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 distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/MET_BaseLineTauId")
drh1.setName("Base")
drh1.normalizeToOne()
drh2 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/MET_InvertedTauId")
drh2.setName("Inv")
drh2.normalizeToOne()
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Base": "Baseline Tau ID",
"Inv": "Inverted Tau ID"})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyle("Base", "P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Base", "EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame("METbaseVSinvertedTauID", opts={"ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -10, "ymax": 50}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Arbitrary units")
# Draw the plot
plot.draw()
# Add the various texts to
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=datasets.getDataset("Data").getLuminosity())
# Save the plot to files
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 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 cutefficiency(self, histo1, histo2):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
h1.Scale(1 / h1.GetMaximum())
h2.Scale(1 / h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin, 0)
if value2 < 0:
h2.SetBinContent(iBin, 0)
iBin = iBin + 1
h1cut = h1.Clone("h1cut")
h1cut.Reset()
h1cut.GetYaxis().SetTitle("Efficiency")
h1cut.GetXaxis().SetTitle("PF MET cut (GeV)")
h2cut = h2.Clone("h2cut")
h2cut.Reset()
h2cut.SetLineColor(2)
integralError = ROOT.Double(0.0)
integralValue = h1.IntegralAndError(1, h1cut.GetNbinsX(),
integralError)
h1_integral = h1.Integral(0, h1.GetNbinsX())
h2_integral = h2.Integral(0, h2.GetNbinsX())
iBin = 1
nBins = h1cut.GetNbinsX()
while iBin < nBins:
error = ROOT.Double(0.0)
selected1 = h1.IntegralAndError(iBin, nBins, error)
if selected1 > 0:
error = error / selected1
else:
error = integralError / integralValue
efficiency1 = selected1 / h1_integral
h1cut.SetBinContent(iBin, efficiency1)
if self.errorBars:
h1cut.SetBinError(iBin, error)
error = ROOT.Double(0.0)
selected2 = h2.IntegralAndError(iBin, nBins, error)
if selected2 > 0:
error = error / selected2
else:
error = integralError / integralValue
efficiency2 = selected2 / h2_integral
h2cut.SetBinContent(iBin, efficiency2)
if self.errorBars:
h2cut.SetBinError(iBin, error)
iBin = iBin + 1
plot = plots.ComparisonPlot(
histograms.Histo(h1cut, "Inv"),
histograms.Histo(h2cut, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Inv": h1.GetTitle(),
"Base": h2.GetTitle()
})
# Set the legend styles
#plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
#plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame(
"cuteff" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2
},
createRatio=True,
opts2={
"ymin": 0,
"ymax": 2
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4, 0.82, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
######
hError = h1cut.Clone("hError")
hError.Divide(h2cut)
iBin = 1
nBins = hError.GetNbinsX()
while iBin < nBins:
hError.SetBinContent(iBin, abs(hError.GetBinContent(iBin) - 1))
iBin = iBin + 1
hError.GetYaxis().SetTitle(
"abs( (#varepsilon^{Inverted} - #varepsilon^{Baseline})/#varepsilon^{Baseline} )"
)
hError.GetXaxis().SetTitle("PF MET cut (GeV)")
plot2 = plots.PlotBase()
plot2.histoMgr.appendHisto(histograms.Histo(hError,
"ShapeUncertainty"))
plot2.histoMgr.forHisto("ShapeUncertainty", st1)
plot2.histoMgr.setHistoDrawStyleAll("EP")
# plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-1, "ymax": 1})
plot2.createFrame("shapeUncertainty" + self.label,
opts={
"ymin": -0.1,
"ymax": 1.1
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
rangeMin = hError.GetXaxis().GetXmin()
rangeMax = hError.GetXaxis().GetXmax()
# rangeMax = 80
rangeMax = 120
# rangeMax = 380
numberOfParameters = 2
class FitFunction:
def __call__(self, x, par):
# return Linear(x,par)
return ErrorFunction(x, par)
theFit = TF1('theFit', FitFunction(), rangeMin, rangeMax,
numberOfParameters)
theFit.SetParLimits(0, 0.01, 0.03)
theFit.SetParLimits(1, 50, 150)
# theFit.FixParameter(0,0.02)
# theFit.FixParameter(1,100)
hError.Fit(theFit, "LRN")
print "Error MET > 40", theFit.Eval(40)
print "Error MET > 50", theFit.Eval(50)
print "Error MET > 70", theFit.Eval(70)
plot2.histoMgr.appendHisto(histograms.Histo(theFit, "Fit"))
plot2.draw()
plot2.save()
def comparison(self, histo1, histo2, norm=1):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
if norm == 1:
h1.Scale(1 / h1.GetMaximum())
h2.Scale(1 / h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin, 0)
if value2 < 0:
h2.SetBinContent(iBin, 0)
iBin = iBin + 1
if norm > 0:
h1.GetYaxis().SetTitle("Arbitrary units")
plot = plots.ComparisonPlot(
histograms.Histo(h1, "Inv"),
histograms.Histo(h2, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Inv": h1.GetTitle(),
"Base": h2.GetTitle()
})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame(
"comparison" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2
},
createRatio=True,
opts2={
"ymin": 0,
"ymax": 2
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4, 0.82, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def getEfficiency(postfix, lumi):
f = ROOT.TFile.Open("tagprobe_output_%s.root" % postfix)
trig = ""
if "Mu20" in postfix:
trig = "HLT_Mu20"
elif "Mu24" in postfix:
trig = "HLT_Mu24"
elif "Mu30" in postfix:
trig = "HLT_Mu30"
elif "Mu40" in postfix:
trig = "HLT_Mu40"
l = ROOT.TLatex()
l.SetNDC()
l.SetTextFont(l.GetTextFont()-20) # bold -> normal
# cntfit = "cnt"
cntfit = "fit"
path = "tnpTree/All/%s_eff_plots" % cntfit
plot = "pt_PLOT"
graph = "hxy_%s_eff" % cntfit
pathPt = "tnpTree/All_pt/%s_eff_plots" % cntfit
grahpPt = "hxy_%s_eff" % cntfit
canv = f.Get(path+"/"+plot)
canv.SetName("TagProbe_%s_%s_%s" % (postfix, plot, graph))
# canv.SaveAs(".eps")
canv.SaveAs(".png")
eff = canv.FindObject(graph)
#print eff
eff_value = eff.GetY()[0]
eff_plus = eff.GetErrorYhigh(0)
eff_minus = eff.GetErrorYlow(0)
canv = f.Get(pathPt+"/"+plot)
gr = canv.FindObject(graph).Clone()
name = "TagProbe_Pt100_%s_%s_%s" % (postfix, plot, graph)
c = ROOT.TCanvas(name, name)
frame = c.DrawFrame(40, 0, 100, 1.1)
gr.Draw("EP")
frame.GetXaxis().SetTitle("Probe muon p_{T} (GeV/c)")
frame.GetYaxis().SetTitle("Trigger and ID efficiency")
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
l.DrawLatex(0.2, 0.4, trig)
c.SaveAs(".png")
gr = canv.FindObject(graph).Clone()
name = "TagProbe_Pt400_%s_%s_%s" % (postfix, plot, graph)
c = ROOT.TCanvas(name, name)
frame = c.DrawFrame(40, 0, 400, 1.1)
gr.Draw("EP")
frame.GetXaxis().SetTitle("Probe muon p_{T} (GeV/c)")
frame.GetYaxis().SetTitle("Trigger and ID efficiency")
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
l.DrawLatex(0.2, 0.4, trig)
c.SaveAs(".png")
# if postfix == "Run2011A_Mu40":
# eff_plus = 0.006
# eff_minus = 0.006
print "%s overall efficiency %f + %f - %f" % (postfix, eff_value, eff_plus, eff_minus)
return (eff_value, eff_plus, eff_minus)
def doPlots(table, onlyWjets, mcEvents, normalize, lumi):
