tiled.adapters.hdf5.HDF5Adapter

class tiled.adapters.hdf5.HDF5Adapter(tree: dict[str, Any] | Sentinel, *data_uris: str, dataset: str | None = None, structure: ArrayStructure | None = None, metadata: Mapping[str, str | int | float | Mapping[str, JSON_ITEM] | Sequence[JSON_ITEM] | None] | None = None, specs: List[Spec] | None = None, **kwargs: Any | None)

Adapter for HDF5 files

This map the structure of an HDF5 file onto a “Tree” of array structures.

Parameters:

treedict

A dictionary representing the HDF5 file or group. The keys are the names of the groups or datasets, and the values are either dictionaries (representing groups) or None (representing datasets). HDF5 datasets will be mapped to HDF5ArrayAdapter instances, and groups will be mapped to HDF5Adapter instances. The tree is rooted at the ‘dataset’ node.

data_urisstr

The URI of the file, or a list of URIs if the dataset spans multiple files.

datasetstr

The dataset to read, for example, “/path/to/dataset” within the file. If supplied, this path will effectively become the root of the adapter.

metadatadict

Metadata for the adapter

specslist

A list of specs for the adapter

kwargsdict

Additional keyword arguments, such as swmr, libver, etc. – they are not stored as separate attributes

Examples

From the root node of a file given a filepath

>>> import h5py
>>> HDF5Adapter.from_uri("file://localhost/path/to/file.h5")

__init__(tree: dict[str, Any] | Sentinel, *data_uris: str, dataset: str | None = None, structure: ArrayStructure | None = None, metadata: Mapping[str, str | int | float | Mapping[str, JSON_ITEM] | Sequence[JSON_ITEM] | None] | None = None, specs: List[Spec] | None = None, **kwargs: Any | None) → None

Methods

__class_getitem__	Parameterizes a generic class.
__contains__(key)
__delattr__(name, /)	Implement delattr(self, name).
__dir__(/)	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getitem__(key)
__getstate__(/)	Helper for pickle.
__gt__(value, /)	Return self>value.
__init__(tree, *data_uris[, dataset, ...])
__init_subclass__	Function to initialize subclasses.
__iter__()	Iterate over the keys of the tree
__le__(value, /)	Return self<=value.
__len__()
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(*args, **kwargs)
__reduce__(/)	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__(/)	Size of object in memory, in bytes.
__str__(/)	Return str(self).
__subclasshook__(C)	Abstract classes can override this to customize issubclass().
_items_slice(start, stop, direction[, page_size])
_keys_slice(start, stop, direction[, page_size])
from_catalog(data_source, node, /[, ...])
from_uris(*data_uris[, dataset, swmr, ...])
get(key, *args)	Overwrite to always raise KeyErrors for broken links and missing items
inlined_contents_enabled(depth)
items()
keys()
metadata()
read([fields])
search(query)
structure()
supported_storage()
values()

Attributes

__abstractmethods__
__annotations__
__dict__
__doc__
__hash__
__module__
__orig_bases__
__parameters__
__reversed__
__slots__
__weakref__	list of weak references to the object
_abc_impl
items_indexer
keys_indexer
specs
structure_family
values_indexer
fn