def setUp(self):
self.fitting_context = BasicFittingContext()
self.dataset_names = ["Name1", "Name2"]
self.fit_function = FunctionFactory.createFunction("FlatBackground")
self.single_fit_functions = [
self.fit_function.clone(),
self.fit_function.clone()
]
def setup_context(freq=False):
loaded_data = MuonLoadData()
loaded_data.get_main_field_direction = mock.MagicMock(return_value='transverse')
data_context = MuonDataContext(load_data=loaded_data)
gui_context = MuonGuiContext()
group_context = MuonGroupPairContext(data_context.check_group_contains_valid_detectors)
corrections_context = CorrectionsContext(loaded_data)
phase_table_context = PhaseTableContext()
freq_context = FrequencyContext()
plot_panes_context = PlotPanesContext()
if freq:
return FrequencyDomainAnalysisContext(muon_data_context=data_context,
muon_group_context=group_context,
muon_gui_context=gui_context,
muon_phase_context=phase_table_context,
corrections_context=corrections_context,
fitting_context=BasicFittingContext(allow_double_pulse_fitting=True),
frequency_context=freq_context,
plot_panes_context=plot_panes_context)
else:
return DataAnalysisContext(muon_data_context=data_context,
muon_group_context=group_context,
muon_gui_context=gui_context,
corrections_context=corrections_context,
muon_phase_context=phase_table_context,
fitting_context=TFAsymmetryFittingContext(allow_double_pulse_fitting=True),
results_context=ResultsContext(),
model_fitting_context=ModelFittingContext(),
plot_panes_context=plot_panes_context)
def __init__(self, parent=None, window_flags=None):
super(FrequencyAnalysisGui, self).__init__(parent)
if window_flags:
self.setWindowFlags(window_flags)
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
self.setFocusPolicy(QtCore.Qt.StrongFocus)
try:
check_facility()
except AttributeError as error:
self.warning_popup(error.args[0])
# load the feature flags
feature_dict = load_features()
# initialise the data storing classes of the interface
self.loaded_data = MuonLoadData()
self.data_context = MuonDataContext('Frequency Domain Data',
self.loaded_data)
self.gui_context = MuonGuiContext()
self.plot_panes_context = PlotPanesContext()
self.group_pair_context = MuonGroupPairContext(
self.data_context.check_group_contains_valid_detectors)
self.corrections_context = CorrectionsContext(self.loaded_data)
self.phase_context = PhaseTableContext()
self.fitting_context = BasicFittingContext(
allow_double_pulse_fitting=True)
self.results_context = ResultsContext()
self.model_fitting_context = ModelFittingContext(
allow_double_pulse_fitting=False)
self.frequency_context = FrequencyContext()
self.context = FrequencyDomainAnalysisContext(
muon_data_context=self.data_context,
muon_gui_context=self.gui_context,
muon_group_context=self.group_pair_context,
corrections_context=self.corrections_context,
muon_phase_context=self.phase_context,
plot_panes_context=self.plot_panes_context,
fitting_context=self.fitting_context,
results_context=self.results_context,
model_fitting_context=self.model_fitting_context,
frequency_context=self.frequency_context)
# create the dockable widget
self.plot_widget = FrequencyAnalysisPlotWidget(self.context,
parent=self)
self.dockable_plot_widget_window = PlottingDockWidget(
parent=self, plotting_widget=self.plot_widget.view)
self.dockable_plot_widget_window.setMinimumWidth(575)
# Add dock widget to main Muon analysis window
self.addDockWidget(QtCore.Qt.RightDockWidgetArea,
self.dockable_plot_widget_window)
# Need this line to stop the bug where the dock window snaps back to its original size after resizing.
# 0 argument is arbitrary and has no effect on fit widget size
# This is a qt bug reported at (https://bugreports.qt.io/browse/QTBUG-65592)
if QT_VERSION >= LooseVersion("5.6"):
self.resizeDocks({self.dockable_plot_widget_window}, {1},
QtCore.Qt.Horizontal)
