def doCounters(datasets, MyCuts, MyCutsName):
### Define the counters to be used
#eventCounter = counter.EventCounter(datasets, counters=myAnalysis + myDataEra + "/counters")
eventCounter = counter.EventCounter(datasets)
### Normalise the MC sample to a luminosity before creating a table
eventCounter.normalizeMCToLuminosity(GetLumi(datasets))
myRowNames = []
for iCut, iCutName in zip(MyCuts, MyCutsName):
#print "*** Cut: %s\n*** CutName: %s" % (iCut, iCutName)
print "*** Processing TCut with:\n Name = \"%s\" \n Expr = \"%s\"" % (iCutName, iCut)
### Define the event "weight" to be used
EvtWeight = GetEventWeight(iCut)
### Define the TTree to be used
treePath = "tree" # treePath = myAnalysis+"/tree"
treeDraw = dataset.TreeDraw(treePath, weight=EvtWeight, selection=iCut)
### Append custom rows to the event counter. An asterisk denotes that the counter row was added. Informative and makes things easier
myRowName = iCutName #"*" + iCutName
myRowNames.append(myRowName)
eventCounter.getMainCounter().appendRow(myRowName, treeDraw)
### Get table with all default rows removed and manage the table format
myTable = GetCustomTable(eventCounter, myRowNames)
if getBool("bMergeEwk"):
DataMinusEwkMc = counter.subtractColumn("DataMinusEwkMc", myTable.getColumn(name="Data"), myTable.getColumn(name="EWK MC"))
QcdPurity = counter.divideColumn("QCD Purity", DataMinusEwkMc, myTable.getColumn(name="Data"))
myTable.appendColumn(QcdPurity)
### See http://docs.python.org/2/library/string.html for string format
cellTextFormat = counter.TableFormatText(counter.CellFormatTeX(valueFormat='%.2E', valueOnly=True)) #%.2e
cellLaTeXFormat = counter.TableFormatLaTeX(counter.CellFormatTeX(valueFormat='%.2E', valueOnly=True)) #%.2e
purityFormat = counter.CellFormatTeX(valueFormat='%.2f', valueOnly=True)
### Customise the "QCD Purity" column
cellTextFormat.setColumnFormat(purityFormat, name="QCD Purity") #does nothing
cellLaTeXFormat.setColumnFormat(purityFormat, name="QCD Purity") #does nothing
# between construction of table format and table format
### Print the final table with the desired format
print "============================================================"
print "Data-Era: %s (%s pb-1)" % (myDataEra, GetLumi(datasets))
print "============================================================"
print myTable.format(cellTextFormat)
print "============================================================"
print "Data-Era: %s (%s pb-1)" % (myDataEra, GetLumi(datasets))
print "============================================================"
print myTable.format(cellLaTeXFormat)
return
def addPurityColumn(table):
mcSumColumn = table.indexColumn("MCsum")
purity = counter.divideColumn("TT+W purity", ttwSum(table), table.getColumn(mcSumColumn))
purity.multiply(100) # -> %
table.appendColumn(purity)
def addTtwFractionColumn(table):
ttColumn = table.indexColumn("TTJets")
fraction = counter.divideColumn("TTJets/(TTJets+WJets)", table.getColumn(ttColumn), ttwSum(table))
fraction.multiply(100) # -> %
table.appendColumn(fraction)
def addPurityColumn(table):
mcSumColumn = table.indexColumn("MCsum")
purity = counter.divideColumn("TT+W purity", ttwSum(table),
table.getColumn(mcSumColumn))
purity.multiply(100) # -> %
table.appendColumn(purity)
def addTtwFractionColumn(table):
ttColumn = table.indexColumn("TTJets")
fraction = counter.divideColumn("TTJets/(TTJets+WJets)",
table.getColumn(ttColumn), ttwSum(table))
