def test_multiple_runs(self):
w = self.widgetClass
previewer = WidgetPreview(w)
previewer.run(42, no_exit=True)
w.int1(43)
previewer.send_signals([(44, 1), (45, 2)])
previewer.run(46, no_exit=True)
w.int1.assert_has_calls(
[call(42), call(43),
call(44, 1),
call(45, 2),
call(46)])
def test_widget_is_shown_and_ran(self):
w = self.widgetClass
app.exec_.reset_mock()
previewer = WidgetPreview(w)
previewer.run()
w.show.assert_called()
w.show.reset_mock()
app.exec_.assert_called()
app.exec_.reset_mock()
w.saveSettings.assert_called()
w.saveSettings.reset_mock()
sys.exit.assert_called()
sys.exit.reset_mock()
self.assertIsNone(previewer.widget)
previewer.run(no_exit=True)
w.show.assert_called()
w.show.reset_mock()
app.exec_.assert_called()
app.exec_.reset_mock()
w.saveSettings.assert_not_called()
sys.exit.assert_not_called()
self.assertIsNotNone(previewer.widget)
widget = previewer.widget
previewer.run(no_exec=True, no_exit=True)
w.show.assert_not_called()
app.exec_.assert_not_called()
w.saveSettings.assert_not_called()
sys.exit.assert_not_called()
self.assertIs(widget, previewer.widget)
previewer.run(no_exec=True)
w.show.assert_not_called()
app.exec_.assert_not_called()
w.saveSettings.assert_called()
sys.exit.assert_called()
self.assertIsNone(previewer.widget)
super().closeEvent(event)
def closeContext(self) -> None:
"""
Gather configs in contextVariables and close context.
"""
if not self.features:
# only close in case of when features are not present if they are
# feature selection is defined by the input and context should
# not have impact
attrs, is_logit = [], []
for config in self.configs:
attrs.append(config.get_selection())
is_logit.append(config.is_logarithmic)
self.attrs = attrs
self.is_logit = is_logit
super().closeContext()
if __name__ == "__main__":
from orangecontrib.timeseries import ARIMA, VAR
airpassengers = Timeseries.from_file('airpassengers')
msft = airpassengers.interp()
model1 = ARIMA((3, 1, 1)).fit(airpassengers)
model2 = VAR(4).fit(msft)
ow = WidgetPreview(OWLineChart)
ow.run(set_data=airpassengers,
set_forecast=[(model1.predict(10, as_table=True), 0),
(model2.predict(10, as_table=True), 1)])