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, [])
model = ResultsTabModel(FittingContext(fits_func1 + fits_func2))
model.on_new_fit_performed()
self.assertEqual(model.selected_fit_function(), 'func2')
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 = FittingContext()
for fit in fits:
fitting_context.add_fit(fit)
return fitting_context, ResultsTabModel(fitting_context)
def __init__(self, fit_context, context, parent):
"""
Initialize the widget.
:param fit_context: A reference to the a FitContext object used to store fit results
:param parent: A parent widget for the view
"""
self.results_tab_view = ResultsTabView(parent=parent)
self.results_tab_presenter = ResultsTabPresenter(
self.results_tab_view, ResultsTabModel(fit_context))
context.update_view_from_model_notifier.add_subscriber(
self.results_tab_presenter.update_view_from_model_observer)
def test_create_results_table_raises_error_if_number_params_different(
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, [])
model = ResultsTabModel(FittingContext(fits_func1 + fits_func2))
selected_results = [('ws1', 0), ('ws2', 1)]
self.assertRaises(RuntimeError, model.create_results_table, [],
selected_results)
def test_create_results_table_with_mixed_global_non_global_raises_error(
self):
parameters = OrderedDict([('f0.Height', (100, 0.1)),
('f1.Height', (90, 0.001)),
('Cost function value', (1.5, 0))])
fits_func1 = create_test_fits(('ws1', ), 'func1', parameters, [])
fits_globals = create_test_fits(('ws2', ),
'func1',
parameters, [],
global_parameters=['Height'])
model = ResultsTabModel(FittingContext(fits_func1 + fits_globals))
selected_results = [('ws1', 0), ('ws2', 1)]
self.assertRaises(RuntimeError, model.create_results_table, [],
selected_results)
def test_create_results_table_with_logs_missing_from_some_workspaces_raises(
self):
parameters = OrderedDict([('f0.Height', (100, 0.1))])
logs = [('log1', (1., 2.)), ('log2', (3., 4.)), ('log3', (4., 5.)),
('log4', (5., 6.))]
fits_logs1 = create_test_fits(('ws1', ), 'func1', parameters, output_workspace_names=('ws1', ))
add_logs(fits_logs1[0].input_workspaces[0], logs[:2])
fits_logs2 = create_test_fits(('ws2', ), 'func1', parameters, output_workspace_names=('ws2', ))
add_logs(fits_logs2[0].input_workspaces[0], logs[2:])
model = ResultsTabModel(FittingContext(fits_logs1 + fits_logs2))
selected_results = [('ws1', 0), ('ws2', 1)]
selected_logs = ['log1', 'log3']
self.assertRaises(RuntimeError, model.create_results_table,
selected_logs, selected_results)
def test_create_results_table_raises_error_if_number_params_different(
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()
fitting_context.fit_list = fits_func1 + fits_func2
model = ResultsTabModel(fitting_context, ResultsContext())
selected_results = [('ws1', 0), ('ws2', 1)]
self.assertRaises(IndexError, model.create_results_table, [],
selected_results)
def test_create_results_table_with_logs_missing_from_some_workspaces_raises(self):
workspace = WorkspaceFactory.create("Workspace2D", NVectors=3, YLength=5, XLength=5)
parameters = OrderedDict([('f0.Height', (100, 0.1))])
logs = [('log1', (1., 2.)), ('log2', (3., 4.)), ('log3', (4., 5.)),
('log4', (5., 6.))]
fits_logs1 = create_test_fits(('ws1',), 'func1', parameters,
output_workspace_names=[StaticWorkspaceWrapper('test-ws1-ws', workspace)])
add_logs(fits_logs1[0].input_workspaces[0], logs[:2])
fits_logs2 = create_test_fits(('ws2',), 'func1', parameters,
output_workspace_names=[StaticWorkspaceWrapper('test-ws2-ws', workspace)])
add_logs(fits_logs2[0].input_workspaces[0], logs[2:])
fitting_context = TFAsymmetryFittingContext()
fitting_context.fit_list = fits_logs1 + fits_logs2
model = ResultsTabModel(fitting_context, ResultsContext())
selected_results = [('ws1', 0), ('ws2', 1)]
selected_logs = ['log1', 'log3']
self.assertRaises(IndexError, model.create_results_table,
selected_logs, selected_results)
def test_model_returns_no_log_selection_if_no_fits_present(self):
model = ResultsTabModel(FittingContext())
self.assertEqual(0, len(model.log_selection({})))
def test_updating_model_selected_fit_function(self):
model = ResultsTabModel(FittingContext())
new_selection = 'func2'
model.set_selected_fit_function(new_selection)
self.assertEqual(model.selected_fit_function(), new_selection)
def test_default_model_has_no_selected_function_without_fits(self):
model = ResultsTabModel(FittingContext())
self.assertTrue(model.selected_fit_function() is None)
def test_updating_model_results_table_name(self):
table_name = 'table_name'
model = ResultsTabModel(FittingContext())
model.set_results_table_name(table_name)
self.assertEqual(model.results_table_name(), table_name)
def test_default_model_has_results_table_name(self):
model = ResultsTabModel(FittingContext())
self.assertEqual(model.results_table_name(), DEFAULT_TABLE_NAME)
def test_model_returns_no_fit_selection_if_no_fits_present(self):
model = ResultsTabModel(TFAsymmetryFittingContext(), ResultsContext())
self.assertEqual(0, len(model.fit_selection({})))