fraction.multiply(100) # -> %
table.appendColumn(fraction)
def doCounters(datasets, MyCuts, MyCutsName):
### Define the counters to be used
#eventCounter = counter.EventCounter(datasets, counters=myAnalysis + myDataEra + "/counters")
eventCounter = counter.EventCounter(datasets)
### Normalise the MC sample to a luminosity before creating a table
eventCounter.normalizeMCToLuminosity(GetLumi(datasets))
myRowNames = []
for iCut, iCutName in zip(MyCuts, MyCutsName):
#print "*** Cut: %s\n*** CutName: %s" % (iCut, iCutName)
print "*** Processing TCut with:\n Name = \"%s\" \n Expr = \"%s\"" % (
iCutName, iCut)
### Define the event "weight" to be used
EvtWeight = GetEventWeight(iCut)
### Define the TTree to be used
treePath = "tree" # treePath = myAnalysis+"/tree"
treeDraw = dataset.TreeDraw(treePath, weight=EvtWeight, selection=iCut)
### Append custom rows to the event counter. An asterisk denotes that the counter row was added. Informative and makes things easier
myRowName = iCutName #"*" + iCutName
myRowNames.append(myRowName)
eventCounter.getMainCounter().appendRow(myRowName, treeDraw)
### Get table with all default rows removed and manage the table format
myTable = GetCustomTable(eventCounter, myRowNames)
if getBool("bMergeEwk"):
DataMinusEwkMc = counter.subtractColumn(
"DataMinusEwkMc", myTable.getColumn(name="Data"),
myTable.getColumn(name="EWK MC"))
QcdPurity = counter.divideColumn("QCD Purity", DataMinusEwkMc,
myTable.getColumn(name="Data"))
myTable.appendColumn(QcdPurity)
### See http://docs.python.org/2/library/string.html for string format
cellTextFormat = counter.TableFormatText(
counter.CellFormatTeX(valueFormat='%.2E', valueOnly=True)) #%.2e
cellLaTeXFormat = counter.TableFormatLaTeX(
counter.CellFormatTeX(valueFormat='%.2E', valueOnly=True)) #%.2e
purityFormat = counter.CellFormatTeX(valueFormat='%.2f', valueOnly=True)
### Customise the "QCD Purity" column
cellTextFormat.setColumnFormat(purityFormat,
name="QCD Purity") #does nothing
cellLaTeXFormat.setColumnFormat(purityFormat,
name="QCD Purity") #does nothing
# between construction of table format and table format
### Print the final table with the desired format
print "============================================================"
print "Data-Era: %s (%s pb-1)" % (myDataEra, GetLumi(datasets))
print "============================================================"
print myTable.format(cellTextFormat)
print "============================================================"
print "Data-Era: %s (%s pb-1)" % (myDataEra, GetLumi(datasets))
print "============================================================"
print myTable.format(cellLaTeXFormat)
return
def addDataMcRatioColumn(table):
dataColumn = table.indexColumn("Data")
mcSumColumn = table.indexColumn("MCsum")
ratio = counter.divideColumn("Data/MCsum", table.getColumn(dataColumn), table.getColumn(mcSumColumn))
table.appendColumn(ratio)
def addDyFractionColumn(table):
dyColumn = table.indexColumn("DYJetsToLL")
mcSumColumn = table.indexColumn("MCsum")
fraction = counter.divideColumn("DY/MCsum", table.getColumn(dyColumn), table.getColumn(mcSumColumn))
fraction.multiply(100) # -> %
table.appendColumn(fraction)
def addQcdFractionColumn(table):
qcdColumn = table.indexColumn("QCD_Pt20_MuEnriched")
mcSumColumn = table.indexColumn("MCsum")
fraction = counter.divideColumn("QCD/MCsum", table.getColumn(qcdColumn), table.getColumn(mcSumColumn))
