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 addCurve(self, name, label):
def getHisto(dataset):
tmp = self.datasets.getDataset(dataset).getDatasetRootHisto(name)
#tmp.normalizeToLuminosity(46)
tmp.normalizeByCrossSection()
return tmp.getHistogram()
signalRootHisto = getHisto("Signal")
signalEff = muonAnalysis.dist2eff(signalRootHisto)
signalEffValues = dataset.histoToList(signalEff)
tmpHistoManager = histograms.HistoManager(self.datasets, name)
tmpHistoManager.normalizeMCByCrossSection()
#tmpHistoManager.normalizeMCByLuminosity()
mcSum = histograms.sumRootHistos([
muonAnalysis.getSumOrRootHisto(histo) for histo in filter(
lambda h: h.isMC(), tmpHistoManager.getHistos())
])
mcSum = muonAnalysis.dist2pass(mcSum)
bkgHisto = tmpHistoManager.getHisto("Background")
bkg = muonAnalysis.getSumOrRootHisto(bkgHisto).Clone(name +
"_bkgfraction")
bkg = muonAnalysis.dist2pass(bkg)
bkg.Divide(mcSum)
bkgValues = dataset.histoToList(bkg)
curve = ROOT.TGraph(len(signalEffValues),
array.array("d", signalEffValues),
array.array("d", bkgValues))
self.histoMgr.appendHisto(histograms.HistoGraph(curve, label))
def addEmbStatSyst(p):
rhwu = p.histoMgr.getHisto("Embedded").getRootHistoWithUncertainties()
embStatSyst = rhwu.getSystematicUncertaintyGraph(addStatistical=True)
for i in xrange(0, embStatSyst.GetN()):
embStatSyst.SetPointEXhigh(i, 0)
embStatSyst.SetPointEXlow(i, 0)
aux.copyStyle(rhwu.getRootHisto(), embStatSyst)
p.appendPlotObject(
histograms.HistoGraph(embStatSyst,
"EmbStatSyst",
legendStyle=None,
drawStyle="[]"))
def createPlot(graph_, **kwargs):
if isinstance(graph_, ROOT.TH1):
defaults = {"legendStyle": "l"}
defaults.update(kwargs)
graph_.GetZaxis().SetTitle("")
#### return plots.PlotBase([histograms.HistoBase(graph_, "efficiency", **defaults)])
return plots.PlotBase(
[histograms.Histo(graph_, "efficiency", **defaults)])
else:
defaults = {"drawStyle": "EP"}
defaults.update(kwargs)
return plots.PlotBase(
[histograms.HistoGraph(graph_, "efficiency", **defaults)])
def addCurve(self, name, label):
def getHisto(dataset):
tmp = self.datasets.getDataset(dataset).getDatasetRootHisto(name)
tmp.normalizeByCrossSection()
return tmp.getHistogram()
signalRootHisto = getHisto("Signal")
bkgRootHisto = getHisto("Background")
signalEff = muonAnalysis.dist2eff(signalRootHisto)
bkgRej = muonAnalysis.dist2rej(bkgRootHisto)
# bkgRej = muonAnalysis.dist2eff(bkgRootHisto)
signalEffValues = dataset.histoToList(signalEff)
bkgRejValues = dataset.histoToList(bkgRej)
roc = ROOT.TGraph(len(signalEffValues),
array.array("d", signalEffValues),
array.array("d", bkgRejValues))
self.histoMgr.appendHisto(histograms.HistoGraph(roc, label))
def plotEfficiency(datasets,
datasetNames,
allPath,
passedPath,
name,
xlabel,
rebinBins=None,
**kwargs):
if mcOnly:
return
def rebin(h):
return h.Rebin(
len(rebinBins) - 1, h.GetName(), array.array("d", rebinBins))
for dname in datasetNames:
dset = datasets.getDataset(dname)
allHisto = dset.getDatasetRootHisto(allPath).getHistogram()
