[Numpy-discussion] Adding an nd generalization of np.ma.mask_rowscols

[Eric Wieser]

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.edison at 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.com at python.org> on behalf of Eric Wieser <
> wieser.eric+numpy at gmail.com>
> *Reply to: *Discussion of Numerical Python <numpy-discussion at python.org>
> *Date: *Friday, 17. January 2020 at 11:40
> *To: *Discussion of Numerical Python <numpy-discussion at 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?
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