Looks gorgeous, thank you to all who worked on the implementation, API, and review, and thank you Sebastian for saving me a click! 😂
> On 13 Oct 2020, at 2:25 am, Sebastian Berg <sebast...@sipsolutions.net> wrote: > > On Mon, 2020-10-12 at 08:39 +0000, Zimmermann Klaus wrote: >> Hello, >> >> I would like to draw the attention of this list to PR #17394 [1] that >> adds the implementation of a sliding window view to numpy. >> > > Hi, > > thanks for working on this and driving going forward. I like the choice of a > minimal API. I have pasted the doc-string (html, hope that works fine) below > to allow a quicker idea of what is being proposed. To me it looks good! (I > wonder if we need `subok`, but I guess we probably do.) > > Cheers, > > Sebastian > > > numpy.sliding_window_view > <https://16171-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.sliding_window_view.html#numpy-sliding-window-view> > numpy.sliding_window_view(x, window_shape, axis=None, *, subok=False, > writeable=False) > <https://16171-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.sliding_window_view.html#numpy.sliding_window_view> > Create a sliding window view into the array with the given window shape. > > Creates a sliding window view of the N dimensional array with the given > window shape. Window slides across each dimension of the array and extract a > subsets of the array at any window position. > > Parameters > x : array_like > Array to create the sliding window view from. > window_shape : int or tuple of int > Size of window over each axis that takes part in the sliding window. If axis > is not present, must have same length as the number of input array > dimensions. Single integers i are treated as if they were the tuple (i,). > axis : int or tuple of int, optional > Axis or axes along which the sliding window is applied. By default, the > sliding window is applied to all axes and window_shape[i] will refer to axis > i of x. If axis is given as a tuple of int, window_shape[i] will refer to the > axis axis[i] of x. Single integers i are treated as if they were the tuple > (i,). > subok : bool, optional > If True, sub-classes will be passed-through, otherwise the returned array > will be forced to be a base-class array (default). > writeable : bool, optional > When true, allow writing to the returned view. The default is false, as this > should be used with caution: the returned view contains the same memory > location multiple times, so writing to one location will cause others to > change. > Returns > view : ndarray > Sliding window view of the array. The sliding window dimensions are inserted > at the end, and the original dimensions are trimmed as required by the size > of the sliding window. > That is, view.shape = x_shape_trimmed + window_shape, where x_shape_trimmed > is x.shape with every entry reduced by one less than the corresponding window > size. > See also > lib.stride_tricks.as_strided > <https://16171-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.lib.stride_tricks.as_strided.html#numpy.lib.stride_tricks.as_strided> > Create a view into the array with the given shape and strides. > broadcast_to > <https://16171-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.broadcast_to.html#numpy.broadcast_to> > broadcast an array to a given shape. > Notes > > For some cases there may be more efficient approaches to calculate > transformations across multi-dimensional arrays, for instance > scipy.signal.fftconvolve > <https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.fftconvolve.html#scipy.signal.fftconvolve>, > where combining the iterating step with the calculation itself while storing > partial results can result in significant speedups. > > Examples > > >>> x = np.arange(6) > >>> x.shape > (6,) > >>> v = np.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 = np.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 = np.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 = np.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) > >>> np.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) > >>> np.sliding_window_view(x, 3)[::2, :] > array([[0, 1, 2], > [2, 3, 4], > [4, 5, 6]]) > > > > > >> Having a sliding window view in numpy is a longstanding open issue (cf >> #7753 [2] from 2016). A brief summary of the discussions surrounding it >> can be found in the description of the PR. >> >> This PR implements a sliding window view based on stride tricks. >> Following the discussion in issue #7753, a first implementation was >> provided by Fanjin Zeng in PR #10771. After some discussion, that PR >> stalled and I picked up the issue in the present PR #17394. It is based >> on the first implementation, but follows the changed API as suggested by >> Eric Wieser. >> >> Code reviews have been provided by Bas van Beek, Stephen Hoyer, and Eric >> Wieser. Sebastian Berg added the "62 - Python API" label. >> >> >> Do you think this is suitable for inclusion in numpy? >> >> Do you consider the PR ready? >> >> Do you have suggestions or requests? >> >> >> Thanks for your time and consideration! >> Klaus >> >> >> [1] https://github.com/numpy/numpy/pull/17394 >> <https://github.com/numpy/numpy/pull/17394> >> [2] https://github.com/numpy/numpy/issues/7753 >> <https://github.com/numpy/numpy/issues/7753> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> >> https://mail.python.org/mailman/listinfo/numpy-discussion >> <https://mail.python.org/mailman/listinfo/numpy-discussion> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> > https://mail.python.org/mailman/listinfo/numpy-discussion > <https://mail.python.org/mailman/listinfo/numpy-discussion>
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