# construct all the widgets.
self.load_widget = LoadWidget(self.loaded_data, self.context, self)
self.grouping_tab_widget = GroupingTabWidget(self.context, parent)
self.corrections_tab = CorrectionsTabWidget(self.context, self)
self.home_tab = HomeTabWidget(self.context, self)
self.phase_tab = PhaseTabWidget(self.context, self)
self.transform = TransformWidget(self.context,
FFTWidget,
MaxEntWidget,
parent=self)
self.fitting_tab = FittingTabWidget(self.context, self)
self.seq_fitting_tab = SeqFittingTabWidget(
self.context, self.fitting_tab.fitting_tab_model, self)
self.results_tab = ResultsTabWidget(self.context.fitting_context,
self.context, self)
self.add_model_analysis = AddModelAnalysis(self, feature_dict)
self.add_raw_plots = AddRawPlots(self, feature_dict)
self.add_fitting = AddFitting(self, feature_dict)
setup_group_ws = AddGroupingWorkspaces(self, feature_dict)
self.setup_tabs()
self.plot_widget.insert_plot_panes()
self.help_widget = HelpWidget(self.context.window_title)
central_widget = QtWidgets.QWidget()
vertical_layout = QtWidgets.QVBoxLayout()
vertical_layout.addWidget(self.load_widget.load_widget_view)
vertical_layout.addWidget(self.tabs)
vertical_layout.addWidget(self.help_widget.view)
central_widget.setLayout(vertical_layout)
central_widget.setSizePolicy(
QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Maximum,
QtWidgets.QSizePolicy.Maximum))
self.disable_notifier = GenericObservable()
self.disable_observer = GenericObserver(
self.disable_notifier.notify_subscribers)
self.enable_notifier = GenericObservable()
self.enable_observer = GenericObserver(
self.enable_notifier.notify_subscribers)
self.setup_disable_notifier()
self.setup_enable_notifier()
self.setCentralWidget(central_widget)
self.setWindowTitle(self.context.window_title)
self.setup_load_observers()
self.setup_gui_variable_observers()
self.setup_grouping_changed_observers()
self.setup_corrections_changed_observers()
self.setup_instrument_changed_notifier()
self.setup_group_calculation_enable_notifier()
self.setup_group_calculation_disabler_notifier()
self.setup_on_load_enabler()
self.setup_on_load_disabler()
self.setup_phase_quad_changed_notifier()
self.setup_phase_table_changed_notifier()
self.setup_fitting_notifier()
self.setup_counts_calculation_finished_notifier()
self.setup_asymmetry_pair_and_diff_calculations_finished_notifier()
self.setup_transform()
self.context.data_context.message_notifier.add_subscriber(
self.grouping_tab_widget.group_tab_presenter.message_observer)
self.add_model_analysis.add_observers_to_feature(self)
self.add_model_analysis.set_feature_observables(self)
self.add_raw_plots.add_observers_to_feature(self)
setup_group_ws.add_observers_to_feature(self)
def test_that_allow_double_pulse_fitting_is_set_to_false_by_default(self):
self.assertFalse(self.fitting_context.allow_double_pulse_fitting)
self.fitting_context = BasicFittingContext(
allow_double_pulse_fitting=True)
self.assertTrue(self.fitting_context.allow_double_pulse_fitting)
class BasicFittingContextTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
FrameworkManager.Instance()
def setUp(self):
self.fitting_context = BasicFittingContext()
self.dataset_names = ["Name1", "Name2"]
self.fit_function = FunctionFactory.createFunction("FlatBackground")
self.single_fit_functions = [
self.fit_function.clone(),
self.fit_function.clone()
]
def test_that_allow_double_pulse_fitting_is_set_to_false_by_default(self):
self.assertFalse(self.fitting_context.allow_double_pulse_fitting)
self.fitting_context = BasicFittingContext(
allow_double_pulse_fitting=True)
self.assertTrue(self.fitting_context.allow_double_pulse_fitting)
def test_that_the_context_has_been_instantiated_with_empty_fit_data(self):
self.assertEqual(self.fitting_context.dataset_names, [])
self.assertEqual(self.fitting_context.current_dataset_index, None)
self.assertEqual(self.fitting_context.start_xs, [])
self.assertEqual(self.fitting_context.end_xs, [])
self.assertEqual(self.fitting_context.exclude_range, False)
self.assertEqual(self.fitting_context.exclude_start_xs, [])
self.assertEqual(self.fitting_context.exclude_end_xs, [])
self.assertEqual(self.fitting_context.single_fit_functions, [])
self.assertEqual(self.fitting_context.dataset_indices_for_undo, [])
