gexp_frac = RealVar(Name = "gexp_frac", Value = 0.1, MinMax = (0.0001, 0.9999))
add_model = AddModel("add_model", Models = [gexp_model, gauss_model], Fractions = [gexp_frac], ConditionalObservables = [st])
from ROOT import RooDecay as Decay
peak_tau = RealVar(Name = "peak_tau", Value = 0, Constant = True)
peak = Pdf(Name = "peak", Type = Decay, Parameters = [t_diff, peak_tau, add_model, 'SingleSided'], ConditionalObservables = [st])
## peak2 = Pdf(Name = "peak2", Type = Decay, Parameters = [t_diff, peak_tau, gexp_model, 'SingleSided'], ConditionalObservables = [st])
## peak3 = Pdf(Name = "peak3", Type = Decay, Parameters = [t_diff, peak_tau, gauss_model, 'SingleSided'], ConditionalObservables = [st])
# signal component
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_Mass
sig_m = Signal_Mass(Name = 'sig_m', mass = m)
signal = Component('signal', (sig_m.pdf(), st_pdf, peak), Yield = (150000, 10000, 1000000))
sig_mass_pdf = buildPdf(Components = (signal, background), Observables = (m,), Name = 'sig_mass_pdf')
signal_name = signal.GetName()
mass_pdf = sig_mass_pdf
fitOpts = dict(NumCPU = 4, Timer = 1, Save = True, Minimizer = 'Minuit2', Optimize = 2, Offset = True,
Verbose = False)
gen_pdf = buildPdf(Components = (signal,), Observables = (t_diff, st), Name = 'gen_pdf')
fit_pdf = buildPdf(Components = (signal,), Observables = (t_diff,), Name = 'fit_pdf')
toy.set_fit_opts(**dict(Verbose = False))
toy.run(Observables = [t_diff, st], Pdf = fit_pdf, GenPdf = gen_pdf)
prefix = '/stuff/PhD' if os.path.exists('/stuff') else '/bfys/raaij'
if dataSample == '2011':
input_file = os.path.join(prefix, 'p2vv/data/Bs2JpsiPhi_ntupleB_for_fitting_20121012_MagDownMagUp.root')
elif dataSample == '2012':
input_file = os.path.join(prefix, '/stuff/PhD/p2vv/data/Bs2JpsiPhi_2012_ntupleB_20121212.root')
else:
raise RuntimeError
cut = 'runNumber > 0 && sel == 1 && sel_cleantail == 1 && (hlt1_biased == 1 || hlt1_unbiased_dec == 1) && hlt2_biased == 1 && '
## cut = 'runNumber > 0 && sel == 1 && sel_cleantail == 1 && hlt1_unbiased_dec == 1 && hlt2_unbiased == 1 && '
cut += ' && '.join(['%s < 4' % e for e in ['muplus_track_chi2ndof', 'muminus_track_chi2ndof', 'Kplus_track_chi2ndof', 'Kminus_track_chi2ndof']])
data = readData(input_file, tree_name, ntupleCuts = cut,
NTuple = True, observables = observables)
# Create signal component
signal = Component('signal', (sig_m.pdf(), psi_m), Yield = (27437,10000,50000))
## Build PDF
mass_pdf = buildPdf(Components = (signal, background), Observables = (m, ), Name = 'mass_pdf')
mass_pdf.Print("t")
## Fit options
fitOpts = dict(NumCPU = 4, Timer = 1, Save = True,
Verbose = False, Optimize = 2, Minimizer = 'Minuit2')
# make sweighted dataset. TODO: use mumu mass as well...
from P2VV.Utilities.SWeights import SData, splot
single_bin_result = mass_pdf.fitTo(data, **fitOpts)
from P2VV.Utilities.General import getSplitPar
# J/psi signal component
psi_ll = Component('psi_ll', (psi_m, bkg_m, psi_t), Yield= (8.5575e+03,100,500000) )
# Background component
from P2VV.Parameterizations.TimePDFs import Prompt_Peak
background = Component('background', (bkg_mpsi.pdf(), bkg_m), Yield = (19620,100,500000) )
if options.add_background:
bkg_t = Prompt_Peak(time_obs, bkg_tres.model(), Name = 'bkg_prompt_pdf')
background[t] = bkg_t.pdf()
# B signal component
sig_t = Prompt_Peak(time_obs, resolutionModel = sig_tres.model(), Name = 'sig_t')
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_Mass
sig_m = Signal_Mass(Name = 'sig_m', mass = m)
signal = Component('signal', (sig_m.pdf(), sig_t.pdf()), Yield = (3e5, 10000, 1e7))
# Prompt component
prompt_pdf = Prompt_Peak(time_obs, sig_tres.model(), Name = 'prompt_pdf')
prompt = Component('prompt', (prompt_pdf.pdf(), psi_m), Yield = (160160, 100, 500000))
# Read data
fit_mass = (options.fit_mass or not options.cache) or options.reduce
## from profiler import heap_profiler_start, heap_profiler_stop
## heap_profiler_start("profile.log")
# Tree and cut
tree_name = 'DecayTree'
if signal_MC and options.excl_biased:
hlt1_cut = 'hlt1_excl_biased == 1'