passedHisto = dset.getDatasetRootHisto(passedPath).getHistogram()
if allHisto.Integral() < 1:
continue
if rebinBins != None:
allHisto = rebin(allHisto)
passedHisto = rebin(passedHisto)
eff = ROOT.TGraphAsymmErrors(passedHisto, allHisto,
"cp") # 0.683 cl is default
p = plots.PlotBase([histograms.HistoGraph(eff, dname, "p", "P")])
if dset.isData():
p.setLuminosity(dset.getLuminosity())
opts = {"ymin": 0.0, "ymax": 1.1}
plots.drawPlot(p,
name + "_" + dname,
xlabel,
addLuminosityText=dset.isData(),
opts=opts,
**kwargs)
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 addDataStatSyst(p):
dataStatSyst = p.histoMgr.getHisto("Data").getRootHistoWithUncertainties().getSystematicUncertaintyGraph(addStatistical=True)
for i in xrange(0, dataStatSyst.GetN()):
dataStatSyst.SetPointEXhigh(i, 0)
dataStatSyst.SetPointEXlow(i, 0)
p.appendPlotObject(histograms.HistoGraph(dataStatSyst, "DataStatSyst", legendStyle=None, drawStyle="[]"))
def addPoint(self, sigeff, bkgfrac, label):
gr = ROOT.TGraph(1, array.array("d", [sigeff]),
array.array("d", [bkgfrac]))
self.histoMgr.appendHisto(histograms.HistoGraph(gr, label))
self.pointLabels.append(label)
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("Ratio")
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 doCounters(datasets):
eventCounter = counter.EventCounter(datasets)
eventCounter.normalizeMCByLuminosity()
# eventCounter.normalizeMCToLuminosity(73)
print "============================================================"
print "Main counter (MC normalized by collision data luminosity)"
print eventCounter.getMainCounterTable().format()
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
### plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
style = tdrstyle.TDRStyle()
eventCounter = counter.EventCounter(datasets)
eventCounter.normalizeMCByCrossSection()
mainTable = eventCounter.getMainCounterTable()
signalDatasets = [
"TTToHplusBWB_M80",
"TTToHplusBWB_M90",
"TTToHplusBWB_M100",
"TTToHplusBWB_M120",
"TTToHplusBWB_M140",
"TTToHplusBWB_M150",
"TTToHplusBWB_M155",
"TTToHplusBWB_M160",
# "TTToHplusBHminusB_M80",
# "TTToHplusBHminusB_M90",
# "TTToHplusBHminusB_M100",
# "TTToHplusBHminusB_M120",
# "TTToHplusBHminusB_M140",
# "TTToHplusBHminusB_M150",
# "TTToHplusBHminusB_M155",
# "TTToHplusBHminusB_M160",
]
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", "DeltaPhi(Tau,MET) upper limit"
]
xvalues = [80, 90, 100, 120, 140, 150, 155, 160]
# xvalues = [80, 90, 120, 140, 150, 155, 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"))
for cut in cuts:
cutCount = column.getCount(column.getRowNames().index(cut))
if column.getRowNames().index(cut) == 1: ## trigger
allCount = column.getCount(
column.getRowNames().index("Offline selection begins"))
if column.getRowNames().index(cut) == 4: ## tau id
allCount = column.getCount(
column.getRowNames().index("Trigger and HLT_MET cut"))
if column.getRowNames().index(cut) == 9: ## electron veto
allCount = column.getCount(
column.getRowNames().index("taus == 1"))
if column.getRowNames().index(cut) == 10: ## muon veto
