def calc_stat_info(self):
dep, staterror, syserror = self.data.to_fit(self.stat.calc_staterror)
model = self.data.eval_model_to_fit(self.model)
stat, fvec = self.stat.calc_stat(dep, model, staterror, syserror)
qval = None
rstat = None
numpoints = len(model)
dof = numpoints - len(self.model.thawedpars)
if (isinstance(self.stat, (CStat,Chi2)) and
not isinstance(self.stat, LeastSq)):
if stat >= 0.0:
qval = igamc(dof/2., stat/2.)
rstat = stat/dof
name = self.stat.name
if isinstance(self.stat, Chi2) and not isinstance(self.stat, LeastSq):
isSimulFit = isinstance(self.data, DataSimulFit)
if isSimulFit:
is_error_set = [d.staterror is not None for d in self.data.datasets]
if all(is_error_set):
name = 'chi2'
elif self.data.staterror is not None:
name = 'chi2'
return StatInfoResults(name, stat, numpoints, model,
dof, qval, rstat)
def goodness_of_fit(self, statval, dof):
"""Return the reduced statistic and q value.
The reduced statisitc is conceptually simple, as it is just
statistic / degrees-of-freedom, but it is not meaningful for
all statistics, and it is only valid if there are any degrees
of freedom.
Parameters
----------
statval : float
The statistic value. It is assumed to be finite.
dof : int
The number of degrees of freedom, which may be 0 or negative.
Returns
-------
rstat, qval : float or NaN or None, float or NaN or None
The reduced statistic and q value. If the statistic does not support
a goodness of fit then the return values are None. If it does then
NaN is returned if either the number of degrees of freedom is 0
(or less), or the statistic value is less than 0.
"""
if not self._can_calculate_rstat:
return None, None
if dof > 0 and statval >= 0.0:
qval = igamc(dof / 2.0, statval / 2.0)
rstat = statval / dof
return rstat, qval
return numpy.nan, numpy.nan
def __init__(self, fit, results, init_stat, param_warnings):
_vals = fit.data.eval_model_to_fit(fit.model)
_dof = len(_vals) - len(tuple(results[1]))
_qval = None
_rstat = None
_covarerr = results[4].get('covarerr')
if (isinstance(fit.stat, (CStat,Chi2)) and
not isinstance(fit.stat, LeastSq)):
if _dof > 0 and results[2] >= 0.0:
_qval = igamc(_dof/2., results[2]/2.)
_rstat = results[2]/_dof
else:
_rstat = nan
self.succeeded = results[0]
self.parnames = tuple(p.fullname for p in fit.model.pars
if not p.frozen)
self.parvals = tuple(results[1])
self.istatval = init_stat
self.statval = results[2]
self.dstatval = abs(results[2]-init_stat)
self.numpoints = len(_vals)
self.dof = _dof
self.qval = _qval
self.rstat = _rstat
self.message = results[3]
if _covarerr is not None:
self.covarerr = tuple(_covarerr)
else:
self.covarerr = None
self.nfev = results[4].get('nfev')
self.extra_output = results[4]
self.modelvals = _vals
self.methodname = type(fit.method).__name__.lower()
self.itermethodname = fit._iterfit.itermethod_opts['name']
statname = type(fit.stat).__name__.lower()
if isinstance(fit.stat, Chi2) and not isinstance(fit.stat, LeastSq):
isSimulFit = isinstance(fit.data, DataSimulFit)
if isSimulFit:
is_error_set = [d.staterror is not None for d in fit.data.datasets]
if all(is_error_set):
statname = 'chi2'
elif fit.data.staterror is not None:
statname = 'chi2'
self.statname = statname
self.datasets = None # To be filled by calling function
self.param_warnings = param_warnings
NoNewAttributesAfterInit.__init__(self)
