def transverseMass(h, name="transverseMass", rebin=2, log=True):
# name = h.getRootHistoPath()
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, MET) (GeV/c^{2})"
ylabel = "A.u."
# h.stackMCHistograms()
# h.addMCUncertainty()
opts = {"xmax": 200}
# opts = {"ymin": 0.0001,"xmax": 200, "ymaxfactor": 1.3}
# opts2 = {"ymin": 0.5, "ymax": 1.5}
# opts = {"xmax": 200}
if log:
name += "_log"
opts["ymin"] = 1e-4
opts["ymaxfactor"] = 2
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
legend = histograms.createLegend()
if log:
h.getPad().SetLogy(True)
histograms.moveLegend(legend, dx=-0.5, dy=-0.3)
h.setLegend(legend)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# h.addLuminosityText()
h.save()
def transverseMass(h, prefix="", rebin=1, ratio=False):
xlabel = "m_{T}(#mu, E_{T}^{miss}) (GeV)"
ylabel = "Events / %.0f GeV"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"mt", opts=_optsLin)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"mt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def drawPlotData(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()
_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")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
common(h, xlabel, ylab, **kwargs)
def compare(raw, type1, quantity, name):
rawTh1 = raw.histoMgr.getHisto(name).getRootHisto().Clone("Raw")
type1Th1 = type1.histoMgr.getHisto(name).getRootHisto().Clone("Type1")
styles.dataStyle.apply(rawTh1)
styles.mcStyle.apply(type1Th1)
plot = plots.ComparisonPlot(
histograms.Histo(rawTh1, "Raw"),
histograms.Histo(type1Th1, "Type 1")
)
q = " "+quantity
if quantity == "Et":
q = ""
plot.setLegend(histograms.moveLegend(histograms.createLegend(), dh=0.15))
plot.createFrame("metres_%s_%s" % (name, quantity))
plot.frame.GetXaxis().SetTitle("MET%s/genMET%s" % (q, q))
plot.frame.GetYaxis().SetTitle("Events / %.2f" % raw.binWidth())
plot.draw()
l = ROOT.TLatex()
l.SetNDC()
x = 0.5
y = 0.7
dy = 0.05
l.DrawLatex(x, y, name); y -= dy
l.SetTextSize(l.GetTextSize()*0.7)
l.SetTextColor(rawTh1.GetLineColor())
l.DrawLatex(x, y, "Raw mean %.2f, stddev %.2f" % (rawTh1.GetMean(), rawTh1.GetRMS())); y -= dy
l.SetTextColor(type1Th1.GetLineColor())
l.DrawLatex(x, y, "Type 1 mean %.2f, stddev %.2f" % (type1Th1.GetMean(), type1Th1.GetRMS())); y -= dy
plot.save()
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 doPlot(datasetsEmb, analyses, path, name, text):
histos = []
legends = {"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"}
for aname, analysis in analyses:
(rootHisto, tmp) = datasetsEmb.getHistogram("Data", analysis+"/"+path)
h = histograms.Histo(rootHisto, aname)
h.setLegendLabel(legends.get(aname, aname))
h.setDrawStyle("EP")
h.setLegendStyle("p")
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
styles.dataStyle(p.histoMgr.getHisto("Baseline"))
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasetsEmb.getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 35})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.appendPlotObject(histograms.PlotText(0.5, 0.55, text, size=20))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
p.addStandardTexts()
p.save()
def plotEff(h, prefix):
h.createFrame(prefix+"eff_%s"%h.name)
h.frame.GetXaxis().SetTitle("Cut on isolation")
h.frame.GetYaxis().SetTitle(h.ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend(), dx=0.55))
h.draw()
h.save()
return
def createTransverseMassPlotInternal(dset, name, postfix, normalizeToOne, nominalSelection, compareSelection, nominalLegend="Nominal", compareLegend="Compare", moveLegend={}):
datasetName = dset.getName()
treeDraw = dataset.TreeDraw("tree", varexp="TauMETTransverseMass >>tmp(10,0,200)")
tdNominal = treeDraw.clone(selection=nominalSelection)#selection="Obj2Type == 3 && LeptonVetoStatus == 0 && (TauIDStatus == 1 || TauIDStatus == 2 || TauIDStatus == 3)") # FIXME
