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