def read(self,
path: str,
s3_kwargs: Dict[str, Any] = None,
s3_client_kwargs: Dict[str, Any] = None,
max_cache_size: int = None,
**kwargs) -> xr.Dataset:
"""
Read dataset from some Zarr storage.
:param path: File path or object storage URL.
:param s3_kwargs: if *path* is an object storage URL, keyword-arguments passed to S3 file system,
that is ``s3fs.S3FileSystem(**s3_kwargs, ...)``.
:param s3_client_kwargs: if *path* is an object storage URL, keyword-arguments passed to S3 (boto3) client,
that is ``s3fs.S3FileSystem(..., client_kwargs=s3_client_kwargs)``.
:param max_cache_size: if this is a positive integer, the store will be wrapped in an in-memory cache,
that is ``store = zarr.LRUStoreCache(store, max_size=max_cache_size)``.
:param kwargs: Keyword-arguments passed to xarray Zarr adapter,
that is ``xarray.open_zarr(..., **kwargs)``. In addition, the parameter **
:return:
"""
path_or_store = path
consolidated = False
if isinstance(path, str):
path_or_store, consolidated = get_path_or_s3_store(
path_or_store,
s3_kwargs=s3_kwargs,
s3_client_kwargs=s3_client_kwargs,
mode='r')
if max_cache_size is not None and max_cache_size > 0:
path_or_store = zarr.LRUStoreCache(path_or_store,
max_size=max_cache_size)
return xr.open_zarr(path_or_store, consolidated=consolidated, **kwargs)
def open(
self,
mode: str = "r",
cached: bool = True,
cache_size_bytes: int = int(1e9)) -> "ChunkedDataset":
"""Opens a zarr dataset from disk from the path supplied in the constructor.
:param mode: Mode to open dataset in, default to read-only (default: {"r"})
:param cached: Whether to cache files read from disk using a LRU cache. (default: {True})
:param cache_size_bytes: Size of cache in bytes (default: {1e9} (1GB))
"""
if cached:
self.root = zarr.open_group(store=zarr.LRUStoreCache(
zarr.DirectoryStore(self.path), max_size=cache_size_bytes),
mode=mode)
else:
self.root = zarr.open_group(self.path, mode=mode)
self.frames = self.root[FRAME_ARRAY_KEY]
self.agents = self.root[AGENT_ARRAY_KEY]
self.scenes = self.root[SCENE_ARRAY_KEY]
try:
self.tl_faces = self.root[TL_FACE_ARRAY_KEY]
except KeyError:
# the real issue here is that frame doesn't have traffic_light_faces_index_interval
warnings.warn(
f"{TL_FACE_ARRAY_KEY} not found in {self.path}! "
f"You won't be able to use this zarr into an Ego/AgentDataset",
RuntimeWarning,
stacklevel=2,
)
self.tl_faces = np.empty((0, ), dtype=TL_FACE_DTYPE)
return self
def open_ml_dataset_from_object_storage(
ctx: ServiceContext,
dataset_descriptor: DatasetDescriptor) -> MultiLevelDataset:
ds_id = dataset_descriptor.get('Identifier')
path = dataset_descriptor.get('Path')
if not path:
raise ServiceConfigError(
f"Missing 'path' entry in dataset descriptor {ds_id}")
data_format = dataset_descriptor.get('Format', FORMAT_NAME_ZARR)
s3_client_kwargs = {}
if 'Endpoint' in dataset_descriptor:
s3_client_kwargs['endpoint_url'] = dataset_descriptor['Endpoint']
if 'Region' in dataset_descriptor:
s3_client_kwargs['region_name'] = dataset_descriptor['Region']
obs_file_system = s3fs.S3FileSystem(anon=True,
client_kwargs=s3_client_kwargs)
if data_format == FORMAT_NAME_ZARR:
store = s3fs.S3Map(root=path, s3=obs_file_system, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
with measure_time(tag=f"opened remote zarr dataset {path}"):
ds = xr.open_zarr(cached_store)
return BaseMultiLevelDataset(ds)
if data_format == FORMAT_NAME_LEVELS:
with measure_time(tag=f"opened remote levels dataset {path}"):
return ObjectStorageMultiLevelDataset(
ds_id,
obs_file_system,
path,
exception_type=ServiceConfigError)
def _get_dataset_lazily(self, index: int, **zarr_kwargs) -> xr.Dataset:
"""
Read the dataset for the level at given *index*.
:param index: the level index
:param zarr_kwargs: kwargs passed to xr.open_zarr()
:return: the dataset for the level at *index*.
