def _get_fit(self, model_function=None, cost_function=None, error=None):
'''convenience'''
model_function = model_function or simple_indexed_model
# TODO: fix default
cost_function = cost_function or IndexedCostFunction_Chi2(
errors_to_use='covariance')
error = error or 1.0
_fit = IndexedFit(data=self._ref_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER)
_fit.add_error(err_val=error)
return _fit
def _get_fit(self, errors=None):
'''convenience'''
errors = errors or [dict(err_val=1.0)]
_fit = IndexedFit(
data=self._ref_data,
model_function=simple_indexed_model,
cost_function=IndexedCostFunction_Chi2(errors_to_use='covariance'),
minimizer=self.MINIMIZER)
for _err in errors:
_fit.add_error(**_err)
return _fit
def _get_fit(self, errors=None):
'''convenience'''
_fit = IndexedFit(
data=self._ref_data,
model_function=simple_indexed_model,
cost_function=IndexedCostFunction_Chi2(errors_to_use='covariance'),
minimizer=self.MINIMIZER)
if errors is None:
_fit.add_matrix_error(err_matrix=np.eye(self._n_points),
matrix_type='cov')
else:
for _err in errors:
if 'err_matrix' in _err:
_fit.add_matrix_error(**_err)
else:
_fit.add_error(**_err)
return _fit
def _get_fit(self,
model_function=None,
cost_function=None,
errors=None,
dynamic_error_algorithm=None):
'''convenience'''
model_function = model_function or simple_indexed_model
# TODO: fix default
cost_function = cost_function or IndexedCostFunction_Chi2(
errors_to_use='covariance')
errors = errors or [dict(err_val=1.0)]
dynamic_error_algorithm = dynamic_error_algorithm or "nonlinear"
_fit = IndexedFit(data=self._ref_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER,
dynamic_error_algorithm=dynamic_error_algorithm)
for _err in errors:
_fit.add_error(**_err)
return _fit