v3

CodecLike `module-attribute` #

CodecLike = str | AnyNamedConfig

A type modelling the permissible declarations for codecs

NamedConfig #

Bases: TypedDict, Generic[TName, TConfig]

A Zarr V3 metadata object.

This class is parametrized by two type parameters: TName and TConfig.

Attributes:

Name	Type	Description
`name`	`TName`	The name of the metadata object.
`configuration`	`NotRequired[TConfig]`	The configuration of the metadata object.

AnyNamedConfig #

Bases: NamedConfig[str, Mapping[str, object]]

This class models any Zarr metadata object that takes the form of a {"name": ..., "configuration": ...} dict, where the "configuration" key is not required.

AllowedExtraField #

Bases: TypedDict

The type of additional fields that may be added to Zarr V3 Array or Group metadata documents.

NodeSpec #

Bases: StrictBase

The base class for V3 ArraySpec and GroupSpec.

Attributes:

Name	Type	Description
`zarr_format`	`Literal[3]`	The Zarr version represented by this node. Must be 3.

validate_extra_fields #

validate_extra_fields()

Validate that extra fields conform to the Zarr V3 spec.

Extra fields must be dicts with a 'must_understand' key set to False.

Source code in pydantic_zarr/experimental/v3.py

@model_validator(mode="after")
def validate_extra_fields(self) -> Self:
    """
    Validate that extra fields conform to the Zarr V3 spec.

    Extra fields must be dicts with a 'must_understand' key set to False.
    """
    extra_checker.validate_python(self.__pydantic_extra__)
    return self

ArraySpec #

Bases: NodeSpec

A model of a Zarr Version 3 Array.

Attributes:

Name	Type	Description
`node_type`	`Literal['array']`	The node type. Must be the string 'array'.
`attributes`	`BaseAttributes`	JSON-serializable metadata associated with this array.
`shape`	`Sequence[int]`	The shape of this array.
`data_type`	`str`	The data type of this array.
`chunk_grid`	`NamedConfig`	A `NamedConfig` object defining the chunk shape of this array.
`chunk_key_encoding`	`NamedConfig`	A `NamedConfig` object defining the chunk_key_encoding for the array.
`fill_value`	`FillValue`	The fill value for this array.
`codecs`	`Sequence[NamedConfig]`	The sequence of codices for this array.
`storage_transformers`	`Optional[Sequence[NamedConfig]]`	An optional sequence of `NamedConfig` objects that define the storage transformers for this array.
`dimension_names`	`Optional[Sequence[str]]`	An optional sequence of strings that gives names to each axis of the array.

model_dump #

model_dump(**kwargs)

Override this method because the Zarr V3 spec requires that the dimension_names field be omitted from metadata entirely if it's empty.

Source code in pydantic_zarr/experimental/v3.py

def model_dump(self, **kwargs: Any) -> dict[str, Any]:
    """
    Override this method because the Zarr V3 spec requires that the dimension_names
    field be omitted from metadata entirely if it's empty.
    """
    d = super().model_dump(**kwargs)

    if d["dimension_names"] is None:
        d.pop("dimension_names")
    return d

from_array `classmethod` #

from_array(
    array,
    *,
    attributes="auto",
    chunk_grid="auto",
    chunk_key_encoding="auto",
    fill_value="auto",
    codecs="auto",
    storage_transformers="auto",
    dimension_names="auto"
)

Create an ArraySpec from a numpy array-like object.

Parameters:

Name	Type	Description	Default
`array`	`NDArray[Any] \| Array`	object that conforms to the numpy array API. The shape and dtype of this object will be used to construct an ArraySpec. If the `chunks` keyword argument is not given, the shape of the array will be used for the chunks.	required

Returns:

Type	Description
`An instance of ArraySpec with properties derived from the provided array.`

Source code in pydantic_zarr/experimental/v3.py

@classmethod
def from_array(
    cls,
    array: npt.NDArray[Any] | zarr.Array,
    *,
    attributes: Literal["auto"] | BaseAttributes = "auto",
    chunk_grid: Literal["auto"] | AnyNamedConfig = "auto",
    chunk_key_encoding: Literal["auto"] | AnyNamedConfig = "auto",
    fill_value: Literal["auto"] | FillValue = "auto",
    codecs: Literal["auto"] | Sequence[CodecLike] = "auto",
    storage_transformers: Literal["auto"] | Sequence[AnyNamedConfig] = "auto",
    dimension_names: Literal["auto"] | Sequence[str | None] = "auto",
) -> Self:
    """
    Create an ArraySpec from a numpy array-like object.

    Parameters
    ----------
    array :
        object that conforms to the numpy array API.
        The shape and dtype of this object will be used to construct an ArraySpec.
        If the `chunks` keyword argument is not given, the shape of the array will
        be used for the chunks.

    Returns
    -------
    An instance of ArraySpec with properties derived from the provided array.

    """
    if attributes == "auto":
        attributes_actual = auto_attributes(array)
    else:
        attributes_actual = attributes  # type: ignore[assignment]

    if chunk_grid == "auto":
        chunk_grid_actual = auto_chunk_grid(array)
    else:
        chunk_grid_actual = chunk_grid

    chunk_key_actual: AnyNamedConfig
    if chunk_key_encoding == "auto":
        chunk_key_actual = {"name": "default", "configuration": {"separator": "/"}}
    else:
        chunk_key_actual = chunk_key_encoding

    if fill_value == "auto":
        fill_value_actual = auto_fill_value(array)
    else:
        fill_value_actual = fill_value

    codecs_actual: tuple[CodecLike, ...]
    if codecs == "auto":
        codecs_actual = auto_codecs(array)
    else:
        codecs_actual = tuple(codecs)
    storage_transformers_actual: Sequence[AnyNamedConfig]
    if storage_transformers == "auto":
        storage_transformers_actual = auto_storage_transformers(array)
    else:
        storage_transformers_actual = storage_transformers

    dimension_names_actual: Sequence[str | None] | None
    if dimension_names == "auto":
        dimension_names_actual = auto_dimension_names(array)
    else:
        dimension_names_actual = dimension_names

    return cls(
        shape=array.shape,
        data_type=str(array.dtype),
        chunk_grid=chunk_grid_actual,
        attributes=attributes_actual,
        chunk_key_encoding=chunk_key_actual,
        fill_value=fill_value_actual,
        codecs=tuple(codecs_actual),
        storage_transformers=tuple(storage_transformers_actual),
        dimension_names=dimension_names_actual,
    )

from_zarr `classmethod` #

from_zarr(array)

Create an ArraySpec from a zarr.Array.

Parameters:

Name	Type	Description	Default
`array`	`Array`		required

Returns:

Type	Description
`An instance of ArraySpec with properties derived from the provided zarr`
`array.`

Examples:

>>> import zarr
>>> from pydantic_zarr.v3 import ArraySpec
>>> x = zarr.create((10,10))
>>> ArraySpec.from_zarr(x)
ArraySpec(zarr_format=2, attributes={}, shape=(10, 10), chunks=(10, 10), dtype='<f8', fill_value=0.0, order='C', filters=None, dimension_separator='.', compressor={'id': 'blosc', 'cname': 'lz4', 'clevel': 5, 'shuffle': 1, 'blocksize': 0})

Source code in pydantic_zarr/experimental/v3.py

@classmethod
def from_zarr(cls, array: zarr.Array) -> Self:
    """
    Create an ArraySpec from a `zarr.Array`.

