def test_logs_from_workspace_without_logs_returns_emtpy_list(self):
fake_ws = create_test_workspace()
fit = FitInformation(mock.MagicMock(), 'func1', fake_ws.name(),
fake_ws.name())
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', [fake1.name()],
[fake1.name()])
self.assertEqual(float(single_value_logs[0][1]),
fit.log_value(single_value_logs[0][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',
[fake1.name(), fake2.name()],
[fake1.name(), fake2.name()])
self.assertTrue(fit.has_log('ts_1'))
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', fake1.name(),
fake1.name())
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_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',
[fake1.name(), fake2.name()],
[fake1.name(), fake2.name()])
self.assertFalse(
fit.has_log('ts_1'),
msg='All input workspaces should have the requested log')
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', fake_ws.name(),
fake_ws.name())
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_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',
[fake1.name()],
[fake1.name()],
)
time_average = (10 * 5 + 290 * 20) / 300.
self.assertAlmostEqual(time_average, fit.log_value('ts_1'), places=6)
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
"""
output_workspace_names = output_workspace_names if output_workspace_names is not None else [
'test-output-ws'
]
# 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(parameter_workspace, function_name, name,
output_workspace_names, global_parameters))
return fits
def test_handle_fit_completed_adds_appropriate_fits_to_plot(self):
self.model.plotted_workspaces = self.workspace_list
self.model.plotted_workspaces_inverse_binning = {}
fit_information = FitInformation(
mock.MagicMock(), 'GaussOsc',
['MUSR62260; Group; bottom; Asymmetry; MA'],
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted;'])
self.context.fitting_context.fit_list.__getitem__.return_value = fit_information
self.context.fitting_context.number_of_fits = 1
self.presenter.handle_fit_completed(fit_information)
self.assertEqual(self.model.add_workspace_to_plot.call_count, 2)
# check fit and diff workspaces plotted
self.model.add_workspace_to_plot.assert_any_call(
self.view.get_axes()[0],
'MUSR62260; Group; bottom; Asymmetry; MA; Fitted;', [1],
errors=False,
plot_kwargs=mock.ANY)
self.model.add_workspace_to_plot.assert_called_with(
self.view.get_axes()[0],
'MUSR62260; Group; bottom; Asymmetry; MA; Fitted;', [2],
errors=False,
plot_kwargs=mock.ANY)
def test_global_parameters_are_captured(self):
fit_information_object = FitInformation(mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(), ['A'])
self.assertEqual(['A'],
fit_information_object.parameters.global_parameters)
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_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',
[fake1.name(), fake2.name()],
[fake1.name(), fake2.name()])
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_empty_global_parameters_if_none_specified(self):
fit_information_object = FitInformation(mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock(),
mock.MagicMock())
self.assertEqual([],
fit_information_object.parameters.global_parameters)
def create_simulatenous_fit_over_groups(self, workspace_list):
output_workspaces = [ws + '; ' + 'Fitted;' for ws in workspace_list]
fit_information = FitInformation(mock.MagicMock(),
'GaussOsc',
workspace_list,
output_workspaces)
return fit_information
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_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_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',
[fake1.name(), fake2.name()],
[fake1.name(), fake2.name()])
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_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_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))
def test_handle_plot_selected_fits_correctly_calls_model(self):
fit = FitInformation(mock.MagicMock(), 'GaussOsc',
['MUSR62260; Group; bottom; Asymmetry; MA'],
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted'])
self.model.get_fit_workspace_and_indices.return_value = [["MUSR62260; Group; bottom; Asymmetry; MA; Fitted"],
[1]]
fit_information = FitPlotInformation(input_workspaces=["MUSR62260; Group; bottom; Asymmetry; MA"], fit=fit)
self.view.is_plot_diff.return_value = False
self.presenter.handle_plot_selected_fits([fit_information])
self.model.get_fit_workspace_and_indices.assert_called_once_with(fit,False)
def test_get_fit_workspaces_to_plot_returns_correctly(self):
fit = FitInformation(
mock.MagicMock(), 'GaussOsc',
['MUSR62260; Group; bottom; Asymmetry; MA'],
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted'])
expected_workspaces = [
'MUSR62260; Group; bottom; Asymmetry; MA; Fitted'
] * 2
expected_indices = [1, 2]
workspaces, indices = self.model.get_fit_workspace_and_indices(fit)
self.assertEqual(workspaces, expected_workspaces)
self.assertEqual(expected_indices, indices)
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_handle_plot_selected_fits_correctly_calls_plot(self):
fit = FitInformation(mock.MagicMock(), 'GaussOsc',
['MUSR62260; Group; bottom; Asymmetry; MA'],
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted'])
self.model.get_fit_workspace_and_indices.return_value = [["MUSR62260; Group; bottom; Asymmetry; MA; Fitted"],
[1]]
fit_information = FitPlotInformation(input_workspaces=["MUSR62260; Group; bottom; Asymmetry; MA"], fit=fit)
expected_workspace_list = ["MUSR62260; Group; bottom; Asymmetry; MA"] + \
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted']
expected_indices = [0] + [1]
self.presenter.handle_plot_selected_fits([fit_information])
self.figure_presenter.plot_workspaces.assert_called_once_with(expected_workspace_list, expected_indices,
hold_on=False, autoscale=False)
def test_handle_fit_completed_adds_appropriate_fits_to_plot(self):
self.model.plotted_workspaces = self.workspace_list
self.model.plotted_workspaces_inverse_binning = []
fit_information = FitInformation(mock.MagicMock(),
'GaussOsc',
['MUSR62260; Group; bottom; Asymmetry; MA'],
['MUSR62260; Group; bottom; Asymmetry; MA; Fitted;'])
self.context.fitting_context.fit_list.__getitem__.return_value = fit_information
self.context.fitting_context.number_of_fits = 1
self.presenter.handle_fit_completed()
self.assertEqual(self.model.add_workspace_to_plot.call_count, 2)
self.model.add_workspace_to_plot.assert_any_call('MUSR62260; Group; bottom; Asymmetry; MA; Fitted;', 2,
'MUSR62260; Group; bottom; Asymmetry; MA; Fitted;: Fit')
self.model.add_workspace_to_plot.assert_called_with('MUSR62260; Group; bottom; Asymmetry; MA; Fitted;', 3,
'MUSR62260; Group; bottom; Asymmetry; MA; Fitted;: Diff')
def test_parameters_are_readonly(self):
fit_info = FitInformation(mock.MagicMock(), mock.MagicMock(),
mock.MagicMock(), mock.MagicMock())
self.assertRaises(AttributeError, setattr, fit_info, "parameters",
mock.MagicMock())
def test_inequality_with_globals(self):
fit_info1 = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), ['A'])
fit_info2 = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), ['B'])
self.assertNotEqual(fit_info1, fit_info2)
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_equality_with_globals(self):
fit_info = FitInformation(mock.MagicMock(), 'MuonGuassOsc',
mock.MagicMock(), mock.MagicMock(), ['A'])
self.assertEqual(fit_info, fit_info)