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codecs

zarr.codecs

BloscCname

Bases: Enum

Enum for compression library used by blosc.

Source code in zarr/codecs/blosc.py 
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class BloscCname(Enum):
    """
    Enum for compression library used by blosc.
    """

    lz4 = "lz4"
    lz4hc = "lz4hc"
    blosclz = "blosclz"
    zstd = "zstd"
    snappy = "snappy"
    zlib = "zlib"

BloscCodec dataclass

Bases: BytesBytesCodec

blosc codec

Source code in zarr/codecs/blosc.py 
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@dataclass(frozen=True)
class BloscCodec(BytesBytesCodec):
    """blosc codec"""

    is_fixed_size = False

    typesize: int | None
    cname: BloscCname = BloscCname.zstd
    clevel: int = 5
    shuffle: BloscShuffle | None = BloscShuffle.noshuffle
    blocksize: int = 0

    def __init__(
        self,
        *,
        typesize: int | None = None,
        cname: BloscCname | str = BloscCname.zstd,
        clevel: int = 5,
        shuffle: BloscShuffle | str | None = None,
        blocksize: int = 0,
    ) -> None:
        typesize_parsed = parse_typesize(typesize) if typesize is not None else None
        cname_parsed = parse_enum(cname, BloscCname)
        clevel_parsed = parse_clevel(clevel)
        shuffle_parsed = parse_enum(shuffle, BloscShuffle) if shuffle is not None else None
        blocksize_parsed = parse_blocksize(blocksize)

        object.__setattr__(self, "typesize", typesize_parsed)
        object.__setattr__(self, "cname", cname_parsed)
        object.__setattr__(self, "clevel", clevel_parsed)
        object.__setattr__(self, "shuffle", shuffle_parsed)
        object.__setattr__(self, "blocksize", blocksize_parsed)

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(data, "blosc")
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    def to_dict(self) -> dict[str, JSON]:
        if self.typesize is None:
            raise ValueError("`typesize` needs to be set for serialization.")
        if self.shuffle is None:
            raise ValueError("`shuffle` needs to be set for serialization.")
        return {
            "name": "blosc",
            "configuration": {
                "typesize": self.typesize,
                "cname": self.cname.value,
                "clevel": self.clevel,
                "shuffle": self.shuffle.value,
                "blocksize": self.blocksize,
            },
        }

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        item_size = 1
        if isinstance(array_spec.dtype, HasItemSize):
            item_size = array_spec.dtype.item_size
        new_codec = self
        if new_codec.typesize is None:
            new_codec = replace(new_codec, typesize=item_size)
        if new_codec.shuffle is None:
            new_codec = replace(
                new_codec,
                shuffle=(BloscShuffle.bitshuffle if item_size == 1 else BloscShuffle.shuffle),
            )

        return new_codec

    @cached_property
    def _blosc_codec(self) -> Blosc:
        if self.shuffle is None:
            raise ValueError("`shuffle` needs to be set for decoding and encoding.")
        map_shuffle_str_to_int = {
            BloscShuffle.noshuffle: 0,
            BloscShuffle.shuffle: 1,
            BloscShuffle.bitshuffle: 2,
        }
        config_dict = {
            "cname": self.cname.name,
            "clevel": self.clevel,
            "shuffle": map_shuffle_str_to_int[self.shuffle],
            "blocksize": self.blocksize,
        }
        # See https://github.com/zarr-developers/numcodecs/pull/713
        if Version(numcodecs.__version__) >= Version("0.16.0"):
            config_dict["typesize"] = self.typesize
        return Blosc.from_config(config_dict)

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer:
        return await asyncio.to_thread(
            as_numpy_array_wrapper, self._blosc_codec.decode, chunk_bytes, chunk_spec.prototype
        )

    async def _encode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        # Since blosc only support host memory, we convert the input and output of the encoding
        # between numpy array and buffer
        return await asyncio.to_thread(
            lambda chunk: chunk_spec.prototype.buffer.from_bytes(
                self._blosc_codec.encode(chunk.as_numpy_array())
            ),
            chunk_bytes,
        )

    def compute_encoded_size(self, _input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        raise NotImplementedError

compute_encoded_size 

compute_encoded_size(
    _input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/blosc.py 
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def compute_encoded_size(self, _input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    raise NotImplementedError

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/blosc.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    item_size = 1
    if isinstance(array_spec.dtype, HasItemSize):
        item_size = array_spec.dtype.item_size
    new_codec = self
    if new_codec.typesize is None:
        new_codec = replace(new_codec, typesize=item_size)
    if new_codec.shuffle is None:
        new_codec = replace(
            new_codec,
            shuffle=(BloscShuffle.bitshuffle if item_size == 1 else BloscShuffle.shuffle),
        )

    return new_codec

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/blosc.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(data, "blosc")
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/blosc.py 
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def to_dict(self) -> dict[str, JSON]:
    if self.typesize is None:
        raise ValueError("`typesize` needs to be set for serialization.")
    if self.shuffle is None:
        raise ValueError("`shuffle` needs to be set for serialization.")
    return {
        "name": "blosc",
        "configuration": {
            "typesize": self.typesize,
            "cname": self.cname.value,
            "clevel": self.clevel,
            "shuffle": self.shuffle.value,
            "blocksize": self.blocksize,
        },
    }

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

BloscShuffle

Bases: Enum

Enum for shuffle filter used by blosc.