"""
ext, level_path = self._level_paths[index]
if ext == ".link":
with self._obs_file_system.open(level_path, "w") as fp:
level_path = fp.read()
# if file_path is a relative path, resolve it against the levels directory
if not os.path.isabs(level_path):
base_dir = os.path.dirname(self._dir_path)
level_path = os.path.join(base_dir, level_path)
store = s3fs.S3Map(root=level_path,
s3=self._obs_file_system,
check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
with measure_time(
tag=f"opened remote dataset {level_path} for level {index}"):
return assert_cube(xr.open_zarr(cached_store, **zarr_kwargs),
name=level_path)
def read(self, path: str, **kwargs) -> xr.Dataset:
path_or_store = path
if isinstance(path, str):
endpoint_url = None
root = None
if 'endpoint_url' in kwargs:
endpoint_url = kwargs.pop('endpoint_url')
root = path
if path.startswith("http://") or path.startswith("https://"):
import urllib3.util
url = urllib3.util.parse_url(path_or_store)
if url.port is not None:
endpoint_url = f'{url.scheme}://{url.host}:{url.port}'
else:
endpoint_url = f'{url.scheme}://{url.host}'
root = url.path
if root.startswith('/'):
root = root[1:]
if endpoint_url and root is not None:
s3 = s3fs.S3FileSystem(anon=True, client_kwargs=dict(endpoint_url=endpoint_url))
path_or_store = s3fs.S3Map(root=root, s3=s3, check=False)
if 'max_cache_size' in kwargs:
max_cache_size = kwargs.pop('max_cache_size')
if max_cache_size > 0:
path_or_store = zarr.LRUStoreCache(path_or_store, max_size=max_cache_size)
return xr.open_zarr(path_or_store, **kwargs)
def open(self, mode: str = "r", cached: bool = True, cache_size_bytes: int = int(1e9)) -> "ChunkedDataset":
"""Opens a zarr dataset from disk from the path supplied in the constructor.
Keyword Arguments:
mode (str): Mode to open dataset in, default to read-only (default: {"r"})
cached (bool): Whether to cache files read from disk using a LRU cache. (default: {True})
cache_size_bytes (int): Size of cache in bytes (default: {1e9} (1GB))
Raises:
Exception: When any of the expected arrays (frames, agents, scenes) is missing or the store couldn't be
opened.
"""
if cached:
self.root = zarr.open_group(
store=zarr.LRUStoreCache(zarr.DirectoryStore(self.path), max_size=cache_size_bytes), mode=mode
)
else:
self.root = zarr.open_group(self.path, mode=mode)
self.frames = self.root[FRAME_ARRAY_KEY]
self.agents = self.root[AGENT_ARRAY_KEY]
self.scenes = self.root[SCENE_ARRAY_KEY]
try:
self.tl_faces = self.root[TL_FACE_ARRAY_KEY]
except KeyError:
warnings.warn(
f"{TL_FACE_ARRAY_KEY} not found in {self.path}! Traffic lights will be disabled",
RuntimeWarning,
stacklevel=2,
)
self.tl_faces = np.empty((0,), dtype=TL_FACE_DTYPE)
return self
def open_cube(cube_config: CubeConfig,
observer: Callable = None,
trace_store_calls: bool = False,
max_cache_size: int = 2 ** 30,
sentinel_hub: SentinelHub = None,
**sh_kwargs) -> xr.Dataset:
"""
Open a data cube from SentinelHub.
This is a facade function that hides the details of opening a volatile data cube from SentinelHub.
:param cube_config: The cube configuration.
:param observer: A observer function or callable that is called on every request made to SentinelHub.
:param trace_store_calls: Whether to trace and dump calls made into the Zarr store.
:param max_cache_size: Cache size in bytes. Defaults to 1 GB. If zero or None, no caching takes place:
:param sentinel_hub: Optional instance of SentinelHub, the object representing the SENTINEL Hub API.
:param sh_kwargs: Optional keyword arguments passed to the SentinelHub constructor. Only valid if
*sentinel_hub* is not given.
:return: the data cube represented by an xarray Dataset object.
"""
if sentinel_hub is None:
sentinel_hub = SentinelHub(**sh_kwargs)
elif sh_kwargs:
raise ValueError(f'unexpected keyword-arguments: {", ".join(sh_kwargs.keys())}')
cube_store = SentinelHubChunkStore(sentinel_hub, cube_config, observer=observer,
trace_store_calls=trace_store_calls)
if max_cache_size:
cube_store = zarr.LRUStoreCache(cube_store, max_cache_size)
return xr.open_zarr(cube_store)
def read(self, path: str, **kwargs) -> xr.Dataset:
path_or_store = path
consolidated = False
if isinstance(path, str):
region_name = None
if 'endpoint_url' in kwargs:
endpoint_url = kwargs.pop('endpoint_url')
root = path
else:
endpoint_url, root = split_bucket_url(path)
if 'region_name' in kwargs:
region_name = kwargs.pop('region_name')
if endpoint_url and root:
s3 = s3fs.S3FileSystem(anon=True,
client_kwargs=dict(
endpoint_url=endpoint_url,
region_name=region_name))
consolidated = s3.exists(f'{root}/.zmetadata')
path_or_store = s3fs.S3Map(root=root, s3=s3, check=False)
if 'max_cache_size' in kwargs:
max_cache_size = kwargs.pop('max_cache_size')
if max_cache_size > 0:
path_or_store = zarr.LRUStoreCache(
path_or_store, max_size=max_cache_size)
else:
consolidated = os.path.exists(
os.path.join(path_or_store, '.zmetadata'))
return xr.open_zarr(path_or_store, consolidated=consolidated, **kwargs)
def _get_dataset_lazily(self, index: int,
parameters: Dict[str, Any]) -> xr.Dataset:
"""
Read the dataset for the level at given *index*.
