OWBoxPlot: Fix crash with missing values

Author: VesnaT

Orange/widgets/visualize/tests/test_owboxplot.py

@@ -0,0 +1,55 @@
+# Test methods with long descriptive names can omit docstrings
+# pylint: disable=missing-docstring
+from unittest import skip
+
+import numpy as np
+from Orange.data import Table, ContinuousVariable
+from Orange.widgets.visualize.owboxplot import OWBoxPlot
+from Orange.widgets.tests.base import WidgetTest
+
+
+class TestOWBoxPlot(WidgetTest):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.iris = Table("iris")
+        cls.zoo = Table("zoo")
+        cls.housing = Table("housing")
+
+    def setUp(self):
+        self.widget = self.create_widget(OWBoxPlot)
+
+    @skip("Known bug, FIXME!")
+    def test_input_data(self):
+        """Check widget's data"""
+        self.send_signal("Data", self.iris)
+        self.assertGreater(len(self.widget.attrs), 0)
+        self.send_signal("Data", None)
+        self.assertEqual(len(self.widget.attrs), 0)
+
+    def test_input_data_missings_cont_group_var(self):
+        """Check widget with continuous data with missing values and group variable"""
+        data = self.iris
+        data.X[:, 0] = np.nan
+        self.send_signal("Data", data)
+        # used to crash, see #1568
+
+    def test_input_data_missings_cont_no_group_var(self):
+        """Check widget with continuous data with missing values and no group variable"""
+        data = self.housing
+        data.X[:, 0] = np.nan
+        self.send_signal("Data", data)
+        # used to crash, see #1568
+
+    def test_input_data_missings_disc_group_var(self):
+        """Check widget with discrete data with missing values and group variable"""
+        data = self.zoo
+        data.X[:, 0] = np.nan
+        self.send_signal("Data", data)
+
+    def test_input_data_missings_disc_no_group_var(self):
+        """Check widget discrete data with missing values and no group variable"""
+        data = self.zoo
+        data.domain.class_var = ContinuousVariable("cls")
+        data.X[:, 0] = np.nan
+        self.send_signal("Data", data)