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:])
table_workspace = WorkspaceFactory.createTable()
fit1 = FitInformation(mock.MagicMock(), 'func1', [StaticWorkspaceWrapper(fake1.name(), fake1)],
StaticWorkspaceWrapper(fake1.name(), table_workspace), mock.MagicMock())
fit2 = FitInformation(mock.MagicMock(), 'func1', [StaticWorkspaceWrapper(fake2.name(), fake2)],
StaticWorkspaceWrapper(fake2.name(), table_workspace), mock.MagicMock())
self.mock_active_fit_history = mock.PropertyMock(return_value=[fit1, fit2])
type(self.fitting_context).active_fit_history = self.mock_active_fit_history
self.fitting_context.all_latest_fits = mock.MagicMock(return_value=[fit1, fit2])
self.fitting_context.add_fit(fit1)
self.fitting_context.add_fit(fit2)
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))
def test_that_all_latest_fits_will_return_the_two_most_recent_unique_fits(
self):
output_ws = WorkspaceFactory.create("Workspace2D",
NVectors=3,
YLength=5,
XLength=5)
table_workspace = WorkspaceFactory.createTable()
output_ws_wrap1 = StaticWorkspaceWrapper("Output1", output_ws)
parameter_ws_wrap1 = StaticWorkspaceWrapper("Parameter1",
table_workspace)
covariance_ws_wrap1 = StaticWorkspaceWrapper("Covariance1",
table_workspace)
output_ws_wrap2 = StaticWorkspaceWrapper("Output2", output_ws)
parameter_ws_wrap2 = StaticWorkspaceWrapper("Parameter2",
table_workspace)
covariance_ws_wrap2 = StaticWorkspaceWrapper("Covariance2",
table_workspace)
fit1 = FitInformation(["Input1"], "GausOsc", [output_ws_wrap1],
parameter_ws_wrap1, covariance_ws_wrap1)
fit2 = FitInformation(["Input2"], "GausOsc", [output_ws_wrap2],
parameter_ws_wrap2, covariance_ws_wrap2)
fit3 = FitInformation(["Input1"], "GausOsc", [output_ws_wrap1],
parameter_ws_wrap1, covariance_ws_wrap1)
self.fitting_context.active_fit_history = [fit1, fit2, fit3]
self.assertEqual(self.fitting_context.active_fit_history[0], fit1)
self.assertEqual(self.fitting_context.active_fit_history[1], fit2)
self.assertEqual(self.fitting_context.active_fit_history[2], fit3)
# fit3 == fit1 and fit3 is more recent, so 'all_latest_fits' will only return the most recent unique fits.
self.assertEqual(self.fitting_context.all_latest_fits()[0], fit2)
self.assertEqual(self.fitting_context.all_latest_fits()[1], fit3)
def test_remove_workspace_by_name_will_remove_a_fit_containing_a_specific_parameter_workspace(
self):
output_ws = WorkspaceFactory.create("Workspace2D",
NVectors=3,
YLength=5,
XLength=5)
table_workspace = WorkspaceFactory.createTable()
output_ws_wrap1 = StaticWorkspaceWrapper("Output1", output_ws)
parameter_ws_wrap1 = StaticWorkspaceWrapper("Parameter1",
table_workspace)
covariance_ws_wrap1 = StaticWorkspaceWrapper("Covariance1",
table_workspace)
output_ws_wrap2 = StaticWorkspaceWrapper("Output2", output_ws)
parameter_ws_wrap2 = StaticWorkspaceWrapper("Parameter2",
table_workspace)
covariance_ws_wrap2 = StaticWorkspaceWrapper("Covariance2",
table_workspace)