:param index: the level index
:param parameters: keyword arguments passed to xr.open_zarr()
:return: the dataset for the level at *index*.
"""
ext, level_path = self._level_paths[index]
if ext == ".link":
with self._s3_file_system.open(level_path, "w") as fp:
level_path = fp.read()
# if file_path is a relative path, resolve it against the levels directory
if not os.path.isabs(level_path):
base_dir = os.path.dirname(self._dir_path)
level_path = os.path.join(base_dir, level_path)
store = s3fs.S3Map(root=level_path,
s3=self._s3_file_system,
check=False)
max_size = self.get_chunk_cache_capacity(index)
if max_size:
store = zarr.LRUStoreCache(store, max_size=max_size)
with measure_time(
tag=f"opened remote dataset {level_path} for level {index}"):
consolidated = self._s3_file_system.exists(
f'{level_path}/.zmetadata')
return assert_cube(xr.open_zarr(store,
consolidated=consolidated,
**parameters),
name=level_path)
def read(self, path: str, **kwargs) -> xr.Dataset:
path_or_store = path
consolidated = False
mode = 'read'
root = None
if isinstance(path, str):
client_kwargs = {}
if 'client_kwargs' in kwargs:
client_kwargs = kwargs.pop('client_kwargs')
if 'endpoint_url' in kwargs:
client_kwargs['endpoint_url'] = kwargs.pop('endpoint_url')
root = path
if 'region_name' in kwargs:
client_kwargs['region_name'] = kwargs.pop('region_name')
path_or_store, root, client_kwargs = _get_path_or_store(path_or_store, client_kwargs, mode, root)
if 'endpoint_url' in client_kwargs and root is not None:
s3 = s3fs.S3FileSystem(anon=True,
client_kwargs=client_kwargs)
consolidated = s3.exists(f'{root}/.zmetadata')
path_or_store = s3fs.S3Map(root=root, s3=s3, check=False)
if 'max_cache_size' in kwargs:
max_cache_size = kwargs.pop('max_cache_size')
if max_cache_size > 0:
path_or_store = zarr.LRUStoreCache(path_or_store, max_size=max_cache_size)
else:
consolidated = os.path.exists(os.path.join(path_or_store, '.zmetadata'))
return xr.open_zarr(path_or_store, consolidated=consolidated, **kwargs)
def open_zarr(s3_path, anon=False, cache=False):
"""Open a zarr archive and return its root."""
s3 = s3fs.S3FileSystem(anon=anon)
store = s3fs.S3Map(s3_path, s3=s3, check=False, create=False)
if cache:
lrucache = zarr.LRUStoreCache(store=store, max_size=1 << 29)
root = zarr.group(store=lrucache)
else:
root = zarr.group(store=store)
return root
def get_storage_map(url: str, creds: dict = None, memcache: float = None):
fs, path, store = _get_storage_map(url, creds)
# TODO: Make use that fs.listdir and store.get do not cache locally filenames,
# because in that case if something is added to s3 or gcs it won't be notified by the program
if (store.get(".zarray") is None and store.get(".zgroup") is None
and len(fs.listdir(path)) > 0):
raise NotZarrFolderException(
"This url is not empty but not zarr url either, for safety reasons refusing to overwrite this folder"
)
return store if not memcache else zarr.LRUStoreCache(
store, memcache * (2**20))
def open_data(self, data_id: str, **open_params) -> Any:
cci_schema = self.get_open_data_params_schema(data_id)
cci_schema.validate_instance(open_params)
cube_kwargs, open_params = cci_schema.process_kwargs_subset(
open_params, ('variable_names', 'time_range', 'bbox'))
max_cache_size: int = 2**30
chunk_store = CciChunkStore(self._cci_odp, data_id, cube_kwargs)
if max_cache_size:
chunk_store = zarr.LRUStoreCache(chunk_store, max_cache_size)
ds = xr.open_zarr(chunk_store)
ds = self._normalize_dataset(ds, cci_schema, **open_params)
return ds
def __init__(self, store, cache_size=128 * (1024**2)):
# don't cache meta-data read once
self.cache = zarr.LRUStoreCache(store, max_size=cache_size)
self.root = zarr.open(self.cache, mode="r")
meta_data, recmd, time_seconds = load_meta(self.root)
self.depth = recmd
self.time_seconds = time_seconds
self.sample_events = self.root["sample_events"][:]
self.segy_filenames = self.root["segy_filenames"][:]
def read(self,
path: str,
client_kwargs: Dict[str, Any] = None,
**kwargs) -> xr.Dataset:
path_or_store = path
consolidated = False
if isinstance(path, str):
path_or_store, consolidated = get_path_or_obs_store(path_or_store,
client_kwargs,
mode='r')
if 'max_cache_size' in kwargs:
max_cache_size = kwargs.pop('max_cache_size')
if max_cache_size > 0:
path_or_store = zarr.LRUStoreCache(path_or_store,
max_size=max_cache_size)
return xr.open_zarr(path_or_store, consolidated=consolidated, **kwargs)
def open_from_obs(path: str,
endpoint_url: str = None,
max_cache_size: int = 2**28) -> xr.Dataset:
"""
Open an xcube (xarray dataset) from S3 compatible object storage (OBS).