nrows = table.getNrows()
function = ROOT.TF1("fitFunction", "[0]")
function.SetParameter(0, 0)
f2 = ROOT.TF1("fitG", "gaus")
f2.SetLineColor(ROOT.kRed)
f2.SetLineWidth(2)
binning = {
"Data": (8, 60, 100),
"Diboson": (8, 0, 2),
"DYJetsToLL": (8, 1, 5),
"EWKMCsum": (8, 40, 120),
"SingleTop": (8, 3, 6),
"TTJets": (10, 25, 35),
"W3Jets": (10, 6, 11),
"WJets": (14, 10, 80),
}
if onlyWjets:
binning["WJets"] = (24, 10, 90)
if not normalize:
binning["Data"] = (10, 70, 120)
binning["EWKMCsum"] = (6, 60, 120)
binning["SingleTop"] = (8, 4, 6)
binning["TTJets"] = (10, 32, 42)
binning["W3Jets"] = (12, 6, 12)
if onlyWjets:
binning["WJets"] = (10, 20, 60)
if mcEvents:
binning["TTJets"] = (12, 320, 440)
binning["WJets"] = (24, 30, 90)
for icol in xrange(table.getNcolumns()):
name = table.getColumnNames()[icol]
label = plots._legendLabels.get(name, name)
if name != "Data":
label += " simulation"
h = ROOT.TH1F(name, name, nrows, 0, nrows)
h2 = ROOT.TH1F(name+"_dist", name, *(binning.get(name, (10, 0, 100))))
mean = dataset.Count(0, 0)
for irow in xrange(nrows):
count = table.getCount(irow, icol)
h.SetBinContent(irow+1, count.value())
h.SetBinError(irow+1, count.uncertainty())
h2.Fill(count.value())
mean.add(count)
mean = dataset.Count(mean.value()/nrows, mean.uncertainty()/nrows)
h.Fit("fitFunction")
value = function.GetParameter(0)
error = function.GetParError(0)
# function.SetParameters(1., 40., 1.);
# function.SetParLimits(0, 0.0, 1.0);
# fitResult = graph.Fit(function, "NRSE+EX0");
# print "Fit status", fitResult.Status()
# #fitResult.Print("V");
# #fitResult.GetCovarianceMatrix().Print();
# function.SetLineColor(graph.GetMarkerColor());
# function.SetLineWidth(2);
function.Draw("same")
ROOT.gPad.Update()
stat = h.FindObject("stats")
if stat:
stat.SetX1NDC(0.2)
stat.SetX2NDC(0.44)
stat.SetY1NDC(0.2)
stat.SetY2NDC(0.3)
stat.SetTextColor(ROOT.kRed)
stat.SetLineColor(ROOT.kRed)
# return (function, fitResult)
styles.dataStyle.apply(h)
p = plots.PlotBase([h])
p.histoMgr.setHistoDrawStyle(name, "EP")
p.createFrame("fluctuation_"+name, opts={"ymin": 0, "ymaxfactor": 1.2, "nbins": nrows})
p.frame.GetXaxis().SetTitle("Embedding trial number")
ylabel = "Simulation"
if name == "Data":
ylabel = "Data"
ylabel += " events"
p.frame.GetYaxis().SetTitle(ylabel)
step = 1
start = 0
if onlyWjets:
start = 4
step = 5
for irow in xrange(start, nrows, step):
p.frame.GetXaxis().SetBinLabel(irow+1, "%d"%(irow+1))
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
leg = histograms.moveLegend(histograms.createLegend(), dx=-0.07, dy=-0.6, dh=-0.15)
leg.AddEntry(h, "Trial values", "P")
def createLine(val, st=1, col=ROOT.kRed):
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineStyle(st)
l.SetLineColor(col)
return l
fv = createLine(value)
leg.AddEntry(fv, "Fitted value", "L")
p.appendPlotObject(fv)
# fe = createLine(value+error, ROOT.kDashed)
# leg.AddEntry(fe, "Fit uncertainty", "L")
# p.appendPlotObject(fe)
# p.appendPlotObject(createLine(value-error, ROOT.kDashed))
v = createLine(mean.value(), col=ROOT.kBlue)
leg.AddEntry(v, "Mean", "L")
p.appendPlotObject(v)
ve = createLine(mean.value()+mean.uncertainty(), st=ROOT.kDashed, col=ROOT.kBlue)
leg.AddEntry(ve, "Mean uncertainty", "L")
p.appendPlotObject(ve)
p.appendPlotObject(createLine(mean.value()-mean.uncertainty(), st=ROOT.kDashed, col=ROOT.kBlue))
p.legend = leg
p.appendPlotObject(histograms.PlotText(0.65, 0.33, label, size=20))
p.draw()
if name != "Data":
histograms.addCmsPreliminaryText(text="Simulation")
