IMHO, masked arrays and extending masks like that is a weird API. To give some context, I needed this nd generalization internally in order to fix the issue on these lines <https://github.com/numpy/numpy/blob/ccfbcc1cd9a4035a467f2e982a565ab27de25b6b/numpy/ma/core.py#L7464-L7468> .
I would prefer a more functional approach Can you elaborate on that with an example of the API you’d prefer instead of np.ma.mask_extend_axis(a, axis=(0, 1))? On Fri, 17 Jan 2020 at 15:30, Hameer Abbasi <einstein.edi...@gmail.com> wrote: > IMHO, masked arrays and extending masks like that is a weird API. I would > prefer a more functional approach: Where we take in an input 1-D or N-D > boolean array in addition to a masked array with multiple axes over which > to extend the mask. > > > > *From: *NumPy-Discussion <numpy-discussion-bounces+einstein.edison= > gmail....@python.org> on behalf of Eric Wieser < > wieser.eric+nu...@gmail.com> > *Reply to: *Discussion of Numerical Python <numpy-discussion@python.org> > *Date: *Friday, 17. January 2020 at 11:40 > *To: *Discussion of Numerical Python <numpy-discussion@python.org> > *Subject: *[Numpy-discussion] Adding an nd generalization of > np.ma.mask_rowscols > > > > Today, numpy has a np.ma.mask_rowcols function, which stretches masks > along > the full length of an axis. For example, given the matrix:: > > >>> a2d = np.zeros((3, 3), dtype=int) > > >>> a2d[1, 1] = 1 > > >>> a2d = np.ma.masked_equal(a2d, 1) > > >>> print(a2d) > > [[0 0 0] > > [0 -- 0] > > [0 0 0]] > > The API allows:: > > >>> print(np.ma.mask_rowcols(a2d, axis=0)) > > [[0 0 0] > > [-- -- --] > > [0 0 0]] > > > > >>> print(np.ma.mask_rowcols(a2d, axis=1)) > > [[0 -- 0] > > [0 -- 0] > > [0 -- 0]] > > > > >>> print(np.ma.mask_rowcols(a2d, axis=None)) > > [[0 -- 0] > > [-- -- --] > > [0 -- 0]] > > However, this function only works for 2D arrays. > It would be useful to generalize this to work on ND arrays as well. > > Unfortunately, the current function is messy to generalize, because axis=0 > means “spread the mask along axis 1”, and vice versa. Additionally, the > name is not particularly good for an ND function. > > My proposal in PR 14998 <https://github.com/numpy/numpy/pull/14998> is to > introduce a new function, mask_extend_axis, which fixes this shortcoming. > Given an 3D array:: > > >>> a3d = np.zeros((2, 2, 2), dtype=int) > > >>> a3d[0, 0, 0] = 1 > > >>> a3d = np.ma.masked_equal(a3d, 1) > > >>> print(a3d) > > [[[-- 0] > > [0 0]] > > > > [[0 0] > > [0 0]]] > > This, in my opinion, has clearer axis semantics: > > >>> print(np.ma.mask_extend_axis(a2d, axis=0)) > > [[[-- 0] > > [0 0]] > > > > [[-- 0] > > [0 0]]] > > > > >>> print(np.ma.mask_extend_axis(a2d, axis=1)) > > [[[-- 0] > > [-- 0]] > > > > [[0 0] > > [0 0]]] > > > > >>> print(np.ma.mask_extend_axis(a2d, axis=2)) > > [[[-- --] > > [0 0]] > > > > [[0 0] > > [0 0]]] > > Stretching over multiple axes remains possible: > > >>> print(np.ma.mask_extend_axis(a2d, axis=(1, 2))) > > [[[-- --] > > [-- 0]] > > > > [[0 0] > > [0 0]]] > > > > # extending sequentially is not the same as extending in parallel > > >>> print(np.ma.mask_extend_axis(np.ma.mask_extend_axis(a2d, axis=1), axis=2)) > > [[[-- --] > > [-- --]] > > > > [[0 0] > > [0 0]]] > > Questions for the mailing list then: > > · Can you think of a better name than mask_extend_axis? > > · Does my proposed meaning of axis make more sense to you than > the one used by mask_rowcols? > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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