on.

Examples
--------
>>> import numpy as np
>>> from numpy.lib.stride_tricks import sliding_window_view
>>> x = np.arange(6)
>>> x.shape
(6,)
>>> v = sliding_window_view(x, 3)
>>> v.shape
(4, 3)
>>> v
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4],
       [3, 4, 5]])

This also works in more dimensions, e.g.

>>> i, j = np.ogrid[:3, :4]
>>> x = 10*i + j
>>> x.shape
(3, 4)
>>> x
array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [20, 21, 22, 23]])
>>> shape = (2,2)
>>> v = sliding_window_view(x, shape)
>>> v.shape
(2, 3, 2, 2)
>>> v
array([[[[ 0,  1],
         [10, 11]],
        [[ 1,  2],
         [11, 12]],
        [[ 2,  3],
         [12, 13]]],
       [[[10, 11],
         [20, 21]],
        [[11, 12],
         [21, 22]],
        [[12, 13],
         [22, 23]]]])

The axis can be specified explicitly:

>>> v = sliding_window_view(x, 3, 0)
>>> v.shape
(1, 4, 3)
>>> v
array([[[ 0, 10, 20],
        [ 1, 11, 21],
        [ 2, 12, 22],
        [ 3, 13, 23]]])

The same axis can be used several times. In that case, every use reduces
the corresponding original dimension:

>>> v = sliding_window_view(x, (2, 3), (1, 1))
>>> v.shape
(3, 1, 2, 3)
>>> v
array([[[[ 0,  1,  2],
         [ 1,  2,  3]]],
       [[[10, 11, 12],
         [11, 12, 13]]],
       [[[20, 21, 22],
         [21, 22, 23]]]])

Combining with stepped slicing (`::step`), this can be used to take sliding
views which skip elements:

>>> x = np.arange(7)
>>> sliding_window_view(x, 5)[:, ::2]
array([[0, 2, 4],
       [1, 3, 5],
       [2, 4, 6]])

or views which move by multiple elements

>>> x = np.arange(7)
>>> sliding_window_view(x, 3)[::2, :]
array([[0, 1, 2],
       [2, 3, 4],
       [4, 5, 6]])

A common application of `sliding_window_view` is the calculation of running
statistics. The simplest example is the
`moving average <https://en.wikipedia.org/wiki/Moving_average>`_:

>>> x = np.arange(6)
>>> x.shape
(6,)
>>> v = sliding_window_view(x, 3)
>>> v.shape
(4, 3)
>>> v
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4],
       [3, 4, 5]])
>>> moving_average = v.mean(axis=-1)
>>> moving_average
array([1., 2., 3., 4.])

Note that a sliding window approach is often **not** optimal (see Notes).
Nr&