:param path: Path having format "<bucket>/<my>/<sub>/<path>"
:param endpoint_url: Optional URL of the OBS service endpoint. If omitted, AWS S3 service URL is used.
:param max_cache_size: If > 0, size of a memory cache in bytes, e.g. 2**30 = one giga bytes.
If None or size <= 0, no memory cache will be used.
:return: an xarray dataset
"""
s3 = s3fs.S3FileSystem(anon=True,
client_kwargs=dict(endpoint_url=endpoint_url))
store = s3fs.S3Map(root=path, s3=s3, check=False)
if max_cache_size is not None and max_cache_size > 0:
store = zarr.LRUStoreCache(store, max_size=max_cache_size)
return xr.open_zarr(store)
def load_dask_array_from_s3(plate_id, index, resolution='0'):
cache_size_mb = 2048
cfg = {
'anon': True,
'client_kwargs': {
'endpoint_url': 'https://minio-dev.openmicroscopy.org/',
},
'root': 'idr/zarr/v0.1-extra/plate-%s.zarr/%s/%s' % (plate_id, index,
resolution)
}
s3 = s3fs.S3FileSystem(
anon=cfg['anon'],
client_kwargs=cfg['client_kwargs'],
)
store = s3fs.S3Map(root=cfg['root'], s3=s3, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=(cache_size_mb * 2**20))
# data.shape is (t, c, z, y, x) by convention
return da.from_zarr(cached_store)
def __getattr__(self, name: str) -> Any:
if name in self._cube_config:
if name in self._dataset_cache:
return self._dataset_cache[name]
else:
dataset_descriptor = self._cube_config[name]
fs_type = dataset_descriptor.get("FileSystem", "local")
path = dataset_descriptor.get('Path')
ds = None
if not path:
print("Missing 'path' entry in dataset descriptor")
if fs_type == 'obs':
data_format = dataset_descriptor.get('Format', 'zarr')
if data_format != 'zarr':
print("Invalid format=" + data_format +
"!r} in dataset descriptor ")
client_kwargs = {}
if 'Endpoint' in dataset_descriptor:
client_kwargs['endpoint_url'] = dataset_descriptor[
'Endpoint']
if 'Region' in dataset_descriptor:
client_kwargs['region_name'] = dataset_descriptor[
'Region']
s3 = s3fs.S3FileSystem(anon=True,
client_kwargs=client_kwargs)
store = s3fs.S3Map(root=path, s3=s3, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
ds = xr.open_zarr(cached_store)
elif fs_type == 'local':
if not os.path.isabs(path):
path = os.path.join(self.base_dir, path)
data_format = dataset_descriptor.get('Format', 'nc')
if data_format == 'nc':
ds = xr.open_dataset(path)
elif data_format == 'zarr':
ds = xr.open_zarr(path)
else:
print("Invalid format=" + data_format +
"!r} in dataset descriptor")
self._dataset_cache[name] = ds
return ds
return super().__getattribute__(name)
def open_ml_dataset_from_object_storage(path: str,
data_format: str = None,
ds_id: str = None,
exception_type: type = ValueError,
client_kwargs: Mapping[str,
Any] = None,
**kwargs) -> MultiLevelDataset:
data_format = data_format or guess_ml_dataset_format(path)
endpoint_url, root = split_bucket_url(path)
if endpoint_url:
kwargs['endpoint_url'] = endpoint_url
path = root
client_kwargs = dict(client_kwargs or {})
for arg_name in ['endpoint_url', 'region_name']:
if arg_name in kwargs:
client_kwargs[arg_name] = kwargs.pop(arg_name)
obs_file_system = s3fs.S3FileSystem(anon=True, client_kwargs=client_kwargs)
if data_format == FORMAT_NAME_ZARR:
store = s3fs.S3Map(root=path, s3=obs_file_system, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
with measure_time(tag=f"opened remote zarr dataset {path}"):
consolidated = obs_file_system.exists(f'{path}/.zmetadata')
ds = assert_cube(
xr.open_zarr(cached_store, consolidated=consolidated,
**kwargs))
return BaseMultiLevelDataset(ds, ds_id=ds_id)
elif data_format == FORMAT_NAME_LEVELS:
with measure_time(tag=f"opened remote levels dataset {path}"):
return ObjectStorageMultiLevelDataset(
obs_file_system,
path,
zarr_kwargs=kwargs,
ds_id=ds_id,
exception_type=exception_type)
raise exception_type(
f'Unrecognized multi-level dataset format {data_format!r} for path {path!r}'
)
def __init__(self, store, cache_size=512 * (1024**2)):
"""
rss format data access.
Parameters
----------
store - Instance of s ZArr storage object,
see s3fs.S3Map for remote s3 storage, or zarr.DirectoryStore as common
types of store.