Source code in zarr/codecs/blosc.py 
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class BloscShuffle(Enum):
    """
    Enum for shuffle filter used by blosc.
    """

    noshuffle = "noshuffle"
    shuffle = "shuffle"
    bitshuffle = "bitshuffle"

    @classmethod
    def from_int(cls, num: int) -> BloscShuffle:
        blosc_shuffle_int_to_str = {
            0: "noshuffle",
            1: "shuffle",
            2: "bitshuffle",
        }
        if num not in blosc_shuffle_int_to_str:
            raise ValueError(f"Value must be between 0 and 2. Got {num}.")
        return BloscShuffle[blosc_shuffle_int_to_str[num]]

BytesCodec dataclass

Bases: ArrayBytesCodec

bytes codec

Source code in zarr/codecs/bytes.py 
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@dataclass(frozen=True)
class BytesCodec(ArrayBytesCodec):
    """bytes codec"""

    is_fixed_size = True

    endian: Endian | None

    def __init__(self, *, endian: Endian | str | None = default_system_endian) -> None:
        endian_parsed = None if endian is None else parse_enum(endian, Endian)

        object.__setattr__(self, "endian", endian_parsed)

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(
            data, "bytes", require_configuration=False
        )
        configuration_parsed = configuration_parsed or {}
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    def to_dict(self) -> dict[str, JSON]:
        if self.endian is None:
            return {"name": "bytes"}
        else:
            return {"name": "bytes", "configuration": {"endian": self.endian.value}}

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        if not isinstance(array_spec.dtype, HasEndianness):
            if self.endian is not None:
                return replace(self, endian=None)
        elif self.endian is None:
            raise ValueError(
                "The `endian` configuration needs to be specified for multi-byte data types."
            )
        return self

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> NDBuffer:
        assert isinstance(chunk_bytes, Buffer)
        # TODO: remove endianness enum in favor of literal union
        endian_str = self.endian.value if self.endian is not None else None
        if isinstance(chunk_spec.dtype, HasEndianness):
            dtype = replace(chunk_spec.dtype, endianness=endian_str).to_native_dtype()  # type: ignore[call-arg]
        else:
            dtype = chunk_spec.dtype.to_native_dtype()
        as_array_like = chunk_bytes.as_array_like()
        if isinstance(as_array_like, NDArrayLike):
            as_nd_array_like = as_array_like
        else:
            as_nd_array_like = np.asanyarray(as_array_like)
        chunk_array = chunk_spec.prototype.nd_buffer.from_ndarray_like(
            as_nd_array_like.view(dtype=dtype)
        )

        # ensure correct chunk shape
        if chunk_array.shape != chunk_spec.shape:
            chunk_array = chunk_array.reshape(
                chunk_spec.shape,
            )
        return chunk_array

    async def _encode_single(
        self,
        chunk_array: NDBuffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        assert isinstance(chunk_array, NDBuffer)
        if (
            chunk_array.dtype.itemsize > 1
            and self.endian is not None
            and self.endian != chunk_array.byteorder
        ):
            # type-ignore is a numpy bug
            # see https://github.com/numpy/numpy/issues/26473
            new_dtype = chunk_array.dtype.newbyteorder(self.endian.name)  # type: ignore[arg-type]
            chunk_array = chunk_array.astype(new_dtype)

        nd_array = chunk_array.as_ndarray_like()
        # Flatten the nd-array (only copy if needed) and reinterpret as bytes
        nd_array = nd_array.ravel().view(dtype="B")
        return chunk_spec.prototype.buffer.from_array_like(nd_array)

    def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        return input_byte_length

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/bytes.py 
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def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    return input_byte_length

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/bytes.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    if not isinstance(array_spec.dtype, HasEndianness):
        if self.endian is not None:
            return replace(self, endian=None)
    elif self.endian is None:
        raise ValueError(
            "The `endian` configuration needs to be specified for multi-byte data types."
        )
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/bytes.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(
        data, "bytes", require_configuration=False
    )
    configuration_parsed = configuration_parsed or {}
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/bytes.py 
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def to_dict(self) -> dict[str, JSON]:
    if self.endian is None:
        return {"name": "bytes"}
    else:
        return {"name": "bytes", "configuration": {"endian": self.endian.value}}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

Crc32cCodec dataclass

Bases: BytesBytesCodec

crc32c codec

Source code in zarr/codecs/crc32c_.py 
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@dataclass(frozen=True)
class Crc32cCodec(BytesBytesCodec):
    """crc32c codec"""

    is_fixed_size = True

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        parse_named_configuration(data, "crc32c", require_configuration=False)
        return cls()

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "crc32c"}

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer:
        data = chunk_bytes.as_numpy_array()
        crc32_bytes = data[-4:]
        inner_bytes = data[:-4]

        # Need to do a manual cast until https://github.com/numpy/numpy/issues/26783 is resolved
        computed_checksum = np.uint32(
            crc32c(cast("typing_extensions.Buffer", inner_bytes))
        ).tobytes()
        stored_checksum = bytes(crc32_bytes)
        if computed_checksum != stored_checksum:
            raise ValueError(
                f"Stored and computed checksum do not match. Stored: {stored_checksum!r}. Computed: {computed_checksum!r}."
            )
        return chunk_spec.prototype.buffer.from_array_like(inner_bytes)

    async def _encode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        data = chunk_bytes.as_numpy_array()
        # Calculate the checksum and "cast" it to a numpy array
        checksum = np.array([crc32c(cast("typing_extensions.Buffer", data))], dtype=np.uint32)
        # Append the checksum (as bytes) to the data
        return chunk_spec.prototype.buffer.from_array_like(np.append(data, checksum.view("B")))

    def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        return input_byte_length + 4

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/crc32c_.py 
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def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    return input_byte_length + 4

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/abc/codec.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    """Fills in codec configuration parameters that can be automatically
    inferred from the array metadata.

    Parameters
    ----------
    array_spec : ArraySpec

    Returns
    -------
    Self
    """
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/crc32c_.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    parse_named_configuration(data, "crc32c", require_configuration=False)
    return cls()

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/crc32c_.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "crc32c"}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

Endian

Bases: Enum

Enum for endian type used by bytes codec.