    Parameters
    ----------
    array : zarr.Array

    Returns
    -------
    An instance of ArraySpec with properties derived from the provided zarr
    array.

    Examples
    --------
    >>> import zarr
    >>> from pydantic_zarr.v3 import ArraySpec
    >>> x = zarr.create((10,10))
    >>> ArraySpec.from_zarr(x)
    ArraySpec(zarr_format=2, attributes={}, shape=(10, 10), chunks=(10, 10), dtype='<f8', fill_value=0.0, order='C', filters=None, dimension_separator='.', compressor={'id': 'blosc', 'cname': 'lz4', 'clevel': 5, 'shuffle': 1, 'blocksize': 0})

    """
    try:
        from zarr.core.metadata import ArrayV3Metadata
    except ImportError as e:
        raise ImportError("zarr must be installed to use from_zarr") from e

    meta_json: Mapping[str, object]

    if not isinstance(array.metadata, ArrayV3Metadata):
        raise ValueError("Only zarr v3 arrays are supported")  # noqa: TRY004
    if Version(version("zarr")) < Version("3.1.0"):
        # this class was removed from zarr python 3.1.0
        from zarr.core.metadata.v3 import V3JsonEncoder  # type: ignore[attr-defined]

        meta_json = json.loads(json.dumps(array.metadata.to_dict(), cls=V3JsonEncoder))
    else:
        meta_json = array.metadata.to_dict()
    return cls(
        attributes=meta_json["attributes"],
        shape=array.shape,
        data_type=meta_json["data_type"],
        chunk_grid=meta_json["chunk_grid"],
        chunk_key_encoding=meta_json["chunk_key_encoding"],
        fill_value=meta_json["fill_value"],
        codecs=meta_json["codecs"],
        storage_transformers=meta_json["storage_transformers"],
        dimension_names=meta_json.get("dimension_names", None),
    )

to_zarr #

to_zarr(store, path, *, overwrite=False, config=None)

Serialize an ArraySpec to a zarr array at a specific path in a zarr store.

Parameters:

Name	Type	Description	Default
`store`	`instance of zarr.abc.store.Store`	The storage backend that will manifest the array.	required
`path`	`str`	The location of the array inside the store.	required
`overwrite`	`bool`	Whether to overwrite an existing array or group at the path. If overwrite is False and an array or group already exists at the path, an exception will be raised. Defaults to False.	`False`
`config`	`ArrayConfigParams \| None`	An instance of `ArrayConfigParams` that defines the runtime configuration for the array.	`= None`

Returns:

Type	Description
`A zarr array that is structurally identical to the ArraySpec.`
`This operation will create metadata documents in the store.`

Source code in pydantic_zarr/experimental/v3.py

def to_zarr(
    self,
    store: Store,
    path: str,
    *,
    overwrite: bool = False,
    config: ArrayConfigParams | None = None,
) -> zarr.Array:
    """
    Serialize an ArraySpec to a zarr array at a specific path in a zarr store.

    Parameters
    ----------
    store : instance of zarr.abc.store.Store
        The storage backend that will manifest the array.
    path : str
        The location of the array inside the store.
    overwrite : bool
        Whether to overwrite an existing array or group at the path. If overwrite is
        False and an array or group already exists at the path, an exception will be
        raised. Defaults to False.
    config : ArrayConfigParams | None, default = None
        An instance of `ArrayConfigParams` that defines the runtime configuration for the array.

    Returns
    -------
    A zarr array that is structurally identical to the ArraySpec.
    This operation will create metadata documents in the store.
    """
    try:
        import zarr
        from zarr.core.array import Array, AsyncArray
        from zarr.core.metadata.v3 import ArrayV3Metadata
        from zarr.core.sync import sync
        from zarr.errors import ContainsArrayError, ContainsGroupError
        from zarr.storage._common import make_store_path
    except ImportError as e:
        raise ImportError("zarr must be installed to use to_zarr") from e

    store_path = sync(make_store_path(store, path=path))
    extant_node = maybe_node(store, path, zarr_format=3)
    if isinstance(extant_node, zarr.Array):
        if not self.like(extant_node) and not overwrite:
            raise ContainsArrayError(store, path)
        else:
            # If there's an existing array that is identical to the model, and overwrite is False,
            # we can just return that existing array.
            if not overwrite:
                return extant_node
    if isinstance(extant_node, zarr.Group) and not overwrite:
        raise ContainsGroupError(store, path)

    meta: ArrayV3Metadata = ArrayV3Metadata.from_dict(self.model_dump())
    async_array = AsyncArray(metadata=meta, store_path=store_path, config=config)
    sync(async_array._save_metadata(meta))
    return Array(_async_array=async_array)

like #

like(other, *, include=None, exclude=None)

Compare am ArraySpec to another ArraySpec or a zarr.Array, parameterized over the fields to exclude or include in the comparison. Models are first converted to dict via the model_dump method of pydantic.BaseModel, then compared with the == operator.

Parameters:

Name	Type	Description	Default
`other`	`ArraySpec \| Array`	The array (model or actual) to compare with. If other is a `zarr.Array`, it will be converted to `ArraySpec` first.	required
`include`	`IncEx`	A specification of fields to include in the comparison. The default value is `None`, which means that all fields will be included. See the documentation of `pydantic.BaseModel.model_dump` for more details.	`= None`
`exclude`	`IncEx`	A specification of fields to exclude from the comparison. The default value is `None`, which means that no fields will be excluded. See the documentation of `pydantic.BaseModel.model_dump` for more details.	`= None`

Returns:

Type	Description
`bool`	`True` if the two models have identical fields, `False` otherwise, given the set of fields specified by the `include` and `exclude` keyword arguments.

Examples:

>>> import zarr
>>> from pydantic_zarr.v3 import ArraySpec
>>> x = zarr.create((10,10), zarr_format=3)
>>> x.attrs.put({'foo': 10})
>>> x_model = ArraySpec.from_zarr(x)
>>> print(x_model.like(x_model)) # it is like itself.
True
>>> print(x_model.like(x))
True
>>> y = zarr.create((10,10))
>>> y.attrs.put({'foo': 11}) # x and y are the same, other than their attrs
>>> print(x_model.like(y))
False
>>> print(x_model.like(y, exclude={'attributes'}))
True

Source code in pydantic_zarr/experimental/v3.py

def like(
    self,
    other: ArraySpec | zarr.Array,
    *,
    include: IncEx = None,
    exclude: IncEx = None,
) -> bool:
    """
    Compare am `ArraySpec` to another `ArraySpec` or a `zarr.Array`, parameterized over the
    fields to exclude or include in the comparison. Models are first converted to `dict` via the
    `model_dump` method of `pydantic.BaseModel`, then compared with the `==` operator.

