def test_volume_identity_downsample_returns_self():
resolution = (27, 185, 90)
v = volumes.Volume(resolution,
image_data=np.zeros((1, 1, 1)),
label_data=np.zeros((1, 1, 1)))
dv = v.downsample(resolution)
assert v == dv
def test_volume_transforms():
mock_image = np.arange(64 * 64 * 64, dtype=np.uint8).reshape((64, 64, 64))
mock_label = np.zeros((64, 64, 64), dtype=np.int64)
v = volumes.Volume((1, 1, 1), image_data=mock_image, label_data=mock_label)
pv = v.partition([1, 1, 2], [0, 0, 1])
dpv = pv.downsample((4, 4, 1))
np.testing.assert_array_equal(dpv.world_coord_to_local(np.array([2, 2, 2])), np.array([8, 8, 34]))
np.testing.assert_array_equal(dpv.local_coord_to_world(np.array([8, 8, 34])), np.array([2, 2, 2]))
svb = volumes.SubvolumeBounds(np.array((0, 0, 32), dtype=np.int64),
np.array((4, 4, 33), dtype=np.int64))
sv = v.get_subvolume(svb)
dpsvb = volumes.SubvolumeBounds(np.array((0, 0, 0), dtype=np.int64),
np.array((1, 1, 1), dtype=np.int64))
dpsv = dpv.get_subvolume(dpsvb)
np.testing.assert_array_equal(dpsv.image, sv.image.reshape((1, 4, 1, 4, 1, 1)).mean(5).mean(3).mean(1))