"""
# don't cache meta-data read once
self.root = zarr.open(store, mode="r")
clear_output()
print("Mounting line access.")
cache = zarr.LRUStoreCache(store, max_size=cache_size)
inline_root = zarr.open(cache, mode="r")
self.inline_root = inline_root["inline"]
crossline_root = zarr.open(cache, mode="r")
self.crossline_root = crossline_root["crossline"]
clear_output()
print("Configuring meta-data.")
self.bounds = self.root["bounds"]
self.ilxl = np.vstack([
self.root["coords"]["inlines"][:],
self.root["coords"]["crosslines"][:],
]).T
self.xy = np.vstack(
[self.root["coords"]["cdpx"][:], self.root["coords"]["cdpy"][:]]).T
self.kdtree = None
clear_output()
print("Connection complete.")
def open_data(self, data_id: str, **open_params) -> xr.Dataset:
assert_instance(data_id, str, name='data_id')
fs, root, open_params = self.load_fs(open_params)
zarr_store = fs.get_mapper(data_id)
cache_size = open_params.pop('cache_size', None)
if isinstance(cache_size, int) and cache_size > 0:
zarr_store = zarr.LRUStoreCache(zarr_store, max_size=cache_size)
log_access = open_params.pop('log_access', None)
if log_access:
zarr_store = LoggingStore(zarr_store,
name=f'zarr_store({data_id!r})')
consolidated = open_params.pop('consolidated',
fs.exists(f'{data_id}/.zmetadata'))
try:
return xr.open_zarr(zarr_store,
consolidated=consolidated,
**open_params)
except ValueError as e:
raise DataStoreError(f'Failed to open'
f' dataset {data_id!r}: {e}') from e
def open(self, mode: str = "r", cached: bool = True, cache_size_bytes: int = int(1e9)) -> None:
"""Opens a zarr dataset from disk from the path supplied in the constructor.
Keyword Arguments:
mode (str): Mode to open dataset in, default to read-only (default: {"r"})
cached (bool): Whether to cache files read from disk using a LRU cache. (default: {True})
cache_size (int): Size of cache in bytes (default: {1e9} (1GB))
Raises:
Exception: When any of the expected arrays (frames, agents, scenes) is missing or the store couldn't be
opened.
"""
if cached:
self.root = zarr.open_group(
store=zarr.LRUStoreCache(zarr.DirectoryStore(self.path), max_size=cache_size_bytes), mode=mode
)
else:
self.root = zarr.open_group(self.path, mode=mode)
self.frames = self.root[FRAME_ARRAY_KEY]
self.agents = self.root[AGENT_ARRAY_KEY]
self.scenes = self.root[SCENE_ARRAY_KEY]
def __init__(
self,
zarr_dataset_path: str,
cache_zarr: bool = False,
with_history: bool = False,
return_indices: bool = False,
agents_from_standard_mask_only: bool = False,
):
if cache_zarr:
zarr_root = zarr.open_group(
store=zarr.LRUStoreCache(
zarr.DirectoryStore(zarr_dataset_path), max_size=int(1e9)),
mode="r",
)
else:
zarr_root = zarr.open_group(zarr_dataset_path, mode="r")
self.cumulative_sizes = zarr_root[SCENE_ARRAY_KEY][
"frame_index_interval"][:, 1]
if with_history:
raise NotImplementedError
if agents_from_standard_mask_only:
self.sample_function = partial(
generate_frame_sample_without_hist,
agents=zarr_root[AGENT_ARRAY_KEY],
tl_faces=zarr_root[TL_FACE_ARRAY_KEY],
agents_from_standard_mask_only=True,
mask_agent_indices=zarr_root[MASK_AGENT_INDICES_ARRAY_KEY],
)
else:
self.sample_function = partial(
generate_frame_sample_without_hist,
agents=zarr_root[AGENT_ARRAY_KEY],
tl_faces=zarr_root[TL_FACE_ARRAY_KEY],
)
self.with_history = with_history
self.return_indices = return_indices
self.zarr_root = zarr_root
def open_cube(cube_config: CubeConfig,
observer: Callable = None,
trace_store_calls: bool = False,
max_cache_size: int = 2 ** 30,
**sh_kwargs) -> xr.Dataset:
"""
Open a data cube from SentinelHub.
This is a facade function that hides the details of opening a volatile data cube from SentinelHub.
:param cube_config: The cube configuration.
:param observer: A observer function or callable that is called on every request made to SentinelHub.
:param trace_store_calls: Whether to trace and dump calls made into the Zarr store.
:param max_cache_size: Cache size in bytes. Defaults to 1 GB. If zero or None, no caching takes place:
:param sh_kwargs: Keyword arguments passed to the SentinelHub constructor.
:return: the data cube represented by an xarray Dataset object.
