GeneratePythonFitScript_SequentialFit.py.in

# Perform a sequential fit
output_workspaces, parameter_tables, normalised_matrices = [], [], []
for input_workspace, domain_data in input_data.items():
    fit_output = Fit(Function=function, InputWorkspace=input_workspace, WorkspaceIndex=domain_data[0],
                     StartX=domain_data[1], EndX=domain_data[2], MaxIterations=max_iterations,
                     Minimizer=minimizer, CostFunction=cost_function, EvaluationType=evaluation_type,
                     Output=output_base_name + input_workspace)

    output_workspaces.append(output_base_name + input_workspace + "_Workspace")
    parameter_tables.append(output_base_name + input_workspace + "_Parameters")
    normalised_matrices.append(output_base_name + input_workspace + "_NormalisedCovarianceMatrix")

    # Use the parameters in the previous function as the start parameters of the next fit
    function = fit_output.Function

# Group the output workspaces from the sequential fit
GroupWorkspaces(InputWorkspaces=output_workspaces, OutputWorkspace=output_base_name + "Workspaces")
GroupWorkspaces(InputWorkspaces=parameter_tables, OutputWorkspace=output_base_name + "Parameters")
GroupWorkspaces(InputWorkspaces=normalised_matrices, OutputWorkspace=output_base_name + "NormalisedCovarianceMatrices")