def fit_form_factor(self,
nstates,
chain=False,
constrain=False,
**fitter_kwargs):
"""Run the joint fit of 2- and 3-point functions for form factor."""
# Handle pedestal
pedestal = fitter_kwargs.pop('pedestal', None)
if pedestal is not None:
self.pedestal = pedestal
# Handle prior
prior = fitter_kwargs.get('prior')
if prior is not None:
self.prior = prior
else:
prior = self.prior
fitter_kwargs['prior'] = prior
# Handle models
models_list = []
for tag in self.ds:
model = get_model(self.ds, tag, nstates, self.pedestal, constrain)
if model is not None:
models_list.append(model)
# Abort if too few models found
if len(models_list) != len(set(self.ds.keys())):
self.fitter = None
fit = None
LOGGER.warning('Insufficient models found. Skipping joint fit.')
return
# Run fit
self.fitter = cf.CorrFitter(models=models_list)
if chain:
_lsqfit = self.fitter.chained_lsqfit
else:
_lsqfit = self.fitter.lsqfit
fit = _lsqfit(data=self.ds, **fitter_kwargs)
# fit = serialize.SerializableNonlinearFit(fit)
fit = serialize.SerializableFormFactor(fit, tags=self.ds.tags)
self.fits['full'] = fit
if fit.failed:
LOGGER.warning('Full joint fit failed.')
else:
# Update mass estimates in dataset, since the visualization of
# the ratio R (or Rbar) is rather sensitive to the masses
self.ds.set_masses(fit.p['light-light:dE'][0],
fit.p['heavy-light:dE'][0])
# Tidy up final results
if self.pedestal is not None:
sign = np.sign(self.pedestal)
vnn = self.pedestal + sign * _abs(fit.p['fluctuation'])
fit.p['Vnn'][0, 0] = vnn
fit.palt['Vnn'][0, 0] = vnn
else:
vnn = fit.p['Vnn'][0, 0]
self.r = convert_vnn_to_ratio(self.m_src, vnn)
def main():
data, basis = make_data('etab.h5')
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in range(1, 8):
print(30 * '=', 'nterm =', N)
prior = make_prior(N, basis)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=SVDCUT)
print(fit.format(pstyle=None if N < 7 else 'v'))
p0 = fit.pmean
print_results(fit, basis, prior, data)
if SHOWPLOTS:
fit.show_plots(save='etab.{}.png', view='ratio')
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.lsqfit(
data=data,
prior=prior,
p0=fit.pmean,
svdcut=SVDCUT,
noise=True,
)
print(noisy_fit.format(pstyle=None))
dE = fit.p['etab.dE'][:3]
noisy_dE = noisy_fit.p['etab.dE'][:3]
print(' dE:', dE)
print('noisy dE:', noisy_dE)
print(' ', gv.fmt_chi2(gv.chi2(dE - noisy_dE)))
if SHOWPLOTS:
fit.qqplot_residuals().show()
def main():
data, basis = make_data('etab.h5')
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in range(1, 8):
print(30 * '=', 'nterm =', N)
prior = make_prior(N, basis)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=SVDCUT)
print(fit.format(pstyle=None if N < 7 else 'm'))
p0 = fit.pmean
print_results(fit, basis, prior, data)
if DISPLAYPLOTS:
fitter.display_plots()
print('\n==================== add svd, prior noise')
noisy_fit = fitter.lsqfit(
data=data,
prior=prior,
p0=fit.pmean,
svdcut=SVDCUT,
noise=True,
)
print(noisy_fit.format(pstyle=None))
dE = fit.p['etab.dE'][:3]
noisy_dE = noisy_fit.p['etab.dE'][:3]
print(' dE:', dE)
print('noisy dE:', noisy_dE)
print(' ', gv.fmt_chi2(gv.chi2(dE - noisy_dE)))
def run_fit(self, nstates=Nstates(1, 0), prior=None, **fitter_kwargs):
"""
Run the fit.
Args:
nstates: tuple (n_decay, n_osc) specifying the number of decaying
and oscillating states, respectively. Defaults to (1,0).
prior: BasicPrior object. Default is None, for which the fitter
tries to constuct a prior itself.