fit1 = FitInformation(["Input1"], "GausOsc", [output_ws_wrap1],
parameter_ws_wrap1, covariance_ws_wrap1)
fit2 = FitInformation(["Input2"], "GausOsc", [output_ws_wrap2],
parameter_ws_wrap2, covariance_ws_wrap2)
self.fitting_context.active_fit_history = [fit1, fit2]
self.fitting_context.remove_workspace_by_name("Parameter1")
self.assertEqual(self.fitting_context.active_fit_history[0], fit2)
self.assertEqual(self.fitting_context.all_latest_fits()[0], fit2)
def test_logs_from_workspace_without_logs_returns_emtpy_list(self):
fake_ws = create_test_workspace()
fit = FitInformation(mock.MagicMock(), 'func1',
[StaticWorkspaceWrapper(fake_ws.name(), fake_ws)],
mock.MagicMock(), mock.MagicMock())
allowed_logs = fit.log_names()
self.assertEqual(0, len(allowed_logs))
def test_string_log_value_from_fit_with_single_workspace(self):
single_value_logs = [('sv_1', '5')]
fake1 = create_test_workspace(ws_name='fake1',
string_value_logs=single_value_logs)
fit = FitInformation(mock.MagicMock(), 'func1',
[StaticWorkspaceWrapper(fake1.name(), fake1)],
mock.MagicMock(), mock.MagicMock())
self.assertEqual(float(single_value_logs[0][1]),
fit.log_value(single_value_logs[0][0]))
def test_log_names_uses_filter_fn(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)
fit = FitInformation(mock.MagicMock(), 'func1',
[StaticWorkspaceWrapper(fake1.name(), fake1)],
mock.MagicMock(), mock.MagicMock())
log_names = fit.log_names(lambda log: log.name == 'ts_1')
self.assertEqual(1, len(log_names))
self.assertEqual(time_series_logs[0][0], log_names[0])
def test_has_log_returns_true_if_all_workspaces_have_the_log(self):
time_series_logs = (('ts_1', (1., )), ('ts_2', (3., )))
fake1 = create_test_workspace(ws_name='fake1',
time_series_logs=time_series_logs)
fake2 = create_test_workspace(ws_name='fake2',
time_series_logs=time_series_logs)
fit = FitInformation(mock.MagicMock(), 'func1', [
StaticWorkspaceWrapper(fake1.name(), fake1),
StaticWorkspaceWrapper(fake2.name(), fake2)
], mock.MagicMock(), mock.MagicMock())
self.assertTrue(fit.has_log('ts_1'))
def test_has_log_returns_false_if_all_workspaces_do_not_have_log(self):
time_series_logs = [('ts_1', (1., ))]
fake1 = create_test_workspace(ws_name='fake1',
time_series_logs=time_series_logs)
fake2 = create_test_workspace(ws_name='fake2')
fit = FitInformation(mock.MagicMock(), 'func1', [
StaticWorkspaceWrapper(fake1.name(), fake1),
StaticWorkspaceWrapper(fake2.name(), fake2)
], mock.MagicMock(), mock.MagicMock())
self.assertFalse(
fit.has_log('ts_1'),
msg='All input workspaces should have the requested log')
def test_time_series_log_value_from_fit_with_single_workspace_uses_time_average(
self):
time_series_logs = \
[('ts_1', (("2000-05-01T12:00:00", 5.),
("2000-05-01T12:00:10", 20.),
("2000-05-01T12:05:00", 30.)))]
fake1 = create_test_workspace('fake1', time_series_logs)
fit = FitInformation(mock.MagicMock(), 'func1',
[StaticWorkspaceWrapper(fake1.name(), fake1)],
mock.MagicMock(), mock.MagicMock())
time_average = (10 * 5 + 290 * 20) / 300.
