elemwise

Apply a function to any number of arguments.

Parameters:

Name	Type	Description	Default
func	Callable	The function to apply. Must support broadcasting.	required
*args	tuple	The arguments to the function. Can be sparse.SparseArray objects or scipy.sparse.spmatrix objects.	()
**kwargs	dict	Any additional arguments to pass to the function.	{}

Returns:

Type	Description
SparseArray	The result of applying the function.

Raises:

Type	Description
ValueError	If the operation would result in a dense matrix, or if the operands don't have broadcastable shapes.

See Also

numpy.ufunc : A similar Numpy construct. Note that any ufunc can be used as the func input to this function.

Notes

Previously, operations with Numpy arrays were sometimes supported. Now, it is necessary to convert Numpy arrays to sparse.COO objects.

Source code in sparse/numba_backend/_umath.py

def elemwise(func, *args, **kwargs):
    """
    Apply a function to any number of arguments.

    Parameters
    ----------
    func : Callable
        The function to apply. Must support broadcasting.
    *args : tuple, optional
        The arguments to the function. Can be [`sparse.SparseArray`][] objects
        or [`scipy.sparse.spmatrix`][] objects.
    **kwargs : dict, optional
        Any additional arguments to pass to the function.

    Returns
    -------
    SparseArray
        The result of applying the function.

    Raises
    ------
    ValueError
        If the operation would result in a dense matrix, or if the operands
        don't have broadcastable shapes.

    See Also
    --------
    [`numpy.ufunc`][] :
        A similar Numpy construct. Note that any `ufunc` can be used
        as the `func` input to this function.

    Notes
    -----
    Previously, operations with Numpy arrays were sometimes supported. Now,
    it is necessary to convert Numpy arrays to [`sparse.COO`][] objects.
    """

    return _Elemwise(func, *args, **kwargs).get_result()