"""
sentinel_hub = SentinelHub(**sh_kwargs)
cube_store = SentinelHubStore(sentinel_hub, cube_config, observer=observer, trace_store_calls=trace_store_calls)
if max_cache_size:
cube_store = zarr.LRUStoreCache(cube_store, max_cache_size)
return xr.open_zarr(cube_store)
def open_ml_dataset_from_object_storage(path: str,
data_format: str = None,
ds_id: str = None,
exception_type: type = ValueError,
s3_kwargs: Mapping[str, Any] = None,
s3_client_kwargs: Mapping[str,
Any] = None,
chunk_cache_capacity: int = None,
**kwargs) -> MultiLevelDataset:
data_format = data_format or guess_ml_dataset_format(path)
s3, root = parse_s3_fs_and_root(path,
s3_kwargs=s3_kwargs,
s3_client_kwargs=s3_client_kwargs,
mode='r')
if data_format == FORMAT_NAME_ZARR:
store = s3fs.S3Map(root=root, s3=s3, check=False)
if chunk_cache_capacity:
store = zarr.LRUStoreCache(store, max_size=chunk_cache_capacity)
with measure_time(tag=f"opened remote zarr dataset {path}"):
consolidated = s3.exists(f'{root}/.zmetadata')
ds = assert_cube(
xr.open_zarr(store, consolidated=consolidated, **kwargs))
return BaseMultiLevelDataset(ds, ds_id=ds_id)
elif data_format == FORMAT_NAME_LEVELS:
with measure_time(tag=f"opened remote levels dataset {path}"):
return ObjectStorageMultiLevelDataset(
s3,
root,
zarr_kwargs=kwargs,
ds_id=ds_id,
chunk_cache_capacity=chunk_cache_capacity,
exception_type=exception_type)
raise exception_type(
f'Unrecognized multi-level dataset format {data_format!r} for path {path!r}'
)
import urllib.request
import matplotlib.colors
from .structure_graph import acronym_to_allen_id, allen_id_to_acronym, structure_graph, allen_id_to_tree_node
from .allen_id_label import labels_for_allen_id
from .swc_morphology import swc_morphology_geometry
from .structure_mesh import structure_mesh
from IPython.core.debugger import set_trace
_image_fs = HTTPFileSystem()
# Todo: Use AWS store after Scott / Lydia upload
_image_store = _image_fs.get_mapper(
"https://thewtex.github.io/allen-ccf-itk-vtk-zarr/average_template_50_chunked.zarr"
)
_image_store_cached = zarr.LRUStoreCache(_image_store, max_size=None)
_image_ds = xr.open_zarr(_image_store_cached, consolidated=True)
_image_da = _image_ds.average_template_50
_label_image_fs = HTTPFileSystem()
# Todo: Use AWS store after Scott / Lydia upload
_label_image_store = _label_image_fs.get_mapper(
"https://thewtex.github.io/allen-ccf-itk-vtk-zarr/allen_ccfv3_annotation_50_contiguous.zarr"
)
_label_image_store_cached = zarr.LRUStoreCache(_label_image_store,
max_size=None)
_label_image_ds = xr.open_zarr(_label_image_store_cached, consolidated=True)
_label_image_da = _label_image_ds.allen_ccfv3_annotation
@register
def test_cached(self):
store_cache = zarr.LRUStoreCache(self.store, max_size=2 * 24)
cube = xr.open_zarr(store_cache)
self.assert_4d_cube_is_valid(cube)
def get_dataset(self, ds_name: str) -> xr.Dataset:
if ds_name in self.dataset_cache:
ds, _, _ = self.dataset_cache[ds_name]
else:
dataset_descriptor = self.get_dataset_descriptor(ds_name)
path = dataset_descriptor.get('Path')
if not path:
raise ServiceConfigError(
f"Missing 'path' entry in dataset descriptor {ds_name}")
t1 = time.clock()
fs_type = dataset_descriptor.get('FileSystem', 'local')
if fs_type == 'obs':
data_format = dataset_descriptor.get('Format', 'zarr')
if data_format != 'zarr':
raise ServiceConfigError(
f"Invalid format={data_format!r} in dataset descriptor {ds_name!r}"
)
client_kwargs = {}
if 'Endpoint' in dataset_descriptor:
client_kwargs['endpoint_url'] = dataset_descriptor[
'Endpoint']
if 'Region' in dataset_descriptor:
client_kwargs['region_name'] = dataset_descriptor['Region']
s3 = s3fs.S3FileSystem(anon=True, client_kwargs=client_kwargs)
store = s3fs.S3Map(root=path, s3=s3, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
with log_time(f"opened remote dataset {path}"):
ds = xr.open_zarr(cached_store)
elif fs_type == 'local':
if not os.path.isabs(path):
path = os.path.join(self.base_dir, path)
data_format = dataset_descriptor.get('Format', 'nc')
if data_format == 'nc':
with log_time(f"opened local NetCDF dataset {path}"):
ds = xr.open_dataset(path)
elif data_format == 'zarr':
with log_time(f"opened local zarr dataset {path}"):
ds = xr.open_zarr(path)
else:
raise ServiceConfigError(
f"Invalid format={data_format!r} in dataset descriptor {ds_name!r}"
)
elif fs_type == 'computed':
if not os.path.isabs(path):
path = os.path.join(self.base_dir, path)
with open(path) as fp:
python_code = fp.read()
local_env = dict()
global_env = None
try:
exec(python_code, global_env, local_env)