    Parameters
    ----------
    other : ArraySpec | zarr.Array
        The array (model or actual) to compare with. If other is a `zarr.Array`, it will be
        converted to `ArraySpec` first.
    include : IncEx, default = None
        A specification of fields to include in the comparison. The default value is `None`,
        which means that all fields will be included. See the documentation of
        `pydantic.BaseModel.model_dump` for more details.
    exclude : IncEx, default = None
        A specification of fields to exclude from the comparison. The default value is `None`,
        which means that no fields will be excluded. See the documentation of
        `pydantic.BaseModel.model_dump` for more details.

    Returns
    -------
    bool
        `True` if the two models have identical fields, `False` otherwise, given
        the set of fields specified by the `include` and `exclude` keyword arguments.

    Examples
    --------
    >>> import zarr
    >>> from pydantic_zarr.v3 import ArraySpec
    >>> x = zarr.create((10,10), zarr_format=3)
    >>> x.attrs.put({'foo': 10})
    >>> x_model = ArraySpec.from_zarr(x)
    >>> print(x_model.like(x_model)) # it is like itself.
    True
    >>> print(x_model.like(x))
    True
    >>> y = zarr.create((10,10))
    >>> y.attrs.put({'foo': 11}) # x and y are the same, other than their attrs
    >>> print(x_model.like(y))
    False
    >>> print(x_model.like(y, exclude={'attributes'}))
    True
    """

    other_parsed: ArraySpec
    if (zarr := sys.modules.get("zarr")) and isinstance(other, zarr.Array):
        other_parsed = ArraySpec.from_zarr(other)
    else:
        other_parsed = other  # type: ignore[assignment]

    return model_like(self, other_parsed, include=include, exclude=exclude)

with_attributes #

with_attributes(attributes)

Return a copy of this model with a new attributes field.

Parameters:

Name	Type	Description	Default
`attributes`	`BaseAttributes`	The new `attributes` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `attributes` field.

Source code in pydantic_zarr/experimental/v3.py

def with_attributes(self, attributes: BaseAttributes) -> Self:
    """
    Return a copy of this model with a new `attributes` field.

    Parameters
    ----------
    attributes : BaseAttributes
        The new `attributes` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `attributes` field.
    """
    return type(self)(**{**self.model_dump(), "attributes": attributes})

with_shape #

with_shape(shape)

Return a copy of this model with a new shape field.

Parameters:

Name	Type	Description	Default
`shape`	`tuple[int, ...]`	The new `shape` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `shape` field.

Source code in pydantic_zarr/experimental/v3.py

def with_shape(self, shape: tuple[int, ...]) -> Self:
    """
    Return a copy of this model with a new `shape` field.

    Parameters
    ----------
    shape : tuple[int, ...]
        The new `shape` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `shape` field.
    """
    return type(self)(**{**self.model_dump(), "shape": shape})

with_data_type #

with_data_type(data_type)

Return a copy of this model with a new data_type field.

Parameters:

Name	Type	Description	Default
`data_type`	`DTypeLike`	The new `data_type` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `data_type` field.

Source code in pydantic_zarr/experimental/v3.py

def with_data_type(self, data_type: DTypeLike) -> Self:
    """
    Return a copy of this model with a new `data_type` field.

    Parameters
    ----------
    data_type : DTypeLike
        The new `data_type` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `data_type` field.
    """
    return type(self)(**{**self.model_dump(), "data_type": data_type})

with_chunk_grid #

with_chunk_grid(chunk_grid)

Return a copy of this model with a new chunk_grid field.

Parameters:

Name	Type	Description	Default
`chunk_grid`	`RegularChunking`	The new `chunk_grid` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `chunk_grid` field.

Source code in pydantic_zarr/experimental/v3.py

def with_chunk_grid(self, chunk_grid: AnyNamedConfig) -> Self:
    """
    Return a copy of this model with a new `chunk_grid` field.

    Parameters
    ----------
    chunk_grid : RegularChunking
        The new `chunk_grid` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `chunk_grid` field.
    """
    return type(self)(**{**self.model_dump(), "chunk_grid": chunk_grid})

with_chunk_key_encoding #

with_chunk_key_encoding(chunk_key_encoding)

Return a copy of this model with a new chunk_key_encoding field.

Parameters:

Name	Type	Description	Default
`chunk_key_encoding`	`DefaultChunkKeyEncoding`	The new `chunk_key_encoding` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `chunk_key_encoding` field.

Source code in pydantic_zarr/experimental/v3.py

def with_chunk_key_encoding(self, chunk_key_encoding: AnyNamedConfig) -> Self:
    """
    Return a copy of this model with a new `chunk_key_encoding` field.

    Parameters
    ----------
    chunk_key_encoding : DefaultChunkKeyEncoding
        The new `chunk_key_encoding` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `chunk_key_encoding` field.
    """
    return type(self)(**{**self.model_dump(), "chunk_key_encoding": chunk_key_encoding})

with_fill_value #

with_fill_value(fill_value)

Return a copy of this model with a new fill_value field.

Parameters:

Name	Type	Description	Default
`fill_value`	`FillValue`	The new `fill_value` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `fill_value` field.

Source code in pydantic_zarr/experimental/v3.py

def with_fill_value(self, fill_value: FillValue) -> Self:
    """
    Return a copy of this model with a new `fill_value` field.

    Parameters
    ----------
    fill_value : FillValue
        The new `fill_value` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `fill_value` field.
    """
    return type(self)(**{**self.model_dump(), "fill_value": fill_value})

with_codecs #

with_codecs(codecs)

Return a copy of this model with a new codecs field.

Parameters:

Name	Type	Description	Default
`codecs`	`CodecTuple`	The new `codecs` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `codecs` field.

Source code in pydantic_zarr/experimental/v3.py

def with_codecs(self, codecs: CodecTuple) -> Self:
    """
    Return a copy of this model with a new `codecs` field.

    Parameters
    ----------
    codecs : CodecTuple
        The new `codecs` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `codecs` field.
    """
    return type(self)(**{**self.model_dump(), "codecs": codecs})

with_storage_transformers #

with_storage_transformers(storage_transformers)

Return a copy of this model with a new storage_transformers field.

Parameters:

Name	Type	Description	Default
`storage_transformers`	`tuple[AnyNamedConfig, ...]`	The new `storage_transformers` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `storage_transformers` field.

Source code in pydantic_zarr/experimental/v3.py

def with_storage_transformers(self, storage_transformers: tuple[AnyNamedConfig, ...]) -> Self:
    """
    Return a copy of this model with a new `storage_transformers` field.

    Parameters
    ----------
    storage_transformers : tuple[AnyNamedConfig, ...]
        The new `storage_transformers` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `storage_transformers` field.
    """
    return type(self)(**{**self.model_dump(), "storage_transformers": storage_transformers})

with_dimension_names #

with_dimension_names(dimension_names)

Return a copy of this model with a new dimension_names field.

Parameters:

Name	Type	Description	Default
`dimension_names`	`tuple[str \| None, ...] \| None`	The new `dimension_names` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `dimension_names` field.

Source code in pydantic_zarr/experimental/v3.py

def with_dimension_names(self, dimension_names: tuple[str | None, ...] | None) -> Self:
    """
    Return a copy of this model with a new `dimension_names` field.

