def _no_fit(self, xmin, xmax):
# We do not use constraint fits here as this fit is for parameter estimation
lg.info()
start_params_no = self._get_start_params(self._res_no,
self._res_no_err,
ratio=1,
ignore_ext=True)
try:
lg.info("Fit non-osc. correlator...")
res_no, res_no_err, chi_dof_no, aicc_no, pcov_no = self._no_corr_fitter.corr_fit(
xmin,
xmax,
start_params_no,
correlated=False,
nstates=self._nstates,
nstates_osc=0)
self._res_no.append(res_no)
self._res_no_err.append(res_no_err)
self._chi_dof_no.append(chi_dof_no)
self._aicc_no.append(aicc_no)
except Exception as e:
lg.info("\nWarning. Fit failed:", e, "\n")
if lg.isLevel("DEBUG"):
traceback.print_exc()
self._res_no.append(np.full(2 * self._nstates, np.nan))
self._res_no_err.append(np.full(2 * self._nstates, np.nan))
self._chi_dof_no.append(np.nan)
self._aicc_no.append(np.nan)
def func_wrapper(inst, *args, **kwargs):
try:
res, res_err, chi_dof, aicc, pcov = fit_func(
inst, *args, **kwargs)
if any(res_err == 0.0):
raise ValueError("Fit error is zero!")
except Exception as e:
lg.info("\nWarning: Fit failed! Exception was:", e, "\n")
if lg.isLevel("DEBUG"):
traceback.print_exc()
res = np.full(inst._nparams, np.nan)
res_err = np.full(inst._nparams, np.nan)
pcov = np.full((inst._nparams, inst._nparams), np.nan)
chi_dof = np.inf
aicc = np.inf
if inst._btstr_fit:
inst._samples.append([[(np.nan, ) * inst._nparams,
(np.nan, ) * inst._nparams, np.nan,
np.nan]
for sitt in range(inst._nfit_samples)
])
return res, res_err, chi_dof, aicc, pcov
def _est_params_one_state(self, xmin, xmax, ind=None):
# estimation of starting parameters via linear fit does not work for periodic
# boundaries. Therefore we have to fit on the half interval.
start_params = [1, 1]
est_range = [int(xmin), int(min(xmax, self._Nt / 2))]
#For estimating parameters of oscillating correlators, we use this function to estimate
#the parameters for the even/odd part. We have to make sure that we use even/odd part
#by this routine.
lg.info("Estimate parameters: Linear fit for one state...")
if ind is None:
ind = (self._xdata > est_range[0]) & (self._xdata < est_range[1])
else:
ind = (self._xdata > est_range[0]) & (self._xdata <
est_range[1]) & ind
try:
(tmp_start_params, tmp_start_params_cov) = curve_fit(
linear,
self._xdata[ind],
np.log(np.abs(self._ydata[ind])),
sigma=1 / self._ydata[ind] * self._edata[ind])
if tmp_start_params[0] > 50 or tmp_start_params[1] > 50:
start_params = [1, 1]
else:
start_params[0] = 1 / self._mult_const * np.exp(tmp_start_params[0])\
* np.exp(-tmp_start_params[1] * self._Nt / 2) * 2
start_params[1] = tmp_start_params[1]
except Exception as e:
lg.info("Linear fit failed. Error was", e)
if lg.isLevel("DEBUG"):
traceback.print_exc()
start_params = [1, 1]
print_res("Final start parameters for uncorrelated fit",
start_params,
level="INFO")
return start_params
def corr_fit(self,
xmin=-np.inf,
xmax=np.inf,
start_params=None,
nstates=None,
nstates_osc=None,
correlated=None,
priorsigma=None,
priorval=None):
if nstates == 0:
raise ValueError("Require at least one non-oscillating state")
if nstates is None:
nstates = self._nstates
if nstates_osc is None:
nstates_osc = self._nstates_osc
if correlated is None:
correlated = self._cov_avail
#To estimate parameters we usually perform a non-correlated fit before the actual
#correlated fit. This is not neccessary if we already get start parameters.
#However, for an oscillating fit is is reasonable to perform a non correlated fit
#in any case. This is because start parameter estimation is usually performed outside
#for oscillating fits.
if correlated and start_params is not None:
if nstates_osc > 0:
skip_uncorr = False
else:
skip_uncorr = True
else:
skip_uncorr = False
try:
start_params, priorval, priorsigma = self.init_start_params(
xmin, xmax, start_params, priorval, priorsigma, nstates,
nstates_osc)
except Exception as e:
lg.info("Failed to estimate start parameters. Try direct fit")
lg.details("Error was", e)
if lg.isLevel("DEBUG"):
traceback.print_exc()
start_params = None
#Save the states of the last fit. This must be ensured, even if the parameter
#estimation fails.
finally:
self._nstates = nstates
self._nstates_osc = nstates_osc
print_res("Start parameters for %d + %d fit" % (nstates, nstates_osc),
start_params,
level="INFO")
if not skip_uncorr:
res, res_err, chi_dof, aicc, pcov = self.simple_corr_fit(
xmin,
xmax,
start_params,
correlated=False,
priorval=priorval,
priorsigma=priorsigma,
nstates=nstates,
nstates_osc=nstates_osc)
start_params = np.copy(res)
self._change_order(res, res_err, nstates, nstates_osc)
res, res_err = self.remove_mult_const(res, res_err)
pcov = self.remove_mult_const_pcov(pcov)
lg.info()
print_res("Fit result for uncorrelated %d + %d fit" %
(nstates, nstates_osc),
res,
res_err,
chi_dof,
level="INFO")
print_scl("AICc", aicc, level='INFO')
if correlated:
if not self._cov_avail:
raise NotAvailableError("Covariance matrix is not available")
res, res_err, chi_dof, aicc, pcov = self.simple_corr_fit(
xmin,
xmax,
start_params=start_params,
correlated=True,
priorval=priorval,
priorsigma=priorsigma,
nstates=nstates,
nstates_osc=nstates_osc)
self._change_order(res, res_err, nstates, nstates_osc)
res, res_err = self.remove_mult_const(res, res_err)
pcov = self.remove_mult_const_pcov(pcov)
lg.info()
print_res("Fit result for correlated %d + %d fit" %
(nstates, nstates_osc),
res,
res_err,
chi_dof,
level="INFO")
print_scl("AICc", aicc)
return res, res_err, chi_dof, aicc, pcov