[Numpy-discussion] ENH: Proposal to add np.neighborwise in PR#9514
ben.v.root at gmail.com
Fri Aug 4 21:44:18 EDT 2017
So, this is a kernel mechanism?
On Fri, Aug 4, 2017 at 6:31 PM, Joseph Fox-Rabinovitz <
jfoxrabinovitz at gmail.com> wrote:
> I would like to propose the addition of a new function,
> `np.neighborwise` in PR#9514. It is based on the discussion relating
> to my proposal for `np.ratio` (PR#9481) and Eric Wieser's
> `np.neighborwise` in PR#9428. This function accepts an array `a`, a
> vectorized function of two arguments `func`, and applies the function
> to all of the neighboring elements of the array across multiple
> dimensions. There are options for masking out parts of the calculation
> and for applying the function recursively.
> The name of the function is not written in stone. The current name is
> taken directly from PR#9428 because I can not think of a better one.
> This function can serve as a backend for the existing `np.diff`, which
> has been re-implemented in this PR, as well as for the `ratio`
> function I propsed earlier. This adds the diagonal diffs feature,
> which is tested and backwards compatible. `ratio` can be implemented
> very simply with or without a mask. With a mask, it can be expressed
> `np.neighborwise(a, np.*_divide, axis=axis, n=n, mask=lambda *args:
> args)` (The conversion to bool is done automatically).
> The one potentially non-backwards-compatible API change that this PR
> introduces is that `np.diff` now returns an `ndarray` version of the
> input, instead of the original array itself if `n==0`. Previously, the
> exact input reference was returned for `n==0`. I very seriously doubt
> that this feature was ever used outside the numpy test suite anyway.
> The advantage of this change is that an invalid axis input can now be
> caught before returning the unaltered array. If this change is
> considered too drastic, I can remove it without removing the axis
> The two main differences between this PR and PR#9428 are the addition
> of masks to the computation, and the interpretation of multiple axes.
> PR#9428 applies `func` successively along each axis. This provides no
> way of doing diagonal diffs. I chose to shift along all the axes
> simultaneously before applying `func`. To clarify with an example, if
> we take `a=[[1, 2], [3, 4]]`, `axis=[0, 1]` and `func=np.subtract`,
> PR#9428 would take two diffs, `(4 - 2) - (3 - 1) = 0`, while the
> version I propose here just takes the diagonal diff `4 - 1 = 3`.
> Besides being more intuitive in my opinion, taking diagonal diffs
> actually adds a new feature that can not be obtained directly by
> taking successive diffs.
> Please let me know your thoughts.
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
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