    Parameters
    ----------
    dimension_names : tuple[str | None, ...] | None
        The new `dimension_names` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `dimension_names` field.
    """
    return type(self)(**{**self.model_dump(), "dimension_names": dimension_names})

BaseGroupSpec #

Bases: NodeSpec

A base GroupSpec class that only has core Zarr V3 group attributes

with_attributes #

with_attributes(attributes)

Return a copy of this model with a new attributes field.

The new model will be validated.

Parameters:

Name	Type	Description	Default
`attributes`	`BaseAttributes`	The new `attributes` field for the copy of this model.	required

Returns:

Type	Description
`ArraySpec`	A copy of this model with a new `attributes` field.

Source code in pydantic_zarr/experimental/v3.py

def with_attributes(self, attributes: BaseAttributes) -> Self:
    """
    Return a copy of this model with a new `attributes` field.

    The new model will be validated.

    Parameters
    ----------
    attributes : BaseAttributes
        The new `attributes` field for the copy of this model.

    Returns
    -------
    ArraySpec
        A copy of this model with a new `attributes` field.

    """
    return type(self)(**{**self.model_dump(), "attributes": attributes})

GroupSpec #

Bases: BaseGroupSpec

A model of a Zarr Version 3 Group.

Attributes:

Name	Type	Description
`node_type`	`Literal['group']`	The type of this node. Must be the string "group".
`attributes`	`BaseAttributes`	The user-defined attributes of this group.
`members`	`dict[str, ArraySpec \| GroupSpec \| BaseGroupSpec]`	The members of this group. This is a dict with string keys and values that must be ArraySpec, GroupSpec, or BaseGroupSpec instances.

with_members #

with_members(members)

Return a copy of this model with a new members field.

The new model will be validated.

Parameters:

Name	Type	Description	Default
`members`	`Mapping[str, ArraySpec \| GroupSpec]`	The new `members` field for the copy of this model.	required

Returns:

Type	Description
A copy of this model with a new `members` field.

Source code in pydantic_zarr/experimental/v3.py

def with_members(self, members: BaseMember) -> Self:
    """
    Return a copy of this model with a new `members` field.

    The new model will be validated.

    Parameters
    ----------
    members : Mapping[str, ArraySpec | GroupSpec]
        The new `members` field for the copy of this model.

    Returns
    -------
    A copy of this model with a new `members` field.
    """
    return type(self)(**{**self.model_dump(), "members": members})

from_flat `classmethod` #

from_flat(data)

Create a GroupSpec from a flat hierarchy representation.

The flattened hierarchy is a dict with the following constraints: keys must be valid paths; values must be ArraySpec or GroupSpec instances.

Parameters:

Name	Type	Description	Default
`data`	`dict[str, ArraySpec \| GroupSpec]`	A flattened representation of a Zarr hierarchy.	required

Returns:

Type	Description
`GroupSpec`	A `GroupSpec` representation of the hierarchy.

Examples:

from pydantic_zarr.experimental.v3 import GroupSpec, ArraySpec, BaseGroupSpec
import numpy as np
flat = {'' : BaseGroupSpec(attributes={'foo': 10})}
GroupSpec.from_flat(flat)
# GroupSpec(zarr_format=3, node_type='group', attributes={'foo': 10}, members={})
flat = {
    '': BaseGroupSpec(attributes={'foo': 10}),
    '/a': ArraySpec.from_array(np.arange(10))}
GroupSpec.from_flat(flat)
# GroupSpec(
#    zarr_format=3,
#    node_type='group',
#    attributes={'foo': 10},
#    members={
#        'a': ArraySpec(
#                zarr_format=3,
#                node_type='array',
#                attributes={},
#                shape=(10,),
#                data_type='int64',
#                chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (10,)}},
#                chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
#                fill_value=0,
#                codecs=(),
#                storage_transformers=(),
#                dimension_names=None)})

Source code in pydantic_zarr/experimental/v3.py

@classmethod
def from_flat(cls, data: Mapping[str, ArraySpec | BaseGroupSpec]) -> Self:
    """
    Create a `GroupSpec` from a flat hierarchy representation.

    The flattened hierarchy is a
    `dict` with the following constraints: keys must be valid paths; values must
    be `ArraySpec` or `GroupSpec` instances.

    Parameters
    ----------
    data : dict[str, ArraySpec | GroupSpec]
        A flattened representation of a Zarr hierarchy.

    Returns
    -------
    GroupSpec
        A `GroupSpec` representation of the hierarchy.

    Examples
    --------
    ```py
    from pydantic_zarr.experimental.v3 import GroupSpec, ArraySpec, BaseGroupSpec
    import numpy as np
    flat = {'' : BaseGroupSpec(attributes={'foo': 10})}
    GroupSpec.from_flat(flat)
    # GroupSpec(zarr_format=3, node_type='group', attributes={'foo': 10}, members={})
    flat = {
        '': BaseGroupSpec(attributes={'foo': 10}),
        '/a': ArraySpec.from_array(np.arange(10))}
    GroupSpec.from_flat(flat)
    # GroupSpec(
    #    zarr_format=3,
    #    node_type='group',
    #    attributes={'foo': 10},
    #    members={
    #        'a': ArraySpec(
    #                zarr_format=3,
    #                node_type='array',
    #                attributes={},
    #                shape=(10,),
    #                data_type='int64',
    #                chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (10,)}},
    #                chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
    #                fill_value=0,
    #                codecs=(),
    #                storage_transformers=(),
    #                dimension_names=None)})
    ```
    """
    from_flated = from_flat_group(data)
    return cls(**from_flated.model_dump())

to_flat #

to_flat(root_path='')

Flatten this GroupSpec. This method returns a dict with string keys and values that are BaseGroupSpec or ArraySpec.

Then the resulting dict will contain a copy of the input with a null members attribute under the key root_path, as well as copies of the result of calling node.to_flat on each element of node.members, each under a key created by joining root_path with a '/` character to the name of each member, and so on recursively for each sub-member.

Parameters:

Name	Type	Description	Default
`root_path`	`str`	The root path. The keys in `self.members` will be made relative to `root_path` when used as keys in the result dictionary.	`= ''`

Returns:

Type	Description
`dict[str, ArraySpec \| GroupSpec]`	A flattened representation of the hierarchy.

Examples:

>>> from pydantic_zarr.v3 import to_flat, GroupSpec, BaseGroupSpec
>>> g1 = GroupSpec(members={}, attributes={'foo': 'bar'})
>>> to_flat(g1)
{'': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
>>> to_flat(g1, root_path='baz')
{'baz': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
>>> to_flat(GroupSpec(members={'g1': g1}, attributes={'foo': 'bar'}))
{'/g1': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'}), '': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}

Source code in pydantic_zarr/experimental/v3.py

def to_flat(self, root_path: str = "") -> dict[str, ArraySpec | BaseGroupSpec]:
    """
    Flatten this `GroupSpec`.
    This method returns a `dict` with string keys and values that are `BaseGroupSpec` or
    `ArraySpec`.

    Then the resulting `dict` will contain a copy of the input with a null `members` attribute
    under the key `root_path`, as well as copies of the result of calling `node.to_flat` on each
    element of `node.members`, each under a key created by joining `root_path` with a '/`
    character to the name of each member, and so on recursively for each sub-member.