tdCompare = treeDraw.clone(selection=compareSelection)
drhNominal = dset.getDatasetRootHisto(tdNominal)
drhCompare = dset.getDatasetRootHisto(tdCompare)
integrate = lambda h: h.Integral(0, h.GetNbinsX()+1)
nNominal = integrate(drhNominal.getHistogram())
nCompare = integrate(drhCompare.getHistogram())
if normalizeToOne:
drhNominal.normalizeToOne()
drhCompare.normalizeToOne()
drhNominal.setName("Nominal")
drhCompare.setName("Compare")
p = plots.ComparisonPlot(drhNominal, drhCompare)
p.histoMgr.forEachHisto(styles.generator())
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
p.histoMgr.setHistoLegendLabelMany({
"Nominal": nominalLegend+ " (%d)" % int(nNominal),
"Compare": compareLegend+ " (%d)" % int(nCompare)
})
pfix = postfix
if normalizeToOne:
pfix += "_unit"
p.createFrame("mt_"+datasetName+"_"+name+"_"+pfix, createRatio=normalizeToOne, invertRatio=True, opts2={"ymin": 0, "ymax": 2})
p.frame.GetXaxis().SetTitle("Transverse mass (GeV/c^{2})")
if normalizeToOne:
p.frame.GetYaxis().SetTitle("Arbitrary units")
else:
p.frame.GetYaxis().SetTitle("MC events")
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.93, y2=0.80, x1=0.45, x2=0.85), **moveLegend))
nomErr = p.histoMgr.getHisto("Nominal").getRootHisto().Clone("Nominal_err")
nomErr.SetFillColor(ROOT.kBlue-7)
nomErr.SetFillStyle(3004)
nomErr.SetMarkerSize(0)
p.prependPlotObject(nomErr, "E2")
comErr = p.histoMgr.getHisto("Compare").getRootHisto().Clone("Compare_err")
comErr.SetFillColor(ROOT.kRed-7)
comErr.SetFillStyle(3013)
comErr.SetMarkerSize(0)
p.prependPlotObject(comErr, "E2")
if normalizeToOne:
p.appendPlotObject(histograms.PlotText(0.6, 0.75, "Normalized to unit area", size=17))
p.draw()
p.save()
def plotClosure(mt_nom, mt_var, name, optMode):
style = tdrstyle.TDRStyle()
plot = plots.ComparisonPlot(mt_var, mt_nom)
plot.createFrame(optMode.replace("Opt","mT_Closure_"+ name +"_"), createRatio=True, opts2={"ymin": 0.7, "ymax": 1.3})#0.73,1.27
#plot.createFrame(optMode.replace("Opt","mT_Closure_"+ name +"_"), createRatio=True, opts2={"ymin": 0.73, "ymax": 1.27}, ratio = True, ratioType = "errorScale")
plot.frame.GetXaxis().SetTitle("m_{T}(tau,MET), GeV")
plot.frame.GetYaxis().SetTitle("#LT Events / bin #GT")
moveLegend={"dx": -0.3,"dy": 0.04}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addStandardTexts()
plot.draw()
plot.save()
def doPlot(datasets, analyses, path, name, rootFile=None, rootHistoName=None):
histos = []
legends = {"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"}
for aname, analysis in analyses:
p = None
if isinstance(path, basestring):
p = plots.DataMCPlot(datasets, analysis+"/"+path)
else:
p = plots.DataMCPlot(datasets, path.clone(tree=analysis+"/tree"))
if normalize:
tauEmbedding.scaleNormalization(p)
h = p.histoMgr.getHisto("Data")
h.setName(aname)
h.setLegendLabel(legends.get(aname, aname))
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
if rootFile:
baseline = histos[0].getRootHisto().Clone("EWKtau_"+rootHistoName)
baseline.SetDirectory(rootFile)
baseline.Write()
plus = histos[1].getRootHisto().Clone("EWKtau_JESUp_"+rootHistoName)
plus.SetDirectory(rootFile)
plus.Write()
minus = histos[2].getRootHisto().Clone("EWKtau_JESDown_"+rootHistoName)
minus.SetDirectory(rootFile)
minus.Write()
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasets.getDataset("Data").getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 40})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.addLuminosityText()
p.save()
def plotRoc(h, prefix, postfix="", log=False, opts={}, zoom_opts={}, moveLegend={}):
args = {"ymin": 0, "ymax": 1.1, "xmin": 0, "xmax": 1.1}
name = prefix+"isolation_%s"%h.name + postfix
if log:
name += "_log"
args["ymin"] = 1e-3
args.update(opts)
h.createFrame(name, **args)
