sigMassComps += signalBMass.pdf()
# build the combinatorial background mass PDF
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
backgroundBMass = BackgroundBMass( Name = 'cbkg_m', mass = observables['mass'] )
cbkgMassComps += backgroundBMass.pdf()
# build total mass PDF
from P2VV.RooFitWrappers import buildPdf
massPdf = buildPdf( massComps, Observables = [ observables['mass'] ], Name = 'JpsiKKMass' )
# get splitting categories and parameters
yieldNames = [ par.GetName() for par in massPdf.Parameters() if par.getAttribute('Yield') ]
allParNames = [ par.GetName() for par in massPdf.Parameters() if not par.getAttribute('Yield') if not par.isConstant() ]
sigParNames = [ par.GetName() for par in signalBMass.parameters() if not par.isConstant() ]
bkgParNames = [ par.GetName() for par in backgroundBMass.parameters() if not par.isConstant() ]
splitCats = { observables['runPeriod'].GetName() : set( yieldNames + bkgParNames )
, observables['KKMassCat'].GetName() : set( yieldNames )
}
if not biasedOnly :
splitCats[ observables['hlt2B'].GetName() ] = set( yieldNames + bkgParNames )
if model == 'ipatia2' :
# split Ipatia 2 signal parameters
hlt2BName = observables['hlt2B'].GetName()
KKCatName = observables['KKMassCat'].GetName()
for parName in sigParNames :
if not biasedOnly and 'sigma' in parName : splitCats[hlt2BName].add(parName)
splitCats[KKCatName].add(parName)
for parName in constSplitPars :
splitCats[KKCatName].add(parName)
print 120 * '='
print 'P2VV - INFO: createB2CCFitNTuple: fitting with mass PDF'
massFitResult = massPdf.fitTo( dataSets['pre'][0], Save = True, **fitOpts )
from P2VV.Imports import parNames
massFitResult.PrintSpecial( text = True, ParNames = parNames )
if runPeriods or triggerSel in ['paper2012', 'timeEffFit'] or len(KKMassBinBounds) > 2 :
# get splitting categories and parameters
splitCats = { }
yieldNames = [ par.GetName() for par in massPdf.Parameters() if par.getAttribute('Yield') ]
if runPeriods :
splitCats[observables['runPeriod'].GetName()] = set([ par.GetName() for par in massPdf.Parameters() if not par.isConstant() ])
if len(KKMassBinBounds) > 2 :
splitCats[ observables['KKMassCat'].GetName() ] = set(yieldNames)
for par in backgroundBMass.parameters() :
if not par.isConstant() : splitCats[ observables['KKMassCat'].GetName() ].add( par.GetName() )
if triggerSel == 'paper2012' or triggerSel == 'timeEffFit' :
splitCats[ observables['hlt1ExclB'].GetName() ] = set(yieldNames)
if triggerSel == 'timeEffFit' :
splitCats[ observables['hlt2B'].GetName() ] = set(yieldNames)
splitParsDict = { }
pdfVars = massPdf.getVariables()
for cat, params in splitCats.iteritems() :
assert ws.cat(cat), 'P2VV - ERROR: createB2CCFitNTuple: category "%s" not in workspace' % cat
for par in params :
assert ws.var(par), 'P2VV - ERROR: createB2CCFitNTuple: no variable "%s" in workspace' % par
assert pdfVars.find(par), 'P2VV - ERROR: createB2CCFitNTuple: variable "%s" not in PDF' % par
if ws[par] not in splitParsDict :
splitParsDict[ ws[par] ] = set( [ ws[cat] ] )