    Parameters
    ----------
    root_path : `str`, default = ''
        The root path. The keys in `self.members` will be
        made relative to `root_path` when used as keys in the result dictionary.

    Returns
    -------
    dict[str, ArraySpec | GroupSpec]
        A flattened representation of the hierarchy.

    Examples
    --------

    >>> from pydantic_zarr.v3 import to_flat, GroupSpec, BaseGroupSpec
    >>> g1 = GroupSpec(members={}, attributes={'foo': 'bar'})
    >>> to_flat(g1)
    {'': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    >>> to_flat(g1, root_path='baz')
    {'baz': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    >>> to_flat(GroupSpec(members={'g1': g1}, attributes={'foo': 'bar'}))
    {'/g1': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'}), '': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    """
    return to_flat(self, root_path=root_path)

from_zarr `classmethod` #

from_zarr(group, *, depth=-1)

Create a GroupSpec from a zarr group. Subgroups and arrays contained in the zarr group will be converted to instances of GroupSpec and ArraySpec, respectively, and these spec instances will be stored in the .members attribute of the parent GroupSpec. This occurs recursively, so the entire zarr hierarchy below a given group can be represented as a GroupSpec.

Parameters:

Name	Type	Description	Default
`group`	`Group`	The Zarr group to model.	required
`depth`	`int`	An integer which may be no lower than -1. Determines how far into the tree to parse. The default value of -1 indicates that the entire hierarchy should be parsed.	`= -1`

Returns:

Type	Description
`An instance of GroupSpec that represents the structure of the zarr hierarchy.`

Source code in pydantic_zarr/experimental/v3.py

@classmethod
def from_zarr(cls, group: zarr.Group, *, depth: int = -1) -> Self:
    """
    Create a GroupSpec from a zarr group. Subgroups and arrays contained in the zarr
    group will be converted to instances of GroupSpec and ArraySpec, respectively,
    and these spec instances will be stored in the .members attribute of the parent
    GroupSpec. This occurs recursively, so the entire zarr hierarchy below a given
    group can be represented as a GroupSpec.

    Parameters
    ----------
    group : zarr.Group
        The Zarr group to model.
    depth : int, default = -1
        An integer which may be no lower than -1. Determines how far into the tree to parse.
        The default value of -1 indicates that the entire hierarchy should be parsed.

    Returns
    -------
    An instance of GroupSpec that represents the structure of the zarr hierarchy.
    """
    try:
        import zarr
    except ImportError as e:
        raise ImportError("zarr must be installed to use from_zarr") from e

    result: GroupSpec
    attributes = group.attrs.asdict()
    members: dict[str, dict[str, object]] = {}

    if depth < -1:
        msg = (
            f"Invalid value for depth. Got {depth}, expected an integer "
            "greater than or equal to -1."
        )
        raise ValueError(msg)
    if depth == 0:
        extra_fields = {}
        if group.metadata.consolidated_metadata is not None:
            extra_fields["consolidated_metadata"] = (
                group.metadata.consolidated_metadata.to_dict()
            )

        return cls(attributes=attributes, members={}, **extra_fields)
    new_depth = max(depth - 1, -1)
    for name, item in group.members():
        if isinstance(item, zarr.Array):
            members[name] = ArraySpec.from_zarr(item).model_dump()
        elif isinstance(item, zarr.Group):
            members[name] = GroupSpec.from_zarr(item, depth=new_depth).model_dump()
        else:
            msg = (  # type: ignore[unreachable]
                f"Unparsable object encountered: {type(item)}. Expected zarr.Array"
                " or zarr.Group."
            )

            raise ValueError(msg)  # noqa: TRY004

    extra_fields = {}
    if group.metadata.consolidated_metadata is not None:
        extra_fields["consolidated_metadata"] = group.metadata.consolidated_metadata.to_dict()

    result = cls(attributes=attributes, members=members, **extra_fields)
    return result

to_zarr #

to_zarr(store, path, *, overwrite=False, **kwargs)

Serialize a GroupSpec to a zarr group at a specific path in a zarr store.

Parameters:

Name	Type	Description	Default
`store`	`instance of zarr.abc.store.Store`	The storage backend that will manifest the group and its contents.	required
`path`	`str`	The location of the group inside the store.	required
`overwrite`	`bool`	Whether to overwrite an existing array or group at the path. If overwrite is False and an array or group already exists at the path, an exception will be raised. Defaults to False.	`False`

Returns:

Type	Description
`A zarr group that is structurally identical to the GroupSpec.`
`This operation will create metadata documents in the store.`

Source code in pydantic_zarr/experimental/v3.py

def to_zarr(
    self, store: Store, path: str, *, overwrite: bool = False, **kwargs: Any
) -> zarr.Group:
    """
    Serialize a GroupSpec to a zarr group at a specific path in a zarr store.

    Parameters
    ----------
    store : instance of zarr.abc.store.Store
        The storage backend that will manifest the group and its contents.
    path : str
        The location of the group inside the store.
    overwrite : bool
        Whether to overwrite an existing array or group at the path. If overwrite is
        False and an array or group already exists at the path, an exception will be
        raised. Defaults to False.

    Returns
    -------
    A zarr group that is structurally identical to the GroupSpec.
    This operation will create metadata documents in the store.
    """
    try:
        import zarr
        from zarr.core.group import GroupMetadata
        from zarr.core.sync import sync
        from zarr.errors import ContainsArrayError, ContainsGroupError
        from zarr.storage._common import make_store_path
    except ImportError as e:
        raise ImportError("zarr must be installed to use to_zarr") from e

    spec_dict = self.model_dump(exclude={"members": True})
    extant_node = maybe_node(store, path, zarr_format=3)
    if isinstance(extant_node, zarr.Group):
        if not self.like(extant_node):
            if not overwrite:
                """
                msg = (
                    f"A group already exists at path {path}. "
                    "That group is structurally dissimilar to the group you are trying to store. "
                    "Call `to_zarr` with `overwrite=True` to overwrite that group."
                )
                """
                # TODO: use the above message when we fix the ContainsGroupError in zarr python
                # To accept a proper message
                raise ContainsGroupError(store, path)
        else:
            if not overwrite:
                # if the extant group is structurally identical to self, and overwrite is false,
                # then just return the extant group
                return extant_node

    elif isinstance(extant_node, zarr.Array) and not overwrite:
        """
        msg = (
            f"An array already exists at path {path}. "
            "Call to_zarr with overwrite=True to overwrite the array."
        )
        """
        # TODO: use the above message when we fix the ContainsArrayError in zarr python
        raise ContainsArrayError(store, path)

    # This indirect routine for creating a group is required because zarr python does
    # not have a convenient way to simply create a group from a metadata dict.
    # we need to create a group directly from the metadata dict to support consolidated metadata
    # or any other extra metadata fields.

    spath = sync(make_store_path(store, path=path))
    result_dict = dict(
        zarr.create_hierarchy(
            store=spath.store,
            nodes={path: GroupMetadata.from_dict(spec_dict)},
            overwrite=overwrite,
        )
    )
    result = result_dict[path.removeprefix("/")]
    # consider raising an exception if a partial GroupSpec is provided
    if self.members is not None:
        for name, member in self.members.items():
            subpath = f"{path.rstrip('/')}/{name.lstrip('/')}"
            member.to_zarr(store, subpath, overwrite=overwrite, **kwargs)

    return result  # type: ignore[return-value]

like #

like(other, include=None, exclude=None)

Compare a GroupSpec to another GroupSpec or a zarr.Group.

