def test_parallel_significance_toys():
"""Perform toy-study for significance of the signal
- generate `nToys` pseudoexperiments using background-only hypothesis
- fit each experiment with signal+background hypothesis
- store fit results
- fill distributions for fit results
"""
## only background hypothesis
bkg_only = Models.Bkg_pdf("BKG", xvar=mass, power=0, tau=0)
signal = Models.Gauss_pdf('S', xvar=mass, mean=0.5, sigma=0.1)
signal.mean.fix(0.4)
signal.sigma.fix(0.1)
## signal + background hypothesis
model = Models.Fit1D(signal=signal, background=1)
model.background.tau.fix(0)
results, stats = Toys.parallel_toys2(
gen_pdf=bkg_only,
fit_pdf=model,
nToys=1000,
nSplit=20,
data=[mass],
gen_config={
'nEvents': 100,
'sample': True
},
fit_config={'silent': True},
gen_pars={'tau_BKG': 0.}, ## initial values for generation
fit_pars={
'B': 100,
'S': 10,
'phi0_Bkg_S': 0.0
}, ## initial fit values for parameters
silent=True,
progress=False)
for p in stats:
logger.info("Toys: %-20s : %s" % (p, stats[p]))
h_S = ROOT.TH1F(hID(), '#S', 60, 0, 60)
for r in results['S']:
h_S.Fill(r)
for h in (h_S, ):
h.draw()
logger.info("%s :\n%s" % (h.GetTitle(), h.dump(30, 10)))
time.sleep(1)
def test_parallel_toys2():
"""Perform toys-study for possible fit bias and correct uncertainty evaluation
- generate `nToys` pseudoexperiments with some PDF `gen_pdf`
- fit teach experiment with the PDF `fit_pdf`
- store fit results
- fill distributions of fit results
"""
results, stats = Toys.parallel_toys2(gen_pdf=gen_gauss,
fit_pdf=fit_gauss,
nToys=1000,
nSplit=20,
data=[mass],
gen_config={
'nEvents': 200,
'sample': True
},
fit_config={'silent': True},
gen_pars={
'mean_GG': 0.4,
'sigma_GG': 0.1
},
fit_pars={
'mean_GF': 0.4,
'sigma_GF': 0.1
},
silent=True,
progress=False)
for p in stats:
logger.info("Toys: %-20s : %s" % (p, stats[p]))
## make histos
h_mean = ROOT.TH1F(hID(), 'mean of Gauss ', 50, 0, 0.80)
h_sigma = ROOT.TH1F(hID(), 'sigma of Gauss', 50, 0.05, 0.15)
for r in results['mean_FG']:
h_mean.Fill(r)
for r in results['sigma_FG']:
h_sigma.Fill(r)
for h in (h_mean, h_sigma):
h.draw()
logger.info("%s :\n%s" % (h.GetTitle(), h.dump(30, 10)))
time.sleep(1)
