reshape

Reshapes an array without changing its data.

Parameters:

Name	Type	Description	Default
x		input array to reshape.	required
shape		a new shape compatible with the original shape. One shape dimension is allowed to be -1. When a shape dimension is -1, the corresponding output array shape dimension must be inferred from the length of the array and the remaining dimensions.	required
copy		whether or not to copy the input array. If True, the function always copies. If False, the function must never copies. If None, the function avoids copying, if possible. Default: None.	None

Returns:

Name	Type	Description
out	array	an output array having the same data type and elements as x.

Raises:

Type	Description
ValueError	If copy=False and a copy would be necessary, a ValueError will be raised.

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.reshape(a, shape=(1, 4))
>>> o.todense()
array([[0, 1, 2, 0]])

Source code in sparse/numba_backend/_common.py

def reshape(x, /, shape, *, copy=None):
    """
    Reshapes an array without changing its data.

    Parameters
    ----------
    x: array
        input array to reshape.
    shape: Tuple[int, ...]
        a new shape compatible with the original shape. One shape dimension is allowed to be ``-1``.
        When a shape dimension is ``-1``, the corresponding output array shape dimension must be inferred
        from the length of the array and the remaining dimensions.
    copy: Optional[bool]
        whether or not to copy the input array.
        If ``True``, the function always copies.
        If ``False``, the function must never copies.
        If ``None``, the function avoids copying, if possible.
        Default: ``None``.

    Returns
    -------
    out: array
        an output array having the same data type and elements as ``x``.

    Raises
    ------
    ValueError
        If ``copy=False`` and a copy would be necessary, a ``ValueError``
        will be raised.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.reshape(a, shape=(1, 4))
    >>> o.todense()
    array([[0, 1, 2, 0]])
    """
    return x.reshape(shape=shape)