Function asanyarray

dask/array/core.py:4936–5016

Convert the input to a dask array. Subclasses of ``np.ndarray`` will be passed through as chunks unchanged. Parameters ---------- a : array-like Input data, in any form that can be converted to a dask array. This includes lists, lists of tuples, tuples, tuples of tu

(a, dtype=None, order=None, *, like=None, inline_array=False)

def asanyarray(a, dtype=None, order=None, *, like=None, inline_array=False):
    """Convert the input to a dask array.

    Subclasses of ``np.ndarray`` will be passed through as chunks unchanged.

    Parameters
    ----------
    a : array-like
        Input data, in any form that can be converted to a dask array. This
        includes lists, lists of tuples, tuples, tuples of tuples, tuples of
        lists and ndarrays.
    dtype : data-type, optional
        By default, the data-type is inferred from the input data.
    order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
        Memory layout. ‘A’ and ‘K’ depend on the order of input array a.
        ‘C’ row-major (C-style), ‘F’ column-major (Fortran-style) memory
        representation. ‘A’ (any) means ‘F’ if a is Fortran contiguous, ‘C’
        otherwise ‘K’ (keep) preserve input order. Defaults to ‘C’.
    like: array-like
        Reference object to allow the creation of Dask arrays with chunks
        that are not NumPy arrays. If an array-like passed in as ``like``
        supports the ``__array_function__`` protocol, the chunk type of the
        resulting array will be defined by it. In this case, it ensures the
        creation of a Dask array compatible with that passed in via this
        argument. If ``like`` is a Dask array, the chunk type of the
        resulting array will be defined by the chunk type of ``like``.
        Requires NumPy 1.20.0 or higher.
    inline_array:
        Whether to inline the array in the resulting dask graph. For more information,
        see the documentation for ``dask.array.from_array()``.

    Returns
    -------
    out : dask array
        Dask array interpretation of a.

    Examples
    --------
    >>> import dask.array as da
    >>> import numpy as np
    >>> x = np.arange(3)
    >>> da.asanyarray(x)
    dask.array<array, shape=(3,), dtype=int64, chunksize=(3,), chunktype=numpy.ndarray>

    >>> y = [[1, 2, 3], [4, 5, 6]]
    >>> da.asanyarray(y)
    dask.array<array, shape=(2, 3), dtype=int64, chunksize=(2, 3), chunktype=numpy.ndarray>

    .. warning::
        `order` is ignored if `a` is an `Array`, has the attribute ``to_dask_array``,
        or is a list or tuple of `Array`&#x27;s.
    """
    if like is None:
        if isinstance(a, Array):
            return _as_dtype(a, dtype)
        elif hasattr(a, "to_dask_array"):
            return _as_dtype(a.to_dask_array(), dtype)
        elif type(a).__module__.split(".")[0] == "xarray" and hasattr(a, "data"):