def do_simultaneous_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_simultaneous_Fit(parameters_dict, alg)
if len(parameters_dict['InputWorkspace']) > 1:
for input_workspace, output in zip(parameters_dict['InputWorkspace'],
mantid.api.AnalysisDataService.retrieve(output_workspace).getNames()):
CopyLogs(InputWorkspace=input_workspace, OutputWorkspace=output, StoreInADS=False)
else:
CopyLogs(InputWorkspace=parameters_dict['InputWorkspace'][0], OutputWorkspace=output_workspace,
StoreInADS=False)
return output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix
def _do_simultaneous_fit_and_return_workspace_parameters_and_fit_function(
self, parameters: dict) -> tuple:
"""Performs a simultaneous fit and returns the resulting function, status and chi squared."""
alg = self._create_fit_algorithm()
output_workspace, parameter_table, function, fit_status, chi_squared, covariance_matrix = run_simultaneous_Fit(
parameters, alg)
self._copy_logs(parameters["InputWorkspace"], output_workspace)
return output_workspace, parameter_table, function, fit_status, chi_squared, covariance_matrix
def do_simultaneous_fit_and_return_workspace_parameters_and_fit_function(self, parameters_dict):
alg = mantid.AlgorithmManager.create("Fit")
return run_simultaneous_Fit(parameters_dict, alg)