[Numpy-discussion] Fwd: [numpy] ENH: Initial implementation of a 'neighbor' calculation (#303)
Wed Oct 10 08:42:13 CDT 2012
This PR submitted a few months ago adds a substantial new API to numpy, so
it'd be great to get more review. No-one's replied yet, though...
Any thoughts, anyone? Is it useful, could it be better...?
On 9 Jun 2012 22:47, "Nathaniel Smith" <email@example.com> wrote:
> [Manual PR notification]
> ---------- Forwarded message ----------
> From: timcera
> Date: Sat, Jun 9, 2012 at 10:13 PM
> Subject: [numpy] ENH: Initial implementation of a 'neighbor' calculation
> To: njsmith <firstname.lastname@example.org>
> Each element is assigned the result of a function based on it's neighbors.
> Neighbors are selected based on a weight array.
> It uses the new pad routines to pad arrays if neighboring values are
> required that would be off the edge of the input array.
> Will be great to have the masked array settled because right now you
> can only sort of exclude from the neighborhood using a zero in the
> weight array. Zero or np.IGNORE don't affect np.sum, but functions
> like np.mean and np.std would give different answers. Because of this
> my early implementations of neighbor included an optional mask array
> along with the weight array, but I decided would be best to wait for
> the new masked arrays.
> This in some ways could be considered a generalization of a
> convolution, and comparison with existing numpy/scipy convolution
> results are included in the tests. The advantage to neighbor is that
> any function that accepts a 1-d array, and returns a single result,
> can be used instead of convolution only using summation. The
> convolution functions require the weight array to be flipped to get
> the same answer as neighbor.
> You can merge this Pull Request by running:
> git pull https://github.com/timcera/numpy neighbor
> Or you can view, comment on it, or merge it online at:
> -- Commit Summary --
> * ENH: Initial implementation of a 'neighbor' calculation where the each
> -- File Changes --
> M numpy/lib/__init__.py (2)
> A numpy/lib/neighbor.py (305)
> A numpy/lib/tests/test_neighbor.py (278)
> -- Patch Links --
> Reply to this email directly or view it on GitHub:
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