tagged_mass.setRange("full", 5.0, 5.6)
thevars = RooArgSet()
thevars.add(tagged_mass)
thevars.add(mumuMass)
thevars.add(mumuMassE)
thevars.add(tagB0)
fulldata = RooDataSet('fulldata', 'fulldataset', tData, RooArgSet(thevars))
## add to the data dataset a random variable, in order to scale it to desired stat
nDataEntries = fulldata.sumEntries()
randVar = RooRealVar("rand", "rand", 0, 1)
p0 = RooPolynomial("px", "px", randVar)
rDataset = p0.generate(RooArgSet(randVar), int(nDataEntries))
fulldata.merge(rDataset)
## add to the input tree the combination of the variables, to be used for the cuts on the dimuon mass
deltaB0Mfunc = RooFormulaVar("deltaB0M", "deltaB0M", "@0 - @1",
RooArgList(tagged_mass, B0Mass))
deltaJMfunc = RooFormulaVar("deltaJpsiM", "deltaJpsiM", "@0 - @1",
RooArgList(mumuMass, JPsiMass))
deltaPMfunc = RooFormulaVar("deltaPsiPM", "deltaPsiPM", "@0 - @1",
RooArgList(mumuMass, PsiPMass))
deltaB0M = fulldata.addColumn(deltaB0Mfunc)
deltaJpsiM = fulldata.addColumn(deltaJMfunc)
deltaPsiPM = fulldata.addColumn(deltaPMfunc)
thevars.add(deltaB0M)
thevars.add(deltaJpsiM)
thevars.add(deltaPsiPM)