Source code in zarr/codecs/bytes.py 
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class Endian(Enum):
    """
    Enum for endian type used by bytes codec.
    """

    big = "big"
    little = "little"

GzipCodec dataclass

Bases: BytesBytesCodec

gzip codec

Source code in zarr/codecs/gzip.py 
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@dataclass(frozen=True)
class GzipCodec(BytesBytesCodec):
    """gzip codec"""

    is_fixed_size = False

    level: int = 5

    def __init__(self, *, level: int = 5) -> None:
        level_parsed = parse_gzip_level(level)

        object.__setattr__(self, "level", level_parsed)

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(data, "gzip")
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "gzip", "configuration": {"level": self.level}}

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer:
        return await asyncio.to_thread(
            as_numpy_array_wrapper, GZip(self.level).decode, chunk_bytes, chunk_spec.prototype
        )

    async def _encode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        return await asyncio.to_thread(
            as_numpy_array_wrapper, GZip(self.level).encode, chunk_bytes, chunk_spec.prototype
        )

    def compute_encoded_size(
        self,
        _input_byte_length: int,
        _chunk_spec: ArraySpec,
    ) -> int:
        raise NotImplementedError

compute_encoded_size 

compute_encoded_size(
    _input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/gzip.py 
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def compute_encoded_size(
    self,
    _input_byte_length: int,
    _chunk_spec: ArraySpec,
) -> int:
    raise NotImplementedError

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/abc/codec.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    """Fills in codec configuration parameters that can be automatically
    inferred from the array metadata.

    Parameters
    ----------
    array_spec : ArraySpec

    Returns
    -------
    Self
    """
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/gzip.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(data, "gzip")
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/gzip.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "gzip", "configuration": {"level": self.level}}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

ShardingCodec dataclass

Bases: ArrayBytesCodec, ArrayBytesCodecPartialDecodeMixin, ArrayBytesCodecPartialEncodeMixin

Sharding codec

Source code in zarr/codecs/sharding.py 
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@dataclass(frozen=True)
class ShardingCodec(
    ArrayBytesCodec, ArrayBytesCodecPartialDecodeMixin, ArrayBytesCodecPartialEncodeMixin
):
    """Sharding codec"""

    chunk_shape: tuple[int, ...]
    codecs: tuple[Codec, ...]
    index_codecs: tuple[Codec, ...]
    index_location: ShardingCodecIndexLocation = ShardingCodecIndexLocation.end

    def __init__(
        self,
        *,
        chunk_shape: ShapeLike,
        codecs: Iterable[Codec | dict[str, JSON]] = (BytesCodec(),),
        index_codecs: Iterable[Codec | dict[str, JSON]] = (BytesCodec(), Crc32cCodec()),
        index_location: ShardingCodecIndexLocation | str = ShardingCodecIndexLocation.end,
    ) -> None:
        chunk_shape_parsed = parse_shapelike(chunk_shape)
        codecs_parsed = parse_codecs(codecs)
        index_codecs_parsed = parse_codecs(index_codecs)
        index_location_parsed = parse_index_location(index_location)

        object.__setattr__(self, "chunk_shape", chunk_shape_parsed)
        object.__setattr__(self, "codecs", codecs_parsed)
        object.__setattr__(self, "index_codecs", index_codecs_parsed)
        object.__setattr__(self, "index_location", index_location_parsed)

        # Use instance-local lru_cache to avoid memory leaks

        # numpy void scalars are not hashable, which means an array spec with a fill value that is
        # a numpy void scalar will break the lru_cache. This is commented for now but should be
        # fixed. See https://github.com/zarr-developers/zarr-python/issues/3054
        # object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec))
        object.__setattr__(self, "_get_index_chunk_spec", lru_cache()(self._get_index_chunk_spec))
        object.__setattr__(self, "_get_chunks_per_shard", lru_cache()(self._get_chunks_per_shard))

    # todo: typedict return type
    def __getstate__(self) -> dict[str, Any]:
        return self.to_dict()

    def __setstate__(self, state: dict[str, Any]) -> None:
        config = state["configuration"]
        object.__setattr__(self, "chunk_shape", parse_shapelike(config["chunk_shape"]))
        object.__setattr__(self, "codecs", parse_codecs(config["codecs"]))
        object.__setattr__(self, "index_codecs", parse_codecs(config["index_codecs"]))
        object.__setattr__(self, "index_location", parse_index_location(config["index_location"]))

        # Use instance-local lru_cache to avoid memory leaks
        # object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec))
        object.__setattr__(self, "_get_index_chunk_spec", lru_cache()(self._get_index_chunk_spec))
        object.__setattr__(self, "_get_chunks_per_shard", lru_cache()(self._get_chunks_per_shard))

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(data, "sharding_indexed")
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    @property
    def codec_pipeline(self) -> CodecPipeline:
        return get_pipeline_class().from_codecs(self.codecs)

    def to_dict(self) -> dict[str, JSON]:
        return {
            "name": "sharding_indexed",
            "configuration": {
                "chunk_shape": self.chunk_shape,
                "codecs": tuple(s.to_dict() for s in self.codecs),
                "index_codecs": tuple(s.to_dict() for s in self.index_codecs),
                "index_location": self.index_location.value,
            },
        }

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        shard_spec = self._get_chunk_spec(array_spec)
        evolved_codecs = tuple(c.evolve_from_array_spec(array_spec=shard_spec) for c in self.codecs)
        if evolved_codecs != self.codecs:
            return replace(self, codecs=evolved_codecs)
        return self

    def validate(
        self,
        *,
        shape: tuple[int, ...],
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunk_grid: ChunkGrid,
    ) -> None:
        if len(self.chunk_shape) != len(shape):
            raise ValueError(
                "The shard's `chunk_shape` and array's `shape` need to have the same number of dimensions."
            )
        if not isinstance(chunk_grid, RegularChunkGrid):
            raise TypeError("Sharding is only compatible with regular chunk grids.")
        if not all(
            s % c == 0
            for s, c in zip(
                chunk_grid.chunk_shape,
                self.chunk_shape,
                strict=False,
            )
        ):
            raise ValueError(
                "The array's `chunk_shape` needs to be divisible by the shard's inner `chunk_shape`."
            )

    async def _decode_single(
        self,
        shard_bytes: Buffer,
        shard_spec: ArraySpec,
    ) -> NDBuffer:
        shard_shape = shard_spec.shape
        chunk_shape = self.chunk_shape
        chunks_per_shard = self._get_chunks_per_shard(shard_spec)
        chunk_spec = self._get_chunk_spec(shard_spec)

        indexer = BasicIndexer(
            tuple(slice(0, s) for s in shard_shape),
            shape=shard_shape,
            chunk_grid=RegularChunkGrid(chunk_shape=chunk_shape),
        )