h.frame.GetXaxis().SetTitle("Non-QCD efficiency")
h.frame.GetYaxis().SetTitle(h.ylabel)
if log:
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
h.draw()
h.save()
def drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, addMCUncertainty=True, ratio=True, opts={}, opts2={}, moveLegend={}, normalize=True, cutLine=None, cutBox=None, function=None):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
if normalize:
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 2
_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)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
common(h, xlabel, ylab)
def drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, addMCUncertainty=True, ratio=False, opts={}, opts2={}, moveLegend={}, textFunction=None, cutLine=None, cutBox=None):
if cutLine != None and cutBox != None:
raise Exception("Both cutLine and cutBox were given, only either one can exist")
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_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)
if log:
h.getPad().SetLogy(log)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
common(h, xlabel, ylab, textFunction=textFunction)
def muonPt(h, prefix="", rebin=1, ratio=False, cutBox=None):
xlabel = "Muon p_{T} (GeV/c)"
ylabel = "Events / %.0f GeV/c"
#ylabel = "Number of events / 5.0 GeV/c"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
# tmp = h.histoMgr.getHisto("Data").getRootHisto()
# dataEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# tmp = h.histoMgr.getHisto("StackedMC").getSumRootHisto()
# mcEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# print "Muon pt Data/MC = %f/%f = %f" % (dataEvents, mcEvents, dataEvents/mcEvents)
h.createFrame(prefix+"muon_pt", opts=_optsLin)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"muon_pt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def plotMet(h, name, xlabel, rebin=1, ratio=False, opts={}, opts2={}):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events / %.0f GeV" % h.binWidth()
h.stackMCHistograms(stackSignal=False)
h.addMCUncertainty()
opts_ = {"ymin": 1e-2, "ymaxfactor": 2} #10}
opts_.update(opts)
#h.createFrameFraction(name, opts=opts)
h.createFrame(name+"_"+runs, opts=opts_, createRatio=ratio, opts2=opts2)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.moveLegend(histograms.createLegend())) #, dx=-0.12))
h.appendPlotObject(histograms.PlotText(0.42, 0.89, "Runs %s"%runs, size=17))
common(h, xlabel, ylabel)
def plotTurnOnData(datasets, name, pathAll, pathPassed1, pathPassed2, dataText1, dataText2, rebin=1, ratio=False):
dataLabel1 = "Data: "+dataText1
dataLabel2 = "Data: "+dataText2
dataset = datasets.getDataset("Data")
all = dataset.getDatasetRootHisto(pathAll).getHistogram()
passed1 = dataset.getDatasetRootHisto(pathPassed1).getHistogram()
passed2 = dataset.getDatasetRootHisto(pathPassed2).getHistogram()
data_eff_gr1 = ROOT.TGraphAsymmErrors(passed1, all, "cp") # 0.683 cl is default
data_eff_gr2 = ROOT.TGraphAsymmErrors(passed2, all, "cp") # 0.683 cl is default
p = plots.ComparisonPlot(
histograms.HistoGraph(data_eff_gr1, "Data1", "p", "P"),
histograms.HistoGraph(data_eff_gr2, "Data2", "p", "P")
)
p.histoMgr.forEachHisto(styles.generator2(styles.StyleMarker(markerSizes=[1.0, 2.0]), [styles.dataStyle, styles.mcStyle2]))
p.histoMgr.setHistoLegendLabelMany({
"Data1": dataLabel1,
"Data2": dataLabel2,
})
p.setLuminosity(dataset.getLuminosity())
opts = {"ymin": 0.0, "ymax": 1.1}
#opts2 = {"ymin": 0.5, "ymax": 1.5}
opts2 = {"ymin": 0.8, "ymax": 1.2}
name = "calomet_turnon_"+runs+"_data_"+name
p.createFrame(name, createRatio=ratio, invertRatio=True, opts=opts, opts2=opts2)
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.95, y2=0.85),
#dx=-0.55, dy=-0.05
dx=-0.44, dy=-0.58
))
common(p, "Uncorrected PF E_{T}^{miss} (GeV)", "Efficiency")
def doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)")
(hall, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(name, tdEff.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")), tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {True: "Btagging", False: "NoBTag"}[btag], npure/nall, (1-npure/nall)*100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_"+name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_"+name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue-7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed-7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1-0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(histograms.PlotText(0.5, 0.9, plots._legendLabels.get(name, name), size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
def doPlots(table):
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 += " MC"
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 = "MC"
if name == "Data":
ylabel = "Data"
ylabel += " events"
p.frame.GetYaxis().SetTitle(ylabel)
for irow in xrange(nrows):
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=-0.05,
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(0.5, 0.2, label, size=20))
p.draw()
if name == "Data":
p.setLuminosity(lumi)
p.addStandardTexts()
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(0.75, 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()
p.addStandardTexts()
p.save()
def drawPlot(h,
name,
xlabel,
ylabel="Events / %.0f GeV/c",
rebin=1,
log=True,
addMCUncertainty=True,
ratio=False,
opts={},
opts2={},
moveLegend={},
textFunction=None,
cutLine=None,
cutBox=None):
if cutLine != None and cutBox != None:
raise Exception(
"Both cutLine and cutBox were given, only either one can exist")
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
scaleMCfromWmunu(h)
# h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=True) #stackSignal=True)
# h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
## _opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts2 = {"ymin": 0.0, "ymax": 2.0}
_opts.update(opts)
_opts2.update(opts2)
#if log:
# name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
if log:
h.getPad().SetLogy(log)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
common(h, xlabel, ylab, textFunction=textFunction)
def drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, ratio=True, opts={}, opts2={}, moveLegend={}, cutLine=None, cutBox=None, function=None):
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()
embErr = h.histoMgr.getHisto("Embedded").getRootHisto().Clone("Embedded_err")
sigErr = h.histoMgr.getHisto("Normal").getRootHisto().Clone("Normal_err")
embErr.SetFillColor(ROOT.kBlue-7)
embErr.SetFillStyle(3004)
embErr.SetMarkerSize(0)
sigErr.SetFillColor(ROOT.kRed-7)
sigErr.SetMarkerSize(0)
sigErr.SetFillStyle(3005)
h.prependPlotObject(sigErr, "E2")
h.prependPlotObject(embErr, "E2")
_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")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
tmp = embErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
h.legend.AddEntry(tmp, "Stat. unc.", "F")
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
common(h, xlabel, ylab)
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = sort(QCDInvertedNormalizationFactors.QCDInvertedNormalization)
analysis = "signalAnalysisInvertedTau"
optModes = []
optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
optModes.append("OptQCDTailKillerVeryTightPlus")
plot = plots.PlotBase()
color = 1
for optMode in optModes:
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
histonames = datasets.getDataset("Data").getDirectoryContent(HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME,"")
if not binname == "Inclusive":
bins.append(binname)
for i,bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets, HISTONAME+"/"+HISTONAME+bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
