def write_to_n5(self, folder, dataset):
"""
Note: because diluvian works with z,y,x and pyn5 assumes x,y,z:
when writing coordinates must be flipped
"""
pyn5.create_dataset(
folder, dataset, self.bounds[1], self.leaf_shape, str(self.dtype).upper()
)
dataset = pyn5.open(folder, dataset)
for leaf in self.iter_leaves():
pyn5.write(dataset, leaf.bounds, leaf.data, self.dtype)
def test_read_write(self):
# make sure n5 is initialized to all zeros
self.assertTrue(
np.array_equal(
pyn5.read(self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size))),
np.zeros([10, 10, 10]),
))
# write ones to whole dataset, and then
# write on partial blocks to make sure data isn't overwritten
pyn5.write(
self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size)),
np.ones(self.dataset_size),
)
self.assertTrue(
np.array_equal(
pyn5.read(self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size))),
np.ones([10, 10, 10]),
))
pyn5.write(self.n5, (np.array([1, 1, 1]), np.array([3, 3, 3])),
np.ones([2, 2, 2]) * 2)
self.assertTrue(
np.array_equal(
pyn5.read(self.n5, (np.array([1, 1, 1]), np.array([3, 3, 3]))),
np.ones([2, 2, 2]) * 2,
))
# test writting non-uniform block to make sure axis orderings are correct
pyn5.write(
self.n5,
(np.array([1, 1, 1]), np.array([5, 5, 5])),
np.array(range(64), dtype=int).reshape([4, 4, 4]),
)
self.assertTrue(
np.array_equal(
pyn5.read(self.n5, (np.array([1, 1, 1]), np.array([5, 5, 5]))),
np.array(range(64), dtype=int).reshape([4, 4, 4]),
))
# test writing data in non block shapes
bounds = (np.array([0, 0, 0]), np.array([1, 2, 3]))
data = np.array(range(6)).reshape([1, 2, 3])
pyn5.write(self.n5, bounds, data)
self.assertTrue(np.array_equal(pyn5.read(self.n5, bounds), data))
def save_data_for_CATMAID(self, folder_path: Path):
"""
Save the segmentation confidence score
"""
pyn5.create_dataset(
folder_path + "/segmentations.n5",
"confidence",
[int(x) for x in self.end_voxel],
[int(x) for x in self.leaf_shape_voxels],
"UINT8",
)
dataset = pyn5.open(folder_path + "/segmentations.n5", "confidence")
for leaf in self.distances.iter_leaves():
pyn5.write(
dataset,
leaf.bounds,
(
255
* self._view_weighted_mask(
tuple(map(slice, leaf.bounds[0], leaf.bounds[1]))
)
).astype(np.uint8),
np.uint8,
)
for file_path in (zarr_root / "volumes").iterdir():
if not file_path.name.startswith("affs") and not file_path.name.startswith(
"."):
dataset_name = "volumes/{}".format(file_path.name)
zarr_dataset = zarr_data[dataset_name]
dtype = "{}".format(zarr_dataset.dtype).upper()
if (n5_root / "volumes" / file_path.name).exists():
shutil.rmtree(n5_root / "volumes" / file_path.name)
all_data = zarr_dataset[:, :, :].transpose([2, 1, 0])
# if dtype == "UINT64":
# vals = list(set(all_data.flatten()))
# mapping = {v: i for i, v in enumerate(vals)}
# fv = np.vectorize(lambda x: mapping[x])
# all_data = fv(all_data)
# dtype = "UINT64"
pyn5.create_dataset(
str(n5_root.absolute()),
dataset_name,
zarr_dataset.shape[::-1],
[64, 64, 64],
dtype,
)
n5_dataset = pyn5.open(str(n5_root.absolute()), dataset_name)
pyn5.write(
n5_dataset,
(np.array([0, 0, 0]), np.array(zarr_dataset.shape[::-1])),
all_data,
dtype,
)
def test_read_write(self):
# make sure n5 is initialized to all zeros
self.assertTrue(
np.array_equal(
pyn5.read(self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size))),
np.zeros([10, 10, 10]),
))
# write ones to whole dataset, and then
# write on partial blocks to make sure data isn't overwritten
pyn5.write(
self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size)),
np.ones(self.dataset_size),
)
self.assertTrue(
np.array_equal(
pyn5.read(self.n5,
(np.array([0, 0, 0]), np.array(self.dataset_size))),
np.ones([10, 10, 10]),
))
pyn5.write(self.n5, (np.array([1, 1, 1]), np.array([3, 3, 3])),
np.ones([2, 2, 2]) * 2)
self.assertTrue(
np.array_equal(
pyn5.read(self.n5, (np.array([1, 1, 1]), np.array([3, 3, 3]))),
np.ones([2, 2, 2]) * 2,
))
# test writting non-uniform block to make sure axis orderings are correct
self.n5.write_ndarray(
np.array([1, 1, 1]),
np.array(range(64), dtype=np.uint8).reshape([4, 4, 4]),
0,
)
self.assertTrue(
np.array_equal(
self.n5.read_ndarray(np.array([1, 1, 1]), np.array([4, 4, 4])),
np.array(range(64), dtype=int).reshape([4, 4, 4]),
))
# sanity check axis orderings
data = np.zeros([5, 7, 1], dtype=np.uint8)
data[0, :, 0] = 1
data[1, :, 0] = 2
data[2, :, 0] = 3
self.n5.write_ndarray(np.array([0, 0, 0]), data, 0)
self.assertTrue(
np.all(
self.n5.read_ndarray(np.array([1, 0, 0]), np.array([1, 7, 1]))
== 2))
np.testing.assert_equal(
self.n5.read_ndarray(np.array([0, 0, 0]), np.array([3, 1,
1])).flatten(),
np.array([1, 2, 3]),
)
# test writing data in non block shapes
data = np.array(range(6), dtype=np.uint8).reshape([1, 2, 3])
self.n5.write_ndarray(np.array([0, 0, 0]), data, 0)
self.assertTrue(
np.array_equal(
self.n5.read_ndarray(np.array([0, 0, 0]), np.array([1, 2, 3])),
data))