        # setup output array
        out = chunk_spec.prototype.nd_buffer.empty(
            shape=shard_shape,
            dtype=shard_spec.dtype.to_native_dtype(),
            order=shard_spec.order,
        )
        shard_dict = await _ShardReader.from_bytes(shard_bytes, self, chunks_per_shard)

        if shard_dict.index.is_all_empty():
            out.fill(shard_spec.fill_value)
            return out

        # decoding chunks and writing them into the output buffer
        await self.codec_pipeline.read(
            [
                (
                    _ShardingByteGetter(shard_dict, chunk_coords),
                    chunk_spec,
                    chunk_selection,
                    out_selection,
                    is_complete_shard,
                )
                for chunk_coords, chunk_selection, out_selection, is_complete_shard in indexer
            ],
            out,
        )

        return out

    async def _decode_partial_single(
        self,
        byte_getter: ByteGetter,
        selection: SelectorTuple,
        shard_spec: ArraySpec,
    ) -> NDBuffer | None:
        shard_shape = shard_spec.shape
        chunk_shape = self.chunk_shape
        chunks_per_shard = self._get_chunks_per_shard(shard_spec)
        chunk_spec = self._get_chunk_spec(shard_spec)

        indexer = get_indexer(
            selection,
            shape=shard_shape,
            chunk_grid=RegularChunkGrid(chunk_shape=chunk_shape),
        )

        # setup output array
        out = shard_spec.prototype.nd_buffer.empty(
            shape=indexer.shape,
            dtype=shard_spec.dtype.to_native_dtype(),
            order=shard_spec.order,
        )

        indexed_chunks = list(indexer)
        all_chunk_coords = {chunk_coords for chunk_coords, *_ in indexed_chunks}

        # reading bytes of all requested chunks
        shard_dict: ShardMapping = {}
        if self._is_total_shard(all_chunk_coords, chunks_per_shard):
            # read entire shard
            shard_dict_maybe = await self._load_full_shard_maybe(
                byte_getter=byte_getter,
                prototype=chunk_spec.prototype,
                chunks_per_shard=chunks_per_shard,
            )
            if shard_dict_maybe is None:
                return None
            shard_dict = shard_dict_maybe
        else:
            # read some chunks within the shard
            shard_index = await self._load_shard_index_maybe(byte_getter, chunks_per_shard)
            if shard_index is None:
                return None
            shard_dict = {}
            for chunk_coords in all_chunk_coords:
                chunk_byte_slice = shard_index.get_chunk_slice(chunk_coords)
                if chunk_byte_slice:
                    chunk_bytes = await byte_getter.get(
                        prototype=chunk_spec.prototype,
                        byte_range=RangeByteRequest(chunk_byte_slice[0], chunk_byte_slice[1]),
                    )
                    if chunk_bytes:
                        shard_dict[chunk_coords] = chunk_bytes

        # decoding chunks and writing them into the output buffer
        await self.codec_pipeline.read(
            [
                (
                    _ShardingByteGetter(shard_dict, chunk_coords),
                    chunk_spec,
                    chunk_selection,
                    out_selection,
                    is_complete_shard,
                )
                for chunk_coords, chunk_selection, out_selection, is_complete_shard in indexer
            ],
            out,
        )

        if hasattr(indexer, "sel_shape"):
            return out.reshape(indexer.sel_shape)
        else:
            return out

    async def _encode_single(
        self,
        shard_array: NDBuffer,
        shard_spec: ArraySpec,
    ) -> Buffer | None:
        shard_shape = shard_spec.shape
        chunk_shape = self.chunk_shape
        chunks_per_shard = self._get_chunks_per_shard(shard_spec)
        chunk_spec = self._get_chunk_spec(shard_spec)

        indexer = list(
            BasicIndexer(
                tuple(slice(0, s) for s in shard_shape),
                shape=shard_shape,
                chunk_grid=RegularChunkGrid(chunk_shape=chunk_shape),
            )
        )

        shard_builder = _ShardBuilder.create_empty(chunks_per_shard)

        await self.codec_pipeline.write(
            [
                (
                    _ShardingByteSetter(shard_builder, chunk_coords),
                    chunk_spec,
                    chunk_selection,
                    out_selection,
                    is_complete_shard,
                )
                for chunk_coords, chunk_selection, out_selection, is_complete_shard in indexer
            ],
            shard_array,
        )

        return await shard_builder.finalize(self.index_location, self._encode_shard_index)

    async def _encode_partial_single(
        self,
        byte_setter: ByteSetter,
        shard_array: NDBuffer,
        selection: SelectorTuple,
        shard_spec: ArraySpec,
    ) -> None:
        shard_shape = shard_spec.shape
        chunk_shape = self.chunk_shape
        chunks_per_shard = self._get_chunks_per_shard(shard_spec)
        chunk_spec = self._get_chunk_spec(shard_spec)

        shard_dict = _MergingShardBuilder(
            await self._load_full_shard_maybe(
                byte_getter=byte_setter,
                prototype=chunk_spec.prototype,
                chunks_per_shard=chunks_per_shard,
            )
            or _ShardReader.create_empty(chunks_per_shard),
            _ShardBuilder.create_empty(chunks_per_shard),
        )

        indexer = list(
            get_indexer(
                selection, shape=shard_shape, chunk_grid=RegularChunkGrid(chunk_shape=chunk_shape)
            )
        )

        await self.codec_pipeline.write(
            [
                (
                    _ShardingByteSetter(shard_dict, chunk_coords),
                    chunk_spec,
                    chunk_selection,
                    out_selection,
                    is_complete_shard,
                )
                for chunk_coords, chunk_selection, out_selection, is_complete_shard in indexer
            ],
            shard_array,
        )

        if shard_dict.is_empty():
            await byte_setter.delete()
        else:
            await byte_setter.set(
                await shard_dict.finalize(
                    self.index_location,
                    self._encode_shard_index,
                )
            )

    def _is_total_shard(
        self, all_chunk_coords: set[tuple[int, ...]], chunks_per_shard: tuple[int, ...]
    ) -> bool:
        return len(all_chunk_coords) == product(chunks_per_shard) and all(
            chunk_coords in all_chunk_coords for chunk_coords in c_order_iter(chunks_per_shard)
        )

