def printMuonOriginHelper(datasets, selection):
mother = histograms.HistoManager(datasets, selection+"/muon_genMother")
mother.normalizeMCByCrossSection()
grandMother = histograms.HistoManager(datasets, selection+"/muon_genGrandMother")
grandMother.normalizeMCByCrossSection()
for datasetName in datasets.getMCDatasetNames():
m = mother.getHisto(datasetName).getRootHisto()
gm = grandMother.getHisto(datasetName).getRootHisto()
allMus = m.Integral(0, m.GetNbinsX()+1)
muFromW = m.GetBinContent(25)
muFromZ = m.GetBinContent(24)
muFromTau = m.GetBinContent(16)
muFromTauW = gm.GetBinContent(25)
muFromTauZ = gm.GetBinContent(24)
otherMus = allMus - muFromW - muFromZ - muFromTauW - muFromTauZ
print "Dataset %s" % datasetName
# print " All mus %f" % allMus
print " Mus from W %f" % (muFromW/allMus)
print " Mus from Z %f" % (muFromZ/allMus)
print " Mus from tau %f" % (muFromTau/allMus)
print " Mus from W->tau %f" % (muFromTauW/allMus)
print " Mus from Z->tau %f" % (muFromTauZ/allMus)
print " Other mus %f" % (otherMus/allMus)
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 main():
datasets = dataset.getDatasetsFromMulticrabCfg()
datasets.loadLuminosities()
#mc = "WJets"
mc = "QCD"
data = "2010"
#data = "2011"
# maxVtx = 15
maxVtx = 20
if data == "2010":
datasets.remove(
filter(lambda name: "Prompt" in name,
datasets.getAllDatasetNames()))
elif data == "2011":
datasets.remove(
filter(lambda name: "Dec22" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
style = tdrstyle.TDRStyle()
if mc == "QCD":
datasets.remove(["WJets"])
elif mc == "WJets":
datasets.remove(["QCD"])
h = histograms.HistoManager(datasets,
"signalAnalysis/verticesBeforeWeight")
h.normalizeToOne()
h.forEachMCHisto(styles.generator())
h.forHisto("Data", styles.getDataStyle())
h.setHistoDrawStyle("Data", "EP")
h.setHistoLegendStyle("Data", "p")
cf = histograms.CanvasFrame(h, "vertex_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("A.u.")
legend = histograms.createLegend()
h.addToLegend(legend)
h.draw()
legend.Draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Weight
dataHisto = h.getHisto("Data").getRootHisto()
mcHisto = h.getHisto(mc).getRootHisto()
# For normalization, see https://twiki.cern.ch/twiki/bin/view/CMS/PileupReweighting
weightHisto = dataHisto.Clone("weights")
weightHisto.Divide(mcHisto)
print "Weight histo integral", weightHisto.Integral()
#weightHisto.Scale(1/dataHisto.Integral())
#weightHisto.Scale(1/weightHisto.Integral())
print "Weight histo integral", weightHisto.Integral()
print "Sum of [weight*prob]", sum([
weightHisto.GetBinContent(bin) * mcHisto.GetBinContent(bin)
for bin in xrange(1, weightHisto.GetNbinsX())
])
print "weights = cms.vdouble(%s)" % ", ".join([
"%.8f" % weightHisto.GetBinContent(bin)
for bin in xrange(1,
min(maxVtx, weightHisto.GetNbinsX()) + 1)
])
h = histograms.HistoManager(datasetRootHistos=[])
h.appendHisto(histograms.Histo(weightHisto, "Weight", "", "HIST"))
h.forEachHisto(styles.generator())
cf = histograms.CanvasFrame(h,
"vertex_weight_%s_%s" % (data, mc),
xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("Weight")
h.draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Example how to merge histograms of several datasets
#datasets.merge("QCD", ["QCD_Pt30to50_TuneZ2_Winter10",
# "QCD_Pt50to80_TuneZ2_Winter10",
# "QCD_Pt80to120_TuneZ2_Winter10",
# "QCD_Pt120to170_TuneZ2_Winter10",
# "QCD_Pt170to300_TuneZ2_Winter10",
# "QCD_Pt300to470_TuneZ2_Winter10"])
datasets.merge("QCD", ["QCD_Pt30to50_TuneZ2_Winter10", "QCD_Pt80to120_TuneZ2_Winter10", "QCD_Pt120to170_TuneZ2_Winter10", "QCD_Pt170to300_TuneZ2_Winter10", "QCD_Pt300to470_TuneZ2_Winter10"])
# Get set of histograms with the given path. The returned object is of
# type HistoManager, which contains a histogram from each dataset in
# DatasetManager. The histograms can be e.g. merged/stacked or normalized
# in various ways before drawing.
tauPts = histograms.HistoManager(datasets, "qcdMeasurementMethod2Part1/TauSelection_all_tau_candidates_pt")
# The default normalization is no normalization (i.e. number of MC
# events for MC, and number of events for data)
# Normalize MC histograms to their cross section
#tauPts.normalizeMCByCrossSection()
#ylabel = "Cross section (pb)"
# Normalize MC histograms to the luminosity of the collision data in
# the HistoManager
#tauPts.normalizeMCByLuminosity()
#tauPts.normalizeMCByCrossSection()
tauPts.normalizeToOne()
#tauPts.normalizeMCToLuminosity(33.69)
ylabel = "#tau cands / 1 GeV/c"