This is parameterized over the fields to exclude or include in the comparison. Models are first converted to dict via the model_dump method of pydantic.BaseModel, then compared with the == operator.

Parameters:

Name	Type	Description	Default
`other`	`GroupSpec \| Group`	The group (model or actual) to compare with. If other is a `zarr.Group`, it will be converted to a `GroupSpec`.	required
`include`	`IncEx`	A specification of fields to include in the comparison. The default is `None`, which means that all fields will be included. See the documentation of `pydantic.BaseModel.model_dump` for more details.	`= None`
`exclude`	`IncEx`	A specification of fields to exclude from the comparison. The default is `None`, which means that no fields will be excluded. See the documentation of `pydantic.BaseModel.model_dump` for more details.	`= None`

Returns:

Type	Description
`bool`	`True` if the two models have identical fields, `False` otherwise.

Examples:

>>> import zarr
>>> from pydantic_zarr.v3 import GroupSpec
>>> import numpy as np
>>> z1 = zarr.group(path='z1')
>>> z1a = z1.array(name='foo', data=np.arange(10))
>>> z1_model = GroupSpec.from_zarr(z1)
>>> print(z1_model.like(z1_model)) # it is like itself
True
>>> print(z1_model.like(z1))
True
>>> z2 = zarr.group(path='z2')
>>> z2a = z2.array(name='foo', data=np.arange(10))
>>> print(z1_model.like(z2))
True
>>> z2.attrs.put({'foo' : 100}) # now they have different attributes
>>> print(z1_model.like(z2))
False
>>> print(z1_model.like(z2, exclude={'attributes'}))
True

Source code in pydantic_zarr/experimental/v3.py

def like(
    self,
    other: GroupSpec | zarr.Group,
    include: IncEx = None,
    exclude: IncEx = None,
) -> bool:
    """
    Compare a `GroupSpec` to another `GroupSpec` or a `zarr.Group`.

    This is parameterized over the fields to exclude or include in the comparison.
    Models are first converted to dict via the `model_dump` method of `pydantic.BaseModel`,
    then compared with the `==` operator.

    Parameters
    ----------
    other : GroupSpec | zarr.Group
        The group (model or actual) to compare with. If other is a `zarr.Group`, it will be
        converted to a `GroupSpec`.
    include : IncEx, default = None
        A specification of fields to include in the comparison. The default is `None`,
        which means that all fields will be included. See the documentation of
        `pydantic.BaseModel.model_dump` for more details.
    exclude : IncEx, default = None
        A specification of fields to exclude from the comparison. The default is `None`,
        which means that no fields will be excluded. See the documentation of
        `pydantic.BaseModel.model_dump` for more details.

    Returns
    -------
    bool
        `True` if the two models have identical fields, `False` otherwise.

    Examples
    --------
    >>> import zarr
    >>> from pydantic_zarr.v3 import GroupSpec
    >>> import numpy as np
    >>> z1 = zarr.group(path='z1')
    >>> z1a = z1.array(name='foo', data=np.arange(10))
    >>> z1_model = GroupSpec.from_zarr(z1)
    >>> print(z1_model.like(z1_model)) # it is like itself
    True
    >>> print(z1_model.like(z1))
    True
    >>> z2 = zarr.group(path='z2')
    >>> z2a = z2.array(name='foo', data=np.arange(10))
    >>> print(z1_model.like(z2))
    True
    >>> z2.attrs.put({'foo' : 100}) # now they have different attributes
    >>> print(z1_model.like(z2))
    False
    >>> print(z1_model.like(z2, exclude={'attributes'}))
    True
    """

    other_parsed: GroupSpec
    if (zarr := sys.modules.get("zarr")) and isinstance(other, zarr.Group):
        other_parsed = GroupSpec.from_zarr(other)
    else:
        other_parsed = other  # type: ignore[assignment]

    return model_like(self, other_parsed, include=include, exclude=exclude)

parse_dtype_v3 #

parse_dtype_v3(dtype)

Todo: refactor this when the zarr python dtypes work is released

Source code in pydantic_zarr/experimental/v3.py

def parse_dtype_v3(dtype: npt.DTypeLike | Mapping[str, object]) -> Mapping[str, object] | str:
    """
    Todo: refactor this when the zarr python dtypes work is released
    """
    if isinstance(dtype, str | Mapping):
        return dtype
    else:
        match np.dtype(dtype):
            case np.dtypes.Int8DType():
                return "int8"
            case np.dtypes.Int16DType():
                return "int16"
            case np.dtypes.Int32DType():
                return "int32"
            case np.dtypes.Int64DType():
                return "int64"
            case np.dtypes.UInt8DType():
                return "uint8"
            case np.dtypes.UInt16DType():
                return "uint16"
            case np.dtypes.UInt32DType():
                return "uint32"
            case np.dtypes.UInt64DType():
                return "uint64"
            case np.dtypes.Float16DType():
                return "float16"
            case np.dtypes.Float32DType():
                return "float32"
            case np.dtypes.Float16DType():
                return "float64"
            case np.dtypes.Float32DType():
                return "complex64"
            case np.dtypes.Complex128DType():
                return "complex128"
            case _:
                raise ValueError(f"Unsupported dtype: {dtype}")

from_zarr #

from_zarr(element: Array, *, depth: int = ...) -> ArraySpec

from_zarr(element: Group, *, depth: int = ...) -> GroupSpec

from_zarr(element, *, depth=-1)

Recursively parse a Zarr group or Zarr array into an ArraySpec or GroupSpec.

Parameters:

Name	Type	Description	Default
`element`	`a zarr Array or zarr Group`		required
`depth`	`int`	An integer which may be no lower than -1. Determines how far into the tree to parse. The default value of -1 indicates that the entire hierarchy should be parsed.	`= -1`

Returns:

Type	Description
`An instance of GroupSpec or ArraySpec that represents the`
`structure of the zarr group or array.`

Source code in pydantic_zarr/experimental/v3.py

def from_zarr(element: zarr.Array | zarr.Group, *, depth: int = -1) -> ArraySpec | GroupSpec:
    """
    Recursively parse a Zarr group or Zarr array into an ArraySpec or GroupSpec.

    Parameters
    ----------
    element : a zarr Array or zarr Group

    depth : int, default = -1
        An integer which may be no lower than -1. Determines how far into the tree to parse.
        The default value of -1 indicates that the entire hierarchy should be parsed.

    Returns
    -------
    An instance of GroupSpec or ArraySpec that represents the
    structure of the zarr group or array.
    """

    if isinstance(element, zarr.Array):
        return ArraySpec.from_zarr(element)
    else:
        return GroupSpec.from_zarr(element, depth=depth)

to_zarr #

to_zarr(
    spec: ArraySpec,
    store: Store,
    path: str,
    overwrite: bool = False,
) -> zarr.Array

to_zarr(
    spec: GroupSpec,
    store: Store,
    path: str,
    overwrite: bool = False,
) -> zarr.Group

to_zarr(spec, store, path, overwrite=False)

Serialize a GroupSpec or ArraySpec to a zarr group or array at a specific path in a zarr store.

