def main():
effDir = "../multicrab_aodAnalysis_taumet_v53_3_131015_122226_triggerTestEff"
bitDir = "."
datasets = aux.MultiObject()
datasets.add(
"eff",
dataset.getDatasetsFromMulticrabCfg(directory=effDir, dataEra=era))
datasets.add(
"bit",
dataset.getDatasetsFromMulticrabCfg(directory=bitDir, dataEra=era))
datasets.forEach(plots.mergeRenameReorderForDataMC)
style = tdrstyle.TDRStyle()
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
histograms.createLegend.setDefaults(y1=0.93, y2=0.8, x1=0.82, x2=0.93)
histograms.createLegend.moveDefaults(dx=-0.05)
plots._plotStyles["Ratio"].extend([
styles.StyleMarker(markerColor=ROOT.kRed),
styles.StyleLine(lineColor=ROOT.kRed)
])
plots._plotStyles["RatioLine"].extend(
[styles.StyleLine(lineColor=ROOT.kBlack)])
plots.drawPlot.setDefaults(addLuminosityText=True)
doPlots(*(datasets.getDataset("TTJets")))
def doPlotsWTauMuRatio(dsetMgr, selectionName, ratios, unweighted,
fitFunction):
prefix = era + "_" + selectionName + "_fitFunction%d" % fitFunction + unweighted + "_"
st = [s.clone() for s in styles.getStyles()]
st[0].marker = 33
st[1].marker = 26
st[2].marker = 32
st = [
styles.StyleCompound(styles=[s, styles.StyleMarker(markerSize=1.5)])
for s in st
]
# Do clone to get rid of statistics box
p = plots.PlotBase([x[0].Clone() for x in ratios])
p.setEnergy(dsetMgr.getEnergies())
p.histoMgr.setHistoDrawStyleAll("P")
p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.forEachHisto(styles.Generator(st))
for i, x in enumerate(ratios):
fitLine = x[1]
st[i].apply(fitLine)
p.appendPlotObject(fitLine)
def customize(plot):
xmin = plot.frame.GetXaxis().GetXmin()
xmax = plot.frame.GetXaxis().GetXmax()
val = 1
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
plot.prependPlotObject(l)
plot.appendPlotObject(
histograms.PlotText(0.18, 0.57, "0.038", size=18,
color=ROOT.kBlue))
plots.drawPlot(p,
prefix + "WMuFraction",
"Muon p_{T} (GeV/c)",
ylabel="W#rightarrow#mu fraction",
opts={
"ymin": 0.9,
"ymax": 1.02
},
moveLegend={
"dh": -0.15,
"dx": -0.5
},
customizeBeforeDraw=customize)
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 __init__(self, datasets, dname, names, labels):
plots.PlotBase.__init__(self, [])
self.name = dname
self.ylabel = "Efficiency"
for hname, label in zip(names, labels):
dh = datasets.getDataset(dname).getDatasetRootHisto(hname)
dh.normalizeByCrossSection()
dh = dataset.DatasetRootHisto(
muonAnalysis.dist2eff(dh.getHistogram()), dh.getDataset())
dh.setName(label)
self.histoMgr.append(dh)
self.histoMgr.forEachHisto(
styles.generator2(styles.StyleMarker(markerSize=1.5), plotStyles))
self.histoMgr.setHistoLegendStyleAll("l")
def finalize(self):
def setLabel(histo):
name = histo.getName()
scic_re = re.compile("Sc(?P<sig>\d+)(Ic(?P<iso>\d+))?")
ic_re = re.compile("Ic(?P<iso>\d+)")
for tauIso in ["VLoose", "Loose", "Medium", "Tight"]:
if "IsoTau" + tauIso in name:
histo.setLegendLabel('Counting iso "' + tauIso + '"')
break
if "IsoTauLike" + tauIso in name:
m = scic_re.search(name)
sc = "0.1"
ic = "0.5"
if m:
sc = m.group("sig").replace("0", "0.")
