asarray

Convert the input to a sparse array.

Parameters:

Name	Type	Description	Default
obj	array_like	Object to be converted to an array.	required
dtype	dtype	Output array data type.	None
format	str	Output array sparse format.	'coo'
device	str	Device on which to place the created array.	None
copy	bool_	Boolean indicating whether or not to copy the input.	False

Returns:

Name	Type	Description
out	Union[SparseArray, ndarray]	Sparse or 0-D array containing the data from obj.

Examples:

>>> x = np.eye(8, dtype="i8")
>>> sparse.asarray(x, format="COO")
<COO: shape=(8, 8), dtype=int64, nnz=8, fill_value=0>

Source code in sparse/numba_backend/_common.py

@_check_device
def asarray(obj, /, *, dtype=None, format="coo", copy=False, device=None):
    """
    Convert the input to a sparse array.

    Parameters
    ----------
    obj : array_like
        Object to be converted to an array.
    dtype : dtype, optional
        Output array data type.
    format : str, optional
        Output array sparse format.
    device : str, optional
        Device on which to place the created array.
    copy : bool, optional
        Boolean indicating whether or not to copy the input.

    Returns
    -------
    out : Union[SparseArray, numpy.ndarray]
        Sparse or 0-D array containing the data from `obj`.

    Examples
    --------
    >>> x = np.eye(8, dtype="i8")
    >>> sparse.asarray(x, format="COO")
    <COO: shape=(8, 8), dtype=int64, nnz=8, fill_value=0>
    """

    if format not in {"coo", "dok", "gcxs", "csc", "csr"}:
        raise ValueError(f"{format} format not supported.")

    from ._compressed import CSC, CSR, GCXS
    from ._coo import COO
    from ._dok import DOK

    format_dict = {"coo": COO, "dok": DOK, "gcxs": GCXS, "csc": CSC, "csr": CSR}

    if isinstance(obj, COO | DOK | GCXS | CSC | CSR):
        return obj.asformat(format)

    if _is_scipy_sparse_obj(obj):
        sparse_obj = format_dict[format].from_scipy_sparse(obj)
        if dtype is None:
            dtype = sparse_obj.dtype
        return sparse_obj.astype(dtype=dtype, copy=copy)

    if np.isscalar(obj) or isinstance(obj, np.ndarray | Iterable):
        sparse_obj = format_dict[format].from_numpy(np.asarray(obj))
        if dtype is None:
            dtype = sparse_obj.dtype
        return sparse_obj.astype(dtype=dtype, copy=copy)

    raise ValueError(f"{type(obj)} not supported.")