def test_orthogonal_indexer(self):
x = np.random.randn(10, 11, 12, 13, 14)
y = np.arange(5)
I = ReturnItem()
# orthogonal and numpy indexing should be equivalent, because we only
# use at most one array and it never in between two slice objects
# (i.e., we try to avoid numpy's mind-boggling "partial indexing"
# http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html)
for i in [
I[:], I[0], I[0, 0], I[:5], I[5:], I[2:5], I[3:-3], I[::-1],
I[::-2], I[5::-2], I[:3:-2], I[2:5:-1], I[7:3:-2], I[:3, :4],
I[:3, 0, :4], I[:3, 0, :4, 0], I[y], I[:, y], I[0, y],
I[:2, :3, y], I[0, y, :, :4, 0]
]:
j = indexing.orthogonal_indexer(i, x.shape)
self.assertArrayEqual(x[i], x[j])
self.assertArrayEqual(self.set_to_zero(x, i),
self.set_to_zero(x, j))
# for more complicated cases, check orthogonal indexing is still
# equivalent to slicing
z = np.arange(2, 8, 2)
for i, j, shape in [(I[y, y], I[:5, :5], (5, 5, 12, 13, 14)),
(I[y, z], I[:5, 2:8:2], (5, 3, 12, 13, 14)),
(I[0, y, y], I[0, :5, :5], (5, 5, 13, 14)),
(I[y, 0, z], I[:5, 0, 2:8:2], (5, 3, 13, 14)),
(I[y, :, z], I[:5, :, 2:8:2], (5, 11, 3, 13, 14)),
(I[0, :, z], I[0, :, 2:8:2], (11, 3, 13, 14)),
(I[0, :2, y, y, 0], I[0, :2, :5, :5,
0], (2, 5, 5)),
(I[0, :, y, :, 0], I[0, :, :5, :, 0], (11, 5, 13)),
(I[:, :, y, :, 0], I[:, :, :5, :,
0], (10, 11, 5, 13)),
(I[:, :, y, z, :], I[:, :, :5,
2:8:2], (10, 11, 5, 3, 14))]:
k = indexing.orthogonal_indexer(i, x.shape)
self.assertEqual(shape, x[k].shape)
self.assertArrayEqual(x[j], x[k])
self.assertArrayEqual(self.set_to_zero(x, j),
self.set_to_zero(x, k))
# standard numpy (non-orthogonal) indexing doesn't work anymore
with self.assertRaisesRegexp(ValueError, 'only supports 1d'):
indexing.orthogonal_indexer(x > 0, x.shape)
with self.assertRaisesRegexp(ValueError, 'invalid subkey'):
print(indexing.orthogonal_indexer((1.5 * y, 1.5 * y), x.shape))
def test_orthogonal_indexer(self):
x = np.random.randn(10, 11, 12, 13, 14)
y = np.arange(5)
I = ReturnItem()
# orthogonal and numpy indexing should be equivalent, because we only
# use at most one array and it never in between two slice objects
# (i.e., we try to avoid numpy's mind-boggling "partial indexing"
# http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html)
for i in [I[:], I[0], I[0, 0], I[:5], I[5:], I[2:5], I[3:-3], I[::-1],
I[::-2], I[5::-2], I[:3:-2], I[2:5:-1], I[7:3:-2], I[:3, :4],
I[:3, 0, :4], I[:3, 0, :4, 0], I[y], I[:, y], I[0, y],
I[:2, :3, y], I[0, y, :, :4, 0]]:
j = indexing.orthogonal_indexer(i, x.shape)
self.assertArrayEqual(x[i], x[j])
self.assertArrayEqual(self.set_to_zero(x, i),
self.set_to_zero(x, j))
# for more complicated cases, check orthogonal indexing is still
# equivalent to slicing
z = np.arange(2, 8, 2)
for i, j, shape in [
(I[y, y], I[:5, :5], (5, 5, 12, 13, 14)),
(I[y, z], I[:5, 2:8:2], (5, 3, 12, 13, 14)),
(I[0, y, y], I[0, :5, :5], (5, 5, 13, 14)),
(I[y, 0, z], I[:5, 0, 2:8:2], (5, 3, 13, 14)),
(I[y, :, z], I[:5, :, 2:8:2], (5, 11, 3, 13, 14)),
(I[0, :, z], I[0, :, 2:8:2], (11, 3, 13, 14)),
(I[0, :2, y, y, 0], I[0, :2, :5, :5, 0], (2, 5, 5)),
(I[0, :, y, :, 0], I[0, :, :5, :, 0], (11, 5, 13)),
(I[:, :, y, :, 0], I[:, :, :5, :, 0], (10, 11, 5, 13)),
(I[:, :, y, z, :], I[:, :, :5, 2:8:2], (10, 11, 5, 3, 14))]:
k = indexing.orthogonal_indexer(i, x.shape)
self.assertEqual(shape, x[k].shape)
self.assertArrayEqual(x[j], x[k])
self.assertArrayEqual(self.set_to_zero(x, j),
self.set_to_zero(x, k))
# standard numpy (non-orthogonal) indexing doesn't work anymore
with self.assertRaisesRegexp(ValueError, 'only supports 1d'):
indexing.orthogonal_indexer(x > 0, x.shape)
with self.assertRaisesRegexp(ValueError, 'invalid subkey'):
print(indexing.orthogonal_indexer((1.5 * y, 1.5 * y), x.shape))
def __getitem__(self, key):
return self.array[indexing.orthogonal_indexer(key, self.shape)]
def __getitem__(self, key):
return self.array[indexing.orthogonal_indexer(key, self.shape)]
