tres_args = dict(time = t, sigmat = st, Cache = True)
tres_1 = Multi_TimeResolution(Name = 'tres', ParNamePrefix = 'one',
TimeResSFParam = 'linear',
timeResMu = dict(Value = 0.),
sf_one_offset = dict(Value = 0, Constant = True),
sf_one_slope = dict(Value = 1.174),
sf_two_offset = dict(Value = 0, Constant = True),
sf_two_slope = dict(Value = 2),
Fractions = [(2, 0.143)], **tres_args)
tres_2 = TimeResolution(Name = 'tres', ParNamePrefix = 'two', PerEventError = True,
timeResSigmaSF = dict(Value = 4.), **tres_args)
# Gaussians for Time
from P2VV.Parameterizations.TimePDFs import Prompt_Peak
g1 = Prompt_Peak(t, tres_1.model(), Name = 'g1')
g2 = Prompt_Peak(t, tres_2.model(), Name = 'g2')
# Mass shapes
from P2VV.Parameterizations.MassPDFs import Background_PsiMass as PsiBkgPdf
bkg_m = PsiBkgPdf(mpsi, Name = 'bkg_mpsi')
from P2VV.Parameterizations.MassPDFs import DoubleCB_Psi_Mass as PsiMassPdf
sig_m = PsiMassPdf(mpsi, Name = 'psi_m', mpsi_alpha_1 = dict(Value = 2, Constant = True),
mpsi_sigma_sf = dict(Value = 2.3), mpsi_frac = dict(Value = 0.8))
one = Component('one', [g1.pdf(), sig_m.pdf(), ln], Yield = (25000, 100, 100000))
two = Component('two', [g2.pdf(), bkg_m.pdf(), ln], Yield = (25000, 100, 100000))
sig_pdf = buildPdf(Name = 'sig_pdf', Components = [one], Observables = [mpsi, t, st])
bkg_pdf = buildPdf(Name = 'bkg_pdf', Components = [two], Observables = [mpsi, t, st])
psi_m = PsiMassPdf(mpsi, Name = 'psi_m', mpsi_alpha_1 = dict(Value = 2, Constant = (year == '2011')),
ParameteriseSigma = options.mass_param)
psi_m = psi_m.pdf()
# J/psi background
from P2VV.Parameterizations.MassPDFs import Background_PsiMass as PsiBkgPdf
bkg_mpsi = PsiBkgPdf(mpsi, Name = 'bkg_mpsi')
# Create combinatorical background component
mean = RealVar(Name = 'mean', Value = 5368, MinMax = (5300, 5400))
sigma = RealVar(Name = 'sigma', Value = 50, MinMax = (1, 500))
bkg_m = Pdf(Name = 'gauss', Type = Gaussian, Parameters = (m, mean, sigma))
# Create psi background component
from P2VV.Parameterizations.TimePDFs import LP2011_Background_Time as Background_Time
psi_t = Background_Time( Name = 'psi_t', time = time_obs, resolutionModel = sig_tres.model()
, psi_t_fml = dict(Name = 'psi_t_fml', Value = 6.7195e-01)
, psi_t_ll_tau = dict(Name = 'psi_t_ll_tau', Value = 1.3672, MinMax = (1, 2.5))
, psi_t_ml_tau = dict(Name = 'psi_t_ml_tau', Value = 1.3405e-01, MinMax = (0.01, 2.5))
)
psi_t = psi_t.pdf()
# 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()
# 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'},
## 'notExclB' : { 'bins' : t.getRange(), 'heights' : [0.7]}},
## hlt2_biased : { 'B' : {'histogram' : 'hlt2_shape'}},
## hlt2_unbiased : { 'UB' : {'histogram' : 'hlt2_shape'}}}
hists = {hlt1_excl_biased_dec : {'exclB' : {'histogram' : 'hlt1_shape'},
'notExclB' : { 'bins' : t.getRange(), 'heights' : [0.7]}},
hlt2_biased : { 'B' : {'histogram' : 'hlt2_shape'}},
hlt2_unbiased : { 'UB' : {'bins' : t.getRange(), 'heights' : [0.7]}}}
from P2VV.Parameterizations.TimePDFs import Single_Exponent_Time
pdf_wrapper = Single_Exponent_Time(Name = 'pdf', time = t, resolutionModel = tres.model())
tau = pdf_wrapper._tau
pdf = pdf_wrapper.pdf()
split_cats = [[run_period]]
from itertools import chain
split_pars = [[tau]]
sim_pdf = SimultaneousPdf(pdf.GetName() + '_simul', MasterPdf = pdf,
SplitCategories = split_cats, SplitParameters = split_pars)
from ROOT import RooArgSet, RooArgList
split_cat_pars = RooArgSet()
sim_pdf.treeNodeServerList(split_cat_pars)
split_cat = sim_pdf.indexCat()
input_cats = RooArgList(split_cat) if split_cat.isFundamental() else split_cat.inputCatList()
prototype = tres.model()