legendName = optMode.replace("OptQCDTailKiller","R_{BB} ")
legendName = legendName.replace("Plus","")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Scale(QCDInvertedNormalization[i])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
h.Scale(QCDInvertedNormalization[i])
mt.Add(h)
plot.histoMgr.appendHisto(histograms.Histo(mt,mt.GetName()))
style = tdrstyle.TDRStyle()
plot.createFrame("mt")
moveLegend={"dx": -0.15,"dy": 0.}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
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,
addMCUncertainty=True,
ratio=True,
opts={},
opts2={},
moveLegend={},
normalize=True,
cutLine=None,
cutBox=None,
function=None):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
if normalize:
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 2
_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)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
common(h, xlabel, ylab)
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 plotTurnOn(datasets, pathAll, pathPassed, commonText, dataText=None, mcText=None, rebin=1, ratio=False):
dataLabel = "Data"
mcLabel = "Simulation"
if dataText != None and mcText == None:
raise Exception("mcText must not be None when dataText is not")
if dataText == None and mcText != None:
raise Exception("dataText must not be None when mcText is not")
# if dataText != None:
# dataLabel += ": "+dataText
# mcLabel += ": "+mcText
mc_effs = []
data_eff_gr = None
binWidth = None
luminosity = 0
for dataset in datasets.getAllDatasets():
all = dataset.getDatasetRootHisto(pathAll).getHistogram()
passed = dataset.getDatasetRootHisto(pathPassed).getHistogram()
if rebin > 1:
all.Rebin(rebin)
passed.Rebin(rebin)
binWidth = all.GetBinWidth(1)
if dataset.isMC() and not "TTTo" in dataset.getName():
#if dataset.isMC() and not "TTTo" in dataset.getName() and not "QCD" in dataset.getName():
#if dataset.isMC() and "QCD" in dataset.getName():
mc_effs.append(HistoEff(all, passed, dataset))
elif dataset.isData():
data_eff_gr = ROOT.TGraphAsymmErrors(passed, all, "cp") # 0.683 cl is default
luminosity += dataset.getLuminosity()
mc_eff_gr = combineHistoEffs(mc_effs)
p = plots.ComparisonPlot(
histograms.HistoGraph(data_eff_gr, "Data", "p", "P"),
histograms.HistoGraph(mc_eff_gr, "Simulation", "p", "P")
)
p.histoMgr.forEachHisto(styles.generator2(styles.StyleMarker(markerSizes=[1.0, 1.5]), plotStyles))
p.histoMgr.setHistoLegendLabelMany({
"Data": dataLabel,
"Simulation": mcLabel,
})
p.setLuminosity(luminosity)
#p.addGraph(data_eff_gr, "Data")
#p.addGraph(mc_eff_gr, "Simulation")
#p.finalize()
opts = {"ymin": 0.0, "ymax": 1.1}
#opts2 = {"ymin": 0.5, "ymax": 1.5}
opts2 = {"ymin": 0.7, "ymax": 1.3}
name = "calomet_turnon_"+runs
if not mcDataDefinition:
name += "_McSummer11"
if caloMetNoHF:
name += "_caloMetNoHF"
p.createFrame(name, createRatio=ratio, opts=opts, opts2=opts2)
if ratio:
p.getFrame2().GetYaxis().SetTitle("Data / MC")
p.cf.canvas.cd(2)
p.line1 = plots._createRatioLine(p.cf.frame.getXmin(), p.cf.frame.getXmax(), 0.9)
p.line2 = plots._createRatioLine(p.cf.frame.getXmin(), p.cf.frame.getXmax(), 1.1)
p.line1.SetLineColor(ROOT.kBlue)
p.line2.SetLineColor(ROOT.kBlue)
p.line1.Draw("L")
p.line2.Draw("L")
p.cf.canvas.cd(1)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.95, y2=0.85),
#dx=-0.55, dy=-0.05
#dx=-0.44, dy=-0.58
dx=-0.3, dy=-0.4
))
x = 0.25
y = 0.3
dy = 0.035
mcColor = mc_eff_gr.GetMarkerColor()
size = 17
p.appendPlotObject(histograms.PlotText(x, y, "Data (runs %s):"%runs, size=size)); y -= dy
p.appendPlotObject(histograms.PlotText(x, y, dataText, size=size)); y -= dy
y -= 0.01
p.appendPlotObject(histograms.PlotText(x, y, "Simulation:", size=size, color=mcColor)); y -= dy
p.appendPlotObject(histograms.PlotText(x, y, mcText, size=size, color=mcColor)); y -= dy