    async def _decode_shard_index(
        self, index_bytes: Buffer, chunks_per_shard: tuple[int, ...]
    ) -> _ShardIndex:
        index_array = next(
            iter(
                await get_pipeline_class()
                .from_codecs(self.index_codecs)
                .decode(
                    [(index_bytes, self._get_index_chunk_spec(chunks_per_shard))],
                )
            )
        )
        assert index_array is not None
        return _ShardIndex(index_array.as_numpy_array())

    async def _encode_shard_index(self, index: _ShardIndex) -> Buffer:
        index_bytes = next(
            iter(
                await get_pipeline_class()
                .from_codecs(self.index_codecs)
                .encode(
                    [
                        (
                            get_ndbuffer_class().from_numpy_array(index.offsets_and_lengths),
                            self._get_index_chunk_spec(index.chunks_per_shard),
                        )
                    ],
                )
            )
        )
        assert index_bytes is not None
        assert isinstance(index_bytes, Buffer)
        return index_bytes

    def _shard_index_size(self, chunks_per_shard: tuple[int, ...]) -> int:
        return (
            get_pipeline_class()
            .from_codecs(self.index_codecs)
            .compute_encoded_size(
                16 * product(chunks_per_shard), self._get_index_chunk_spec(chunks_per_shard)
            )
        )

    def _get_index_chunk_spec(self, chunks_per_shard: tuple[int, ...]) -> ArraySpec:
        return ArraySpec(
            shape=chunks_per_shard + (2,),
            dtype=UInt64(endianness="little"),
            fill_value=MAX_UINT_64,
            config=ArrayConfig(
                order="C", write_empty_chunks=False
            ),  # Note: this is hard-coded for simplicity -- it is not surfaced into user code,
            prototype=default_buffer_prototype(),
        )

    def _get_chunk_spec(self, shard_spec: ArraySpec) -> ArraySpec:
        return ArraySpec(
            shape=self.chunk_shape,
            dtype=shard_spec.dtype,
            fill_value=shard_spec.fill_value,
            config=shard_spec.config,
            prototype=shard_spec.prototype,
        )

    def _get_chunks_per_shard(self, shard_spec: ArraySpec) -> tuple[int, ...]:
        return tuple(
            s // c
            for s, c in zip(
                shard_spec.shape,
                self.chunk_shape,
                strict=False,
            )
        )

    async def _load_shard_index_maybe(
        self, byte_getter: ByteGetter, chunks_per_shard: tuple[int, ...]
    ) -> _ShardIndex | None:
        shard_index_size = self._shard_index_size(chunks_per_shard)
        if self.index_location == ShardingCodecIndexLocation.start:
            index_bytes = await byte_getter.get(
                prototype=numpy_buffer_prototype(),
                byte_range=RangeByteRequest(0, shard_index_size),
            )
        else:
            index_bytes = await byte_getter.get(
                prototype=numpy_buffer_prototype(), byte_range=SuffixByteRequest(shard_index_size)
            )
        if index_bytes is not None:
            return await self._decode_shard_index(index_bytes, chunks_per_shard)
        return None

    async def _load_shard_index(
        self, byte_getter: ByteGetter, chunks_per_shard: tuple[int, ...]
    ) -> _ShardIndex:
        return (
            await self._load_shard_index_maybe(byte_getter, chunks_per_shard)
        ) or _ShardIndex.create_empty(chunks_per_shard)

    async def _load_full_shard_maybe(
        self, byte_getter: ByteGetter, prototype: BufferPrototype, chunks_per_shard: tuple[int, ...]
    ) -> _ShardReader | None:
        shard_bytes = await byte_getter.get(prototype=prototype)

        return (
            await _ShardReader.from_bytes(shard_bytes, self, chunks_per_shard)
            if shard_bytes
            else None
        )

    def compute_encoded_size(self, input_byte_length: int, shard_spec: ArraySpec) -> int:
        chunks_per_shard = self._get_chunks_per_shard(shard_spec)
        return input_byte_length + self._shard_index_size(chunks_per_shard)

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, shard_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/sharding.py 
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def compute_encoded_size(self, input_byte_length: int, shard_spec: ArraySpec) -> int:
    chunks_per_shard = self._get_chunks_per_shard(shard_spec)
    return input_byte_length + self._shard_index_size(chunks_per_shard)

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

decode_partial async 

decode_partial(
    batch_info: Iterable[
        tuple[ByteGetter, SelectorTuple, ArraySpec]
    ],
) -> Iterable[NDBuffer | None]

Partially decodes a batch of chunks. This method determines parts of a chunk from the slice selection, fetches these parts from the store (via ByteGetter) and decodes them.

Parameters:

  • batch_info (Iterable[tuple[ByteGetter, SelectorTuple, ArraySpec]]) –

    Ordered set of information about slices of encoded chunks. The slice selection determines which parts of the chunk will be fetched. The ByteGetter is used to fetch the necessary bytes. The chunk spec contains information about the construction of an array from the bytes.

Returns:

Source code in zarr/abc/codec.py 
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async def decode_partial(
    self,
    batch_info: Iterable[tuple[ByteGetter, SelectorTuple, ArraySpec]],
) -> Iterable[NDBuffer | None]:
    """Partially decodes a batch of chunks.
    This method determines parts of a chunk from the slice selection,
    fetches these parts from the store (via ByteGetter) and decodes them.

    Parameters
    ----------
    batch_info : Iterable[tuple[ByteGetter, SelectorTuple, ArraySpec]]
        Ordered set of information about slices of encoded chunks.
        The slice selection determines which parts of the chunk will be fetched.
        The ByteGetter is used to fetch the necessary bytes.
        The chunk spec contains information about the construction of an array from the bytes.

    Returns
    -------
    Iterable[NDBuffer | None]
    """
    return await concurrent_map(
        list(batch_info),
        self._decode_partial_single,
        config.get("async.concurrency"),
    )

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

encode_partial async 

encode_partial(
    batch_info: Iterable[
        tuple[
            ByteSetter, NDBuffer, SelectorTuple, ArraySpec
        ]
    ],
) -> None

Partially encodes a batch of chunks. This method determines parts of a chunk from the slice selection, encodes them and writes these parts to the store (via ByteSetter). If merging with existing chunk data in the store is necessary, this method will read from the store first and perform the merge.

