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()
p.addStandardTexts()
p.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 main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=baseline+"Counters")
datasets.remove([
# "SingleMu_Mu_160431-163261_May10",
# "SingleMu_Mu_163270-163869_May10",
# "SingleMu_Mu_165088-166150_Prompt",
# "SingleMu_Mu_166161-166164_Prompt",
# "SingleMu_Mu_166346-166346_Prompt",
# "SingleMu_Mu_166374-167043_Prompt",
# "SingleMu_Mu_167078-167913_Prompt",
# "SingleMu_Mu_170722-172619_Aug05",
# "SingleMu_Mu_172620-173198_Prompt",
# "SingleMu_Mu_173236-173692_Prompt",
])
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
values = {}
analyses = [
("Baseline", baseline),
("Plus", plus),
("Minus", minus)
]
mainTable = counter.CounterTable()
tauTable = counter.CounterTable()
cntr = "Counters"
if normalize:
cntr += "/weighted"
for name, analysis in analyses:
eventCounter = counter.EventCounter(datasets, counters=analysis+cntr)
eventCounter.normalizeMCByLuminosity()
if normalize:
tauEmbedding.scaleNormalization(eventCounter)
col = eventCounter.getMainCounterTable().getColumn(name="Data")
col.setName(name)
mainTable.appendColumn(col)
effcol = counter.efficiencyColumn(col.getName()+" eff", col)
mainTable.appendColumn(effcol)
values[name+"Main"] = effcol
value = col.getCount(col.getRowNames().index(count)).value()
values[name] = value
col = eventCounter.getSubCounterTable("TauIDPassedEvt::tauID_HPSTight").getColumn(name="Data")
col.setName(name)
tauTable.appendColumn(col)
effcol = counter.efficiencyColumn(col.getName()+" eff", col)
tauTable.appendColumn(effcol)
values[name+"Tau"] = effcol
mainTable.appendColumn(counter.divideColumn("Plus eff/Baseline", values["PlusMain"], values["BaselineMain"]))
mainTable.appendColumn(counter.divideColumn("Minus eff/Baseline", values["MinusMain"], values["BaselineMain"]))
tauTable.appendColumn(counter.divideColumn("Plus eff/Baseline", values["PlusTau"], values["BaselineTau"]))
tauTable.appendColumn(counter.divideColumn("Minus eff/Baseline", values["MinusTau"], values["BaselineTau"]))
print mainTable.format()
print tauTable.format()
plusDiff = abs(values["Baseline"] - values["Plus"])
minusDiff = abs(values["Baseline"] - values["Minus"])
maxDiff = max(plusDiff, minusDiff)
rel = maxDiff / values["Baseline"]
print "Count %s, baseline %.3f, plus %.3f, minus %.3f" % (count, values["Baseline"], values["Plus"], values["Minus"])
print "Plus diff %.3f, minus diff %.3f" % (plusDiff, minusDiff)
print "Relative uncertainty from tau energy scale %.6f" % (rel)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.32, dh=-0.15)
metCut = "(met_p4.Et() > 50)"
bTaggingCut = "passedBTagging"
tdMt = dataset.TreeDraw("dummy",
weight="weightPileup*weightTrigger*weightBTagging",
selection=metCut+"&&"+bTaggingCut,
varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(40,0,400)"
)
output = ROOT.TFile.Open("mt_variated_ewk.root", "RECREATE")
#doPlot(datasets, analyses, tdMt, "mt_variated")
doPlot(datasets, analyses, "transverseMass", "mt_variated_btagging", output, "Dphi180")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi160", "mt_variated_deltaPhi160", output, "Dphi160")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi130", "mt_variated_deltaPhi130", output, "Dphi130")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi90", "mt_variated_deltaPhi90", output, "Dphi90")
output.Close()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=baseline +
"Counters",
weightedCounters=False)
datasets.remove([
# "SingleMu_Mu_160431-163261_May10",
# "SingleMu_Mu_163270-163869_May10",
# "SingleMu_Mu_165088-166150_Prompt",
# "SingleMu_Mu_166161-166164_Prompt",
# "SingleMu_Mu_166346-166346_Prompt",
# "SingleMu_Mu_166374-167043_Prompt",
# "SingleMu_Mu_167078-167913_Prompt",
# "SingleMu_Mu_170722-172619_Aug05",
# "SingleMu_Mu_172620-173198_Prompt",
# "SingleMu_Mu_173236-173692_Prompt",
])
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
values = {}
analyses = [("Baseline", baseline), ("Plus", plus), ("Minus", minus)]
mainTable = counter.CounterTable()
tauTable = counter.CounterTable()
cntr = "Counters"
if normalize:
cntr += "/weighted"
for name, analysis in analyses:
eventCounter = counter.EventCounter(datasets, counters=analysis + cntr)
eventCounter.normalizeMCByLuminosity()
if normalize:
tauEmbedding.scaleNormalization(eventCounter)
col = eventCounter.getMainCounterTable().getColumn(name="Data")
col.setName(name)
mainTable.appendColumn(col)
effcol = counter.efficiencyColumn(col.getName() + " eff", col)
mainTable.appendColumn(effcol)
values[name + "Main"] = effcol
value = col.getCount(col.getRowNames().index(count)).value()
values[name] = value
col = eventCounter.getSubCounterTable(
"TauIDPassedEvt::tauID_HPSTight").getColumn(name="Data")
col.setName(name)
tauTable.appendColumn(col)
effcol = counter.efficiencyColumn(col.getName() + " eff", col)
tauTable.appendColumn(effcol)
values[name + "Tau"] = effcol
mainTable.appendColumn(
counter.divideColumn("Plus eff/Baseline", values["PlusMain"],
values["BaselineMain"]))
mainTable.appendColumn(
counter.divideColumn("Minus eff/Baseline", values["MinusMain"],
values["BaselineMain"]))
tauTable.appendColumn(
counter.divideColumn("Plus eff/Baseline", values["PlusTau"],
values["BaselineTau"]))
tauTable.appendColumn(
counter.divideColumn("Minus eff/Baseline", values["MinusTau"],
values["BaselineTau"]))
print mainTable.format()
print tauTable.format()
plusDiff = abs(values["Baseline"] - values["Plus"])
minusDiff = abs(values["Baseline"] - values["Minus"])
maxDiff = max(plusDiff, minusDiff)
rel = maxDiff / values["Baseline"]
print "Count %s, baseline %.3f, plus %.3f, minus %.3f" % (
count, values["Baseline"], values["Plus"], values["Minus"])
print "Plus diff %.3f, minus diff %.3f" % (plusDiff, minusDiff)
print "Relative uncertainty from tau energy scale %.6f" % (rel)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.32, dh=-0.15)
metCut = "(met_p4.Et() > 50)"
bTaggingCut = "passedBTagging"
tdMt = dataset.TreeDraw(
"dummy",
weight="weightPileup*weightTrigger*weightBTagging",
selection=metCut + "&&" + bTaggingCut,
varexp=
"sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(40,0,400)"
)
output = ROOT.TFile.Open("mt_variated_ewk.root", "RECREATE")
#doPlot(datasets, analyses, tdMt, "mt_variated")
doPlot(datasets, analyses, "transverseMass", "mt_variated_btagging",
output, "Dphi180")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi160",
"mt_variated_deltaPhi160", output, "Dphi160")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi130",
"mt_variated_deltaPhi130", output, "Dphi130")
doPlot(datasets, analyses, "transverseMassAfterDeltaPhi90",
"mt_variated_deltaPhi90", output, "Dphi90")
output.Close()