fraction.multiply(100) # -> %
table.appendColumn(fraction)
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 doCounters(datasets, mcLumi=None):
createPlot = lambda name: createPlotCommon(name, datasets, mcLumi)
eventCounter = counter.EventCounter(datasets, counters=countersWeighted)
sels = [
# "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 20)",
# "(20 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))", "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 80)",
# "(80 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))", "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 120)",
# "(120 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))",
]
tdCount = treeDraw.clone(weight=weightBTagging)
tdCountMET = tdCount.clone(weight=weight,
selection="&&".join(sels + [metCut]))
tdCountBTagging = tdCount.clone(selection="&&".join(sels +
[metCut, bTaggingCut]))
tdCountDeltaPhi160 = tdCount.clone(
selection="&&".join(sels + [metCut, bTaggingCut, deltaPhi160Cut]))
tdCountDeltaPhi130 = tdCount.clone(
selection="&&".join(sels + [metCut, bTaggingCut, deltaPhi130Cut]))
tdCountDeltaPhi90 = tdCount.clone(
selection="&&".join(sels + [metCut, bTaggingCut, deltaPhi90Cut]))
# eventCounter.getMainCounter().appendRow("JetsForEffs", tdCount.clone(weight=weight, selection="&&".join(sels)))
# eventCounter.getMainCounter().appendRow("METForEffs", tdCountMET)
# eventCounter.getMainCounter().appendRow("BTagging", tdCountBTagging)
# eventCounter.getMainCounter().appendRow("DeltaPhi < 160", tdCountDeltaPhi160)
# eventCounter.getMainCounter().appendRow("DeltaPhi < 130", tdCountDeltaPhi130)
# eventCounter.getMainCounter().appendRow("DeltaPhi < 90", tdCountDeltaPhi90)
td1 = tdCount.clone(selection=metCut + "&&" + bTaggingCut +
"&& (tecalometNoHF_p4.Pt() > 60)")
td2 = tdCount.clone(selection=metCut + "&&" + bTaggingCut +
"&& (tecalomet_p4.Pt() > 60)")
td3 = dataset.TreeDrawCompound(
td1, {
"SingleMu_Mu_170722-172619_Aug05": td2,
"SingleMu_Mu_172620-173198_Prompt": td2,
"SingleMu_Mu_173236-173692_Prompt": td2,
})
# eventCounter.getMainCounter().appendRow("BTagging+CaloMetNoHF", td1)
# eventCounter.getMainCounter().appendRow("BTagging+CaloMet", td2)
# eventCounter.getMainCounter().appendRow("BTagging+CaloMet(NoHF)", td3)
if mcLumi != None:
eventCounter.normalizeMCToLuminosity(mcLumi)
else:
eventCounter.normalizeMCByLuminosity()
tauEmbedding.scaleNormalization(eventCounter)
ewkDatasets = ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"]
table = eventCounter.getMainCounterTable()
mainTable = table
#muonAnalysis.addSumColumn(table)
#mainTable.insertColumn(2, counter.sumColumn("EWKMCsum", [mainTable.getColumn(name=name) for name in ewkDatasets]))
#muonAnalysis.addDataMcRatioColumn(table)
if datasets.hasDataset("EWKSignal"):
mainTable.insertColumn(
7,
counter.divideColumn(
"SignalFraction",
mainTable.getColumn(name="TTToHplus_" + keepSignal),
mainTable.getColumn(name="EWKSignal")))
datasets.printInfo()
print "============================================================"
print "Main counter (%s)" % eventCounter.getNormalizationString()
cellFormat = counter.TableFormatText(
counter.CellFormatTeX(valueFormat='%.3f'))
print table.format(cellFormat)
tauTable = eventCounter.getSubCounterTable(
"TauIDPassedEvt::TauSelection_HPS")
#muonAnalysis.addSumColumn(tauTable)
tauTable.insertColumn(
2,
counter.sumColumn(
"EWKMCsum",
[tauTable.getColumn(name=name) for name in ewkDatasets]))