Parameters:

Name	Type	Description	Default
`spec`	`GroupSpec or ArraySpec`	The GroupSpec or ArraySpec that will be serialized to storage.	required
`store`	`instance of zarr.abc.store.Store`	The storage backend that will manifest the group or array.	required
`path`	`str`	The location of the group or array inside the store.	required
`overwrite`	`bool`	Whether to overwrite an existing array or group at the path. If overwrite is False and an array or group already exists at the path, an exception will be raised. Defaults to False.	`False`

Returns:

Type	Description
`A zarr Group or Array that is structurally equivalent to the spec object.`
`This operation will create metadata documents in the store.`

Source code in pydantic_zarr/experimental/v3.py

def to_zarr(
    spec: ArraySpec | GroupSpec,
    store: Store,
    path: str,
    overwrite: bool = False,
) -> zarr.Array | zarr.Group:
    """
    Serialize a GroupSpec or ArraySpec to a zarr group or array at a specific path in
    a zarr store.

    Parameters
    ----------
    spec : GroupSpec or ArraySpec
        The GroupSpec or ArraySpec that will be serialized to storage.
    store : instance of zarr.abc.store.Store
        The storage backend that will manifest the group or array.
    path : str
        The location of the group or array inside the store.
    overwrite : bool
        Whether to overwrite an existing array or group at the path. If overwrite is
        False and an array or group already exists at the path, an exception will be
        raised. Defaults to False.

    Returns
    -------
    A zarr Group or Array that is structurally equivalent to the spec object.
    This operation will create metadata documents in the store.

    """
    return spec.to_zarr(store, path, overwrite=overwrite)

from_flat #

from_flat(data)

Wraps from_flat_group, handling the special case where a Zarr array is defined at the root of a hierarchy and thus is not contained by a Zarr group.

Parameters:

Name	Type	Description	Default
`data`	`dict[str, ArraySpec \| GroupSpec]`	A flat representation of a Zarr hierarchy. This is a `dict` with keys that are strings, and values that are either `GroupSpec` or `ArraySpec` instances.	required

Returns:

Type	Description
`ArraySpec \| GroupSpec`	The `ArraySpec` or `GroupSpec` representation of the input data.

Examples:

>>> from pydantic_zarr.v3 import from_flat, GroupSpec, ArraySpec
>>> import numpy as np
>>> tree = {'': ArraySpec.from_array(np.arange(10))}
>>> from_flat(tree) # special case of a Zarr array at the root of the hierarchy
ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)
>>> tree = {'/foo': ArraySpec.from_array(np.arange(10))}
>>> from_flat(tree) # note that an implicit Group is created
GroupSpec(zarr_format=2, attributes={}, members={'foo': ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)})

Source code in pydantic_zarr/experimental/v3.py

def from_flat(
    data: Mapping[str, ArraySpec | GroupSpec],
) -> ArraySpec | GroupSpec:
    """
    Wraps `from_flat_group`, handling the special case where a Zarr array is defined at the root of
    a hierarchy and thus is not contained by a Zarr group.

    Parameters
    ----------

    data : dict[str, ArraySpec | GroupSpec]
        A flat representation of a Zarr hierarchy. This is a `dict` with keys that are strings,
        and values that are either `GroupSpec` or `ArraySpec` instances.

    Returns
    -------
    ArraySpec | GroupSpec
        The `ArraySpec` or `GroupSpec` representation of the input data.

    Examples
    --------
    >>> from pydantic_zarr.v3 import from_flat, GroupSpec, ArraySpec
    >>> import numpy as np
    >>> tree = {'': ArraySpec.from_array(np.arange(10))}
    >>> from_flat(tree) # special case of a Zarr array at the root of the hierarchy
    ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)
    >>> tree = {'/foo': ArraySpec.from_array(np.arange(10))}
    >>> from_flat(tree) # note that an implicit Group is created
    GroupSpec(zarr_format=2, attributes={}, members={'foo': ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)})
    """

    # minimal check that the keys are valid
    invalid_keys = [key for key in data if key.endswith("/")]
    if len(invalid_keys) > 0:
        msg = f'Invalid keys {invalid_keys} found in data. Keys may not end with the "/"" character'
        raise ValueError(msg)

    if tuple(data.keys()) == ("",) and isinstance(next(iter(data.values())), ArraySpec):
        return next(iter(data.values()))
    else:
        return from_flat_group(data)

from_flat_group #

from_flat_group(data)

Generate a GroupSpec from a flat representation of a hierarchy, i.e. a dict with string keys (paths) and ArraySpec / GroupSpec values (nodes).

Parameters:

Name	Type	Description	Default
`data`	`dict[str, ArraySpec \| BaseGroupSpec]`	A flat representation of a Zarr hierarchy rooted at a Zarr group.	required

Returns:

Type	Description
`GroupSpec`	A `GroupSpec` that represents the hierarchy described by `data`.

Examples:

>>> from pydantic_zarr.v3 import from_flat_group, GroupSpec, ArraySpec
>>> import numpy as np
>>> tree = {'/foo': ArraySpec.from_array(np.arange(10))}
>>> from_flat_group(tree) # note that an implicit Group is created
GroupSpec(zarr_format=2, attributes={}, members={'foo': ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)})

Source code in pydantic_zarr/experimental/v3.py

def from_flat_group(
    data: Mapping[str, ArraySpec | BaseGroupSpec],
) -> GroupSpec:
    """
    Generate a `GroupSpec` from a flat representation of a hierarchy, i.e. a `dict` with
    string keys (paths) and `ArraySpec` / `GroupSpec` values (nodes).

    Parameters
    ----------
    data : dict[str, ArraySpec | BaseGroupSpec]
        A flat representation of a Zarr hierarchy rooted at a Zarr group.

    Returns
    -------
    GroupSpec
        A `GroupSpec` that represents the hierarchy described by `data`.

    Examples
    --------
    >>> from pydantic_zarr.v3 import from_flat_group, GroupSpec, ArraySpec
    >>> import numpy as np
    >>> tree = {'/foo': ArraySpec.from_array(np.arange(10))}
    >>> from_flat_group(tree) # note that an implicit Group is created
    GroupSpec(zarr_format=2, attributes={}, members={'foo': ArraySpec(zarr_format=2, attributes={}, shape=(10,), chunks=(10,), dtype='<i8', fill_value=0, order='C', filters=None, dimension_separator='/', compressor=None)})
    """
    root_name = ""
    sep = "/"
    # arrays that will be members of the returned GroupSpec
    member_arrays: dict[str, ArraySpec] = {}
    # groups, and their members, that will be members of the returned GroupSpec.
    # this dict is populated by recursively applying `from_flat_group` function.
    member_groups: dict[str, GroupSpec] = {}
    # this dict collects the arrayspecs and groupspecs that belong to one of the members of the
    # groupspecs we are constructing. They will later be aggregated in a recursive step that
    # populates member_groups
    submember_by_parent_name: dict[str, dict[str, ArraySpec | BaseGroupSpec]] = {}
    # copy the input to ensure that mutations are contained inside this function
    data_copy = dict(data).copy()
    # Get the root node
    try:
        # The root node is a GroupSpec with the key ""
        root_node = data_copy.pop(root_name)
        if isinstance(root_node, ArraySpec):
            raise ValueError("Got an ArraySpec as the root node. This is invalid.")  # noqa: TRY004
    except KeyError:
        # If a root node was not found, create a default one
        root_node = BaseGroupSpec(attributes={})