def text():
l = ROOT.TLatex()
l.SetNDC()
# l.SetTextFont(l.GetTextFont()-20) # bold -> normal
l.SetTextSize(l.GetTextSize()*0.65)
#l.DrawLatex(0.35, 0.4, commonText)
l.DrawLatex(0.48, 0.32, commonText)
textFunction = text
if dataText != None:
textFunction = None
#common(p, "PF E_{T}^{miss} (GeV)", "Efficiency / %.0f GeV"%binWidth, afterDraw=textFunction)
common(p, "Uncorrected PF E_{T}^{miss} (GeV)", "Efficiency", afterDraw=textFunction)
def doPlotsWTauMu(datasets, name):
selection = And(muonSelection, muonVeto, electronVeto, jetSelection)
td = treeDraw.clone(varexp="muons_p4.Pt() >> tmp(40,0,400)")
ds = datasets.getDataset(name)
# Take first unweighted histograms for the fraction plot
drh_all = ds.getDatasetRootHisto(td.clone(selection=selection, weight=""))
drh_pure = ds.getDatasetRootHisto(td.clone(selection=And(selection, "abs(muons_mother_pdgid) == 24"), weight=""))
hallUn = drh_all.getHistogram()
hpureUn = drh_pure.getHistogram()
# Then the correctly weighted for the main plot
drh_all = ds.getDatasetRootHisto(td.clone(selection=selection))
drh_pure = ds.getDatasetRootHisto(td.clone(selection=And(selection, "abs(muons_mother_pdgid) == 24")))
lumi = datasets.getDataset("Data").getLuminosity()
drh_all.normalizeToLuminosity(lumi)
drh_pure.normalizeToLuminosity(lumi)
hall = drh_all.getHistogram()
hpure = drh_pure.getHistogram()
hall.SetName("All")
hpure.SetName("Pure")
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#tau#rightarrow#mu"
# "Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue-7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed-7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame("muonPt_wtaumu_"+name, createRatio=True, opts={"ymin": 1e-1, "ymaxfactor": 2}, opts2={"ymin": 0.9, "ymax": 1.05}
)
p.setRatios([plots._createRatio(hpureUn, hallUn, "", isBinomial=True)])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1-0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
#p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.getPad().SetLogy(True)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("Muon p_{T} (GeV/c)")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c" % p.binWidth())
p.appendPlotObject(histograms.PlotText(0.5, 0.9, plots._legendLabels.get(name, name), size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.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 drawPlot(h,
name,
xlabel,
ylabel="Events / %.0f GeV/c",
rebin=1,
log=True,
ratio=True,
opts={},
opts2={},
moveLegend={},
cutLine=None,
cutBox=None,
function=None):
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()
embErr = h.histoMgr.getHisto("Embedded").getRootHisto().Clone(
"Embedded_err")
sigErr = h.histoMgr.getHisto("Normal").getRootHisto().Clone("Normal_err")
embErr.SetFillColor(ROOT.kBlue - 7)
embErr.SetFillStyle(3004)
embErr.SetMarkerSize(0)
sigErr.SetFillColor(ROOT.kRed - 7)
sigErr.SetMarkerSize(0)
sigErr.SetFillStyle(3005)
h.prependPlotObject(sigErr, "E2")
h.prependPlotObject(embErr, "E2")
_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")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
tmp = embErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
h.legend.AddEntry(tmp, "Stat. unc.", "F")
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
common(h, xlabel, ylab)
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 doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(
analysisEmb + "/tree",
weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb + "/tree",
weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(
varexp=
"sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)"
)
(hall, tmp) = datasetsEmb.getHistogram(name,
tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(
name,
tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(
