sliding_window.py

def sliding_window(data, size, stepsize=1, padded=False, axis=-1, copy=True):
    """
    Calculate a sliding window over a signal

    Parameters
    ----------
    data : numpy array
        The array to be slided over.
    size : int
        The sliding window size
    stepsize : int
        The sliding window stepsize. Defaults to 1.
    axis : int
        The axis to slide over. Defaults to the last axis.
    copy : bool
        Return strided array as copy to avoid sideffects when manipulating the
        output array.


    Returns
    -------
    data : numpy array
        A matrix where row in last dimension consists of one instance
        of the sliding window.

    Notes
    -----

    - Be wary of setting `copy` to `False` as undesired sideffects with the
      output values may occurr.

    Examples
    --------

    >>> a = numpy.array([1, 2, 3, 4, 5])
    >>> sliding_window(a, size=3)
    array([[1, 2, 3],
           [2, 3, 4],
           [3, 4, 5]])
    >>> sliding_window(a, size=3, stepsize=2)
    array([[1, 2, 3],
           [3, 4, 5]])

    See Also
    --------
    pieces : Calculate number of pieces available by sliding

    """
    if axis >= data.ndim:
        raise ValueError(
            "Axis value out of range"
        )

    if stepsize < 1:
        raise ValueError(
            "Stepsize may not be zero or negative"
        )

    if size > data.shape[axis]:
        raise ValueError(
            "Sliding window size may not exceed size of selected axis"
        )

    shape = list(data.shape)
    shape[axis] = numpy.floor(data.shape[axis] / stepsize - size / stepsize + 1).astype(int)
    shape.append(size)

    strides = list(data.strides)
    strides[axis] *= stepsize
    strides.append(data.strides[axis])

    strided = numpy.lib.stride_tricks.as_strided(
        data, shape=shape, strides=strides
    )

    if copy:
        return strided.copy()
    else:
        return strided

Nowadays I recommend using skimage.util.view_as_windows() instead:

skimage.util.view_as_windows(x, (3, 3), step=2)
# array([[[[ 0,  1,  2],
#          [10, 11, 12],
#          [20, 21, 22]],
# 
#         [[ 2,  3,  4],
#          [12, 13, 14],
#          [22, 23, 24]],
# 
#         [[ 4,  5,  6],
#          [14, 15, 16],
#          [24, 25, 26]],
# 
#         [[ 6,  7,  8],
#          [16, 17, 18],
#          [26, 27, 28]]]])

Nice, I didn't known sklearn had this util function.