self.assertEqual(self.fitting_context.single_fit_functions_for_undo,
[])
self.assertEqual(self.fitting_context.fit_statuses_for_undo, [])
self.assertEqual(self.fitting_context.chi_squared_for_undo, [])
self.assertEqual(self.fitting_context.fit_statuses, [])
self.assertEqual(self.fitting_context.chi_squared, [])
self.assertEqual(self.fitting_context.plot_guess, False)
self.assertEqual(self.fitting_context.plot_guess_type,
'x from fit range')
self.assertEqual(self.fitting_context.plot_guess_points, None)
self.assertEqual(self.fitting_context.plot_guess_start_x, None)
self.assertEqual(self.fitting_context.plot_guess_end_x, None)
self.assertEqual(self.fitting_context.guess_workspace_name, None)
self.assertEqual(self.fitting_context.function_name, "")
self.assertTrue(self.fitting_context.function_name_auto_update)
self.assertEqual(self.fitting_context.minimizer, "")
self.assertEqual(self.fitting_context.evaluation_type, "")
self.assertTrue(self.fitting_context.fit_to_raw)
def test_that_current_dataset_index_will_raise_if_the_index_is_greater_than_or_equal_to_the_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.current_dataset_index = 2
def test_that_current_dataset_index_does_not_throw_when_provided_None_and_there_are_no_datasets_loaded(
self):
self.fitting_context.current_dataset_index = None
def test_that_number_of_datasets_returns_the_expected_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
self.assertEqual(self.fitting_context.number_of_datasets, 2)
def test_that_start_xs_will_raise_if_the_number_of_xs_is_greater_than_or_equal_to_the_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.start_xs = [1.0, 2.0, 3.0]
def test_that_end_xs_will_raise_if_the_number_of_xs_is_greater_than_or_equal_to_the_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.end_xs = [1.0, 2.0, 3.0]
def test_that_exclude_start_xs_will_raise_if_the_number_of_xs_is_greater_than_or_equal_to_the_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.exclude_start_xs = [1.0, 2.0, 3.0]
def test_that_exclude_end_xs_will_raise_if_the_number_of_xs_is_greater_than_or_equal_to_the_number_of_datasets(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.exclude_end_xs = [1.0, 2.0, 3.0]
def test_that_single_fit_functions_will_raise_if_the_num_of_funcs_is_greater_than_or_equal_to_the_num_of_datasets(
self):
self.fitting_context.dataset_names = ["Name1"]
with self.assertRaises(RuntimeError):
self.fitting_context.single_fit_functions = self.single_fit_functions
def test_that_fit_statuses_will_raise_if_the_number_of_datasets_is_smaller_than_the_provided_list_size(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.fit_statuses = [
"Success", "Success", "Success"
]
def test_that_chi_squared_will_raise_if_the_number_of_datasets_is_smaller_than_the_provided_list_size(
self):
self.fitting_context.dataset_names = self.dataset_names
with self.assertRaises(RuntimeError):
self.fitting_context.chi_squared = [1.0, 2.0, 3.0]
def test_that_guess_workspace_name_will_not_raise_if_the_workspace_does_not_exist(
self):
self.fitting_context.guess_workspace_name = "FakeName"
self.assertEqual(self.fitting_context.guess_workspace_name, None)
def test_that_guess_workspace_name_will_not_raise_if_the_name_provided_is_none(
self):
self.fitting_context.guess_workspace_name = None
self.assertEqual(self.fitting_context.guess_workspace_name, None)
def test_that_clear_will_clear_the_undo_data_and_active_fits_list(self):
self.fitting_context.active_fit_history = [mock.Mock(), mock.Mock()]
self.fitting_context.dataset_indices_for_undo = [0, 1]
self.fitting_context.single_fit_functions_for_undo = [
mock.Mock(), mock.Mock()
]
self.fitting_context.fit_statuses_for_undo = ["Success", "Fail"]
self.fitting_context.chi_squared_for_undo = [2.2, 3.3]
self.fitting_context.clear()
self.assertEqual(self.fitting_context.active_fit_history, [])
self.assertEqual(self.fitting_context.all_latest_fits(), [])
self.assertEqual(self.fitting_context.dataset_indices_for_undo, [])
self.assertEqual(self.fitting_context.single_fit_functions_for_undo,
[])
self.assertEqual(self.fitting_context.fit_statuses_for_undo, [])
self.assertEqual(self.fitting_context.chi_squared_for_undo, [])
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)