print tauTable.format(cellFormat)
# print eventCounter.getSubCounterTable("TauIDPassedJets::tauID_HPSTight").format()
# table = eventCounter.getSubCounterTable("Trigger")
# muonAnalysis.addSumColumn(table)
# print table.format(cellFormat)
mainTable.keepOnlyRows([
"All events",
"Trigger and HLT_MET cut",
"taus == 1",
# "trigger scale factor",
"electron veto",
"muon veto",
"MET",
"njets",
"btagging",
"btagging scale factor",
"JetsForEffs",
"METForEffs",
"BTagging",
"DeltaPhi < 160",
"DeltaPhi < 130"
])
tauTable.keepOnlyRows([
"AllTauCandidates",
"DecayModeFinding",
"TauJetPt",
"TauJetEta",
"TauLdgTrackExists",
"TauLdgTrackPtCut",
"TauECALFiducialCutsCracksAndGap",
"TauAgainstElectronCut",
"TauAgainstMuonCut",
#"EMFractionCut",
"HPS",
"TauOneProngCut",
"TauRtauCut",
])
#effFormat = counter.TableFormatText(counter.CellFormatText(valueFormat='%.4f'))
effFormat = counter.TableFormatText(
counter.CellFormatTeX(valueFormat='%.4f'))
#effFormat = counter.TableFormatConTeXtTABLE(counter.CellFormatTeX(valueFormat='%.4f'))
for name, table in [("Main", mainTable), ("Tau ID", tauTable)]:
effTable = counter.CounterTable()
col = table.getColumn(name="Data")
effTable.appendColumn(col)
effTable.appendColumn(
counter.efficiencyColumn(col.getName() + " eff", col))
col = table.getColumn(name="EWKMCsum")
effTable.appendColumn(col)
effTable.appendColumn(
counter.efficiencyColumn(col.getName() + " eff", col))
print "%s counter efficiencies" % name
print effTable.format(effFormat)
print "Trigger uncertainties"
bins = [40, 50, 60, 80]
tauPtPrototype = ROOT.TH1F("tauPtTrigger", "Tau pt",
len(bins) - 1, array.array("d", bins))
runs = [
"(160431 <= run && run <= 167913)",
"(170722 <= run && run <= 173198)",
"(173236 <= run && run <= 173692)",
#"(160431 <= run && run <= 173692)",
]
for name, td in [("BTagging", tdCountBTagging),
("DeltaPhi160", tdCountDeltaPhi160),
("DeltaPhi130", tdCountDeltaPhi130),
("DeltaPhi90", tdCountDeltaPhi90)]:
t = td.clone(varexp="tau_p4.Pt() >>tauPtTrigger")
NallSum = 0
NSum = 0
absUncSquareSum = 0
for runRegion in runs:
#neventsPlot = createPlot(dataset.treeDrawToNumEntries(t.clone(weight="weightTrigger")))
#uncertaintyPlot = createPlot(dataset.treeDrawToNumEntries(t.clone(weight="weightTriggerAbsUnc*weightTriggerAbsUnc/(weightTrigger*weightTrigger)")))
tmp = t.clone(selection=t.selection + "&&" + runRegion)
nallPlot = createPlot(tmp.clone(weight=""))
neventsPlot = createPlot(tmp.clone(weight="weightTrigger"))
uncertaintyPlot = createPlot(
tmp.clone(weight="weightTriggerAbsUnc"))
th1all = nallPlot.histoMgr.getHisto("Data").getRootHisto()
th1 = neventsPlot.histoMgr.getHisto("Data").getRootHisto()
th12 = uncertaintyPlot.histoMgr.getHisto("Data").getRootHisto()
Nall = th1all.Integral(0, th1all.GetNbinsX() + 1)
N = th1.Integral(0, th1.GetNbinsX() + 1)
#absSum2 = th12.Integral(0, th12.GetNbinsX()+1)
#absUnc = math.sqrt(absSum2)
#absUnc = th12.Integral(0, 2)
NallSum += Nall
NSum += N
absUnc = tauEmbedding.squareSum(th12)
absUncSquareSum += absUnc
absUnc = math.sqrt(absUnc)
relUnc = 0
if N > 0:
relUnc = absUnc / N
print "%-15s for runs %s Nall = %.2f, N = %.2f, absolute uncertainty %.2f, relative uncertainty %.4f" % (
name, runRegion, Nall, N, absUnc, relUnc)
absUnc = math.sqrt(absUncSquareSum)
relUnc = absUnc / NSum
print "%-15s Nall = %.2f, N = %.2f, absolute uncertainty %.2f, relative uncertainty %.4f" % (
name, NallSum, NSum, absUnc, relUnc)
