Function meta_from_array

dask/array/utils.py:27–121

Normalize an array to appropriate meta object Parameters ---------- x: array-like, callable Either an object that looks sufficiently like a Numpy array, or a callable that accepts shape and dtype keywords ndim: int Number of dimensions of the array dtype:

(x, ndim=None, dtype=None)

def meta_from_array(x, ndim=None, dtype=None):
    """Normalize an array to appropriate meta object

    Parameters
    ----------
    x: array-like, callable
        Either an object that looks sufficiently like a Numpy array,
        or a callable that accepts shape and dtype keywords
    ndim: int
        Number of dimensions of the array
    dtype: Numpy dtype
        A valid input for ``np.dtype``

    Returns
    -------
    array-like with zero elements of the correct dtype
    """
    # If using x._meta, x must be a Dask Array, some libraries (e.g. zarr)
    # implement a _meta attribute that are incompatible with Dask Array._meta
    if hasattr(x, "_meta") and is_dask_collection(x) and is_arraylike(x):
        x = x._meta

    if dtype is None and x is None:
        raise ValueError("You must specify the meta or dtype of the array")

    if np.isscalar(x):
        x = np.array(x)

    if x is None:
        x = np.ndarray
    elif dtype is None and hasattr(x, "dtype"):
        dtype = x.dtype

    if isinstance(x, type):
        x = x(shape=(0,) * (ndim or 0), dtype=dtype)

    if isinstance(x, (list, tuple)):
        ndims = [
            (
                0
                if isinstance(a, numbers.Number)
                else a.ndim if hasattr(a, "ndim") else len(a)
            )
            for a in x
        ]
        a = [a if nd == 0 else meta_from_array(a, nd) for a, nd in zip(x, ndims)]
        return a if isinstance(x, list) else tuple(x)

    if (
        not hasattr(x, "shape")
        or not hasattr(x, "dtype")
        or not isinstance(x.shape, tuple)
    ):
        return x

    if ndim is None:
        ndim = x.ndim