self.assertAlmostEqual(time_average, fit.log_value('ts_1'), places=6)
def test_logs_for_single_workspace_return_all_time_series_logs(self):
time_series_logs = (('ts_1', (1., )), ('ts_2', (3., )))
single_value_logs = (('sv_1', 'val1'), ('sv_2', 'val2'))
fake_ws = create_test_workspace(time_series_logs=time_series_logs)
fit = FitInformation(mock.MagicMock(), 'func1',
[StaticWorkspaceWrapper(fake_ws.name(), fake_ws)],
mock.MagicMock(), mock.MagicMock())
log_names = fit.log_names()
for name, _ in time_series_logs:
self.assertTrue(name in log_names,
msg="{} not found in log list".format(name))
for name, _ in single_value_logs:
self.assertFalse(name in log_names,
msg="{} found in log list".format(name))
def test_parameters_are_readonly(self):
fit_info = FitInformation(mock.MagicMock(), mock.MagicMock(),
mock.MagicMock(), mock.MagicMock(),
mock.MagicMock())
self.assertRaises(AttributeError, setattr, fit_info, "parameters",
mock.MagicMock())
def test_global_parameters_are_captured(self):
fit_information_object = FitInformation(mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(), ['A'])
self.assertEqual(['A'],
fit_information_object.parameters.global_parameters)
def test_empty_global_parameters_if_none_specified(self):
fit_information_object = FitInformation(mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock())
self.assertEqual([],
fit_information_object.parameters.global_parameters)
def test_log_names_from_list_of_workspaces_gives_combined_set(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:])
fit = FitInformation(mock.MagicMock(), 'func1', [
StaticWorkspaceWrapper(fake1.name(), fake1),
StaticWorkspaceWrapper(fake2.name(), fake2)
], mock.MagicMock(), mock.MagicMock())
log_names = fit.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_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(mock.MagicMock(), mock.MagicMock(),
mock.MagicMock(), fit_params._parameter_workspace, mock.MagicMock())
self.assertRaises(AttributeError, setattr, fit_info, "parameters",
fit_params)
def test_that_all_latest_fits_will_return_the_two_most_recent_unique_fits(
self):
output_ws = WorkspaceFactory.create("Workspace2D",
NVectors=3,
YLength=5,
XLength=5)
table_workspace = WorkspaceFactory.createTable()
output_ws_wrap1 = StaticWorkspaceWrapper("Output1", output_ws)
parameter_ws_wrap1 = StaticWorkspaceWrapper("Parameter1",
table_workspace)
covariance_ws_wrap1 = StaticWorkspaceWrapper("Covariance1",
table_workspace)
output_ws_wrap2 = StaticWorkspaceWrapper("Output2", output_ws)
parameter_ws_wrap2 = StaticWorkspaceWrapper("Parameter2",
table_workspace)
covariance_ws_wrap2 = StaticWorkspaceWrapper("Covariance2",
table_workspace)
fit1 = FitInformation(["Input1"], "GausOsc", [output_ws_wrap1],
parameter_ws_wrap1, covariance_ws_wrap1)
fit2 = FitInformation(["Input2"], "GausOsc", [output_ws_wrap2],
parameter_ws_wrap2, covariance_ws_wrap2)
fit3 = FitInformation(["Input1"], "GausOsc", [output_ws_wrap1],
parameter_ws_wrap1, covariance_ws_wrap1)
self.fitting_context.tf_asymmetry_mode = True
self.fitting_context.simultaneous_fitting_mode = True
self.fitting_context.active_fit_history = [fit1, fit2, fit3]
self.assertEqual(self.fitting_context.active_fit_history[0], fit1)
self.assertEqual(self.fitting_context.active_fit_history[1], fit2)
self.assertEqual(self.fitting_context.active_fit_history[2], fit3)
self.assertEqual(self.fitting_context.all_latest_fits()[0], fit2)
self.assertEqual(self.fitting_context.all_latest_fits()[1], fit3)
self.fitting_context.tf_asymmetry_mode = False
self.assertEqual(self.fitting_context.active_fit_history, [])
self.assertEqual(self.fitting_context.all_latest_fits()[0], fit2)
self.assertEqual(self.fitting_context.all_latest_fits()[1], fit3)
def test_time_series_log_value_from_fit_with_multiple_workspaces_uses_average_of_time_average(
self):
time_series_logs1 = \
[('ts_1', (("2000-05-01T12:00:00", 5.),
("2000-05-01T12:00:10", 20.),
("2000-05-01T12:05:00", 30.)))]
fake1 = create_test_workspace('fake1', time_series_logs1)
time_series_logs2 = \
[('ts_1', (("2000-05-01T12:00:30", 10.),
("2000-05-01T12:01:45", 30.),
("2000-05-01T12:05:00", 40.)))]
