Class Array

dask/array/core.py:1300–3030

Parallel Dask Array A parallel nd-array comprised of many numpy arrays arranged in a grid. This constructor is for advanced uses only. For normal use see the :func:`dask.array.from_array` function. Parameters ---------- dask : dict Task dependency graph name :

class Array(DaskMethodsMixin):
    """Parallel Dask Array

    A parallel nd-array comprised of many numpy arrays arranged in a grid.

    This constructor is for advanced uses only.  For normal use see the
    :func:`dask.array.from_array` function.

    Parameters
    ----------
    dask : dict
        Task dependency graph
    name : string
        Name of array in dask
    chunks: iterable of tuples
        block sizes along each dimension
    dtype : str or dtype
        Typecode or data-type for the new Dask Array
    meta : empty ndarray
        empty ndarray created with same NumPy backend, ndim and dtype as the
        Dask Array being created (overrides dtype)
    shape : tuple of ints
        Shape of the entire array

    See Also
    --------
    dask.array.from_array
    """

    __slots__ = "dask", "__name", "_cached_keys", "__chunks", "_meta", "__dict__"

    def __new__(cls, dask, name, chunks, dtype=None, meta=None, shape=None):
        self = super().__new__(cls)
        assert isinstance(dask, Mapping)
        if not isinstance(dask, HighLevelGraph):
            dask = HighLevelGraph.from_collections(name, dask, dependencies=())
        self.dask = dask
        self._name = str(name)
        meta = meta_from_array(meta, dtype=dtype)

        if (
            isinstance(chunks, str)
            or isinstance(chunks, tuple)
            and chunks
            and any(isinstance(c, str) for c in chunks)
        ):
            dt = meta.dtype
        else:
            dt = None
        self._chunks = normalize_chunks(chunks, shape, dtype=dt)
        if self.chunks is None:
            raise ValueError(CHUNKS_NONE_ERROR_MESSAGE)
        self._meta = meta_from_array(meta, ndim=self.ndim, dtype=dtype)

        for plugin in config.get("array_plugins", ()):
            result = plugin(self)
            if result is not None:
                self = result