def plotHisto(self, histo, canvasName):
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo, histo.GetName()))
plot.createFrame(canvasName + self.label,
opts={
"ymin": 0.1,
"ymaxfactor": 2.
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.getPad().SetLogy(True)
integralValue = int(0.5 +
histo.Integral(0, histo.GetNbinsX(), "width"))
print histo.GetName(), "Integral", histo.Integral(
0, histo.GetNbinsX(), "width")
histograms.addText(0.4, 0.7, "Integral = %s ev" % integralValue)
match = re.search("aseline", histo.GetName())
if match:
self.nBaseQCD = integralValue
match = re.search("nverted", histo.GetName())
if match:
self.nInvQCD = integralValue
self.plotIntegral(plot, histo.GetName())
def rtauGen(h, name, rebin=2, ratio=False, defaultStyles=True):
if defaultStyles:
h.setDefaultStyles()
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f" % h.binWidth()
if "LeptonsInMt" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "NoLeptonsRealTau" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "Mass" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
kwargs = {"ymin": 0.1, "ymax": 1000}
if "LeptonsInMt" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "NoLeptonsRealTau" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "Rtau" in name:
kwargs = {"ymin": 0.0001, "xmax": 1.1}
kwargs = {"ymin": 0.1, "xmax": 1.1}
h.getPad().SetLogy(True)
# kwargs["opts"] = {"ymin": 0, "xmax": 14, "ymaxfactor": 1.1}}
if ratio:
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# name = name+"_log"
h.createFrame(name, **kwargs)
# histograms.addText(0.65, 0.7, "BR(t #rightarrow bH^{#pm})=0.05", 20)
h.getPad().SetLogy(True)
leg = histograms.createLegend(0.6, 0.75, 0.8, 0.9)
if "LeptonsInMt" in name:
h.getPad().SetLogy(False)
leg = histograms.moveLegend(leg, dx=-0.18)
histograms.addText(0.5, 0.65, "TailKiller cut: Tight", 20)
h.setLegend(leg)
plots._legendLabels["MetNoJetInHole"] = "Jets outside dead cells"
plots._legendLabels["MetJetInHole"] = "Jets within dead cells"
histograms.addText(300, 300, "p_{T}^{jet} > 50 GeV/c", 20)
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# if ratio:
# h.createFrameFraction(name, opts=opts, opts2=opts2)
# h.setLegend(leg)
common(h, xlabel, ylabel)
def vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix + "vertices" + postfix,
opts=opts,
createRatio=ratio,
opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix + "vertices" + postfix + "_log",
opts=opts_log,
createRatio=ratio,
opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix+"vertices"+postfix, opts=opts, createRatio=ratio, opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", opts=opts_log, createRatio=ratio, opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def plot(datasets):
# datasetName = "TTJets_TuneZ2_Summer11"
# datasetName = "TTToHplusBWB_M120_Summer11"
datasetName = "WJets_TuneZ2_Summer11"
den_selection = "jets_p4.Pt()>30 && ( abs(jets_flavour) < 4 || jets_flavour > 20)"
# den_selection = "jets_p4.Pt()>30"
num_selection = den_selection + "&&jets_btag>1.7"
ds = datasets.getDataset(datasetName)
den = ds.getDatasetRootHisto(
treeDraw.clone(varexp="jets_p4.Pt()>>dist1(100,0.,500.)",
selection=den_selection)).getHistogram()
num = ds.getDatasetRootHisto(
treeDraw.clone(varexp="jets_p4.Pt()>>dist2(100,0.,500.)",
selection=num_selection)).getHistogram()
canvas = ROOT.TCanvas("canvas", "", 500, 700)
canvas.Divide(1, 3)
canvas.cd(1)
den.Draw()
canvas.cd(2)
num.Draw()
canvas.cd(3)
# eff = ROOT.TEfficiency(num,den)
eff = num.Clone()
eff.Divide(den)
eff.Draw()
canvas.Print("canvas.C")
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(eff, eff.GetName()))
plot.createFrame("BtaggingEff", opts={"ymin": 0.1, "ymaxfactor": 2.})
plot.frame.GetXaxis().SetTitle("jet E_{T} (GeV)")
plot.frame.GetYaxis().SetTitle("B-tagging eff.")
