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')