Parameters:

  • batch_info (Iterable[tuple[ByteSetter, NDBuffer, SelectorTuple, ArraySpec]]) –

    Ordered set of information about slices of to-be-encoded chunks. The slice selection determines which parts of the chunk will be encoded. The ByteSetter is used to write the necessary bytes and fetch bytes for existing chunk data. The chunk spec contains information about the chunk.

Source code in zarr/abc/codec.py 
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async def encode_partial(
    self,
    batch_info: Iterable[tuple[ByteSetter, NDBuffer, SelectorTuple, ArraySpec]],
) -> None:
    """Partially encodes a batch of chunks.
    This method determines parts of a chunk from the slice selection, encodes them and
    writes these parts to the store (via ByteSetter).
    If merging with existing chunk data in the store is necessary, this method will
    read from the store first and perform the merge.

    Parameters
    ----------
    batch_info : Iterable[tuple[ByteSetter, NDBuffer, SelectorTuple, ArraySpec]]
        Ordered set of information about slices of to-be-encoded chunks.
        The slice selection determines which parts of the chunk will be encoded.
        The ByteSetter is used to write the necessary bytes and fetch bytes for existing chunk data.
        The chunk spec contains information about the chunk.
    """
    await concurrent_map(
        list(batch_info),
        self._encode_partial_single,
        config.get("async.concurrency"),
    )

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/sharding.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    shard_spec = self._get_chunk_spec(array_spec)
    evolved_codecs = tuple(c.evolve_from_array_spec(array_spec=shard_spec) for c in self.codecs)
    if evolved_codecs != self.codecs:
        return replace(self, codecs=evolved_codecs)
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/sharding.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(data, "sharding_indexed")
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/sharding.py 
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def to_dict(self) -> dict[str, JSON]:
    return {
        "name": "sharding_indexed",
        "configuration": {
            "chunk_shape": self.chunk_shape,
            "codecs": tuple(s.to_dict() for s in self.codecs),
            "index_codecs": tuple(s.to_dict() for s in self.index_codecs),
            "index_location": self.index_location.value,
        },
    }

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/codecs/sharding.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    if len(self.chunk_shape) != len(shape):
        raise ValueError(
            "The shard's `chunk_shape` and array's `shape` need to have the same number of dimensions."
        )
    if not isinstance(chunk_grid, RegularChunkGrid):
        raise TypeError("Sharding is only compatible with regular chunk grids.")
    if not all(
        s % c == 0
        for s, c in zip(
            chunk_grid.chunk_shape,
            self.chunk_shape,
            strict=False,
        )
    ):
        raise ValueError(
            "The array's `chunk_shape` needs to be divisible by the shard's inner `chunk_shape`."
        )

ShardingCodecIndexLocation

Bases: Enum

Enum for index location used by the sharding codec.

Source code in zarr/codecs/sharding.py 
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class ShardingCodecIndexLocation(Enum):
    """
    Enum for index location used by the sharding codec.
    """

    start = "start"
    end = "end"

TransposeCodec dataclass

Bases: ArrayArrayCodec

Transpose codec

Source code in zarr/codecs/transpose.py 
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@dataclass(frozen=True)
class TransposeCodec(ArrayArrayCodec):
    """Transpose codec"""

    is_fixed_size = True

    order: tuple[int, ...]

    def __init__(self, *, order: Iterable[int]) -> None:
        order_parsed = parse_transpose_order(order)

        object.__setattr__(self, "order", order_parsed)

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(data, "transpose")
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "transpose", "configuration": {"order": tuple(self.order)}}

    def validate(
        self,
        shape: tuple[int, ...],
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunk_grid: ChunkGrid,
    ) -> None:
        if len(self.order) != len(shape):
            raise ValueError(
                f"The `order` tuple must have as many entries as there are dimensions in the array. Got {self.order}."
            )
        if len(self.order) != len(set(self.order)):
            raise ValueError(
                f"There must not be duplicates in the `order` tuple. Got {self.order}."
            )
        if not all(0 <= x < len(shape) for x in self.order):
            raise ValueError(
                f"All entries in the `order` tuple must be between 0 and the number of dimensions in the array. Got {self.order}."
            )

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        ndim = array_spec.ndim
        if len(self.order) != ndim:
            raise ValueError(
                f"The `order` tuple must have as many entries as there are dimensions in the array. Got {self.order}."
            )
        if len(self.order) != len(set(self.order)):
            raise ValueError(
                f"There must not be duplicates in the `order` tuple. Got {self.order}."
            )
        if not all(0 <= x < ndim for x in self.order):
            raise ValueError(
                f"All entries in the `order` tuple must be between 0 and the number of dimensions in the array. Got {self.order}."
            )
        order = tuple(self.order)

        if order != self.order:
            return replace(self, order=order)
        return self

    def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
        return ArraySpec(
            shape=tuple(chunk_spec.shape[self.order[i]] for i in range(chunk_spec.ndim)),
            dtype=chunk_spec.dtype,
            fill_value=chunk_spec.fill_value,
            config=chunk_spec.config,
            prototype=chunk_spec.prototype,
        )

    async def _decode_single(
        self,
        chunk_array: NDBuffer,
        chunk_spec: ArraySpec,
    ) -> NDBuffer:
        inverse_order = np.argsort(self.order)
        return chunk_array.transpose(inverse_order)

    async def _encode_single(
        self,
        chunk_array: NDBuffer,
        _chunk_spec: ArraySpec,
    ) -> NDBuffer | None:
        return chunk_array.transpose(self.order)

    def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        return input_byte_length

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/transpose.py 
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def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    return input_byte_length