## muon veto for lepton veto
allCount = column.getCount(
column.getRowNames().index("trigger scale factor"))
# allCount = column.getCount(column.getRowNames().index("electron veto"))
if column.getRowNames().index(cut) == 12: ## njets
allCount = column.getCount(
column.getRowNames().index("muon veto"))
if column.getRowNames().index(cut) == 13: ## MET
allCount = column.getCount(column.getRowNames().index("njets"))
if column.getRowNames().index(cut) == 15: ## btagging
allCount = column.getCount(column.getRowNames().index("MET"))
if column.getRowNames().index(
cut) == 16: ## DeltaPhi(Tau,MET) upper limit
allCount = column.getCount(
column.getRowNames().index("btagging scale factor"))
eff = cutCount.clone()
eff.divide(allCount) # N(cut) / N(all)
# print "cutCount ",cutCount.value(),"allCount ",allCount.value()
yvalues[cut].append(eff.value())
yerrs[cut].append(eff.uncertainty())
if column.getRowNames().index(
cut) == 9: ## DeltaPhi(Tau,MET) upper
print cut, eff.value()
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(0.9)
gr.SetLineStyle(1)
gr.SetLineWidth(2)
return gr
gtrig = createErrors("Trigger and HLT_MET cut")
gtrig.SetLineColor(38)
gtrig.SetMarkerColor(38)
gtrig.SetMarkerStyle(20)
gtrig.SetMarkerSize(2)
gtrig.SetLineStyle(1) ## 8
gtrig.SetLineWidth(4)
gtau = createErrors("taus == 1")
gtau.SetLineColor(2)
gtau.SetMarkerColor(2)
gtau.SetMarkerStyle(21)
gtau.SetMarkerSize(2)
gtau.SetLineStyle(1) ##3
gtau.SetLineWidth(4)
#gtau = createErrors("trigger scale factor")
gveto = createErrors("muon veto")
gveto.SetLineColor(6)
gveto.SetMarkerColor(6)
gveto.SetMarkerStyle(22)
gveto.SetMarkerSize(2)
gveto.SetLineStyle(1) ## 5
gveto.SetLineWidth(4)
gjets = createErrors("njets")
gjets.SetLineColor(4)
gjets.SetMarkerColor(4)
gjets.SetMarkerStyle(23)
gjets.SetMarkerSize(2)
gjets.SetLineStyle(1) ## 1
gjets.SetLineWidth(4)
gmet = createErrors("MET")
gmet.SetLineColor(ROOT.kGreen + 2)
gmet.SetMarkerColor(ROOT.kGreen + 2)
gmet.SetMarkerStyle(24)
gmet.SetMarkerSize(2)
gmet.SetLineStyle(1) ##2
gmet.SetLineWidth(4)
gbtag = createErrors("btagging")
gbtag.SetLineColor(1)
gbtag.SetMarkerColor(1)
gbtag.SetMarkerStyle(25)
gbtag.SetMarkerSize(2)
gbtag.SetLineStyle(1) ## 6
gbtag.SetLineWidth(4)
gdphi = createErrors("DeltaPhi(Tau,MET) upper limit")
gdphi.SetLineColor(93)
gdphi.SetMarkerColor(93)
gdphi.SetMarkerStyle(26)
gdphi.SetMarkerSize(2)
gdphi.SetLineStyle(1) ## 6
gdphi.SetLineWidth(4)
#gtau = createErrors("trigger scale factor")
glist = [gtrig, gtau, gveto, gjets, gmet, gbtag, gdphi]
opts = {"xmin": 75, "xmax": 165, "ymin": 0.06}
canvasFrame = histograms.CanvasFrame(
[histograms.HistoGraph(g, "", "") for g in glist],
"SignalEfficiencyConseq", **opts)
canvasFrame.frame.GetYaxis().SetTitle("Selection efficiency")
canvasFrame.frame.GetXaxis().SetTitle("m_{H^{#pm}} (GeV/c^{2})")
canvasFrame.canvas.SetLogy(True)
canvasFrame.frame.Draw()
for gr in glist:
gr.Draw("PC same")
histograms.addStandardTexts()
# tex5 = ROOT.TLatex(120,0.11,"t#bar{t} -> bH^{#pm}bH^{#pm}")
tex5 = ROOT.TLatex(120, 0.3, "t#bar{t} -> bH^{#pm}bW")