if m.group("iso"):
ic = m.group("iso").replace("0", "0.")
else:
m = ic_re.search(name)
if m:
ic = m.group("iso").replace("0", "0.")
label = 'Counting iso "' + tauIso + '" %s < DR < %s' % (sc,
ic)
print label
histo.setLegendLabel(label)
self.histoMgr.setHistoLegendLabelMany({
"sumIsoRel": "Rel iso",
"pfSumIsoRel": "PF rel iso",
"sumIsoRelFull": "Rel iso",
"pfSumIsoRelFull": "PF rel iso",
})
self.histoMgr.forEachHisto(setLabel)
self.histoMgr.forEachHisto(
styles.generator2(styles.StyleMarker(markerSize=1.5), plotStyles))
self.defaultStyle()
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tdrstyle as tdrstyle
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.styles as styles
from HiggsAnalysis.HeavyChHiggsToTauNu.tools.cutstring import * # And, Not, Or
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.crosssection as xsect
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysisCaloMet60TEff"
analysisSig = "signalAnalysisGenuineTau" # require that the selected tau is genuine, valid comparison after njets
plotStyles = [
styles.StyleCompound([s, styles.StyleLine(lineWidth=4)])
for s in styles.styles[0:2]
]
plotStyles[0] = styles.StyleCompound(
[plotStyles[0],
styles.StyleMarker(markerStyle=21, markerSize=1.2)])
#taujet = "#tau jet"
#taujetH = "#tau-jet"
taujet = "#tau_{h}"
taujetH = "#tau_{h}"
def main():
# Adjust paths such that this script can be run inside the first embedding trial directory
dirEmbs = ["."] + [os.path.join("..", d) for d in tauEmbedding.dirEmbs[1:]]
dirSig = "../" + tauEmbedding.dirSig
# Create the dataset objects
datasetsEmb = tauEmbedding.DatasetsMany(dirEmbs,
analysisEmb + "Counters",
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()
return self._cache[dsetName].histogram(name)
systematicsSigMC = SystematicsSigMC()
systematicsEmbData = dataset.Systematics(shapes=[
"SystVarMuonIdDataEff",
"SystVarMuonTrgDataEff",
"SystVarWTauMu",
"SystVarEmbMTWeight",
], additionalNormalizations = {
"CaloMETApproximation": 0.12,
"QCDContamination": 0.02,
})
plotStyles = styles.styles[0:2]
plotStyles[0] = styles.StyleCompound([plotStyles[0], styles.StyleMarker(markerStyle=21, markerSize=1.2)])
def main():
parser = OptionParser(usage="Usage: %prog [options]")
parser.add_option("--dirSig", dest="dirSig", default=None,
help="Path to signalAnalysisGenTau multicrab directory")
(opts, args) = parser.parse_args()
if opts.dirSig is None:
parser.error("--dirSig missing")
dirEmb = "."
dirSig = opts.dirSig
# Apply TDR style
style = tdrstyle.TDRStyle()
def main():
parser = OptionParser(usage="Usage: %prog [options]")
parser.add_option("--dirSig",
dest="dirSig",
default=None,
help="Path to signalAnalysisGenTau multicrab directory")
parser.add_option("--nortau",
dest="nortau",
default=False,
action="store_true",
help="Is Rtau cut disabled?")
parser.add_option("--notrigger",
dest="notrigger",
default=False,
action="store_true",
help="Is tau+MET trigger disabled?")
parser.add_option("--dofit",
dest="dofit",
default=False,
action="store_true",
help="Do the fit on mT slope on ttbar?")