print
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()
def doCounters(datasets, mcLumi=None):
createPlot = lambda name: createPlotCommon(name, datasets, mcLumi)
eventCounter = counter.EventCounter(datasets, counters=countersWeighted)
sels = [
# "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 20)",
# "(20 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))", "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 80)",
# "(80 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))", "(sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) < 120)",
# "(120 < sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))))",
]
tdCount = treeDraw.clone(weight=weightBTagging)
tdCountMET = tdCount.clone(weight=weight, selection="&&".join(sels+[metCut]))
tdCountBTagging = tdCount.clone(selection="&&".join(sels+[metCut, bTaggingCut]))
tdCountDeltaPhi160 = tdCount.clone(selection="&&".join(sels+[metCut, bTaggingCut, deltaPhi160Cut]))
tdCountDeltaPhi130 = tdCount.clone(selection="&&".join(sels+[metCut, bTaggingCut, deltaPhi130Cut]))
tdCountDeltaPhi90 = tdCount.clone(selection="&&".join(sels+[metCut, bTaggingCut, deltaPhi90Cut]))
eventCounter.getMainCounter().appendRow("JetsForEffs", tdCount.clone(weight=weight, selection="&&".join(sels)))
eventCounter.getMainCounter().appendRow("METForEffs", tdCountMET)
eventCounter.getMainCounter().appendRow("BTagging", tdCountBTagging)
eventCounter.getMainCounter().appendRow("DeltaPhi < 160", tdCountDeltaPhi160)
eventCounter.getMainCounter().appendRow("DeltaPhi < 130", tdCountDeltaPhi130)
eventCounter.getMainCounter().appendRow("DeltaPhi < 90", tdCountDeltaPhi90)
td1 = tdCount.clone(selection=metCut+"&&"+bTaggingCut+"&& (tecalometNoHF_p4.Pt() > 60)")
td2 = tdCount.clone(selection=metCut+"&&"+bTaggingCut+"&& (tecalomet_p4.Pt() > 60)")
td3 = dataset.TreeDrawCompound(td1, {
"SingleMu_Mu_170722-172619_Aug05": td2,
"SingleMu_Mu_172620-173198_Prompt": td2,
"SingleMu_Mu_173236-173692_Prompt": td2,
})
eventCounter.getMainCounter().appendRow("BTagging+CaloMetNoHF", td1)
eventCounter.getMainCounter().appendRow("BTagging+CaloMet", td2)
eventCounter.getMainCounter().appendRow("BTagging+CaloMet(NoHF)", td3)
if mcLumi != None:
eventCounter.normalizeMCToLuminosity(mcLumi)
else:
eventCounter.normalizeMCByLuminosity()
tauEmbedding.scaleNormalization(eventCounter)
ewkDatasets = [
"WJets", "TTJets",
"DYJetsToLL", "SingleTop", "Diboson"
]
table = eventCounter.getMainCounterTable()
mainTable = table
muonAnalysis.addSumColumn(table)
mainTable.insertColumn(2, counter.sumColumn("EWKMCsum", [mainTable.getColumn(name=name) for name in ewkDatasets]))
# table = eventCounter.getSubCounterTable("Trigger")
# muonAnalysis.reorderCounterTable(table)
muonAnalysis.addDataMcRatioColumn(table)
if datasets.hasDataset("EWKSignal"):
mainTable.insertColumn(7, counter.divideColumn("SignalFraction", mainTable.getColumn(name="TTToHplus_"+keepSignal), mainTable.getColumn(name="EWKSignal")))
datasets.printInfo()
print "============================================================"
print "Main counter (%s)" % eventCounter.getNormalizationString()
cellFormat = counter.TableFormatText(counter.CellFormatTeX(valueFormat='%.3f'))
print table.format(cellFormat)
tauTable = eventCounter.getSubCounterTable("TauIDPassedEvt::tauID_HPSTight")
#muonAnalysis.addSumColumn(tauTable)
tauTable.insertColumn(2, counter.sumColumn("EWKMCsum", [tauTable.getColumn(name=name) for name in ewkDatasets]))
