def doFit(ws,options):
rap_bins = range(1,len(jpsi.pTRange))
pt_bins = None
if options.testBin is not None:
rap_bins = [int(options.testBin.split(',')[0])]
pt_bins = [int(options.testBin.split(',')[1])-1]
for rap_bin in rap_bins:
if options.testBin is None:
pt_bins = range(len(jpsi.pTRange[rap_bin]))
for pt_bin in pt_bins:
sigMaxMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigBkgHigh*jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigBkgLow*jpsi.sigmaMassJpsi[rap_bin]
jPsiMass = ws.var('JpsiMass')
jPsicTau = ws.var('Jpsict')
jPsiMass.setRange('mlfit_prompt',2.7,3.5)
jPsiMass.setRange('mlfit_nonPrompt',2.7,3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_prompt',2.7,3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_nonPrompt',2.7,3.5)
jPsicTau.setRange('mlfit_signal',-1,2.5)
jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_signal',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_promptSignal',-1,.1)
#jPsicTau.setRange('mlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
#reset parameters
ws.var('CBn').setVal(.5)
ws.var('CBalpha').setVal(.5)
ws.var('CBmass').setVal(3.1)
ws.var('CBsigma').setVal(.02)
ws.var('bkgLambda').setVal(0)
ws.var('bkgTauSSDL').setVal(.5)
#ws.var('bkgTauFDL').setVal(.5)
ws.var('bkgTauDSDL').setVal(.5)
ws.var('fBkgSSDL').setVal(.5)
ws.var('fBkgLR').setVal(.5)
ws.var('bkgTauSSDR').setVal(.5)
#ws.var('bkgTauFDR').setVal(.5)
ws.var('bkgTauDSDR').setVal(.5)
ws.var('fBkgSSDR').setVal(.5)
#ws.var('fBkgFDR').setVal(.25)
#ws.var('nPrompt').setVal(5000)
#ws.var('nNonPrompt').setVal(500)
#ws.var('nBackground').setVal(100)
#ws.var('nBackgroundL').setVal(50)
#ws.var('nBackgroundR').setVal(50)
ws.var('nonPromptTau').setVal(.5)
ws.var('promptMean').setVal(0)
ws.var('ctResolution').setVal(1)
if options.fixBfrac:
cutStringM1 = '('+jPsiMass.GetName()+' > '+str(sigMinMass)+' && '+jPsiMass.GetName()+' < '+str(sigMaxMass)+')'
data2 = ws.data('data_rap'+str(rap_bin)+'_pt'+str(pt_bin+1))
dataSizeBfrac1 = data2.numEntries()
print dataSizeBfrac1
bfracVars = RooArgSet(jPsiMass)
bfracData = data2.reduce(ROOT.RooFit.SelectVars(bfracVars),
ROOT.RooFit.Cut(cutStringM1),
ROOT.RooFit.Name('data_for_normalizing_bfrac'),
ROOT.RooFit.Title('data_for_normalizing_bfrac'))
print rap_bin
print pt_bin
bfrac = jpsi.Bfrac[rap_bin-1][pt_bin]
print bfrac
dataSizeBfrac = bfracData.numEntries()
factPrompt = 1-bfrac
dataSizePrompt = dataSizeBfrac*factPrompt
dataSizeNonPrompt = dataSizeBfrac*bfrac
print factPrompt
print dataSizeBfrac
print dataSizePrompt
print dataSizeNonPrompt
ws.var('nPromptSignal').setVal(dataSizePrompt)
ws.var('nNonPromptSignal').setVal(dataSizeNonPrompt)
ws.var('nPromptSignal').setConstant()
ws.var('nNonPromptSignal').setConstant()
LPdf = ws.pdf('LPdf')
MPdf = ws.pdf('MPdf')
data = ws.data('data_rap'+str(rap_bin)+'_pt'+str(pt_bin+1))
NLLs = RooArgSet()
MassNLL = MPdf.createNLL(data,
ROOT.RooFit.Range('mlfit'),
ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(RooArgSet(ws.var('JpsictErr'))),
ROOT.RooFit.NumCPU(2))
CTauNLL = LPdf.createNLL(data,