fake2 = create_test_workspace('fake2', time_series_logs2)
fit = FitInformation(mock.MagicMock(), 'func1', [
StaticWorkspaceWrapper(fake1.name(), fake1),
StaticWorkspaceWrapper(fake2.name(), fake2)
], mock.MagicMock(), mock.MagicMock())
time_average1 = (10 * 5 + 290 * 20) / 300.
time_average2 = (75 * 10 + 195 * 30) / 270.
all_average = 0.5 * (time_average1 + time_average2)
self.assertAlmostEqual(all_average, fit.log_value('ts_1'), places=6)
def test_can_add_fits_without_first_creating_fit_information_objects(self):
fit_function_name = 'MuonGuassOsc'
fit_information_object = FitInformation(mock.MagicMock(), fit_function_name, mock.MagicMock(),
mock.MagicMock(), mock.MagicMock())
self.fitting_context.all_latest_fits = mock.MagicMock(return_value=[fit_information_object])
self.fitting_context.add_fit_from_values(mock.MagicMock(), fit_function_name, mock.MagicMock(),
mock.MagicMock(), mock.MagicMock())
self.assertEqual(fit_information_object,
self.fitting_context.all_latest_fits()[0])
def test_items_can_be_added_to_fitting_context(self):
fit_information_object = FitInformation(mock.MagicMock(),
'MuonGuassOsc',
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock())
self.fitting_context.all_latest_fits = mock.MagicMock(return_value=[fit_information_object])
self.fitting_context.add_fit(fit_information_object)
self.assertEqual(fit_information_object,
self.fitting_context.all_latest_fits()[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([input_workspace], fit_function_name, mock.MagicMock(),
parameter_workspace, mock.MagicMock(), global_params)
self.fitting_context.all_latest_fits = mock.MagicMock(return_value=[fit_information_object])
self.fitting_context.add_fit_from_values([input_workspace], fit_function_name, mock.MagicMock(),
parameter_workspace, mock.MagicMock(), global_params)
self.assertEqual(fit_information_object,
self.fitting_context.all_latest_fits()[0])
def create_test_fits(input_workspaces,
function_name,
parameters,
output_workspace_names=None,
global_parameters=None):
"""
Create a list of fits
:param input_workspaces: The input workspaces
:param function_name: The name of the function
:param parameters: The parameters list
:param output_workspace_names: A list of workspace names
:param global_parameters: An optional list of tied parameters
:return: A list of Fits
"""
workspace = WorkspaceFactory.create("Workspace2D",
NVectors=3,
YLength=5,
XLength=5)
output_workspace_names = output_workspace_names if output_workspace_names is not None else [
StaticWorkspaceWrapper('test-output-ws', workspace)
]
# Convert parameters to fit table-like structure
fit_table = [{
'Name': name,
'Value': value,
'Error': error
} for name, (value, error) in parameters.items()]
fits = []
for name in input_workspaces:
parameter_workspace = mock.NonCallableMagicMock()
parameter_workspace.workspace.__iter__.return_value = fit_table
parameter_workspace.workspace_name = name + '_Parameters'
fits.append(
FitInformation([name], function_name,
output_workspace_names, parameter_workspace,
mock.Mock(), global_parameters))
return fits
def test_fitfunctions_gives_list_of_unique_function_names(self):
test_fit_function = 'MuonGuassOsc'
self.fitting_context.all_latest_fits = mock.MagicMock(return_value=[FitInformation(mock.MagicMock(),
test_fit_function,
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock())])
self.fitting_context.add_fit_from_values(mock.MagicMock(),
test_fit_function,
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock())
self.fitting_context.add_fit_from_values(mock.MagicMock(),
test_fit_function,
mock.MagicMock(),
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_inequality_with_globals(self):
fit_info1 = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), mock.MagicMock(), ['A'])
fit_info2 = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), mock.MagicMock(), ['B'])
self.assertNotEqual(fit_info1, fit_info2)
def test_equality_with_globals(self):
fit_info = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), mock.MagicMock(),
mock.MagicMock(), ['A'])
self.assertEqual(fit_info, fit_info)