histograms.addText(0.2, 0.8, "MC: " + datasetName, 25)
plot.addStandardTexts()
plot.draw()
plot.save()
def plot(datasets):
# datasetName = "TTJets_TuneZ2_Summer11"
# datasetName = "TTToHplusBWB_M120_Summer11"
datasetName = "WJets_TuneZ2_Summer11"
den_selection = "jets_p4.Pt()>30 && ( abs(jets_flavour) < 4 || jets_flavour > 20)"
# den_selection = "jets_p4.Pt()>30"
num_selection = den_selection + "&&jets_btag>1.7"
ds = datasets.getDataset(datasetName)
den = ds.getDatasetRootHisto(treeDraw.clone(varexp="jets_p4.Pt()>>dist1(100,0.,500.)", selection=den_selection)).getHistogram()
num = ds.getDatasetRootHisto(treeDraw.clone(varexp="jets_p4.Pt()>>dist2(100,0.,500.)", selection=num_selection)).getHistogram()
canvas = ROOT.TCanvas("canvas","",500,700)
canvas.Divide(1,3)
canvas.cd(1)
den.Draw()
canvas.cd(2)
num.Draw()
canvas.cd(3)
# eff = ROOT.TEfficiency(num,den)
eff = num.Clone()
eff.Divide(den)
eff.Draw()
canvas.Print("canvas.C")
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(eff,eff.GetName()))
plot.createFrame("BtaggingEff", opts={"ymin": 0.1, "ymaxfactor": 2.})
plot.frame.GetXaxis().SetTitle("jet E_{T} (GeV)")
plot.frame.GetYaxis().SetTitle("B-tagging eff.")
histograms.addText(0.2, 0.8, "MC: "+datasetName, 25)
plot.addStandardTexts()
plot.draw()
plot.save()
def plotHisto(self,histo,canvasName):
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo,histo.GetName()))
plot.createFrame(canvasName+self.label, opts={"ymin": 0.1, "ymaxfactor": 2.})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.getPad().SetLogy(True)
integralValue = int(0.5 + histo.Integral(0,histo.GetNbinsX(),"width"))
print histo.GetName(),"Integral",histo.Integral(0,histo.GetNbinsX(),"width")
histograms.addText(0.4,0.7,"Integral = %s ev"% integralValue)
match = re.search("aseline",histo.GetName())
if match:
self.nBaseQCD = integralValue
match = re.search("nverted",histo.GetName())
if match:
self.nInvQCD = integralValue
self.plotIntegral(plot, histo.GetName())
def transverseMass(h, name, rebin=1, opts={}, opts_log={}, ratio=False):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
h.addMCUncertainty()
_opts = {"ymaxfactor": 1.5}
_opts.update(opts)
_opts_log = {"ymin": 1e-2, "ymaxfactor": 2}
_opts_log.update(_opts)
_opts_log.update(opts_log)
_opts2 = {"ymin": 0, "ymax": 2}
h.createFrame(name, opts=_opts, opts2=_opts2, createRatio=ratio)
h.setLegend(histograms.createLegend())
deltaPhi = "#Delta#phi(#tau jet, E_{T}^{miss})"
coord = {"x": 0.5, "y": 0.55, "size": 20}
if "AfterBTagging" in name:
histograms.addText(text="Without %s cut" % deltaPhi, **coord)
elif "AfterDeltaPhi160" in name:
histograms.addText(text="%s < 160^{#circ}" % deltaPhi, **coord)
elif "AfterDeltaPhi130" in name:
histograms.addText(text="%s < 130^{#circ}" % deltaPhi, **coord)
common(h, xlabel, ylabel)
name += "_log"
h.createFrame(name, opts=_opts_log, opts2=_opts2, createRatio=ratio)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
common(h, xlabel, ylabel)
def transverseMass(h, name, rebin=1, opts={}, opts_log={}, ratio=False):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
h.addMCUncertainty()
_opts = {"ymaxfactor": 1.5}
_opts.update(opts)
_opts_log = {"ymin": 1e-2, "ymaxfactor": 2}
_opts_log.update(_opts)
_opts_log.update(opts_log)
_opts2 = {"ymin": 0, "ymax": 2}
h.createFrame(name, opts=_opts, opts2=_opts2, createRatio=ratio)
h.setLegend(histograms.createLegend())
deltaPhi = "#Delta#phi(#tau jet, E_{T}^{miss})"
coord = {"x": 0.5, "y": 0.55, "size": 20}
if "AfterBTagging" in name:
histograms.addText(text="Without %s cut"%deltaPhi, **coord)
elif "AfterDeltaPhi160" in name:
histograms.addText(text="%s < 160^{#circ}"%deltaPhi, **coord)
elif "AfterDeltaPhi130" in name:
histograms.addText(text="%s < 130^{#circ}"%deltaPhi, **coord)
common(h, xlabel, ylabel)
name += "_log"
h.createFrame(name, opts=_opts_log, opts2=_opts2, createRatio=ratio)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
common(h, xlabel, ylabel)
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 fitData(self, histo):
parInvQCD = self.parInvQCD
parMCEWK = self.parMCEWK
nInvQCD = self.nInvQCD
nFitInvQCD = self.nFitInvQCD
nMCEWK = self.nMCEWK
class FitFunction:
def __call__(self, x, par):
return par[0] * (
par[1] * QCDFunction(x, parInvQCD, 1 / nFitInvQCD) +
(1 - par[1]) * EWKFunction(x, parMCEWK, 1 / nMCEWK))
class QCDOnly:
def __call__(self, x, par):
return par[0] * par[1] * QCDFunction(x, parInvQCD,
1 / nFitInvQCD)
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
numberOfParameters = 2
print "Fit range ", rangeMin, " - ", rangeMax
theFit = TF1("theFit", FitFunction(), rangeMin, rangeMax,
numberOfParameters)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo, histo.GetName()))
plot.createFrame("combinedfit" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2.