(opts, args) = parser.parse_args()
if opts.dirSig is None:
parser.error("--dirSig missing")
dirEmb = "."
dirSig = opts.dirSig
global analysisSig, systematicsSigMC
if opts.notrigger:
analysisSig = analysisSig.replace("Triggered", "")
systematicsSigMC = dataset.Systematics()
# Apply TDR style
style = tdrstyle.TDRStyle()
styles.styles[1] = styles.StyleCompound(
[styles.styles[1],
styles.StyleMarker(markerStyle=25)])
histograms.cmsTextMode = histograms.CMSMode.SIMULATION_PRELIMINARY
# histograms.cmsTextMode = histograms.CMSMode.SIMULATION
# histograms.cmsTextMode = histograms.CMSMode.SIMULATION_UNPUBLISHED
# histograms.cmsTextMode = histograms.CMSMode.UNPUBLISHED
#histograms.createLegend.setDefaults(y1=0.93, y2=0.75, x1=0.52, x2=0.93)
histograms.createLegend.setDefaults(textSize=0.04)
histograms.createLegend.moveDefaults(dx=-0.25, dh=-0.2, dy=-0.12)
histograms.createLegendRatio.setDefaults(ncolumns=2,
textSize=0.08,
columnSeparation=0.6)
histograms.createLegendRatio.moveDefaults(dx=-0.48, dh=-0.1, dw=0.25)
if opts.dofit:
histograms.uncertaintyMode.set(histograms.uncertaintyMode.StatOnly)
histograms.createLegendRatio.moveDefaults(dw=-0.25)
plots._legendLabels["BackgroundStatError"] = "Emb. stat. unc."
plots._legendLabels[
"BackgroundStatSystError"] = "Emb. stat.#oplussyst. unc."
else:
histograms.uncertaintyMode.set(histograms.uncertaintyMode.StatAndSyst)
# plots._legendLabels["BackgroundStatError"] = "Norm. stat. unc."
# plots._legendLabels["BackgroundStatSystError"] = "Norm. stat.#oplussyst. unc."
plots._legendLabels["BackgroundStatError"] = "Non-emb. stat. unc."
plots._legendLabels[
"BackgroundStatSystError"] = "Non-emb. stat.#oplussyst. unc."
plots._legendLabels["Data"] = "Embedded data"
plots._legendLabels["EWKMC"] = "EWK+t#bar{t}"
# plots._legendLabels["EWKMC"] = "Non-emb. EWK+t#bar{t} with ^{}#tau_{h}"
postfix = ""
if opts.dofit:
postfix = "_fit"
for optMode in [
# "OptQCDTailKillerNoCuts",
"OptQCDTailKillerLoosePlus",
# "OptQCDTailKillerMediumPlus",
# "OptQCDTailKillerTightPlus",
# None
]:
datasetsEmb = dataset.getDatasetsFromMulticrabCfg(
directory=dirEmb,
dataEra=dataEra,
analysisName=analysisEmb,
optimizationMode=optMode)
datasetsSig = dataset.getDatasetsFromMulticrabCfg(
directory=dirSig,
dataEra=dataEra,
analysisName=analysisSig,
optimizationMode=optMode)
doDataset(datasetsEmb, datasetsSig, optMode + postfix, opts)
datasetsEmb.close()
datasetsSig.close()
tauEmbedding.writeToFile(
optMode + postfix, "input.txt",
"Embedded: %s\nSignal analysis (GenTau): %s\n" %
(os.getcwd(), dirSig))
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 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 doPlotsWTauMuRatioUncertaintyEstimate(dsetMgr, ratios, analysisName):
prefix = era + "_"
st = [s.clone() for s in styles.getStyles()]
st[0].marker = 33
st = [
styles.StyleCompound(styles=[s, styles.StyleMarker(markerSize=1.5)])
for s in st
]
# Nominal
p = plots.PlotBase([ratios[analysisName][0].Clone()])
p.setEnergy(dsetMgr.getEnergies())
p.histoMgr.setHistoDrawStyleAll("P")
p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.forEachHisto(styles.Generator(st))