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/transpose.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    ndim = array_spec.ndim
    if len(self.order) != ndim:
        raise ValueError(
            f"The `order` tuple must have as many entries as there are dimensions in the array. Got {self.order}."
        )
    if len(self.order) != len(set(self.order)):
        raise ValueError(
            f"There must not be duplicates in the `order` tuple. Got {self.order}."
        )
    if not all(0 <= x < ndim for x in self.order):
        raise ValueError(
            f"All entries in the `order` tuple must be between 0 and the number of dimensions in the array. Got {self.order}."
        )
    order = tuple(self.order)

    if order != self.order:
        return replace(self, order=order)
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/transpose.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(data, "transpose")
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/codecs/transpose.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    return ArraySpec(
        shape=tuple(chunk_spec.shape[self.order[i]] for i in range(chunk_spec.ndim)),
        dtype=chunk_spec.dtype,
        fill_value=chunk_spec.fill_value,
        config=chunk_spec.config,
        prototype=chunk_spec.prototype,
    )

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/transpose.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "transpose", "configuration": {"order": tuple(self.order)}}

validate 

validate(
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/codecs/transpose.py 
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def validate(
    self,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    if len(self.order) != len(shape):
        raise ValueError(
            f"The `order` tuple must have as many entries as there are dimensions in the array. Got {self.order}."
        )
    if len(self.order) != len(set(self.order)):
        raise ValueError(
            f"There must not be duplicates in the `order` tuple. Got {self.order}."
        )
    if not all(0 <= x < len(shape) for x in self.order):
        raise ValueError(
            f"All entries in the `order` tuple must be between 0 and the number of dimensions in the array. Got {self.order}."
        )

VLenBytesCodec dataclass

Bases: ArrayBytesCodec

Source code in zarr/codecs/vlen_utf8.py 
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@dataclass(frozen=True)
class VLenBytesCodec(ArrayBytesCodec):
    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(
            data, "vlen-bytes", require_configuration=False
        )
        configuration_parsed = configuration_parsed or {}
        return cls(**configuration_parsed)

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "vlen-bytes", "configuration": {}}

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        return self

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> NDBuffer:
        assert isinstance(chunk_bytes, Buffer)

        raw_bytes = chunk_bytes.as_array_like()
        decoded = _vlen_bytes_codec.decode(raw_bytes)
        assert decoded.dtype == np.object_
        decoded.shape = chunk_spec.shape
        return chunk_spec.prototype.nd_buffer.from_numpy_array(decoded)

    async def _encode_single(
        self,
        chunk_array: NDBuffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        assert isinstance(chunk_array, NDBuffer)
        return chunk_spec.prototype.buffer.from_bytes(
            _vlen_bytes_codec.encode(chunk_array.as_numpy_array())
        )

    def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        # what is input_byte_length for an object dtype?
        raise NotImplementedError("compute_encoded_size is not implemented for VLen codecs")

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/vlen_utf8.py 
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def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    # what is input_byte_length for an object dtype?
    raise NotImplementedError("compute_encoded_size is not implemented for VLen codecs")

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/vlen_utf8.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/vlen_utf8.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(
        data, "vlen-bytes", require_configuration=False
    )
    configuration_parsed = configuration_parsed or {}
    return cls(**configuration_parsed)

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/vlen_utf8.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "vlen-bytes", "configuration": {}}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

VLenUTF8Codec dataclass

Bases: ArrayBytesCodec

Variable-length UTF8 codec

Source code in zarr/codecs/vlen_utf8.py 
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@dataclass(frozen=True)
class VLenUTF8Codec(ArrayBytesCodec):
    """Variable-length UTF8 codec"""

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(
            data, "vlen-utf8", require_configuration=False
        )
        configuration_parsed = configuration_parsed or {}
        return cls(**configuration_parsed)

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "vlen-utf8", "configuration": {}}

    def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
        return self

    # TODO: expand the tests for this function
    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> NDBuffer:
        assert isinstance(chunk_bytes, Buffer)

        raw_bytes = chunk_bytes.as_array_like()
        decoded = _vlen_utf8_codec.decode(raw_bytes)
        assert decoded.dtype == np.object_
        decoded.shape = chunk_spec.shape
        as_string_dtype = decoded.astype(chunk_spec.dtype.to_native_dtype(), copy=False)
        return chunk_spec.prototype.nd_buffer.from_numpy_array(as_string_dtype)

    async def _encode_single(
        self,
        chunk_array: NDBuffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        assert isinstance(chunk_array, NDBuffer)
        return chunk_spec.prototype.buffer.from_bytes(
            _vlen_utf8_codec.encode(chunk_array.as_numpy_array())
        )

    def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        # what is input_byte_length for an object dtype?
        raise NotImplementedError("compute_encoded_size is not implemented for VLen codecs")

compute_encoded_size 

compute_encoded_size(
    input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/vlen_utf8.py 
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def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    # what is input_byte_length for an object dtype?
    raise NotImplementedError("compute_encoded_size is not implemented for VLen codecs")

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/vlen_utf8.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/vlen_utf8.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(
        data, "vlen-utf8", require_configuration=False
    )
    configuration_parsed = configuration_parsed or {}
    return cls(**configuration_parsed)

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/vlen_utf8.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "vlen-utf8", "configuration": {}}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """

ZstdCodec dataclass

Bases: BytesBytesCodec

zstd codec

Source code in zarr/codecs/zstd.py 
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@dataclass(frozen=True)
class ZstdCodec(BytesBytesCodec):
    """zstd codec"""

    is_fixed_size = True

    level: int = 0
    checksum: bool = False

    def __init__(self, *, level: int = 0, checksum: bool = False) -> None:
        # numcodecs 0.13.0 introduces the checksum attribute for the zstd codec
        _numcodecs_version = Version(numcodecs.__version__)
        if _numcodecs_version < Version("0.13.0"):
            raise RuntimeError(
                "numcodecs version >= 0.13.0 is required to use the zstd codec. "
                f"Version {_numcodecs_version} is currently installed."
            )

        level_parsed = parse_zstd_level(level)
        checksum_parsed = parse_checksum(checksum)

        object.__setattr__(self, "level", level_parsed)
        object.__setattr__(self, "checksum", checksum_parsed)