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
print "data events ", histo.Integral(0, histo.GetNbinsX())
histo.Fit(theFit, "R")
theFit.SetRange(histo.GetXaxis().GetXmin(), histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
par = theFit.GetParameters()
qcdOnly = TF1("qcdOnly", QCDOnly(), rangeMin, rangeMax,
numberOfParameters)
qcdOnly.FixParameter(0, par[0])
qcdOnly.FixParameter(1, par[1])
qcdOnly.SetLineStyle(2)
qcdOnly.Draw("same")
histograms.addText(0.35, 0.8, "Data, Baseline TauID")
histograms.addText(0.4, 0.3, "QCD", 15)
plot.histoMgr.appendHisto(histograms.Histo(qcdOnly, "qcdOnly"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(par[i])
i = i + 1
print fitPars
nBaseQCD = par[0]
self.QCDfraction = par[1]
if len(self.label) > 0:
print "Bin ", self.label
print "Integral ", nBaseQCD
print "QCD fraction ", self.QCDfraction
return theFit
def fitEWK(self, histo, options="R"):
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
# rangeMin = 120
# rangeMax = 120
numberOfParameters = 4
print "Fit range ", rangeMin, " - ", rangeMax
class FitFunction:
def __call__(self, x, par):
return EWKFunction(x, par, 1, 1)
# return SumFunction(x,par)
# return TestFunction(x,par,1)
class PlotFunction:
def __call__(self, x, par):
return EWKFunction(x, par, 0)
theFit = TF1('theFit', FitFunction(), rangeMin, rangeMax,
numberOfParameters)
thePlot = TF1('thePlot', PlotFunction(), rangeMin, rangeMax,
numberOfParameters)
theFit.SetParLimits(0, 5, 30)
theFit.SetParLimits(1, 90, 120)
theFit.SetParLimits(2, 30, 50)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "4050":
theFit.SetParLimits(0, 5, 20)
theFit.SetParLimits(1, 90, 120)
theFit.SetParLimits(2, 30, 50)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "5060":
theFit.SetParLimits(0, 5, 20)
theFit.SetParLimits(1, 90, 120)
theFit.SetParLimits(2, 20, 50)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "6070":
theFit.SetParLimits(0, 5, 50)
theFit.SetParLimits(1, 90, 150)
theFit.SetParLimits(2, 20, 50)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "7080":
theFit.SetParLimits(0, 5, 60)
theFit.SetParLimits(1, 90, 200)
theFit.SetParLimits(2, 20, 100)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "80100":
theFit.SetParLimits(0, 5, 50)
theFit.SetParLimits(1, 50, 170)
theFit.SetParLimits(2, 20, 60)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "100120":
theFit.SetParLimits(0, 5, 50)
theFit.SetParLimits(1, 90, 170)
theFit.SetParLimits(2, 20, 60)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "120150":
theFit.SetParLimits(0, 5, 50)
theFit.SetParLimits(1, 60, 170)
theFit.SetParLimits(2, 10, 100)
theFit.SetParLimits(3, 0.001, 1)
if self.label == "150":
theFit.SetParLimits(0, 5, 50)
theFit.SetParLimits(1, 70, 170)
theFit.SetParLimits(2, 20, 100)
theFit.SetParLimits(3, 0.001, 1)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo, histo.GetName()))
plot.createFrame("ewkfit" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2.