fitLine = ratios[analysisName][1]
st[0].apply(fitLine)
p.appendPlotObject(fitLine)
# Stat. unc. from parameters
pars = [fitLine.GetParameter(i) for i in xrange(0, fitLine.GetNpar())]
parErrors = [fitLine.GetParError(i) for i in xrange(0, fitLine.GetNpar())]
def evalPar(x, i, shift):
pars_ = pars[:]
pars_[i] += shift
return fitLine.EvalPar(x, array.array("d", pars_))
# Fit function
fits = [
ratios[analysisName + "SystFunc0"][1],
ratios[analysisName + "SystFunc1"][1]
]
def maxDiff(x, tf1s, nominal):
return max([abs(tf1.Eval(x[0]) - nominal) for tf1 in tf1s])
# Sample
samples = [
ratios[analysisName + "SystWJets"][1],
ratios[analysisName + "SystSingleTop"][1]
]
# Weight systematic variations
keys = filter(lambda n: "SystVar" in n, ratios.keys())
weightSources = {}
for key in keys:
src = key[key.find("SystVar") + 7:].replace("Plus",
"").replace("Minus", "")
# if src != "TopPtWeight":
# continue
# if src != "PUWeight":
# continue
# print src
if not src in weightSources:
weightSources[src] = [ratios[key][1]]
else:
weightSources[src].append(ratios[key][1])
def comb(plus, x, p):
nominal = fitLine.Eval(x[0])
diff = 0
# stat unc
uncStat = 0.0
for i, parErr in enumerate(parErrors):
diffI = max(abs(evalPar(x, i, parErr) - nominal),
abs(evalPar(x, i, -parErr)) - nominal)
uncStat += diffI**2
diff += uncStat
# Fit function
diffFit = maxDiff(x, fits, nominal)
diff += diffFit**2
# Sample
diffSample = maxDiff(x, samples, nominal)
diff += diffSample**2
# Weights
for src, tf1s in weightSources.iteritems():
diffWeight = maxDiff(x, tf1s, nominal)
diff += diffWeight**2
diff = math.sqrt(diff)
if plus:
ret = nominal + diff
else:
ret = nominal - diff
if ret > 1:
ret = 1.0
if ret < 0:
ret = 0.0
# if ret > 0:
# print x[0], (ret-nominal)/ret
return ret
p.appendPlotObject(
ROOT.TF1("combPlus", lambda x, p: comb(True, x, p), fitLine.GetXmin(),
fitLine.GetXmax(), fitLine.GetNpar()))
p.appendPlotObject(
ROOT.TF1("combMinus", lambda x, p: comb(False, x, p),
fitLine.GetXmin(), fitLine.GetXmax(), fitLine.GetNpar()))
# Simple relative uncertainty
def rel(x):
return 0.012
def relUnc(plus, x, p):
nominal = fitLine.Eval(x[0])
if plus:
f = 1 + rel(x[0])
else:
f = 1 - rel(x[0])
return nominal * f
relPlus = ROOT.TF1("relPlus", lambda x, p: relUnc(True, x, p),
fitLine.GetXmin(), fitLine.GetXmax(), fitLine.GetNpar())
relMinus = ROOT.TF1("relMinus", lambda x, p: relUnc(False, x, p),
fitLine.GetXmin(), fitLine.GetXmax(),
fitLine.GetNpar())
relPlus.SetLineColor(ROOT.kGreen)
relMinus.SetLineColor(ROOT.kGreen)
p.appendPlotObject(relPlus)
p.appendPlotObject(relMinus)
def customize(plot):
xmin = plot.frame.GetXaxis().GetXmin()
xmax = plot.frame.GetXaxis().GetXmax()
ymin = plot.frame.GetYaxis().GetXmin()
ymax = plot.frame.GetYaxis().GetXmax()
val = 1
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
l = ROOT.TLine(41, ymin, 41, ymax)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
plots.drawPlot(
p,
prefix + "WMuFractionUncertainty",
"Muon p_{T} (GeV/c)",
ylabel="W#rightarrow#mu fraction",
opts={
"ymin": 0.9,
"ymax": 1.02
},
createLegend=None,
#moveLegend={"dh": -0.15, "dx": -0.5},
customizeBeforeDraw=customize)
