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)])