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
self.normEWK = histo.Integral(0, histo.GetNbinsX())
histo.Scale(1 / self.normEWK)
histo.Fit(theFit, options)
theFit.SetRange(histo.GetXaxis().GetXmin(), histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
self.parMCEWK = theFit.GetParameters()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(self.parMCEWK[i])
thePlot.SetParameter(i, theFit.GetParameter(i))
i = i + 1
thePlot.Draw("same")
histograms.addText(0.2, 0.2, "EWK MC, baseline TauID")
plot.histoMgr.appendHisto(histograms.Histo(theFit, "Fit"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
self.parMCEWK = theFit.GetParameters()
print fitPars
self.nMCEWK = theFit.Integral(0, 1000, self.parMCEWK)
print "Integral ", self.normEWK * self.nMCEWK
def fitQCD(self, origHisto):
histo = origHisto.Clone("histo")
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
numberOfParameters = 8
print "Fit range ", rangeMin, " - ", rangeMax
class FitFunction:
def __call__(self, x, par):
return QCDFunction(x, par, 1)
theFit = TF1('theFit', FitFunction(), rangeMin, rangeMax,
numberOfParameters)
theFit.SetParLimits(0, 1, 20)
theFit.SetParLimits(1, 20, 40)
theFit.SetParLimits(2, 10, 25)
theFit.SetParLimits(3, 1, 10)
theFit.SetParLimits(4, 0, 150)
theFit.SetParLimits(5, 10, 100)
theFit.SetParLimits(6, 0.0001, 1)
theFit.SetParLimits(7, 0.001, 0.05)
if self.label == "Baseline":
rangeMax = 240
if self.label == "7080":
theFit.SetParLimits(5, 10, 100)
# if self.label == "100120":
# theFit.SetParLimits(0,1,20)
# theFit.SetParLimits(2,1,25)
# theFit.SetParLimits(3,0.1,20)
if self.label == "120150":
theFit.SetParLimits(0, 1, 20)
theFit.SetParLimits(3, 0.1, 5)
gStyle.SetOptFit(0)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo, histo.GetName()))
plot.createFrame("qcdfit" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2.
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
self.normInvQCD = histo.Integral(0, histo.GetNbinsX())
histo.Scale(1 / self.normInvQCD)
histo.Fit(theFit, "LR")
theFit.SetRange(histo.GetXaxis().GetXmin(), histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
histograms.addText(0.4, 0.8, "Inverted TauID")
plot.histoMgr.appendHisto(histograms.Histo(theFit, "Fit"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
self.parInvQCD = theFit.GetParameters()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(self.parInvQCD[i])
i = i + 1
print fitPars
self.nFitInvQCD = theFit.Integral(0, 1000, self.parInvQCD)
print "Integral ", self.normInvQCD * self.nFitInvQCD
def fitQCD(self,origHisto):
histo = origHisto.Clone("histo")
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
numberOfParameters = 8
print "Fit range ",rangeMin, " - ",rangeMax
class FitFunction:
def __call__( self, x, par ):
return QCDFunction(x,par,1)
theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,numberOfParameters)
theFit.SetParLimits(0,1,20)
theFit.SetParLimits(1,20,40)
theFit.SetParLimits(2,10,25)
theFit.SetParLimits(3,1,10)
theFit.SetParLimits(4,0,150)
theFit.SetParLimits(5,10,100)
theFit.SetParLimits(6,0.0001,1)
theFit.SetParLimits(7,0.001,0.05)
if self.label == "Baseline":
rangeMax = 240
if self.label == "7080":
theFit.SetParLimits(5,10,100)
# if self.label == "100120":
# theFit.SetParLimits(0,1,20)
# theFit.SetParLimits(2,1,25)
# theFit.SetParLimits(3,0.1,20)
if self.label == "120150":
theFit.SetParLimits(0,1,20)
theFit.SetParLimits(3,0.1,5)
gStyle.SetOptFit(0)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo,histo.GetName()))
plot.createFrame("qcdfit"+self.label, opts={"ymin": 1e-5, "ymaxfactor": 2.})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
self.normInvQCD = histo.Integral(0,histo.GetNbinsX())
histo.Scale(1/self.normInvQCD)
histo.Fit(theFit,"LR")
theFit.SetRange(histo.GetXaxis().GetXmin(),histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
histograms.addText(0.4,0.8,"Inverted TauID")
