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)
class FittingContextTest(GuiTest):
def setUp(self):
self.fitting_context = FittingContext()
def test_context_constructor_accepts_fit_list(self):
fit_list = [
FitInformation(mock.MagicMock(), 'MuonGuassOsc', mock.MagicMock(),
mock.MagicMock())
]
context = FittingContext(fit_list)
self.assertEqual(fit_list, context.fit_list)
def test_len_gives_length_of_fit_list(self):
self.assertEqual(0, len(self.fitting_context))
self.fitting_context.add_fit(
FitInformation(mock.MagicMock(), 'MuonGuassOsc', mock.MagicMock(),
mock.MagicMock()))
self.assertEqual(1, len(self.fitting_context))
def test_items_can_be_added_to_fitting_context(self):
fit_information_object = FitInformation(mock.MagicMock(),
'MuonGuassOsc',
mock.MagicMock(),
mock.MagicMock())
self.fitting_context.add_fit(fit_information_object)
self.assertEqual(fit_information_object,
self.fitting_context.fit_list[0])
def test_fitfunctions_gives_list_of_unique_function_names(self):
test_fit_function = 'MuonGuassOsc'
self.fitting_context.add_fit_from_values(mock.MagicMock(),
test_fit_function,
mock.MagicMock(),
mock.MagicMock(), [])
self.fitting_context.add_fit_from_values(mock.MagicMock(),
test_fit_function,
mock.MagicMock(),
mock.MagicMock(), [])
fit_functions = self.fitting_context.fit_function_names()
self.assertEqual(len(fit_functions), 1)
self.assertEqual(test_fit_function, fit_functions[0])
def test_can_add_fits_without_first_creating_fit_information_objects(self):
parameter_workspace = mock.MagicMock()
input_workspace = mock.MagicMock()
output_workspace_names = mock.MagicMock()
fit_function_name = 'MuonGuassOsc'
fit_information_object = FitInformation(parameter_workspace,
fit_function_name,
input_workspace,
output_workspace_names)
self.fitting_context.add_fit_from_values(parameter_workspace,
fit_function_name,
input_workspace,
output_workspace_names)
self.assertEqual(fit_information_object,
self.fitting_context.fit_list[0])
def test_can_add_fits_with_global_parameters_without_creating_fit_information(
self):
parameter_workspace = mock.MagicMock()
input_workspace = mock.MagicMock()
fit_function_name = 'MuonGuassOsc'
global_params = ['A']
fit_information_object = FitInformation(parameter_workspace,
fit_function_name,
input_workspace, global_params)
self.fitting_context.add_fit_from_values(parameter_workspace,
fit_function_name,
input_workspace,
global_params)
self.assertEqual(fit_information_object,
self.fitting_context.fit_list[0])
def test_parameters_are_readonly(self):
test_parameters = OrderedDict([('Height', (10., 0.4)),
('A0', (1, 0.01)),
('Cost function', (0.1, 0.))])
fit_params = create_test_fit_parameters(test_parameters)
fit_info = FitInformation(fit_params._parameter_workspace,
mock.MagicMock(), mock.MagicMock(),
mock.MagicMock())
self.assertRaises(AttributeError, setattr, fit_info, "parameters",
fit_params)
def test_log_names_returns_logs_from_all_fits_by_default(self):
time_series_logs = (('ts_1', (1., )), ('ts_2', (3., )), ('ts_3', [2.]),
('ts_4', [3.]))
fake1 = create_test_workspace(ws_name='fake1',
time_series_logs=time_series_logs[:2])
fake2 = create_test_workspace(ws_name='fake2',
time_series_logs=time_series_logs[2:])
self.fitting_context.add_fit(
FitInformation(mock.MagicMock(), 'func1', fake1.name(),
fake1.name()))
self.fitting_context.add_fit(
FitInformation(mock.MagicMock(), 'func1', fake2.name(),
fake2.name()))
log_names = self.fitting_context.log_names()
self.assertEqual(len(time_series_logs), len(log_names))
for name, _ in time_series_logs:
self.assertTrue(name in log_names,
msg="{} not found in log list".format(name))
def test_log_names_respects_filter(self):
time_series_logs = (('ts_1', (1., )), ('ts_2', (3., )), ('ts_3', [2.]),
('ts_4', [3.]))
fake1 = create_test_workspace(ws_name='fake1',
time_series_logs=time_series_logs[:2])
fake2 = create_test_workspace(ws_name='fake2',
time_series_logs=time_series_logs[2:])
self.fitting_context.add_fit(
FitInformation(mock.MagicMock(), 'func1', fake1.name(),
fake1.name()))
self.fitting_context.add_fit(
FitInformation(mock.MagicMock(), 'func1', fake2.name(),
fake2.name()))
required_logs = ('ts_2', 'ts_4')
log_names = self.fitting_context.log_names(
filter_fn=lambda log: log.name in required_logs)
self.assertEqual(len(required_logs), len(log_names))
for name in required_logs:
self.assertTrue(name in log_names,
msg="{} not found in log list".format(name))