print tauTable.format(cellFormat)
# print eventCounter.getSubCounterTable("TauIDPassedJets::tauID_HPSTight").format()
# table = eventCounter.getSubCounterTable("Trigger")
# muonAnalysis.addSumColumn(table)
# print table.format(cellFormat)
mainTable.keepOnlyRows([
"All events",
"Trigger and HLT_MET cut",
"taus == 1",
# "trigger scale factor",
"electron veto",
"muon veto",
"MET",
"njets",
"btagging",
"btagging scale factor",
"JetsForEffs",
"METForEffs",
"BTagging",
"DeltaPhi < 160",
"DeltaPhi < 130"
])
tauTable.keepOnlyRows([
"AllTauCandidates",
"DecayModeFinding",
"TauJetPt",
"TauJetEta",
"TauLdgTrackExists",
"TauLdgTrackPtCut",
"TauECALFiducialCutsCracksAndGap",
"TauAgainstElectronCut",
"TauAgainstMuonCut",
#"EMFractionCut",
"HPS",
"TauOneProngCut",
"TauRtauCut",
])
#effFormat = counter.TableFormatText(counter.CellFormatText(valueFormat='%.4f'))
effFormat = counter.TableFormatText(counter.CellFormatTeX(valueFormat='%.4f'))
#effFormat = counter.TableFormatConTeXtTABLE(counter.CellFormatTeX(valueFormat='%.4f'))
for name, table in [("Main", mainTable), ("Tau ID", tauTable)]:
effTable = counter.CounterTable()
col = table.getColumn(name="Data")
effTable.appendColumn(col)
effTable.appendColumn(counter.efficiencyColumn(col.getName()+" eff", col))
col = table.getColumn(name="EWKMCsum")
effTable.appendColumn(col)
effTable.appendColumn(counter.efficiencyColumn(col.getName()+" eff", col))
print "%s counter efficiencies" % name
print effTable.format(effFormat)
print "Trigger uncertainties"
bins = [40, 50, 60, 80]
tauPtPrototype = ROOT.TH1F("tauPtTrigger", "Tau pt", len(bins)-1, array.array("d", bins))
runs = [
"(160431 <= run && run <= 167913)",
"(170722 <= run && run <= 173198)",
"(173236 <= run && run <= 173692)",
#"(160431 <= run && run <= 173692)",
]
for name, td in [
("BTagging", tdCountBTagging),
("DeltaPhi160", tdCountDeltaPhi160),
("DeltaPhi130", tdCountDeltaPhi130),
("DeltaPhi90", tdCountDeltaPhi90)
]:
t = td.clone(varexp="tau_p4.Pt() >>tauPtTrigger")
NallSum = 0
NSum = 0
absUncSquareSum = 0
for runRegion in runs:
#neventsPlot = createPlot(dataset.treeDrawToNumEntries(t.clone(weight="weightTrigger")))
#uncertaintyPlot = createPlot(dataset.treeDrawToNumEntries(t.clone(weight="weightTriggerAbsUnc*weightTriggerAbsUnc/(weightTrigger*weightTrigger)")))
tmp = t.clone(selection=t.selection+"&&"+runRegion)
nallPlot = createPlot(tmp.clone(weight=""))
neventsPlot = createPlot(tmp.clone(weight="weightTrigger"))
uncertaintyPlot = createPlot(tmp.clone(weight="weightTriggerAbsUnc"))
th1all = nallPlot.histoMgr.getHisto("Data").getRootHisto()
th1 = neventsPlot.histoMgr.getHisto("Data").getRootHisto()
th12 = uncertaintyPlot.histoMgr.getHisto("Data").getRootHisto()
Nall = th1all.Integral(0, th1all.GetNbinsX()+1)
N = th1.Integral(0, th1.GetNbinsX()+1)
#absSum2 = th12.Integral(0, th12.GetNbinsX()+1)
#absUnc = math.sqrt(absSum2)
#absUnc = th12.Integral(0, 2)
NallSum += Nall
NSum += N
absUnc = squareSum(th12)
absUncSquareSum += absUnc
absUnc = math.sqrt(absUnc)
relUnc = 0
if N > 0:
relUnc = absUnc/N
print "%-15s for runs %s Nall = %.2f, N = %.2f, absolute uncertainty %.2f, relative uncertainty %.4f" % (name, runRegion, Nall, N, absUnc, relUnc)
absUnc = math.sqrt(absUncSquareSum)
relUnc = absUnc/NSum
print "%-15s Nall = %.2f, N = %.2f, absolute uncertainty %.2f, relative uncertainty %.4f" % (name, NallSum, NSum, absUnc, relUnc)
print