Hey Eric, That's a good point. I remember seeing this behavior before and thought it was a bit odd. Best, John

On Mar 16, 2015, at 2:20 AM, numpy-discussion-request@scipy.org wrote:

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Today's Topics:

1. Re: Fix masked arrays to properly edit views (Eric Firing) 2. Rewrite np.histogram in c? (Robert McGibbon) 3. numpy.stack -- which function, if any, deserves the name? (Stephan Hoyer) 4. Re: Rewrite np.histogram in c? (Jaime Fern?ndez del R?o) 5. Re: Rewrite np.histogram in c? (Robert McGibbon) 6. Re: Rewrite np.histogram in c? (Robert McGibbon)

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Message: 1 Date: Sat, 14 Mar 2015 14:01:04 -1000 From: Eric Firing Subject: Re: [Numpy-discussion] Fix masked arrays to properly edit views To: numpy-discussion@scipy.org Message-ID: <5504CBC0.1080502@hawaii.edu> Content-Type: text/plain; charset=windows-1252; format=flowed

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On 2015/03/14 1:02 PM, John Kirkham wrote: The sample case of the issue ( https://github.com/numpy/numpy/issues/5558 ) is shown below. A proposal to address this behavior can be found here ( https://github.com/numpy/numpy/pull/5580 ). Please give me your feedback.

I tried to change the mask of `a` through a subindexed view, but was unable. Using this setup I can reproduce this in the 1.9.1 version of NumPy.

import numpy as np

a = np.arange(6).reshape(2,3) a = np.ma.masked_array(a, mask=np.ma.getmaskarray(a), shrink=False)

b = a[1:2,1:2]

c = np.zeros(b.shape, b.dtype) c = np.ma.masked_array(c, mask=np.ma.getmaskarray(c), shrink=False) c[:] = np.ma.masked

This yields what one would expect for `a`, `b`, and `c` (seen below).

masked_array(data = [[0 1 2] [3 4 5]], mask = [[False False False] [False False False]], fill_value = 999999)

masked_array(data = [[4]], mask = [[False]], fill_value = 999999)

masked_array(data = [[--]], mask = [[ True]], fill_value = 999999)

Now, it would seem reasonable that to copy data into `b` from `c` one can use `__setitem__` (seen below).

b[:] = c

This results in new data and mask for `b`.

masked_array(data = [[--]], mask = [[ True]], fill_value = 999999)

This should, in turn, change `a`. However, the mask of `a` remains unchanged (seen below).

masked_array(data = [[0 1 2] [3 0 5]], mask = [[False False False] [False False False]], fill_value = 999999)

I agree that this behavior is wrong. A related oddity is this:

In [24]: a = np.arange(6).reshape(2,3) In [25]: a = np.ma.array(a, mask=np.ma.getmaskarray(a), shrink=False) In [27]: a.sharedmask True In [28]: a.unshare_mask() In [30]: b = a[1:2, 1:2] In [31]: b[:] = np.ma.masked In [32]: b.sharedmask False In [33]: a masked_array(data = [[0 1 2] [3 -- 5]], mask = [[False False False] [False True False]], fill_value = 999999)

It looks like the sharedmask property simply is not being set and interpreted correctly--a freshly initialized array has sharedmask True; and after setting it to False, changing the mask of a new view *does* change the mask in the original.

Eric

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Best, John

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Message: 2 Date: Sun, 15 Mar 2015 21:32:49 -0700 From: Robert McGibbon Subject: [Numpy-discussion] Rewrite np.histogram in c? To: Discussion of Numerical Python Message-ID: Content-Type: text/plain; charset="utf-8"

Hi,

Numpy.histogram is implemented in python, and is a little sluggish. This has been discussed previously on the mailing list, [1, 2]. It came up in a project that I maintain, where a new feature is bottlenecked by numpy.histogram, and one developer suggested a faster implementation in cython [3].

Would it make sense to reimplement this function in c? or cython? Is moving functions like this from python to c to improve performance within the scope of the development roadmap for numpy? I started implementing this a little bit in c, [4] but I figured I should check in here first.

-Robert

[1] http://scipy-user.10969.n7.nabble.com/numpy-histogram-is-slow-td17208.html [2] http://numpy-discussion.10968.n7.nabble.com/Fast-histogram-td9359.html [3] https://github.com/mdtraj/mdtraj/pull/734 [4] https://github.com/rmcgibbo/numpy/tree/histogram