def test_localized_mean(self) -> None: with self.subTest('It rejects inputs other than numpy ndarrays'): with self.assertRaises(AssertionError): localized_mean(numpy.array([1])) with self.subTest( 'It rejects geometry vectors if it has too many dimensions'): with self.assertRaises(AssertionError): localized_mean(numpy.zeros((1, 2, 3))) with self.subTest('It produces a 1d numpy array of shape (2,)'): means = localized_mean(square[0]) self.assertEqual(numpy.ndim(means), 1) with self.subTest('It computes the centroid of a sample square'): means = localized_mean(square[0]) numpy.testing.assert_array_equal(means, 0.5)
def test_localized_mean_dup_nodes(self) -> None: means = localized_mean(square_duplicate_nodes[0]) numpy.testing.assert_array_equal(means, 0.75)
def test_localized_mean_rectangle(self) -> None: means = localized_mean(rectangle[0]) self.assertEqual(means[0], 0.5) self.assertEqual(means[1], 1)
def test_localized_mean_dup_nodes(self): means = gs.localized_mean(square_duplicate_nodes) self.assertTrue((means == 0.75).all())
def test_localized_mean_rectangle(self): means = gs.localized_mean(rectangle) self.assertEqual(means[0, 0, 0], 0.5) self.assertEqual(means[0, 0, 1], 1)
def test_localized_mean(self): means = gs.localized_mean(square) for mean in means[0]: self.assertTrue((mean == 0.5).all())