ROOT.RooFit.Range('mlfit'),
ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(RooArgSet(ws.var('JpsictErr'))),
ROOT.RooFit.NumCPU(2))
NLLs.add(MassNLL)
NLLs.add(CTauNLL)
simNLL = RooAddition('add','add',NLLs)
minuit = RooMinuit(simNLL)
minuit.setStrategy(2)
minuit.setPrintEvalErrors(-1)
if options.do_fit:
minuit.simplex()
minuit.migrad()
minuit.migrad()
minuit.hesse()
fitresult = minuit.save('polfitresult_rap'+str(rap_bin)+'_pt'+str(pt_bin+1))
getattr(ws,'import')(fitresult)
ws.saveSnapshot('snapshot_rap'+str(rap_bin)+'_pt'+str(pt_bin+1),ws.allVars())
fitresult.Print()
def doFit(ws,options):
rap_bins = range(1,len(jpsi.pTRange))
pt_bins = None
if options.testBin is not None:
rap_bins = [int(options.testBin.split(',')[0])]
pt_bins = [int(options.testBin.split(',')[1])-1]
for rap_bin in rap_bins:
if options.testBin is None:
pt_bins = range(len(jpsi.pTRange[rap_bin]))
for pt_bin in pt_bins:
sigMaxMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigBkgHigh*jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigBkgLow*jpsi.sigmaMassJpsi[rap_bin]
jPsiMass = ws.var('JpsiMass')
jPsicTau = ws.var('Jpsict')
jPsiMass.setRange('mlfit_prompt',2.7,3.5)
jPsiMass.setRange('mlfit_nonPrompt',2.7,3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_prompt',2.7,3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_nonPrompt',2.7,3.5)
jPsicTau.setRange('mlfit_signal',-1,2.5)
jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_signal',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_promptSignal',-1,.1)
#jPsicTau.setRange('mlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
#reset parameters
ws.var('CBn').setVal(.5)
ws.var('CBalpha').setVal(.5)
ws.var('CBmass').setVal(3.1)
ws.var('CBsigma').setVal(.02)
ws.var('bkgLambda').setVal(0)
ws.var('bkgTauSSDL').setVal(.5)
#ws.var('bkgTauFDL').setVal(.5)
ws.var('bkgTauDSDL').setVal(.5)
ws.var('fBkgSSDL').setVal(.5)
ws.var('fBkgLR').setVal(.5)
ws.var('bkgTauSSDR').setVal(.5)
#ws.var('bkgTauFDR').setVal(.5)
ws.var('bkgTauDSDR').setVal(.5)
ws.var('fBkgSSDR').setVal(.5)
#ws.var('fBkgFDR').setVal(.25)
#ws.var('nPrompt').setVal(5000)
#ws.var('nNonPrompt').setVal(500)
#ws.var('nBackground').setVal(100)
#ws.var('nBackgroundL').setVal(50)
#ws.var('nBackgroundR').setVal(50)
ws.var('nonPromptTau').setVal(.5)
ws.var('promptMean').setVal(0)
ws.var('ctResolution').setVal(1)
LPdf = ws.pdf('LPdf')
MPdf = ws.pdf('MPdf')
data = ws.data('data_rap'+str(rap_bin)+'_pt'+str(pt_bin+1))
NLLs = RooArgSet()
MassNLL = MPdf.createNLL(data,
ROOT.RooFit.Range('mlfit'),
ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(RooArgSet(ws.var('JpsictErr'))),
ROOT.RooFit.NumCPU(2))
CTauNLL = LPdf.createNLL(data,
ROOT.RooFit.Range('mlfit'),
ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(RooArgSet(ws.var('JpsictErr'))),
ROOT.RooFit.NumCPU(2))
NLLs.add(MassNLL)
NLLs.add(CTauNLL)
simNLL = RooAddition('add','add',NLLs)
minuit = RooMinuit(simNLL)
minuit.setStrategy(2)
minuit.setPrintEvalErrors(-1)
minuit.simplex()
minuit.migrad()
minuit.migrad()
minuit.hesse()
fitresult = minuit.save('polfitresult_rap'+str(rap_bin)+'_pt'+str(pt_bin+1))