massComps = [ sigMassComps, cbkgMassComps ] yieldNames = [ comp.getYield().GetName() for comp in massComps ] # build the signal mass PDF sigMassArgs = dict( Name = 'sig_m', mass = observables['mass'] ) from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass signalBMass = SignalBMass( **sigMassArgs ) # build the combinatorial background mass PDF cbkgMassArgs = dict( Name = 'cbkg_m', mass = observables['mass'] ) from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass backgroundBMass = BackgroundBMass( **cbkgMassArgs ) # build mass PDF from P2VV.RooFitWrappers import buildPdf sigMassComps += signalBMass.pdf() cbkgMassComps += backgroundBMass.pdf() massPdf = buildPdf( massComps, Observables = [ observables['mass'] ], Name = 'JpsiKKMass' ) ########################################################################################################################################### ## fit J/psiKK mass distributions ## #################################### # determine mass parameters with a fit 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 )
ntupleCuts=cut,
)
sigFrac = 0.504
nEvents = data.sumEntries()
nSignal = nEvents * sigFrac
nBackground = nEvents * (1.0 - sigFrac)
## from P2VV.Parameterizations.MassPDFs import Ipatia2_Signal_Mass as SignalBMass
## sig_m = SignalBMass(Name = 'sig_m', mass = mass, m_sig_lambda = -2.5, m_sig_zeta = 0.01,
## m_sig_alpha_1 = 3.0, m_sig_alpha_2 = 2.5, m_sig_n_1 = 1.5, m_sig_n_2 = 2.0)
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass
sig_m = SignalBMass(Name="sig_m", mass=mass)
signal = Component("signal", [sig_m.pdf()], Yield=(nSignal, 0.0, nEvents))
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
bkg_m = BackgroundBMass(Name="bkg_m", mass=mass)
background = Component("background", [bkg_m.pdf()], Yield=(nBackground, 0.0, nEvents))
mass_pdf = buildPdf([signal, background], Observables=[mass], Name="mass_pdf")
fitOpts = dict(NumCPU=4, Optimize=2, Save=True, Timer=True, Minimizer="Minuit2", Offset=True)
mass_result = mass_pdf.fitTo(data, **fitOpts)
# categories for splitting the PDF
if not options.prescaled:
split_cats = [[excl_biased]]
for o in [mass, t, st]:
cut += ' && {0} > {1} && {0} < {2}'.format(o.GetName(), o.getMin(), o.getMax())
from P2VV.Utilities.DataHandling import readData
data = readData(filePath = input_file, dataSetName = 'DecayTree', NTuple = True,
observables = [mass, t, st, excl_biased], Rename = 'JpsiphiData', ntupleCuts = cut)
sigFrac = 0.504
nEvents = data.sumEntries()
nSignal = nEvents * sigFrac
nBackground = nEvents * ( 1. - sigFrac )
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as SignalBMass
sig_m = SignalBMass(Name = 'sig_m', mass = mass)
signal = Component( 'signal', [sig_m.pdf()], Yield = ( nSignal, 0., nEvents ) )
from P2VV.Parameterizations.MassPDFs import LP2011_Background_Mass as BackgroundBMass
bkg_m = BackgroundBMass(Name = 'bkg_m', mass = mass)
background = Component( 'background', [bkg_m.pdf()], Yield = ( nBackground, 0., nEvents ) )
mass_pdf = buildPdf( [ signal, background ], Observables = [ mass ], Name = 'mass_pdf')
fitOpts = dict(NumCPU = 4, Optimize = 2, Save = True, Timer = True, Minimizer = 'Minuit2',
Offset = True)
mass_result = mass_pdf.fitTo(data, **fitOpts)
# categories for splitting the PDF
if data_type.endswith("signal"):
split_cats = [[excl_biased]]
from ROOT import RooDecay as Decay
from P2VV.Parameterizations.TimeResolution import Gaussian_TimeResolution as TimeResolution
tres = TimeResolution(Name = 'resolution', time = t, BiasScaleFactor = False,
timeResSigma = dict(Value = 0.045, Constant = True),
timeResMu = dict(Value = 0, Constant = True), Cache = False)
# B mass pdf
from P2VV.Parameterizations.MassPDFs import LP2011_Signal_Mass as Signal_BMass, LP2011_Background_Mass as Background_BMass
sig_m = Signal_BMass(Name = 'sig_m', mass = m, m_sig_mean = dict( Value = 5365, MinMax = (5363,5372)))
# Create combinatorical background component
bkg_m = Background_BMass( Name = 'bkg_m', mass = m, m_bkg_exp = dict( Name = 'm_bkg_exp' ) )
# Create components
signal_mass = Component('signal', (sig_m.pdf(),), Yield = (30000,100,100000))
background_mass = Component('background', (bkg_m.pdf(),), Yield = (100000,100,300000) )
## Build mass PDF
mass_pdf = buildPdf(Components = (signal_mass, background_mass), Observables = (m, ), Name='mass_pdf')
mass_pdf.Print("t")
## base_location = '/home/raaij'
base_location = '/stuff/PhD/p2vv'
# Build the acceptance using the histogram as starting values
input_file = os.path.join(base_location, 'data/start_values.root')
## hists = {hlt1_excl_biased_dec : {'excl_biased' : {'histogram' : 'hlt1_shape', 'average' : (6.285e-01, 1.633e-02)},
## 'unbiased' : { 'bins' : t.getRange(), 'heights' : [0.5]}}}
## hists = {hlt1_excl_biased_dec : {'exclB' : {'histogram' : 'hlt1_shape'},