"""
self._nstates = nstates
if prior is None:
prior = bayes_prior.MesonPrior(nstates.n,
nstates.no,
amps=['a', 'ao'],
tag=self.tag,
ffit=self.c2.fastfit,
extend=True)
self.prior = prior
# Model construction infers the fit times from c2
model = get_two_point_model(self.c2, bool(nstates.no))
self.fitter = cf.CorrFitter(models=model)
data = {self.tag: self.c2}
fit = self.fitter.lsqfit(data=data,
prior=prior,
p0=prior.p0,
**fitter_kwargs)
fit = serialize.SerializableNonlinearFit(fit)
self._fit = fit
if fit.failed:
fit = None
return fit
def main():
data, basis = make_data(corrpath)
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in NEXP:
print(30 * '=', 'nterm =', N)
prior = make_prior(N, basis)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=SVDCUT)
#print(fit.format(pstyle=None if N < 12 else 'm'))
p0 = fit.pmean
print_results(fit, basis, prior, data, l)
if SHOWPLOTS:
fit.show_plots(view='ratio')
def main():
data, basis = make_data(file)
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in NEXP:
print(30 * '=', 'nterm =', N)
prior = make_prior(N, basis)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=SVDCUT)
print(fit.format(pstyle=None if N < 12 else 'm'))
p0 = fit.pmean
print_results(fit, basis, prior, data)
if WRITE_LOG:
write_results(fit, basis, prior, data, NEXP)
if SHOWPLOTS:
fit.show_plots(save='etac.{}.png', view='ratio')
def main():
data = make_data(filename='mydata')
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in range(2, 6):
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0)
p0 = fit.pmean
print_results(fit)
fastfit = cf.fastfit(G=data['cdata'],
ampl='1(1)',
dE='0.5(5)',
tmin=tm,
tp=T)
print(fastfit)
def main():
data = make_data('etas-Ds.h5')
models = make_models() # 1a
models = [
models[0], models[1], # 1b
dict(nterm=(2, 1), svdcut=6.3e-5), # 1c
(models[2], models[3]) # 1d
]
fitter = cf.CorrFitter(models=models) # 1e
p0 = None
for N in [1, 2, 3, 4]:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.chained_lsqfit(data=data, prior=prior, p0=p0) # 2
print(fit.format(pstyle=None if N < 4 else 'm'))
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fit.show_plots()
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.chained_lsqfit(
data=data, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
noise=True,
)
print(noisy_fit.format(pstyle=None))
p = key_parameters(fit.p)
noisy_p = key_parameters(noisy_fit.p)
print(' fit:', p)
print('noisy fit:', noisy_p)
print(' ', gv.fmt_chi2(gv.chi2(p - noisy_p)))
# simulated fit
for sim_pdata in fitter.simulated_pdata_iter(
n=2, dataset=cf.read_dataset('etas-Ds.h5'), p_exact=fit.pmean
):
print('\n==================== simulation')
sim_fit = fitter.chained_lsqfit(
pdata=sim_pdata, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
)
print(sim_fit.format(pstyle=None))
p = key_parameters(fit.pmean)
sim_p = key_parameters(sim_fit.p)
print('simulated - exact:', sim_p - p)
print(' ', gv.fmt_chi2(gv.chi2(p - sim_p)))
def main():
data = make_data('etas-Ds.h5')
fitter = cf.CorrFitter(models=make_models())
p0 = None
prior = make_prior(8) # 1
for N in [1, 2]: # 2
print(30 * '=', 'nterm =', N)
fit = fitter.lsqfit(
data=data, prior=prior, p0=p0, nterm=(N, N), svdcut=SVDCUT # 3
)
print(fit) # 4
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fit.show_plots()
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.lsqfit(
data=data, prior=prior, p0=fit.pmean, svdcut=SVDCUT, nterm=(N, N),
noise=True,
)
print(noisy_fit.format(pstyle=None))
p = key_parameters(fit.p)
noisy_p = key_parameters(noisy_fit.p)
print(' fit:', p)
print('noisy fit:', noisy_p)
print(' ', gv.fmt_chi2(gv.chi2(p - noisy_p)))
# simulated fit
for sim_pdata in fitter.simulated_pdata_iter(
n=2, dataset=cf.read_dataset('etas-Ds.h5'), p_exact=fit.pmean
):
print('\n==================== simulation')
sim_fit = fitter.lsqfit(
pdata=sim_pdata, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
nterm=(N, N),
)
print(sim_fit.format(pstyle=None))
p = key_parameters(fit.pmean)
sim_p = key_parameters(sim_fit.p)
print('simulated - exact:', sim_p - p)
print(' ', gv.fmt_chi2(gv.chi2(p - sim_p)))
def main():
data = make_data('Ds-Ds.h5')
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in [1, 2, 3, 4]:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=SVDCUT)
print(fit.format(pstyle=None if N < 4 else 'v'))
p0 = fit.pmean
print_results(fit, prior, data)
if SHOWPLOTS:
fit.show_plots(save='Ds-Ds.{}.png', view='ratio')
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.lsqfit(
data=data, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
noise=True,
)
print(noisy_fit.format(pstyle=None))
p = key_parameters(fit.p)
noisy_p = key_parameters(noisy_fit.p)
print(' fit:', p)
print('noisy fit:', noisy_p)
print(' ', gv.fmt_chi2(gv.chi2(p - noisy_p)))
# simulated fit
for sim_pdata in fitter.simulated_pdata_iter(
n=2, dataset=h5py.File('Ds-Ds.h5', 'r'), p_exact=fit.pmean
):
print('\n==================== simulation')
sim_fit = fitter.lsqfit(
pdata=sim_pdata, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
)
print(sim_fit.format(pstyle=None))
p = key_parameters(fit.pmean)
sim_p = key_parameters(sim_fit.p)
print('simulated - exact:', sim_p - p)
print(' ', gv.fmt_chi2(gv.chi2(p - sim_p)))
def fit_data(filename_in, key, otherkey):
dset = cf.read_dataset(filename_in) # read data
# If we don't have many samples this next part suggests an svdcut
#s = gv.dataset.svd_diagnosis(dset, models=make_models(key, otherkey, n))
#print('svdcut =', s.svdcut) # suggested svdcut
#s.plot_ratio(show=True)
############################################################################################
data = make_data(filename_in)
fitter = cf.CorrFitter(models=make_models(key, otherkey))
p0 = None
for N in NEXP:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.lsqfit(
data=data, prior=prior,
p0=p0) # add_svdnoise=True, add_priornoise=True, svdcut=s.svdcut
p0 = fit.pmean
print(fit.format(pstyle=None if N < 10 else 'm'))
print_results(fit)