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
p.save()
###############
f2.SetParameter(1, value)
h2.Fit("fitG")
# f2.Draw("same")
ROOT.gPad.Update()
stat = h2.FindObject("stats")
if stat:
stat.SetX1NDC(0.62)
stat.SetX2NDC(0.9)
stat.SetY1NDC(0.7)
stat.SetY2NDC(0.85)
stat.SetTextColor(ROOT.kRed)
stat.SetLineColor(ROOT.kRed)
styles.dataStyle.apply(h2)
p = plots.PlotBase([h2])
p.histoMgr.setHistoDrawStyle(name+"_dist", "HIST")
p.createFrame("fluctuation_"+name+"_dist", opts={"ymin": 0, "ymaxfactor": 1.4, "nbins": nrows})
p.frame.GetXaxis().SetTitle(ylabel)
p.frame.GetYaxis().SetTitle("Occurrances")
ymin = p.frame.GetYaxis().GetXmin()
ymax = p.frame.GetYaxis().GetXmax()
leg = histograms.moveLegend(histograms.createLegend(), dx=-0.07, dy=-0.25, dh=-0.15)
leg.AddEntry(h2, "Trials", "F")
leg.AddEntry(f2, "Gaussian fit", "L")
def createLine2(val, st=1):
l = ROOT.TLine(val, ymin, val, ymax)
l.SetLineWidth(1)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(st)
return l
p.appendPlotObject(h2, "FUNC")
p.appendPlotObject(stat)
p.appendPlotObject(histograms.PlotText(0.65, 0.88, label, size=20))
# fv = createLine2(value)
# leg.AddEntry(fv, "Fit of values", "L")
# p.appendPlotObject(fv)
# fe = createLine2(value+error, ROOT.kDashed)
# leg.AddEntry(fe, "Fit of values unc.", "L")
# p.appendPlotObject(fe)
# p.appendPlotObject(createLine2(value-error, ROOT.kDashed))
p.legend = leg
p.draw()
if name != "Data":
histograms.addCmsPreliminaryText(text="Simulation")
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
p.save()
def getEfficiency(postfix, lumi):
f = ROOT.TFile.Open("tagprobe_output_%s.root" % postfix)
trig = ""
if "Mu20" in postfix:
trig = "HLT_Mu20"
elif "Mu24" in postfix:
trig = "HLT_Mu24"
elif "Mu30" in postfix:
trig = "HLT_Mu30"
elif "Mu40" in postfix:
trig = "HLT_Mu40"
l = ROOT.TLatex()
l.SetNDC()
l.SetTextFont(l.GetTextFont() - 20) # bold -> normal
# cntfit = "cnt"
cntfit = "fit"
path = "tnpTree/All/%s_eff_plots" % cntfit
plot = "pt_PLOT"
graph = "hxy_%s_eff" % cntfit
pathPt = "tnpTree/All_pt/%s_eff_plots" % cntfit
grahpPt = "hxy_%s_eff" % cntfit
canv = f.Get(path + "/" + plot)
canv.SetName("TagProbe_%s_%s_%s" % (postfix, plot, graph))
# canv.SaveAs(".eps")
canv.SaveAs(".png")
eff = canv.FindObject(graph)
#print eff
eff_value = eff.GetY()[0]
eff_plus = eff.GetErrorYhigh(0)
eff_minus = eff.GetErrorYlow(0)
canv = f.Get(pathPt + "/" + plot)
gr = canv.FindObject(graph).Clone()
name = "TagProbe_Pt100_%s_%s_%s" % (postfix, plot, graph)
c = ROOT.TCanvas(name, name)
frame = c.DrawFrame(40, 0, 100, 1.1)
gr.Draw("EP")
frame.GetXaxis().SetTitle("Probe muon p_{T} (GeV/c)")
frame.GetYaxis().SetTitle("Trigger and ID efficiency")
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
l.DrawLatex(0.2, 0.4, trig)
c.SaveAs(".png")
gr = canv.FindObject(graph).Clone()
name = "TagProbe_Pt400_%s_%s_%s" % (postfix, plot, graph)
c = ROOT.TCanvas(name, name)
frame = c.DrawFrame(40, 0, 400, 1.1)
gr.Draw("EP")
frame.GetXaxis().SetTitle("Probe muon p_{T} (GeV/c)")
frame.GetYaxis().SetTitle("Trigger and ID efficiency")
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
l.DrawLatex(0.2, 0.4, trig)
c.SaveAs(".png")
# if postfix == "Run2011A_Mu40":
# eff_plus = 0.006
# eff_minus = 0.006
print "%s overall efficiency %f + %f - %f" % (postfix, eff_value, eff_plus,
eff_minus)
return (eff_value, eff_plus, eff_minus)
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 cutefficiency(self,histo1,histo2):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