    # partition the tree (sans root node) into 2 categories: (arrays, groups + their members).
    for key, value in data_copy.items():
        key_parts = key.split(sep)
        if key_parts[0] != root_name:
            raise ValueError(f'Invalid path: {key} does not start with "{root_name}{sep}".')

        subparent_name = key_parts[1]
        if len(key_parts) == 2:
            # this is an array or group that belongs to the group we are ultimately returning
            if isinstance(value, ArraySpec):
                member_arrays[subparent_name] = value
            elif isinstance(value, BaseGroupSpec):
                if subparent_name not in submember_by_parent_name:
                    submember_by_parent_name[subparent_name] = {}
                # Convert BaseGroupSpec to GroupSpec with empty members if needed
                if not isinstance(value, GroupSpec):
                    value = GroupSpec(attributes=value.attributes, members={})
                submember_by_parent_name[subparent_name][root_name] = value
            else:
                raise ValueError(
                    f"Value at '{key}' is not a v3 ArraySpec or BaseGroupSpec (got {type(value)=})"
                )
        else:
            # these are groups or arrays that belong to one of the member groups
            # not great that we repeat this conditional dict initialization
            if subparent_name not in submember_by_parent_name:
                submember_by_parent_name[subparent_name] = {}
            submember_by_parent_name[subparent_name][sep.join([root_name, *key_parts[2:]])] = value

    # recurse
    for subparent_name, submemb in submember_by_parent_name.items():
        member_groups[subparent_name] = from_flat_group(submemb)

    return GroupSpec(members={**member_groups, **member_arrays}, attributes=root_node.attributes)

auto_codecs #

auto_codecs(data)

Automatically create a tuple of codecs from an arbitrary python object.

Source code in pydantic_zarr/experimental/v3.py

def auto_codecs(data: object) -> tuple[CodecLike, ...]:
    """
    Automatically create a tuple of codecs from an arbitrary python object.
    """
    if hasattr(data, "codecs"):
        # todo: type check
        return tuple(data.codecs)
    return ({"name": "bytes"},)

to_flat #

to_flat(node, root_path='')

Flatten a GroupSpec or ArraySpec. Converts a GroupSpec or ArraySpec and a string, into a dict with string keys and values that are GroupSpec or ArraySpec.

If the input is an ArraySpec, then this function just returns the dict {root_path: node}.

If the input is a GroupSpec, then the resulting dict will contain a copy of the input with a null members attribute under the key root_path, as well as copies of the result of calling flatten_node on each element of node.members, each under a key created by joining root_path with a '/` character to the name of each member, and so on recursively for each sub-member.

Parameters:

Name	Type	Description	Default
`node`	`GroupSpec` \| `ArraySpec`	The node to flatten.	required
`root_path`	`str`	The root path. If `node` is a `GroupSpec`, then the keys in `node.members` will be made relative to `root_path` when used as keys in the result dictionary.	`= ''`

Returns:

Type	Description
`dict[str, ArraySpec \| GroupSpec]`	A flattened representation of the hierarchy.

Examples:

>>> from pydantic_zarr.v3 import to_flat, GroupSpec, BaseGroupSpec
>>> g1 = GroupSpec(members={}, attributes={'foo': 'bar'})
>>> to_flat(g1)
{'': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
>>> to_flat(g1, root_path='baz')
{'baz': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
>>> to_flat(GroupSpec(members={'g1': g1}, attributes={'foo': 'bar'}))
{'/g1': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'}), '': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}

Source code in pydantic_zarr/experimental/v3.py

def to_flat(
    node: ArraySpec | GroupSpec, root_path: str = ""
) -> dict[str, ArraySpec | BaseGroupSpec]:
    """
    Flatten a `GroupSpec` or `ArraySpec`.
    Converts a `GroupSpec` or `ArraySpec` and a string, into a `dict` with string keys and
    values that are `GroupSpec` or `ArraySpec`.

    If the input is an `ArraySpec`, then this function just returns the dict `{root_path: node}`.

    If the input is a `GroupSpec`, then the resulting `dict` will contain a copy of the input
    with a null `members` attribute under the key `root_path`, as well as copies of the result of
    calling `flatten_node` on each element of `node.members`, each under a key created by joining
    `root_path` with a '/` character to the name of each member, and so on recursively for each
    sub-member.

    Parameters
    ----------
    node : `GroupSpec` | `ArraySpec`
        The node to flatten.
    root_path : `str`, default = ''
        The root path. If `node` is a `GroupSpec`, then the keys in `node.members` will be
        made relative to `root_path` when used as keys in the result dictionary.

    Returns
    -------
    dict[str, ArraySpec | GroupSpec]
        A flattened representation of the hierarchy.

    Examples
    --------

    >>> from pydantic_zarr.v3 import to_flat, GroupSpec, BaseGroupSpec
    >>> g1 = GroupSpec(members={}, attributes={'foo': 'bar'})
    >>> to_flat(g1)
    {'': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    >>> to_flat(g1, root_path='baz')
    {'baz': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    >>> to_flat(GroupSpec(members={'g1': g1}, attributes={'foo': 'bar'}))
    {'/g1': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'}), '': BaseGroupSpec(zarr_format=3, attributes={'foo': 'bar'})}
    """
    result = {}
    model_copy: ArraySpec | BaseGroupSpec
    if isinstance(node, ArraySpec):
        model_copy = node.model_copy(deep=True)
    else:
        # Create a BaseGroupSpec for the flattened representation (no members)
        model_copy = BaseGroupSpec(zarr_format=node.zarr_format, attributes=node.attributes)
        for name, value in node.members.items():
            result.update(to_flat(value, f"{root_path}/{name}"))

    result[root_path] = model_copy
    # sort by increasing key length
    return dict(sorted(result.items(), key=lambda v: len(v[0])))

v3

CodecLike module-attribute #

NamedConfig #

AnyNamedConfig #

AllowedExtraField #

NodeSpec #

validate_extra_fields #

ArraySpec #

model_dump #

from_array classmethod #

from_zarr classmethod #

to_zarr #

like #

with_attributes #

with_shape #

with_data_type #

with_chunk_grid #

with_chunk_key_encoding #

with_fill_value #

with_codecs #

with_storage_transformers #

with_dimension_names #

BaseGroupSpec #

with_attributes #

GroupSpec #

with_members #

from_flat classmethod #

to_flat #

from_zarr classmethod #

to_zarr #

like #

parse_dtype_v3 #

from_zarr #

to_zarr #

from_flat #

from_flat_group #

auto_codecs #

to_flat #

CodecLike `module-attribute` #

from_array `classmethod` #

from_zarr `classmethod` #

from_flat `classmethod` #

from_zarr `classmethod` #