# tex5.SetNDC()
tex5.SetTextSize(25)
tex5.Draw()
legend = histograms.createLegend(x1=0.2, y1=0.36, x2=0.5, y2=0.65)
legend.AddEntry(gtrig, "Trigger", "lp")
legend.AddEntry(gtau, "#tau identification", "lp")
legend.AddEntry(gveto, "lepton vetoes", "lp")
legend.AddEntry(gjets, "3 jets", "lp")
legend.AddEntry(gmet, "MET ", "lp")
legend.AddEntry(gbtag, "b tagging ", "lp")
legend.AddEntry(gdphi, "DeltaPhi(Tau,MET)", "lp")
legend.Draw()
canvasFrame.canvas.SaveAs(".png")
canvasFrame.canvas.SaveAs(".C")
canvasFrame.canvas.SaveAs(".eps")
def main():
# Read the datasets
#datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets = dataset.getDatasetsFromMulticrabCfg(analysisName=analysis,
searchMode=searchMode,
dataEra=dataEra,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Remove signals other than M120
### datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTToHplusBHminusB" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTToHplus" in name,
datasets.getAllDatasetNames()))
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
### plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
style = tdrstyle.TDRStyle()
eventCounter = counter.EventCounter(datasets)
#eventCounter = counter.EventCounter(datasets, counters=counters)
eventCounter.normalizeMCByCrossSection()
mainTable = eventCounter.getMainCounterTable()
cellFormat = counter.TableFormatText(cellFormat=counter.CellFormatText(
valueOnly=True))
print mainTable.format(cellFormat)
#eventCounterUnweighted = counter.EventCounter(datasets, mainCounterOnly=True, counters="counters")
#eventCounterUnweighted.normalizeMCByCrossSection()
#mainTableUnweighted = eventCounterUnweighted.getMainCounterTable()
#print mainTableUnweighted.format(cellFormat)
signalDatasets = [
"HplusTB_M180",
"HplusTB_M190",
"HplusTB_M200",
"HplusTB_M220",
"HplusTB_M250",
"HplusTB_M300",
#"HplusTB_M400",
#"HplusTB_M500",
#"HplusTB_M600",
#"TTToHplusBWB_M80",
#"TTToHplusBWB_M90",
#"TTToHplusBWB_M100",
#"TTToHplusBWB_M120",
#"TTToHplusBWB_M140",
#"TTToHplusBWB_M150",
#"TTToHplusBWB_M155",
#"TTToHplusBWB_M160",
]
allName = "Trigger and HLT_MET cut"
cuts = [
#"Offline selection begins",
"Trigger and HLT_MET cut",
"primary vertex",
"taus == 1",
"tau trigger scale factor",
"electron veto",
"muon veto",
"njets",
"QCD tail killer collinear",
"MET",
"btagging",
"btagging scale factor",
"QCD tail killer back-to-back"
]
xvalues = [180, 190, 200, 220, 250, 300]
#xvalues = [80, 90, 100, 120, 140, 150, 155, 160]
xerrs = [0] * len(xvalues)
yvalues = {}
yerrs = {}
for cut in cuts:
yvalues[cut] = []
yerrs[cut] = []
for name in signalDatasets:
column = mainTable.getColumn(name=name)
#columnUnweighted = mainTableUnweighted.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("Trigger and HLT_MET cut"))
# Somewhat weird way to get total cross section via unweighted counters
#rowNames = column.getRowNames()
#if "allEvents" in rowNames:
# allCount = columnUnweighted.getCount(rowNames.index("allEvents"))
#else:
# # Hack needed because non-triggered signal pattuples do not have allEvents counter!
# allCount = columnUnweighted.getCount(rowNames.index("primaryVertexAllEvents"))
#dset = datasets.getDataset(name)
#allCount.multiply(dataset.Count(dset.getNAllEvents()/dset.getNAllEventsUnweighted()))
allCount = dataset.Count(
datasets.getDataset(name).getCrossSection(), 0)
for cut in cuts:
cutCount = column.getCount(column.getRowNames().index(cut))
eff = cutCount.clone()
eff.divide(allCount) # N(cut) / N(all)
if column.getRowNames().index(cut) == 9: ## btagging
print cut, eff.value()
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(0.9)
gr.SetLineStyle(1)
gr.SetLineWidth(2)
return gr
#gtrig = createErrors("primary vertex")
gtrig = createErrors("Trigger and HLT_MET cut")
print gtrig
gtrig.SetLineColor(38)
gtrig.SetMarkerColor(38)
gtrig.SetMarkerStyle(20)
gtrig.SetLineStyle(2)
gtrig.SetMarkerSize(2)
gtau = createErrors("taus == 1")
gtau.SetLineColor(2)
gtau.SetMarkerColor(2)
gtau.SetMarkerStyle(20)
gtau.SetMarkerSize(2)
gtau.SetLineStyle(3)
#gtau = createErrors("trigger scale factor")
gveto = createErrors("muon veto")
gveto.SetLineColor(1)
gveto.SetMarkerColor(1)
gveto.SetMarkerStyle(21)
gveto.SetMarkerSize(2)
gveto.SetLineStyle(4)
gjets = createErrors("njets")
gjets.SetLineColor(4)
gjets.SetMarkerColor(4)
gjets.SetMarkerStyle(22)
gjets.SetMarkerSize(2)
gjets.SetLineStyle(1)
gcoll = createErrors("QCD tail killer collinear")
gcoll.SetLineColor(6)
gcoll.SetMarkerColor(6)
gcoll.SetMarkerStyle(26)
gcoll.SetMarkerSize(2)
gcoll.SetLineStyle(2)
gmet = createErrors("MET")
gmet.SetLineColor(1)
gmet.SetMarkerColor(1)
gmet.SetMarkerStyle(24)
gmet.SetMarkerSize(2)
gmet.SetLineStyle(5)
gbtag = createErrors("btagging")
gbtag.SetLineColor(2)
gbtag.SetMarkerColor(2)
gbtag.SetMarkerStyle(25)
gbtag.SetMarkerSize(2)
gbtag.SetLineStyle(6)
print gbtag
gback = createErrors("QCD tail killer back-to-back")
gback.SetLineColor(7)
gback.SetMarkerColor(7)
gback.SetMarkerStyle(23)
gback.SetMarkerSize(2)
gback.SetLineStyle(1)
#gtau = createErrors("trigger scale factor")
glist = [gtrig, gtau, gveto, gjets, gcoll, gmet, gbtag, gback]
opts = {"xmin": 175, "xmax": 310, "ymin": 0.001}
canvasFrame = histograms.CanvasFrame(
[histograms.HistoGraph(g, "", "") for g in glist], "SignalEfficiency",
**opts)
canvasFrame.frame.GetYaxis().SetTitle("Selection efficiency")
canvasFrame.frame.GetXaxis().SetTitle("m_{H^{#pm}} (GeV/c^{2})")
canvasFrame.canvas.SetLogy(True)
canvasFrame.frame.Draw()
for gr in glist:
gr.Draw("PC same")
histograms.addStandardTexts()
legend2 = histograms.createLegend(x1=0.5, y1=0.7, x2=0.9, y2=0.85)
legend2.AddEntry(gtrig, "Trigger", "lp")
legend2.AddEntry(gtau, "Loose #tau identification", "lp")
legend2.AddEntry(gveto, "lepton vetoes", "lp")
legend2.AddEntry(gjets, "3 jets", "lp")
legend2.Draw()
legend = histograms.createLegend(x1=0.35, y1=0.15, x2=0.7, y2=0.3)
legend.AddEntry(gcoll, "QCD tail killer collinear", "lp")