h1.Scale(1/h1.GetMaximum())
h2.Scale(1/h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin,0)
if value2 < 0:
h2.SetBinContent(iBin,0)
iBin = iBin + 1
h1cut = h1.Clone("h1cut")
h1cut.Reset()
h1cut.GetYaxis().SetTitle("Efficiency")
h1cut.GetXaxis().SetTitle("PF MET cut (GeV)")
h2cut = h2.Clone("h2cut")
h2cut.Reset()
h2cut.SetLineColor(2)
integralError = ROOT.Double(0.0)
integralValue = h1.IntegralAndError(1,h1cut.GetNbinsX(),integralError)
h1_integral = h1.Integral(0,h1.GetNbinsX())
h2_integral = h2.Integral(0,h2.GetNbinsX())
iBin = 1
nBins = h1cut.GetNbinsX()
while iBin < nBins:
error = ROOT.Double(0.0)
selected1 = h1.IntegralAndError(iBin,nBins,error)
if selected1 > 0:
error = error/selected1
else:
error = integralError/integralValue
efficiency1 = selected1/h1_integral
h1cut.SetBinContent(iBin,efficiency1)
if self.errorBars:
h1cut.SetBinError(iBin,error)
error = ROOT.Double(0.0)
selected2 = h2.IntegralAndError(iBin,nBins,error)
if selected2 > 0:
error = error/selected2
else:
error = integralError/integralValue
efficiency2 = selected2/h2_integral
h2cut.SetBinContent(iBin,efficiency2)
if self.errorBars:
h2cut.SetBinError(iBin,error)
iBin = iBin + 1
plot = plots.ComparisonPlot(
histograms.Histo(h1cut, "Inv"),
histograms.Histo(h2cut, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Inv": h1.GetTitle(),
"Base": h2.GetTitle()})
# Set the legend styles
#plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
#plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame("cuteff"+self.label, opts={"ymin":1e-5, "ymaxfactor": 2},
createRatio=True, opts2={"ymin": 0, "ymax": 2}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4,0.82,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
######
hError = h1cut.Clone("hError")
hError.Divide(h2cut)
iBin = 1
nBins = hError.GetNbinsX()
while iBin < nBins:
hError.SetBinContent(iBin,abs(hError.GetBinContent(iBin) - 1))
iBin = iBin + 1
hError.GetYaxis().SetTitle("abs( (#varepsilon^{Inverted} - #varepsilon^{Baseline})/#varepsilon^{Baseline} )")
hError.GetXaxis().SetTitle("PF MET cut (GeV)")
plot2 = plots.PlotBase()
plot2.histoMgr.appendHisto(histograms.Histo(hError,"ShapeUncertainty"))
plot2.histoMgr.forHisto("ShapeUncertainty", st1)
plot2.histoMgr.setHistoDrawStyleAll("EP")
# plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-1, "ymax": 1})
plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-0.1, "ymax": 1.1})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
rangeMin = hError.GetXaxis().GetXmin()
rangeMax = hError.GetXaxis().GetXmax()
# rangeMax = 80
rangeMax = 120
# rangeMax = 380
numberOfParameters = 2
class FitFunction:
def __call__( self, x, par ):
# return Linear(x,par)
return ErrorFunction(x,par)
theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,numberOfParameters)
theFit.SetParLimits(0,0.01,0.03)
theFit.SetParLimits(1,50,150)
# theFit.FixParameter(0,0.02)
# theFit.FixParameter(1,100)
hError.Fit(theFit,"LRN")
print "Error MET > 40",theFit.Eval(40)
print "Error MET > 50",theFit.Eval(50)
print "Error MET > 70",theFit.Eval(70)
plot2.histoMgr.appendHisto(histograms.Histo(theFit,"Fit"))
plot2.draw()
plot2.save()
def comparison(self,histo1,histo2,norm=1):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
if norm == 1:
h1.Scale(1/h1.GetMaximum())
h2.Scale(1/h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin,0)
if value2 < 0:
h2.SetBinContent(iBin,0)
iBin = iBin + 1
if norm > 0:
h1.GetYaxis().SetTitle("Arbitrary units")
