Function as_strided

numpy/lib/_stride_tricks_impl.py:39–169

Create a view into the array with the given shape and strides. .. warning:: This function has to be used with extreme care, see notes. Parameters ---------- x : ndarray Array to create a new. shape : sequence of int, optional The shape of the new array. Def

(
    x, shape=None, strides=None, subok=False, writeable=True, *, check_bounds=None
)

@set_module("numpy.lib.stride_tricks")
def as_strided(
    x, shape=None, strides=None, subok=False, writeable=True, *, check_bounds=None
):
    """
    Create a view into the array with the given shape and strides.

    .. warning:: This function has to be used with extreme care, see notes.

    Parameters
    ----------
    x : ndarray
        Array to create a new.
    shape : sequence of int, optional
        The shape of the new array. Defaults to ``x.shape``.
    strides : sequence of int, optional
        The strides of the new array. Defaults to ``x.strides``.
    subok : bool, optional
        If True, subclasses are preserved.
    writeable : bool, optional
        If set to False, the returned array will always be readonly.
        Otherwise it will be writable if the original array was. It
        is advisable to set this to False if possible (see Notes).
    check_bounds : bool or None
        Check new stride and shape for potential out of bound memory
        access.

    Returns
    -------
    view : ndarray

    Raises
    ------
    ValueError
        If `check_bounds` is True the given shape and strides could result in
        out-of-bounds memory access.

    See also
    --------
    broadcast_to : broadcast an array to a given shape.
    reshape : reshape an array.
    lib.stride_tricks.sliding_window_view :
        userfriendly and safe function for a creation of sliding window views.

    Notes
    -----
    `as_strided` creates a view into the array given the exact strides
    and shape. This means it manipulates the internal data structure of
    ndarray and, if done incorrectly, the array elements can point to
    invalid memory and can corrupt results or crash your program.
    It is advisable to always use the original ``x.strides`` when
    calculating new strides to avoid reliance on a contiguous memory
    layout.

    Furthermore, arrays created with this function often contain self
    overlapping memory, so that two elements are identical.
    Vectorized write operations on such arrays will typically be
    unpredictable. They may even give different results for small, large,
    or transposed arrays.