getattr(ws,'import')(fitresult)
ws.saveSnapshot('snapshot_rap'+str(rap_bin)+'_pt'+str(pt_bin+1),ws.allVars())
def doFit(ws, options):
rap_bins = range(1, len(jpsi.pTRange))
pt_bins = None
if options.testBin is not None:
rap_bins = [int(options.testBin.split(',')[0])]
pt_bins = [int(options.testBin.split(',')[1]) - 1]
for rap_bin in rap_bins:
if options.testBin is None:
pt_bins = range(len(jpsi.pTRange[rap_bin]))
for pt_bin in pt_bins:
sigMaxMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigBkgHigh * jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigBkgLow * jpsi.sigmaMassJpsi[rap_bin]
jPsiMass = ws.var('JpsiMass')
jPsicTau = ws.var('Jpsict')
jPsiMass.setRange('mlfit_prompt', 2.7, 3.5)
jPsiMass.setRange('mlfit_nonPrompt', 2.7, 3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_prompt', 2.7, 3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_nonPrompt', 2.7, 3.5)
jPsicTau.setRange('mlfit_signal', -1, 2.5)
jPsicTau.setRange('mlfit_leftMassSideBand', -1, 2.5)
jPsicTau.setRange('mlfit_rightMassSideBand', -1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_signal', -1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',
-1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',
-1, 2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_promptSignal',-1,.1)
#jPsicTau.setRange('mlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
#reset parameters
ws.var('CBn').setVal(.5)
ws.var('CBalpha').setVal(.5)
ws.var('CBmass').setVal(3.1)
ws.var('CBsigma').setVal(.02)
ws.var('bkgLambda').setVal(0)
ws.var('bkgTauSSDL').setVal(.5)
#ws.var('bkgTauFDL').setVal(.5)
ws.var('bkgTauDSDL').setVal(.5)
ws.var('fBkgSSDL').setVal(.5)
ws.var('fBkgLR').setVal(.5)
ws.var('bkgTauSSDR').setVal(.5)
#ws.var('bkgTauFDR').setVal(.5)
ws.var('bkgTauDSDR').setVal(.5)
ws.var('fBkgSSDR').setVal(.5)
#ws.var('fBkgFDR').setVal(.25)
#ws.var('nPrompt').setVal(5000)
#ws.var('nNonPrompt').setVal(500)
#ws.var('nBackground').setVal(100)
#ws.var('nBackgroundL').setVal(50)
#ws.var('nBackgroundR').setVal(50)
ws.var('nonPromptTau').setVal(.5)
ws.var('promptMean').setVal(0)
ws.var('ctResolution').setVal(1)
LPdf = ws.pdf('LPdf')
MPdf = ws.pdf('MPdf')
data = ws.data('data_rap' + str(rap_bin) + '_pt' + str(pt_bin + 1))
NLLs = RooArgSet()
MassNLL = MPdf.createNLL(
data, ROOT.RooFit.Range('mlfit'), ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(
RooArgSet(ws.var('JpsictErr'))), ROOT.RooFit.NumCPU(2))
CTauNLL = LPdf.createNLL(
data, ROOT.RooFit.Range('mlfit'), ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(
RooArgSet(ws.var('JpsictErr'))), ROOT.RooFit.NumCPU(2))
NLLs.add(MassNLL)
NLLs.add(CTauNLL)
simNLL = RooAddition('add', 'add', NLLs)
minuit = RooMinuit(simNLL)
minuit.setStrategy(2)
minuit.setPrintEvalErrors(-1)
minuit.simplex()
minuit.migrad()
minuit.migrad()
minuit.hesse()
fitresult = minuit.save('polfitresult_rap' + str(rap_bin) + '_pt' +
str(pt_bin + 1))
getattr(ws, 'import')(fitresult)
ws.saveSnapshot(
'snapshot_rap' + str(rap_bin) + '_pt' + str(pt_bin + 1),
ws.allVars())