plot = plots.ComparisonPlot(
histograms.Histo(h1, "Inv"),
histograms.Histo(h2, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Inv": h1.GetTitle(),
"Base": h2.GetTitle()})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame("comparison"+self.label, opts={"ymin":1e-5, "ymaxfactor": 2},
createRatio=True, opts2={"ymin": 0, "ymax": 2}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4,0.82,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
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 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 Save(self, name):
if len(self.histogramsNotFound) > 0:
for name in self.histogramsNotFound:
print "Histo",name,"not found"
self.histogramsNotFound = []
return
hObserved = self.histograms[self.FindHistoIndex(self.dataHistos[0].name)].Clone("Data")
hObserved.Reset()
for histo in self.dataHistos:
hObserved.Add(self.histograms[self.FindHistoIndex(histo.label)])
print " Data: ", hObserved.Integral(0,hObserved.GetNbinsX()),"events"
hEstimatedEWKfake = self.histograms[self.FindHistoIndex("EWKfake")].Clone("hEstimatedEWKfake")
print " EWKfake: ",hEstimatedEWKfake.Integral(0,hEstimatedEWKfake.GetNbinsX()),"events"
hEstimatedEWK = self.histograms[self.FindHistoIndex("EWK")].Clone("hEstimatedEWK")
print " EWK: ",hEstimatedEWK.Integral(0,hEstimatedEWK.GetNbinsX()),"events"
hEstimatedEWK.Add(hEstimatedEWKfake)
hEstimatedQCD = self.histograms[self.FindHistoIndex("QCD")].Clone("hEstimatedQCD")
print " QCD: ",hEstimatedQCD.Integral(0,hEstimatedQCD.GetNbinsX()),"events"
hEstimatedQCD.Add(hEstimatedEWK)
hUncertainty = hEstimatedQCD.Clone("BackgrUncertainty")
hUncertainty.SetFillColor(1)
hUncertainty.SetFillStyle(3354)
hUncertainty.SetLineColor(0)
hUncertainty.SetLineStyle(0)
hUncertainty.SetLineWidth(0)
hUncertainty.SetMarkerColor(0)
hUncertainty.SetMarkerSize(0)
hSignal = self.histograms[self.FindHistoIndex("Signal")].Clone("hSignal")
print " Signal: ",hSignal.Integral(0,hSignal.GetNbinsX()),"events"
hSignal.Add(hEstimatedQCD)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(
histograms.Histo(hObserved, "Data"),
[
histograms.Histo(hUncertainty, "Backgr.Uncertainty"),
histograms.Histo(hEstimatedEWKfake, "EWKfake"),
histograms.Histo(hEstimatedEWK, "EWK"),
histograms.Histo(hEstimatedQCD, "QCD"),
histograms.Histo(hSignal, "Signal")
]
)
plot.histoMgr.forHisto("Data", styles.getDataStyle())
plot.histoMgr.forHisto("EWK",styles.getEWKStyle())
plot.histoMgr.forHisto("EWKfake",styles.getEWKFakeStyle())
plot.histoMgr.forHisto("QCD",styles.getQCDStyle())
plot.histoMgr.forHisto("Signal",styles.getSignalStyle())
plot.histoMgr.forHisto("Backgr.Uncertainty",styles.getErrorStyle())
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Data", "EP")
plot.histoMgr.setHistoDrawStyle("Backgr.Uncertainty", "E2")
plot.histoMgr.setHistoLegendStyleAll("F")
plot.histoMgr.setHistoLegendStyle("Data", "P")
plot.histoMgr.setHistoLegendStyle("Signal", "L")
plot.createFrame(name, opts={"ymin":0, "ymaxfactor": 1.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.reorderLegend(["Data", "Signal", "QCD", "EWK", "EWKfake", "Backgr.Uncertainty"])
self.setLegendLabels(plot)
plot.setLegend(histograms.createLegend(0.55,0.68,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def 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 Save(self, name):
if len(self.histogramsNotFound) > 0:
for name in self.histogramsNotFound:
print "Histo", name, "not found"
self.histogramsNotFound = []
return
hObserved = self.histograms[self.FindHistoIndex(
