def calculate_background(self, fit_function: IFunction) -> None: """Calculates the background in the counts workspace.""" params = self._get_parameters_for_background_fit(fit_function, create_output=False) function, fit_status, chi_squared = run_Fit( params, AlgorithmManager.create("Fit")) self._handle_background_fit_output(function, fit_status, chi_squared)
def create_background_output_workspaces(self, fit_function: IFunction) -> tuple: """Creates the output workspaces for the currently stored background data.""" params = self._get_parameters_for_background_fit(fit_function, create_output=True, max_iterations=0) _, parameter_table_name, _, _, _, covariance_matrix_name = run_Fit( params, AlgorithmManager.create("Fit")) return parameter_table_name, covariance_matrix_name
def do_single_fit_and_return_workspace_parameters_and_fit_function( self, parameters_dict): alg = mantid.AlgorithmManager.create("Fit") output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix = run_Fit( parameters_dict, alg) CopyLogs(InputWorkspace=parameters_dict['InputWorkspace'], OutputWorkspace=output_workspace, StoreInADS=False) return output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix
def do_single_fit_and_return_workspace_parameters_and_fit_function( self, parameters_dict): if 'DoublePulseEnabled' in self.context.gui_context and self.context.gui_context[ 'DoublePulseEnabled']: alg = self._create_double_pulse_alg() else: alg = mantid.AlgorithmManager.create("Fit") output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix = run_Fit( parameters_dict, alg) CopyLogs(InputWorkspace=parameters_dict['InputWorkspace'], OutputWorkspace=output_workspace, StoreInADS=False) return output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix
def do_single_fit_and_return_workspace_parameters_and_fit_function(self, parameters_dict): alg = mantid.AlgorithmManager.create("Fit") return run_Fit(parameters_dict, alg)