legend.AddEntry(gmet, "MET > 60 GeV", "lp")
legend.AddEntry(gbtag, "b tagging ", "lp")
legend.AddEntry(gback, "QCD tail killer back-to-back: Tight", "lp")
legend.Draw()
canvasFrame.canvas.SaveAs(".png")
canvasFrame.canvas.SaveAs(".C")
canvasFrame.canvas.SaveAs(".eps")
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Remove signals other than M120
### datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
### datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
### plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
style = tdrstyle.TDRStyle()
eventCounter = counter.EventCounter(datasets, counters=counters)
eventCounter.normalizeMCByCrossSection()
mainTable = eventCounter.getMainCounterTable()
signalDatasets = [
"TTToHplusBWB_M80",
"TTToHplusBWB_M90",
"TTToHplusBWB_M100",
"TTToHplusBWB_M120",
"TTToHplusBWB_M140",
"TTToHplusBWB_M150",
"TTToHplusBWB_M155",
"TTToHplusBWB_M160",
]
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"
]
xvalues = [80, 90, 100, 120, 140, 150, 155, 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"))
for cut in cuts:
cutCount = column.getCount(column.getRowNames().index(cut))
eff = cutCount.clone()
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(0.9)
gr.SetLineStyle(1)
gr.SetLineWidth(2)
return gr
gtrig = createErrors("Trigger and HLT_MET cut")
gtrig.SetLineColor(38)
gtrig.SetMarkerColor(38)
gtrig.SetLineStyle(2)
gtau = createErrors("taus == 1")
gtau.SetLineColor(2)
gtau.SetMarkerColor(2)
gtau.SetLineStyle(3)
#gtau = createErrors("trigger scale factor")
gveto = createErrors("muon veto")
gveto.SetLineColor(1)
gveto.SetMarkerColor(1)
gveto.SetLineStyle(4)
gjets = createErrors("njets")
gjets.SetLineColor(4)
gjets.SetMarkerColor(4)
gjets.SetLineStyle(1)
gmet = createErrors("MET")
gmet.SetLineColor(2)
gmet.SetMarkerColor(2)
gmet.SetLineStyle(5)
gbtag = createErrors("btagging")
gbtag.SetLineColor(1)
gbtag.SetMarkerColor(1)
gbtag.SetLineStyle(6)
#gtau = createErrors("trigger scale factor")
glist = [gtrig, gtau, gveto, gjets, gmet, gbtag]
opts = {"xmin": 75, "xmax": 165, "ymin": 0.001}
canvasFrame = histograms.CanvasFrame(
[histograms.HistoGraph(g, "", "") for g in glist], "SignalEfficiency",
**opts)
canvasFrame.frame.GetYaxis().SetTitle("Selection efficiency")
canvasFrame.frame.GetXaxis().SetTitle("m_{H^{#pm}} (GeV/c^{2})")
canvasFrame.canvas.SetLogy(True)
canvasFrame.frame.Draw()
for gr in glist:
gr.Draw("PC same")
histograms.addEnergyText()
histograms.addCmsPreliminaryText()
legend = histograms.createLegend(x1=0.5, y1=0.53, x2=0.85, y2=0.75)
legend.AddEntry(gtrig, "Trigger", "l")
legend.AddEntry(gtau, "#tau identification", "l")
legend.AddEntry(gveto, "lepton vetoes", "l")
legend.AddEntry(gjets, "3 jets", "l")
legend.AddEntry(gmet, "MET ", "l")
legend.AddEntry(gbtag, "b tagging ", "l")
legend.Draw()
canvasFrame.canvas.SaveAs(".png")
canvasFrame.canvas.SaveAs(".C")
canvasFrame.canvas.SaveAs(".eps")