def main(h5_path: str, n5_path: str, skip_confirmation: bool = False):
crop_name = Path(split_by_suffix(h5_path, ('.h5', ))[0]).parent.name
num_n5_suffixes = len(
tuple(filter(lambda v: v.endswith('.n5'),
Path(n5_path).parts)))
if num_n5_suffixes != 1:
raise ValueError(
'The n5 path must have one and only one element ending with ".n5". The path given has {num_n5_suffixes}'
)
dataset_name = Path(split_by_suffix(n5_path, ('.n5', ))[0]).name
sheet_df = get_sheet_df(credfile, sheetname, sheetpage)
crop_attrs = generate_crop_attrs(dataset_name, crop_name, sheet_df,
classNameDict)
gt_data = read(h5_path)
if not skip_confirmation:
print(
f'The data created at {n5_path} will have the following attributes: '
)
print(crop_attrs)
click.confirm('Do you wish to continue?', default=True)
output_array = access(n5_path,
shape=gt_data.shape,
dtype=gt_data.dtype,
mode='a')
output_array[:] = gt_data
output_array.attrs.update(**crop_attrs)
log.info(f'Saved data and attributes to {n5_path}')
return 0
def test_path_splitting():
path = 's3://0/1/2.n5/3/4'
split = split_by_suffix(path, ('.n5', ))
assert (split == ('s3://0/1/2.n5', '3/4', '.n5'))
path = os.path.join('0', '1', '2.n5', '3', '4')
split = split_by_suffix(path, ('.n5', ))
assert (split == (os.path.join('0', '1',
'2.n5'), os.path.join('3', '4'), '.n5'))
path = os.path.join('0', '1', '2.n5')
split = split_by_suffix(path, ('.n5', ))
assert (split == (os.path.join('0', '1', '2.n5'), '', '.n5'))
def precomputed_to_dask(urlpath: str,
chunks: Union[str, Sequence[int]],
channel: int = 0):
store_path, key, _ = split_by_suffix(urlpath, (".precomputed", ))
tsa = access_precomputed(store_path, key,
mode="r")[ts.d["channel"][channel]]
shape = tuple(tsa.shape)
dtype = tsa.dtype.numpy_dtype
if chunks == "original":
chunks = tsa.spec().to_json()["scale_metadata"]["chunk_size"]
_chunks = normalize_chunks(chunks, shape, dtype=dtype)
def chunk_loader(store_path, key, block_info=None):
idx = tuple(
slice(*idcs) for idcs in block_info[None]["array-location"])
tsa = access_precomputed(store_path, key,
mode="r")[ts.d["channel"][channel]]
result = tsa[idx].read().result()
return result
arr = map_blocks(chunk_loader,
store_path,
key,
chunks=_chunks,
dtype=dtype)
return arr
def n5_to_dask(urlpath: str, chunks: Union[str, Sequence[int]], **kwargs):
store_path, key, _ = split_by_suffix(urlpath, (".n5", ))
arr = access_n5(store_path, key, mode="r", **kwargs)
if not hasattr(arr, "shape"):
raise ValueError(f"{store_path}/{key} is not an n5 array")
if chunks == "original":
_chunks = arr.chunks
else:
_chunks = chunks
darr = da.from_array(arr, chunks=_chunks, inline_array=True)
return darr