hd = ('%d' % hash(hash_str)).replace('-', 'm')
directory = '1bin_%4.2ffs_simple/%s' % (1000 * (t.getMax() - t.getMin()), hd)
cache = Cache(input_data[years]['cache'].rsplit('/', 1), directory)
data, sdatas = cache.read_data()
if component == 'signal':
data = single_bin_sig_sdata = sdatas['sig_sdata']
else:
data = single_bin_bkg_sdata = sdatas['bkg_sdata']
## Fit options
fitOpts = dict(NumCPU = 4, Timer = 1, Save = True,
Verbose = False, Optimize = 2, Minimizer = 'Minuit2')
from P2VV.Parameterizations.SigmatPDFs import DoubleLogNormal
dln = DoubleLogNormal(st)
ln = dln.pdf()
# Fit
result = ln.fitTo(data, **fitOpts)
# Plot
from ROOT import TCanvas
canvas = TCanvas('canvas', 'canvas', 600, 400)
p = canvas.cd(1)
from P2VV.Utilities.Plotting import plot
from ROOT import kBlack, kBlue
plot(p, st, pdf = ln, data = data,
dataOpts = dict(MarkerSize = 0.8, MarkerColor = kBlack, Binning = 60),
pdfOpts = dict(LineWidth = 3),
xTitle = 'estimated decay-time error [ps]',
yTitle = 'Candidates / (0.002 ps)')
, psi_t_ml_tau = dict(Name = 'psi_t_ml_tau', Value = 1.3405e-01, MinMax = (0.01, 0.5))
)
psi_t = psi_t.pdf()
# Sigmat PDFs
from P2VV.Parameterizations.SigmatPDFs import DoubleLogNormal
sig_ln = DoubleLogNormal(st, ParNamePrefix = 'sig')
bkg_ln = DoubleLogNormal(st, ParNamePrefix = 'bkg')
# Background time pdf
from P2VV.Parameterizations.TimePDFs import Prompt_Peak
bkg_peak = Prompt_Peak(time_obs, bkg_tres.model(), Name = 'prompt_bkg')
bkg_t = Background_Time(Name = 'bkg_t', time = time_obs, resolutionModel = bkg_tres.model())
# J/psi signal component
psi_ll = Component('psi_ll', (psi_m, psi_t, sig_ln.pdf()), Yield= (8.5575e+03,100,500000) )
psi_prompt_pdf = Prompt_Peak(time_obs, sig_tres.model(), Name = 'psi_prompt_pdf')
psi_prompt = Component('psi_prompt', (psi_prompt_pdf.pdf(), psi_m, sig_ln.pdf()), Yield = (160160, 100, 500000))
# Background component
bkg_prompt_pdf = Prompt_Peak(time_obs, bkg_tres.model(), Name = 'bkg_prompt_pdf')
bkg_prompt = Component('bkg_prompt', (bkg_mpsi.pdf(), bkg_prompt_pdf.pdf(), bkg_ln.pdf()),
Yield = (19620,100,500000))
bkg_ll = Component('bkg_ll', (bkg_mpsi.pdf(), bkg_t.pdf(), bkg_ln.pdf()),
Yield = (19620,100,500000) )
from ROOT import RooKeysPdf
def observables(self):
return self._observables
def conditional_observables(self):
return set()