self.dataHistos[0].name)].Clone("Data")
hObserved.Reset()
for histo in self.dataHistos:
hObserved.Add(self.histograms[self.FindHistoIndex(histo.label)])
print " Data: ", hObserved.Integral(
0, hObserved.GetNbinsX()), "events"
hEstimatedEWKfake = self.histograms[self.FindHistoIndex(
"EWKfake")].Clone("hEstimatedEWKfake")
print " EWKfake: ", hEstimatedEWKfake.Integral(
0, hEstimatedEWKfake.GetNbinsX()), "events"
hEstimatedEWK = self.histograms[self.FindHistoIndex("EWK")].Clone(
"hEstimatedEWK")
print " EWK: ", hEstimatedEWK.Integral(
0, hEstimatedEWK.GetNbinsX()), "events"
hEstimatedEWK.Add(hEstimatedEWKfake)
hEstimatedQCD = self.histograms[self.FindHistoIndex("QCD")].Clone(
"hEstimatedQCD")
print " QCD: ", hEstimatedQCD.Integral(
0, hEstimatedQCD.GetNbinsX()), "events"
hEstimatedQCD.Add(hEstimatedEWK)
hUncertainty = hEstimatedQCD.Clone("BackgrUncertainty")
hUncertainty.SetFillColor(1)
hUncertainty.SetFillStyle(3354)
hUncertainty.SetLineColor(0)
hUncertainty.SetLineStyle(0)
hUncertainty.SetLineWidth(0)
hUncertainty.SetMarkerColor(0)
hUncertainty.SetMarkerSize(0)
hSignal = self.histograms[self.FindHistoIndex("Signal")].Clone(
"hSignal")
print " Signal: ", hSignal.Integral(0,
hSignal.GetNbinsX()), "events"
hSignal.Add(hEstimatedQCD)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(histograms.Histo(hObserved, "Data"), [
histograms.Histo(hUncertainty, "Backgr.Uncertainty"),
histograms.Histo(hEstimatedEWKfake, "EWKfake"),
histograms.Histo(hEstimatedEWK, "EWK"),
histograms.Histo(hEstimatedQCD, "QCD"),
histograms.Histo(hSignal, "Signal")
])
plot.histoMgr.forHisto("Data", styles.getDataStyle())
plot.histoMgr.forHisto("EWK", styles.getEWKStyle())
plot.histoMgr.forHisto("EWKfake", styles.getEWKFakeStyle())
plot.histoMgr.forHisto("QCD", styles.getQCDStyle())
plot.histoMgr.forHisto("Signal", styles.getSignalStyle())
plot.histoMgr.forHisto("Backgr.Uncertainty", styles.getErrorStyle())
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Data", "EP")
plot.histoMgr.setHistoDrawStyle("Backgr.Uncertainty", "E2")
plot.histoMgr.setHistoLegendStyleAll("F")
plot.histoMgr.setHistoLegendStyle("Data", "P")
plot.histoMgr.setHistoLegendStyle("Signal", "L")
plot.createFrame(name, opts={"ymin": 0, "ymaxfactor": 1.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.reorderLegend(
["Data", "Signal", "QCD", "EWK", "EWKfake", "Backgr.Uncertainty"])
self.setLegendLabels(plot)
plot.setLegend(histograms.createLegend(0.55, 0.68, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def 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 distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/MET_BaseLineTauId")
drh1.setName("Base")
drh1.normalizeToOne()
drh2 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/MET_InvertedTauId")
drh2.setName("Inv")
drh2.normalizeToOne()
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Base": "Baseline Tau ID",
"Inv": "Inverted Tau ID"
})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyle("Base",
"P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Base",
"EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame(
"METbaseVSinvertedTauID",
opts={
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -10,
"ymax": 50
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Arbitrary units")
# Draw the plot
plot.draw()
# Add the various texts to
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(
x=None, y=None, lumi=datasets.getDataset("Data").getLuminosity())
# Save the plot to files
plot.save()
