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_send_signals(self): previewer = WidgetPreview(self.widgetClass) previewer.create_widget() widget = previewer.widget previewer.send_signals(42) widget.int1.assert_called_with(42) widget.int1.reset_mock() previewer.send_signals([(42, 1), (40, 2)], str2="foo", float1=[(3.14, 1), (5.1, 8)]) widget.int1.assert_has_calls([call(42, 1), call(40, 2)]) widget.str2.assert_called_with("foo") widget.float1.assert_has_calls([call(3.14, 1), call(5.1, 8)])
def test_send_signals(self): previewer = WidgetPreview(self.widgetClass) previewer.create_widget() widget = previewer.widget previewer.send_signals(42) widget.int1.assert_called_with(42) widget.int1.reset_mock() previewer.send_signals( [(42, 1), (40, 2)], str2="foo", float1=[(3.14, 1), (5.1, 8)]) widget.int1.assert_has_calls([call(42, 1), call(40, 2)]) widget.str2.assert_called_with("foo") widget.float1.assert_has_calls([call(3.14, 1), call(5.1, 8)])
headerState = headerState[0] if isinstance(headerState, bytes): hview = QHeaderView(Qt.Horizontal) hview.restoreState(headerState) column, order = hview.sortIndicatorSection() - 1, hview.sortIndicatorOrder() settings["sorting"] = (column, order) @classmethod def migrate_context(cls, context, version): if version is None or version < 2: # Old selection was saved as sorted indices. New selection is original indices. # Since we can't devise the latter without first computing the ranks, # just reset the selection to avoid confusion. context.values['selected_rows'] = [] if __name__ == "__main__": # pragma: no cover from Orange.classification import RandomForestLearner previewer = WidgetPreview(OWRank) previewer.run(Table("heart_disease.tab"), no_exit=True) previewer.send_signals( set_learner=(RandomForestLearner(), (3, 'Learner', None))) previewer.run() """ WidgetPreview(OWRank).run( set_learner=(RandomForestLearner(), (3, 'Learner', None)), set_data=Table("heart_disease.tab")) """
if isinstance(headerState, bytes): hview = QHeaderView(Qt.Horizontal) hview.restoreState(headerState) column, order = hview.sortIndicatorSection() - 1, hview.sortIndicatorOrder() settings["sorting"] = (column, order) @classmethod def migrate_context(cls, context, version): if version is None or version < 2: # Old selection was saved as sorted indices. New selection is original indices. # Since we can't devise the latter without first computing the ranks, # just reset the selection to avoid confusion. context.values['selected_rows'] = [] if __name__ == "__main__": # pragma: no cover from Orange.classification import RandomForestLearner previewer = WidgetPreview(OWRank) previewer.run(Table("heart_disease.tab"), no_exit=True) previewer.send_signals( set_learner=(RandomForestLearner(), (3, 'Learner', None))) previewer.run() # pylint: disable=pointless-string-statement """ WidgetPreview(OWRank).run( set_learner=(RandomForestLearner(), (3, 'Learner', None)), set_data=Table("heart_disease.tab")) """