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())