    @classmethod
    def from_dict(cls, data: dict[str, JSON]) -> Self:
        _, configuration_parsed = parse_named_configuration(data, "zstd")
        return cls(**configuration_parsed)  # type: ignore[arg-type]

    def to_dict(self) -> dict[str, JSON]:
        return {"name": "zstd", "configuration": {"level": self.level, "checksum": self.checksum}}

    @cached_property
    def _zstd_codec(self) -> Zstd:
        config_dict = {"level": self.level, "checksum": self.checksum}
        return Zstd.from_config(config_dict)

    async def _decode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer:
        return await asyncio.to_thread(
            as_numpy_array_wrapper, self._zstd_codec.decode, chunk_bytes, chunk_spec.prototype
        )

    async def _encode_single(
        self,
        chunk_bytes: Buffer,
        chunk_spec: ArraySpec,
    ) -> Buffer | None:
        return await asyncio.to_thread(
            as_numpy_array_wrapper, self._zstd_codec.encode, chunk_bytes, chunk_spec.prototype
        )

    def compute_encoded_size(self, _input_byte_length: int, _chunk_spec: ArraySpec) -> int:
        raise NotImplementedError

compute_encoded_size 

compute_encoded_size(
    _input_byte_length: int, _chunk_spec: ArraySpec
) -> int

Given an input byte length, this method returns the output byte length. Raises a NotImplementedError for codecs with variable-sized outputs (e.g. compressors).

Parameters:

  • input_byte_length (int) –
  • chunk_spec (ArraySpec) –

Returns:

Source code in zarr/codecs/zstd.py 
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def compute_encoded_size(self, _input_byte_length: int, _chunk_spec: ArraySpec) -> int:
    raise NotImplementedError

decode async 

decode(
    chunks_and_specs: Iterable[
        tuple[CodecOutput | None, ArraySpec]
    ],
) -> Iterable[CodecInput | None]

Decodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecOutput | None, ArraySpec]]) –

    Ordered set of encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def decode(
    self,
    chunks_and_specs: Iterable[tuple[CodecOutput | None, ArraySpec]],
) -> Iterable[CodecInput | None]:
    """Decodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecOutput | None, ArraySpec]]
        Ordered set of encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecInput | None]
    """
    return await _batching_helper(self._decode_single, chunks_and_specs)

encode async 

encode(
    chunks_and_specs: Iterable[
        tuple[CodecInput | None, ArraySpec]
    ],
) -> Iterable[CodecOutput | None]

Encodes a batch of chunks. Chunks can be None in which case they are ignored by the codec.

Parameters:

  • chunks_and_specs (Iterable[tuple[CodecInput | None, ArraySpec]]) –

    Ordered set of to-be-encoded chunks with their accompanying chunk spec.

Returns:

Source code in zarr/abc/codec.py 
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async def encode(
    self,
    chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]],
) -> Iterable[CodecOutput | None]:
    """Encodes a batch of chunks.
    Chunks can be None in which case they are ignored by the codec.

    Parameters
    ----------
    chunks_and_specs : Iterable[tuple[CodecInput | None, ArraySpec]]
        Ordered set of to-be-encoded chunks with their accompanying chunk spec.

    Returns
    -------
    Iterable[CodecOutput | None]
    """
    return await _batching_helper(self._encode_single, chunks_and_specs)

evolve_from_array_spec 

evolve_from_array_spec(array_spec: ArraySpec) -> Self

Fills in codec configuration parameters that can be automatically inferred from the array metadata.

Parameters:

  • array_spec (ArraySpec) –

Returns:

Source code in zarr/abc/codec.py 
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def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self:
    """Fills in codec configuration parameters that can be automatically
    inferred from the array metadata.

    Parameters
    ----------
    array_spec : ArraySpec

    Returns
    -------
    Self
    """
    return self

from_dict classmethod 

from_dict(data: dict[str, JSON]) -> Self

Create an instance of the model from a dictionary

Source code in zarr/codecs/zstd.py 
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@classmethod
def from_dict(cls, data: dict[str, JSON]) -> Self:
    _, configuration_parsed = parse_named_configuration(data, "zstd")
    return cls(**configuration_parsed)  # type: ignore[arg-type]

resolve_metadata 

resolve_metadata(chunk_spec: ArraySpec) -> ArraySpec

Computed the spec of the chunk after it has been encoded by the codec. This is important for codecs that change the shape, data type or fill value of a chunk. The spec will then be used for subsequent codecs in the pipeline.

Parameters:

  • chunk_spec (ArraySpec) –

Returns:

  • ArraySpec –
Source code in zarr/abc/codec.py 
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def resolve_metadata(self, chunk_spec: ArraySpec) -> ArraySpec:
    """Computed the spec of the chunk after it has been encoded by the codec.
    This is important for codecs that change the shape, data type or fill value of a chunk.
    The spec will then be used for subsequent codecs in the pipeline.

    Parameters
    ----------
    chunk_spec : ArraySpec

    Returns
    -------
    ArraySpec
    """
    return chunk_spec

to_dict 

to_dict() -> dict[str, JSON]

Recursively serialize this model to a dictionary. This method inspects the fields of self and calls x.to_dict() for any fields that are instances of Metadata. Sequences of Metadata are similarly recursed into, and the output of that recursion is collected in a list.

Source code in zarr/codecs/zstd.py 
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def to_dict(self) -> dict[str, JSON]:
    return {"name": "zstd", "configuration": {"level": self.level, "checksum": self.checksum}}

validate 

validate(
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None

Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible.

Parameters:

  • shape (tuple[int, ...]) –

    The array shape

  • dtype (dtype[Any]) –

    The array data type

  • chunk_grid (ChunkGrid) –

    The array chunk grid

Source code in zarr/abc/codec.py 
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def validate(
    self,
    *,
    shape: tuple[int, ...],
    dtype: ZDType[TBaseDType, TBaseScalar],
    chunk_grid: ChunkGrid,
) -> None:
    """Validates that the codec configuration is compatible with the array metadata.
    Raises errors when the codec configuration is not compatible.

    Parameters
    ----------
    shape : tuple[int, ...]
        The array shape
    dtype : np.dtype[Any]
        The array data type
    chunk_grid : ChunkGrid
        The array chunk grid
    """