plot.histoMgr.appendHisto(histograms.Histo(theFit,"Fit"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
self.parInvQCD = theFit.GetParameters()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(self.parInvQCD[i])
i = i + 1
print fitPars
self.nFitInvQCD = theFit.Integral(0,1000,self.parInvQCD)
print "Integral ",self.normInvQCD*self.nFitInvQCD
def fitData(self,histo):
parInvQCD = self.parInvQCD
parMCEWK = self.parMCEWK
nInvQCD = self.nInvQCD
nFitInvQCD = self.nFitInvQCD
nMCEWK = self.nMCEWK
class FitFunction:
def __call__( self, x, par ):
return par[0]*(par[1] * QCDFunction(x,parInvQCD,1/nFitInvQCD) + ( 1 - par[1] ) * EWKFunction(x,parMCEWK,1/nMCEWK))
class QCDOnly:
def __call__( self, x, par ):
return par[0]*par[1] * QCDFunction(x,parInvQCD,1/nFitInvQCD)
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
numberOfParameters = 2
print "Fit range ",rangeMin, " - ",rangeMax
theFit = TF1("theFit",FitFunction(),rangeMin,rangeMax,numberOfParameters)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo,histo.GetName()))
plot.createFrame("combinedfit"+self.label, opts={"ymin": 1e-5, "ymaxfactor": 2.})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
print "data events ",histo.Integral(0,histo.GetNbinsX())
histo.Fit(theFit,"R")
theFit.SetRange(histo.GetXaxis().GetXmin(),histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
par = theFit.GetParameters()
qcdOnly = TF1("qcdOnly",QCDOnly(),rangeMin,rangeMax,numberOfParameters)
qcdOnly.FixParameter(0,par[0])
qcdOnly.FixParameter(1,par[1])
qcdOnly.SetLineStyle(2)
qcdOnly.Draw("same")
histograms.addText(0.35,0.8,"Data, Baseline TauID")
histograms.addText(0.4,0.3,"QCD",15)
plot.histoMgr.appendHisto(histograms.Histo(qcdOnly,"qcdOnly"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(par[i])
i = i + 1
print fitPars
nBaseQCD = par[0]
self.QCDfraction = par[1]
if len(self.label) > 0:
print "Bin ",self.label
print "Integral ", nBaseQCD
print "QCD fraction ",self.QCDfraction
return theFit
def __call__(self, x, y):
mass = "m_{H^{#pm}} = %d GeV/c^{2}" % self.mass
br = "BR(t #rightarrow bH^{#pm})=%.2f" % self.br
histograms.addText(x, y, mass, size=self.size)
histograms.addText(x, y - self.separation, br, size=self.size)
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i, histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i, datasets, histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s" % i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s" % i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s" % i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s" % i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s" % i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s" % i] = "BackToBack cuts"
plot.createFrame("purityLoose",
opts={
"xmin": 40,
"xmax": 160,
"ymin": 0.,
"ymax": 1.05
})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.3, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.2, 0.3, "TailKiller: TightPlus", 18)
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
plot.draw()
plot.save()
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i,histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i,datasets,histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s"%i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s"%i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s"%i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s"%i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s"%i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s"%i] = "BackToBack cuts"
plot.createFrame("purityLoose", opts={"xmin": 40, "xmax": 160, "ymin": 0., "ymax": 1.05})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.3, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.2, 0.3, "TailKiller: TightPlus", 18)
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
plot.draw()
plot.save()
def drawPlot(h,
name,
xlabel,
ylabel="Events / %.0f GeV/c",
rebin=1,
log=True,
ratio=True,
