def create_test_model(input_workspaces, function_name, parameters, output_workspace_names=None, logs=None, global_parameters=None): """ Create a list of fits with time series logs on the workspaces :param input_workspaces: See create_test_fits :param function_name: See create_test_fits :param parameters: See create_test_fits :param logs: A list of (name, (values...), (name, (values...))) :param global_parameters: An optional list of tied parameters :return: A list of Fits with workspaces/logs attached """ fits = create_test_fits(input_workspaces, function_name, parameters, output_workspace_names, global_parameters) logs = logs if logs is not None else [] for fit, workspace_name in zip(fits, input_workspaces): add_logs(workspace_name, logs) fitting_context = TFAsymmetryFittingContext() for fit in fits: fitting_context.add_fit(fit) return fitting_context, ResultsTabModel(fitting_context, ResultsContext())
def test_that_when_new_fit_is_performed_function_name_is_set_to_lastest_fit_name(self): parameters = OrderedDict([('Height', (100, 0.1)), ('Cost function value', (1.5, 0))]) fits_func1 = create_test_fits(('ws1',), 'func1', parameters) parameters = OrderedDict([('Height', (100, 0.1)), ('A0', (1, 0.001)), ('Cost function value', (1.5, 0))]) fits_func2 = create_test_fits(('ws2',), 'func2', parameters) fitting_context = TFAsymmetryFittingContext() fits = fits_func1 + fits_func2 for fit in fits: fitting_context.add_fit(fit) model = ResultsTabModel(fitting_context, ResultsContext()) model.on_new_fit_performed() self.assertEqual(model.selected_fit_function(), 'func2')