except Exception as e:
raise ServiceError(
f"Failed to compute dataset {ds_name!r} from {path!r}: {e}"
) from e
callable_name = dataset_descriptor.get('Function',
COMPUTE_DATASET)
callable_args = dataset_descriptor.get('Args', [])
callable_obj = local_env.get(callable_name)
if callable_obj is None:
raise ServiceConfigError(
f"Invalid dataset descriptor {ds_name!r}: "
f"no callable named {callable_name!r} found in {path!r}"
)
elif not callable(callable_obj):
raise ServiceConfigError(
f"Invalid dataset descriptor {ds_name!r}: "
f"object {callable_name!r} in {path!r} is not callable"
)
args = list()
for arg_value in callable_args:
if isinstance(arg_value, str) and len(arg_value) > 2 \
and arg_value.startswith('@') and arg_value.endswith('@'):
ref_ds_name = arg_value[1:-1]
if not self.get_dataset_descriptor(ref_ds_name):
raise ServiceConfigError(
f"Invalid dataset descriptor {ds_name!r}: "
f"argument {arg_value!r} of callable {callable_name!r} "
f"must reference another dataset")
args.append(self.get_dataset(ref_ds_name))
else:
args.append(arg_value)
try:
with log_time(f"created computed dataset {ds_name}"):
ds = callable_obj(*args)
except Exception as e:
raise ServiceError(
f"Failed to compute dataset {ds_name!r} "
f"from function {callable_name!r} in {path!r}: {e}"
) from e
if not isinstance(ds, xr.Dataset):
raise ServiceError(
f"Failed to compute dataset {ds_name!r} "
f"from function {callable_name!r} in {path!r}: "
f"expected an xarray.Dataset but got a {type(ds)}")
else:
raise ServiceConfigError(
f"Invalid fs={fs_type!r} in dataset descriptor {ds_name!r}"
)
tile_grid_cache = dict()
self.dataset_cache[
ds_name] = ds, dataset_descriptor, tile_grid_cache
t2 = time.clock()
if TRACE_PERF:
print(f'PERF: opening {ds_name!r} took {t2-t1} seconds')
return ds
def __init__(self,
filename: str,
hdf5group: str = None,
hdf5file_mode: str = 'r',
store: Union[MutableMapping, str, Path] = None,
store_path: str = None,
store_mode: str = 'a',
LRU: bool = False,
LRU_max_size: int = 2**30,
max_chunksize=2 * 2**20):
"""
Args:
filename: str or File-like object, file name string or File-like object to be read by zarr
hdf5group: str, hdf5 group in hdf5 file to be read by zarr
along with its children. default is the root group.
hdf5file_mode str, subset of h5py file access modes, filename must exist
'r' readonly, default 'r'
'r+' read and write
store: collections.abc.MutableMapping or str, zarr store.
if string path is passed, zarr.DirectoryStore
is created at the given path, if None, zarr.MemoryStore is used
store_mode: store data access mode, default 'a'
'r' readonly, compatible zarr hierarchy should
already exist in the passed store
'r+' read and write, return error if file does not exist,
for updating zarr hierarchy
'w' create store, remove data if it exists
'w-' or 'x' create store, fail if exists
'a' read and write, create if it does not exist, default 'r'
store_path: string, path in zarr store
LRU: bool, if store is not already zarr.LRUStoreCache, add
a zarr.LRUStoreCache store layer on top of currently used store
LRU_max_size: int, maximum zarr.LRUStoreCache cache size, only used
if store is zarr.LRUStoreCache, or LRU argument is True
max_chunksize: maximum chunk size to use when creating zarr hierarchy, this is useful if
only a small slice of data needs to be read
"""
# Verify arguments
if hdf5file_mode not in ('r', 'r+'):
raise ValueError("hdf5file_mode must be 'r' or 'r+'")
self.hdf5file_mode = hdf5file_mode
# Verify arguments
if not isinstance(LRU, bool):
raise TypeError(f"Expected bool for LRU, recieved {type(LRU)}")
self.LRU = LRU
if not isinstance(LRU_max_size, int):
raise TypeError(
f"Expected int for LRU_max_size, recieved {type(LRU_max_size)}"
)
self.LRU_max_size = LRU_max_size
if not isinstance(max_chunksize, int):
raise TypeError(
f"Expected int for max_chunksize, recieved {type(max_chunksize)}"
)
self.max_chunksize = max_chunksize
# store, store_path, and store_mode are passed through to zarr
self.store_path = store_path
self.store_mode = store_mode
if store is not None and LRU is True and not isinstance(
store, zarr.LRUStoreCache):
self.store = zarr.LRUStoreCache(store, max_size=self.LRU_max_size)
else:
self.store = store
# create dictionary mapping hdf5 filter numbers to compatible zarr codec
self._hdf5_regfilters_subset = {}
self._fill_regfilters()
# dictionary to hold addresses of hdf5 objects in file
self._address_dict = {}
# create zarr format hierarchy for datasets and attributes compatible with hdf5 file,
# dataset contents are not copied, unless it contains variable-length strings
self.zgroup = zarr.open_group(self.store,