opts={},
opts2={},
moveLegend={},
**kwargs):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
#scaleNormalization(h)
#h.stackMCHistograms()
sigErr = h.histoMgr.getHisto("Normal").getRootHisto().Clone("Normal_err")
sigErr.SetFillColor(ROOT.kRed - 7)
sigErr.SetMarkerSize(0)
sigErr.SetFillStyle(3005)
h.prependPlotObject(sigErr, "E2")
if h.histoMgr.hasHisto("Embedded"):
embErr = h.histoMgr.getHisto("Embedded").getRootHisto().Clone(
"Embedded_err")
embErr.SetFillColor(ROOT.kBlue - 7)
embErr.SetFillStyle(3004)
embErr.SetMarkerSize(0)
h.prependPlotObject(embErr, "E2")
if hasattr(h, "embeddingVariation"):
h.prependPlotObject(h.embeddingVariation, "[]")
if hasattr(h, "embeddingDataVariation"):
h.prependPlotObject(h.embeddingDataVariation, "[]")
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
if log:
name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
h.getPad().SetLogy(log)
if ratio:
h.getFrame2().GetYaxis().SetTitle("Ratio")
# Very, very ugly hack
if h.histoMgr.hasHisto("EmbeddedData"):
if h.ratios[1].getName() != "Embedded":
raise Exception("Assumption failed")
h.ratios[1].setDrawStyle("PE2")
rh = h.ratios[1].getRootHisto()
rh.SetFillColor(ROOT.kBlue - 7)
rh.SetFillStyle(3004)
# err = h.ratios[1].getRootHisto().Clone("Embedded_ratio_err")
# err.SetFillColor(ROOT.kBlue-7)
# err.SetFillStyle(3004)
# err.SetMarkerSize(0)
# h.prependPlotObjectToRatio(err, "E2")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(
histograms.moveLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend)))
tmp = sigErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
#tmp.SetFillStyle(sigErr.GetFillStyle()); tmp.SetFillColor(sigErr.GetFillColor())
tmp.SetLineColor(ROOT.kWhite)
h.legend.AddEntry(tmp, "Stat. unc.", "F")
x = h.legend.GetX1()
y = h.legend.GetY1()
x += 0.05
y -= 0.03
if hasattr(h, "embeddingDataVariation"):
histograms.addText(x,
y,
"[ ]",
size=17,
color=h.embeddingDataVariation.GetMarkerColor())
x += 0.05
histograms.addText(x, y, "Embedded data min/max", size=17)
y -= 0.03
if hasattr(h, "embeddingVariation"):
histograms.addText(x,
y,
"[ ]",
size=17,
color=h.embeddingVariation.GetMarkerColor())
x += 0.05
histograms.addText(x, y, "Embedded MC min/max", size=17)
y -= 0.03
#if hasattr(h, "embeddingDataVariation"):
# h.legend.AddEntry(h.embeddingDataVariation, "Embedded data min/max", "p")
#if hasattr(h, "embeddingVariation"):
# h.legend.AddEntry(h.embeddingVariation, "Embedded MC min/max", "p")
common(h, xlabel, ylab, **kwargs)
def __call__(self, x, y):
mass = "m_{H^{#pm}} = %d GeV/c^{2}" % self.mass
br = "BR(t #rightarrow bH^{#pm})=%.2f" % self.br
histograms.addText(x, y, mass, size=self.size)
histograms.addText(x, y-self.separation, br, size=self.size)
def doPlots(mainTable, xvalues, signalDatasets, signalPostfix, consequtive=True):
allName = "All events"
cuts = [
"Trigger and HLT_MET cut",
"primary vertex",
"taus == 1",
"trigger scale factor",
"electron veto",
"muon veto",
"njets",
"MET",
"btagging",
"btagging scale factor",
"deltaPhiTauMET<160",
]
xerrs = [0]*len(xvalues)
yvalues = {}
yerrs = {}
for cut in cuts:
yvalues[cut] = []
yerrs[cut] = []
for name in signalDatasets:
column = mainTable.getColumn(name=name)
# Get the counts (returned objects are of type dataset.Count,
# and have both value and uncertainty
allCount = column.getCount(column.getRowNames().index("All events"))
prevCount = allCount
for cut in cuts:
cutCount = column.getCount(column.getRowNames().index(cut))
eff = cutCount.clone()
if consequtive:
eff.divide(prevCount)
prevCount = cutCount
else:
eff.divide(allCount) # N(cut) / N(all)
yvalues[cut].append(eff.value())
yerrs[cut].append(eff.uncertainty())
def createErrors(cutname):
gr = ROOT.TGraphErrors(len(xvalues), array.array("d", xvalues), array.array("d", yvalues[cutname]),
array.array("d", xerrs), array.array("d", yerrs[cutname]))
gr.SetMarkerStyle(24)
gr.SetMarkerColor(2)
gr.SetMarkerSize(2)
gr.SetLineStyle(1)
