def test_transform():
skelv = Skeleton([(0, 0, 0), (1, 0, 0), (1, 1, 0), (1, 1, 3), (2, 1, 3),
(2, 2, 3)],
edges=[(1, 0), (1, 2), (2, 3), (3, 4), (5, 4)],
radii=[1, 2, 3, 4, 5, 6],
vertex_types=[1, 2, 3, 4, 5, 6],
segid=1337,
transform=np.array([
[2, 0, 0, 0],
[0, 2, 0, 0],
[0, 0, 2, 0],
]))
skelp = skelv.physical_space()
assert np.all(skelp.vertices == skelv.vertices * 2)
assert np.all(skelv.vertices == skelp.voxel_space().vertices)
skelv.transform = [
[1, 0, 0, 1],
[0, 1, 0, 2],
[0, 0, 1, 3],
]
skelp = skelv.physical_space()
tmpskel = skelv.clone()
tmpskel.vertices[:, 0] += 1
tmpskel.vertices[:, 1] += 2
tmpskel.vertices[:, 2] += 3
assert np.all(skelp.vertices == tmpskel.vertices)
assert np.all(skelp.voxel_space().vertices == skelv.vertices)
def test_sharded():
skel = Skeleton([
(0, 0, 0),
(1, 0, 0),
(2, 0, 0),
(0, 1, 0),
(0, 2, 0),
(0, 3, 0),
],
edges=[(0, 1), (1, 2), (3, 4), (4, 5), (3, 5)],
segid=1,
extra_attributes=[{
"id": "radius",
"data_type": "float32",
"num_components": 1,
}]).physical_space()
skels = {}
for i in range(10):
sk = skel.clone()
sk.id = i
skels[i] = sk.to_precomputed()
mkdir('/tmp/removeme/skeletons/sharded/skeletons')
with open('/tmp/removeme/skeletons/sharded/info', 'wt') as f:
f.write(jsonify(info))
for idxenc in ('raw', 'gzip'):
for dataenc in ('raw', 'gzip'):
spec = ShardingSpecification(
'neuroglancer_uint64_sharded_v1',
preshift_bits=1,
hash='murmurhash3_x86_128',
minishard_bits=2,
shard_bits=1,
minishard_index_encoding=idxenc,
data_encoding=dataenc,
)
skel_info['sharding'] = spec.to_dict()
with open('/tmp/removeme/skeletons/sharded/skeletons/info',
'wt') as f:
f.write(jsonify(skel_info))
files = spec.synthesize_shards(skels)
for fname in files.keys():
with open('/tmp/removeme/skeletons/sharded/skeletons/' + fname,
'wb') as f:
f.write(files[fname])
cv = CloudVolume('file:///tmp/removeme/skeletons/sharded/')
assert cv.skeleton.meta.mip == 3
for i in range(10):
sk = cv.skeleton.get(i).physical_space()
sk.id = 1
assert sk == skel
shutil.rmtree('/tmp/removeme/skeletons')
def test_sharded():
skel = Skeleton([
(0, 0, 0),
(1, 0, 0),
(2, 0, 0),
(0, 1, 0),
(0, 2, 0),
(0, 3, 0),
],
edges=[(0, 1), (1, 2), (3, 4), (4, 5), (3, 5)],
segid=1,
extra_attributes=[{
"id": "radius",
"data_type": "float32",
"num_components": 1,
}]).physical_space()
skels = {}
for i in range(10):
sk = skel.clone()
sk.id = i
skels[i] = sk.to_precomputed()
mkdir('/tmp/removeme/skeletons/sharded/skeletons')
with open('/tmp/removeme/skeletons/sharded/info', 'wt') as f:
f.write(jsonify(info))
for idxenc in ('raw', 'gzip'):
for dataenc in ('raw', 'gzip'):
spec = ShardingSpecification(
'neuroglancer_uint64_sharded_v1',
preshift_bits=1,
hash='murmurhash3_x86_128',
minishard_bits=2,
shard_bits=1,
minishard_index_encoding=idxenc,
data_encoding=dataenc,
)
skel_info['sharding'] = spec.to_dict()
with open('/tmp/removeme/skeletons/sharded/skeletons/info',
'wt') as f:
f.write(jsonify(skel_info))
files = spec.synthesize_shards(skels)
for fname in files.keys():
with open('/tmp/removeme/skeletons/sharded/skeletons/' + fname,
'wb') as f:
f.write(files[fname])
cv = CloudVolume('file:///tmp/removeme/skeletons/sharded/')
assert cv.skeleton.meta.mip == 3
for i in range(10):
sk = cv.skeleton.get(i).physical_space()
sk.id = 1
assert sk == skel
labels = []
for fname in files.keys():
lbls = cv.skeleton.reader.list_labels(fname, path='skeletons')
labels += list(lbls)
labels.sort()
assert labels == list(range(10))
for filename, shard in files.items():
decoded_skels = cv.skeleton.reader.disassemble_shard(shard)
for label, binary in decoded_skels.items():
Skeleton.from_precomputed(binary)
exists = cv.skeleton.reader.exists(list(range(11)),
path='skeletons')
assert exists == {
0: 'skeletons/0.shard',
1: 'skeletons/0.shard',
2: 'skeletons/0.shard',
3: 'skeletons/0.shard',
4: 'skeletons/0.shard',
5: 'skeletons/0.shard',
6: 'skeletons/0.shard',
7: 'skeletons/0.shard',
8: 'skeletons/1.shard',
9: 'skeletons/1.shard',
10: None,
}
shutil.rmtree('/tmp/removeme/skeletons')