mode=self.store_mode,
path=self.store_path)
if self.store is None:
self.store = self.zgroup.store
# FileChunkStore requires uri
if isinstance(filename, str):
self.uri = filename
else:
try:
self.uri = getattr(filename, 'path', None)
if self.uri is None:
self.uri = filename.name
except:
self.uri = ''
# Access hdf5 file and create zarr hierarchy
if hdf5group is not None and not isinstance(hdf5group, str):
raise TypeError(
f"Expected str for hdf5group, recieved {type(hdf5group)}")
self.hdf5group = hdf5group
self.filename = filename
if self.store_mode != 'r':
self.file = h5py.File(self.filename, mode=self.hdf5file_mode)
self.group = self.file[
self.hdf5group] if self.hdf5group is not None else self.file
self.create_zarr_hierarchy(self.group, self.zgroup)
self.file.close()
if isinstance(self.filename, str):
self.chunkstore_file = fsspec.open(self.filename, mode='rb')
self.chunk_store = FileChunkStore(
self.store, chunk_source=self.chunkstore_file.open())
else:
self.chunk_store = FileChunkStore(self.store,
chunk_source=self.filename)
if LRU is True and not isinstance(self.chunk_store,
zarr.LRUStoreCache):
self.chunk_store = zarr.LRUStoreCache(self.chunk_store,
max_size=self.LRU_max_size)
# open zarr group
store_mode_cons = 'r' if self.store_mode == 'r' else 'r+'
self.zgroup = zarr.open_group(self.store,
mode=store_mode_cons,
path=self.store_path,
chunk_store=self.chunk_store)
def _create_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, Dict[str, Any]]:
dataset_descriptor = self.get_dataset_descriptor(ds_id)
path = dataset_descriptor.get('Path')
if not path:
raise ServiceConfigError(
f"Missing 'path' entry in dataset descriptor {ds_id}")
t1 = time.perf_counter()
fs_type = dataset_descriptor.get('FileSystem', 'local')
if fs_type == 'obs':
data_format = dataset_descriptor.get('Format', 'zarr')
s3_client_kwargs = {}
if 'Endpoint' in dataset_descriptor:
s3_client_kwargs['endpoint_url'] = dataset_descriptor[
'Endpoint']
if 'Region' in dataset_descriptor:
s3_client_kwargs['region_name'] = dataset_descriptor['Region']
obs_file_system = s3fs.S3FileSystem(anon=True,
client_kwargs=s3_client_kwargs)
if data_format == 'zarr':
store = s3fs.S3Map(root=path, s3=obs_file_system, check=False)
cached_store = zarr.LRUStoreCache(store, max_size=2**28)
with measure_time(tag=f"opened remote zarr dataset {path}"):
ds = xr.open_zarr(cached_store)
ml_dataset = BaseMultiLevelDataset(ds)
elif data_format == 'levels':
with measure_time(tag=f"opened remote levels dataset {path}"):
ml_dataset = ObjectStorageMultiLevelDataset(
ds_id,
obs_file_system,
path,
exception_type=ServiceConfigError)
else:
raise ServiceConfigError(
f"Invalid format={data_format!r} in dataset descriptor {ds_id!r}"
)
elif fs_type == 'local':
if not os.path.isabs(path):
path = os.path.join(self.base_dir, path)
data_format = dataset_descriptor.get('Format', 'nc')
if data_format == 'nc':
with measure_time(tag=f"opened local NetCDF dataset {path}"):
ds = xr.open_dataset(path)
ml_dataset = BaseMultiLevelDataset(ds)
elif data_format == 'zarr':
with measure_time(tag=f"opened local zarr dataset {path}"):
ds = xr.open_zarr(path)
ml_dataset = BaseMultiLevelDataset(ds)
elif data_format == 'levels':
with measure_time(tag=f"opened local levels dataset {path}"):
ml_dataset = FileStorageMultiLevelDataset(path)
else:
raise ServiceConfigError(
f"Invalid format={data_format!r} in dataset descriptor {ds_id!r}"
)
elif fs_type == 'memory':
if not os.path.isabs(path):
path = os.path.join(self.base_dir, path)
callable_name = dataset_descriptor.get('Function', COMPUTE_DATASET)
input_dataset_ids = dataset_descriptor.get('InputDatasets', [])
input_parameters = dataset_descriptor.get('InputParameters', {})
for input_dataset_id in input_dataset_ids:
if not self.get_dataset_descriptor(input_dataset_id):
raise ServiceConfigError(
f"Invalid dataset descriptor {ds_id!r}: "
f"Input dataset {input_dataset_id!r} of callable {callable_name!r} "
f"must reference another dataset")
with measure_time(tag=f"opened memory dataset {path}"):
ml_dataset = ComputedMultiLevelDataset(
ds_id,
path,
callable_name,
input_dataset_ids,
self.get_ml_dataset,
input_parameters,
exception_type=ServiceConfigError)
else:
raise ServiceConfigError(
f"Invalid fs={fs_type!r} in dataset descriptor {ds_id!r}")
t2 = time.perf_counter()
if self.config.get("trace_perf", False):
_LOG.info(f'Opening {ds_id!r} took {t2 - t1} seconds')
return ml_dataset, dataset_descriptor