gr.SetLineWidth(4)
return gr
def setStyle(gr, lc, ls, ms):
gr.SetLineColor(lc)
gr.SetLineStyle(ls)
gr.SetMarkerColor(lc)
gr.SetMarkerStyle(ms)
gtrig = createErrors("Trigger and HLT_MET cut")
setStyle(gtrig, lc=38, ls=8, ms=20)
#gtau = createErrors("trigger scale factor")
gtau = createErrors("taus == 1")
setStyle(gtau, lc=2, ls=3, ms=21)
gveto = createErrors("muon veto")
setStyle(gveto, lc=1, ls=5, ms=22)
gjets = createErrors("njets")
setStyle(gjets, lc=4, ls=1, ms=23)
gmet = createErrors("MET")
setStyle(gmet, lc=2, ls=2, ms=24)
gbtag = createErrors("btagging")
setStyle(gbtag, lc=1, ls=6, ms=24)
gdphi = createErrors("deltaPhiTauMET<160")
setStyle(gdphi, lc=9, ls=3, ms=25)
glist = [gtrig, gtau, gveto, gjets, gmet, gbtag, gdphi]
#opts = {"xmin": 75, "xmax": 165, "ymin": 0.001}
opts = {"xmin": 75, "xmax": 165, "ymin": 7e-4, "ymax": 2e-1}
name = "SignalEfficiency"
if consequtive:
opts.update({"ymin": 2.5e-2, "ymax": 1.15})
name += "Conseq"
name += "_"+signalPostfix
canvasFrame = histograms.CanvasFrame([histograms.HistoGraph(g, "", "") for g in glist], name, **opts)
canvasFrame.frame.GetYaxis().SetTitle("Selection efficiency")
canvasFrame.frame.GetXaxis().SetTitle("m_{H^{+}} (GeV/c^{2})")
canvasFrame.canvas.SetLogy(True)
canvasFrame.frame.Draw()
for gr in glist:
gr.Draw("PL same")
histograms.addEnergyText()
histograms.addCmsPreliminaryText(text="Simulation")
legend = histograms.createLegend(x1=0.5, y1=0.5, x2=0.85, y2=0.75)
legend = histograms.moveLegend(legend, dx=-0.3, dy=-0.04)
if not consequtive and signalPostfix == "HH":
legend = histograms.moveLegend(legend, dy=0.05)
legend.AddEntry(gtrig,"Trigger", "lp");
# legend.AddEntry(gtau, "#tau-jet identification", "lp");
legend.AddEntry(gtau, "#tau_{h} identification", "lp");
legend.AddEntry(gveto ,"lepton vetoes", "lp");
legend.AddEntry(gjets ,"3 jets", "lp");
legend.AddEntry(gmet,"E_{T}^{miss}", "lp")
legend.AddEntry(gbtag,"b tagging", "lp")
# legend.AddEntry(gdphi,"#Delta#phi(#tau, E_{T}^{miss})<160^{o} ", "lp")
legend.AddEntry(gdphi,"#Delta#phi(#tau_{h}, E_{T}^{miss})<160^{o} ", "lp")
legend.Draw()
process = {
"HW": "t#bar{t} #rightarrow W^{+}bH^{-}#bar{b}",
"HH": "t#bar{t} #rightarrow H^{+}bH^{-}#bar{b}"
}[signalPostfix]
histograms.addText(x=legend.GetX1()+0.02, y=legend.GetY2()+0.01,
text=process, size=17)
canvasFrame.canvas.SaveAs(".png")
canvasFrame.canvas.SaveAs(".C")
canvasFrame.canvas.SaveAs(".eps")
def fitEWK(self,histo,options="R"):
rangeMin = histo.GetXaxis().GetXmin()
rangeMax = histo.GetXaxis().GetXmax()
# rangeMin = 120
# rangeMax = 120
numberOfParameters = 4
print "Fit range ",rangeMin, " - ",rangeMax
class FitFunction:
def __call__( self, x, par ):
return EWKFunction(x,par,1,1)
# return SumFunction(x,par)
# return TestFunction(x,par,1)
class PlotFunction:
def __call__( self, x, par ):
return EWKFunction(x,par,0)
theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,numberOfParameters)
thePlot = TF1('thePlot',PlotFunction(),rangeMin,rangeMax,numberOfParameters)
theFit.SetParLimits(0,5,30)
theFit.SetParLimits(1,90,120)
theFit.SetParLimits(2,30,50)
theFit.SetParLimits(3,0.001,1)
if self.label == "4050":
theFit.SetParLimits(0,5,20)
theFit.SetParLimits(1,90,120)
theFit.SetParLimits(2,30,50)
theFit.SetParLimits(3,0.001,1)
if self.label == "5060":
theFit.SetParLimits(0,5,20)
theFit.SetParLimits(1,90,120)
theFit.SetParLimits(2,20,50)
theFit.SetParLimits(3,0.001,1)
if self.label == "6070":
theFit.SetParLimits(0,5,50)
theFit.SetParLimits(1,90,150)
theFit.SetParLimits(2,20,50)
theFit.SetParLimits(3,0.001,1)
if self.label == "7080":
theFit.SetParLimits(0,5,60)
theFit.SetParLimits(1,90,200)
theFit.SetParLimits(2,20,100)
theFit.SetParLimits(3,0.001,1)
if self.label == "80100":
theFit.SetParLimits(0,5,50)
theFit.SetParLimits(1,50,170)
theFit.SetParLimits(2,20,60)
theFit.SetParLimits(3,0.001,1)
if self.label == "100120":
theFit.SetParLimits(0,5,50)
theFit.SetParLimits(1,90,170)