name,
tdEff.clone(
selection=And(selection, "abs(temuon_mother_pdgid) == 24")),
tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {
True: "Btagging",
False: "NoBTag"
}[btag], npure / nall, (1 - npure / nall) * 100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_" + name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_" + name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(
histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(name, name),
size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
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 doPlotsWTauMu(datasets,
name,
datasetName,
ntupleCache,
selectorName,
unweighted,
fitFunction,
rebin=None):
ds = datasets.getDataset(datasetName)
# Take first unweighted histograms for the fraction plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection" + unweighted,
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram(
"selectedMuonPt_AfterJetSelection_MuFromW" + unweighted,
selectorName))
def Rebin(th1, div=False):
if rebin is None:
return th1
else:
h = th1.Rebin(
len(rebin) - 1, th1.GetName(), array.array("d", rebin))
if div:
h.Scale(1, "width")
return h
def createTEfficiency(drhAll, drhPure):
hallUn = Rebin(drhAll.getHistogram())
hpureUn = Rebin(drhPure.getHistogram())
teff = ROOT.TEfficiency(hpureUn, hallUn)
teff.SetDirectory(0)
teff.SetWeight(drhAll.getDataset().getCrossSection())
return teff
teffs = drh_all.forEach(createTEfficiency, drh_pure)
#coll = ROOT.TList()
#for o in teffs:
# coll.AddLast(o)
#ratio = ROOT.TEfficiency.Combine(coll)
ratio = teffs[0]
for e in teffs[1:]:
ratio.Add(e)
styles.getDataStyle().apply(ratio)
ratio.SetName(datasetName)
ROOT.gStyle.SetStatY(0.99)
ROOT.gStyle.SetStatX(0.52)
ROOT.gStyle.SetStatW(0.18)
ROOT.gStyle.SetStatH(0.23)
expFit = ROOT.TF1("purityFit", [
"1/(1+[0]*exp(-[1]*x))",
"1-[0]*exp(-[1]*x)",
"1-([0]/(x^[1]))",
][fitFunction], aux.th1Xmin(ratio.GetPassedHistogram()),
aux.th1Xmax(ratio.GetPassedHistogram()))
expFit.SetParameter(0, 0.05)
ratio.Fit(expFit, "N")
expFit.SetLineColor(ROOT.kRed)
expFit.SetLineWidth(2)
# expFit = None
# Then the correctly weighted for the main plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection",
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection_MuFromW",
selectorName))
if mcOnly:
lumi = mcLuminosity
else:
lumi = datasets.getDataset("Data").getLuminosity()
drh_all.normalizeToLuminosity(lumi)
drh_pure.normalizeToLuminosity(lumi)
hall = Rebin(drh_all.getHistogram(), div=True)
hpure = Rebin(drh_pure.getHistogram(), div=True)
hall.SetName("All")
hpure.SetName("Pure")
p = plots.ComparisonPlot(hall, hpure)
p.setLuminosity(lumi)
p.setEnergy(ds.getEnergy())
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
# "Pure": "W#rightarrow#tau#rightarrow#mu"
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(era + "_" + name +
"_selectedMuonPt_AFterJetSelection_MuFromW_%s_fit%d" %
(datasetName, fitFunction) + unweighted,
createRatio=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 2
},
opts2={
"ymin": 0.9,
"ymax": 1.05
})
p.setRatios([ratio])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
#p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.appendPlotObjectToRatio(
histograms.PlotText(0.18, 0.61, "0.038", size=18, color=ROOT.kBlue))
if expFit is not None:
p.appendPlotObjectToRatio(expFit)
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.getPad().SetLogy(True)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("Muon p_{T} (GeV/c)")
p.frame.GetYaxis().SetTitle("Events / #Deltap_{T} %.0f-%.0f GeV/c" %
(min(p.binWidths()), max(p.binWidths())))
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(datasetName, datasetName),
size=18))
p.draw()
p.addStandardTexts()
p.save()
# Clear list of functions
ratio.GetListOfFunctions().Delete()
return (ratio, expFit)