theFit.SetParLimits(2,20,60)
theFit.SetParLimits(3,0.001,1)
if self.label == "120150":
theFit.SetParLimits(0,5,50)
theFit.SetParLimits(1,60,170)
theFit.SetParLimits(2,10,100)
theFit.SetParLimits(3,0.001,1)
if self.label == "150":
theFit.SetParLimits(0,5,50)
theFit.SetParLimits(1,70,170)
theFit.SetParLimits(2,20,100)
theFit.SetParLimits(3,0.001,1)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo,histo.GetName()))
plot.createFrame("ewkfit"+self.label, opts={"ymin": 1e-5, "ymaxfactor": 2.})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
self.normEWK = histo.Integral(0,histo.GetNbinsX())
histo.Scale(1/self.normEWK)
histo.Fit(theFit,options)
theFit.SetRange(histo.GetXaxis().GetXmin(),histo.GetXaxis().GetXmax())
theFit.SetLineStyle(2)
theFit.Draw("same")
self.parMCEWK = theFit.GetParameters()
fitPars = "fit parameters "
i = 0
while i < numberOfParameters:
fitPars = fitPars + " " + str(self.parMCEWK[i])
thePlot.SetParameter(i,theFit.GetParameter(i))
i = i + 1
thePlot.Draw("same")
histograms.addText(0.2,0.2,"EWK MC, baseline TauID")
plot.histoMgr.appendHisto(histograms.Histo(theFit,"Fit"))
plot.getPad().SetLogy(True)
plot.draw()
plot.save()
self.parMCEWK = theFit.GetParameters()
print fitPars
self.nMCEWK = theFit.Integral(0,1000,self.parMCEWK)
print "Integral ",self.normEWK*self.nMCEWK
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 drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, ratio=True, opts={}, opts2={}, moveLegend={}, **kwargs):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
#scaleNormalization(h)
#h.stackMCHistograms()
sigErr = h.histoMgr.getHisto("Normal").getRootHisto().Clone("Normal_err")
sigErr.SetFillColor(ROOT.kRed-7)
sigErr.SetMarkerSize(0)
sigErr.SetFillStyle(3005)
h.prependPlotObject(sigErr, "E2")
if h.histoMgr.hasHisto("Embedded"):
embErr = h.histoMgr.getHisto("Embedded").getRootHisto().Clone("Embedded_err")
embErr.SetFillColor(ROOT.kBlue-7)
embErr.SetFillStyle(3004)
embErr.SetMarkerSize(0)
h.prependPlotObject(embErr, "E2")
if hasattr(h, "embeddingVariation"):
h.prependPlotObject(h.embeddingVariation, "[]")
if hasattr(h, "embeddingDataVariation"):
h.prependPlotObject(h.embeddingDataVariation, "[]")
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
if log:
name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
h.getPad().SetLogy(log)
if ratio:
h.getFrame2().GetYaxis().SetTitle("Ratio")
# Very, very ugly hack
if h.histoMgr.hasHisto("EmbeddedData"):
if h.ratios[1].getName() != "Embedded":
raise Exception("Assumption failed")
h.ratios[1].setDrawStyle("PE2")
rh = h.ratios[1].getRootHisto()
rh.SetFillColor(ROOT.kBlue-7)
rh.SetFillStyle(3004)
# err = h.ratios[1].getRootHisto().Clone("Embedded_ratio_err")
# err.SetFillColor(ROOT.kBlue-7)
# err.SetFillStyle(3004)
# err.SetMarkerSize(0)
# h.prependPlotObjectToRatio(err, "E2")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(histograms.moveLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend)))
tmp = sigErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
#tmp.SetFillStyle(sigErr.GetFillStyle()); tmp.SetFillColor(sigErr.GetFillColor())
tmp.SetLineColor(ROOT.kWhite)
h.legend.AddEntry(tmp, "Stat. unc.", "F")
x = h.legend.GetX1()
y = h.legend.GetY1()
x += 0.05; y -= 0.03
if hasattr(h, "embeddingDataVariation"):
histograms.addText(x, y, "[ ]", size=17, color=h.embeddingDataVariation.GetMarkerColor()); x += 0.05
histograms.addText(x, y, "Embedded data min/max", size=17); y-= 0.03
if hasattr(h, "embeddingVariation"):
histograms.addText(x, y, "[ ]", size=17, color=h.embeddingVariation.GetMarkerColor()); x += 0.05
histograms.addText(x, y, "Embedded MC min/max", size=17); y-= 0.03
#if hasattr(h, "embeddingDataVariation"):
# h.legend.AddEntry(h.embeddingDataVariation, "Embedded data min/max", "p")
#if hasattr(h, "embeddingVariation"):
# h.legend.AddEntry(h.embeddingVariation, "Embedded MC min/max", "p")
common(h, xlabel, ylab, **kwargs)
