On 10/14/2016 07:49 PM, Juan Nunez-Iglesias wrote:

+1 for propagate_mask. That is the only proposal that immediately makes sense to me. "contagious" may be cute but I think approximately 0% of users would guess its purpose on first use.## Advertising

Can you elaborate on what happens with the masks exactly? I didn't quite get why propagate_mask=False was unintuitive. My expectation is that any mask present in the input will not be set in the output, but the mask will be "respected" by the function.

`Here's an illustration of how the PR currently works with convolve,`

`using the name "propagate_mask":`

>>> m = np.ma.masked >>> a = np.ma.array([1,1,1,m,1,1,1,m,m,m,1,1,1]) >>> b = np.ma.array([1,1,1]) >>> >>> print np.ma.convolve(a, b, propagate_mask=True) [1 2 3 -- -- -- 3 -- -- -- -- -- 3 2 1] >>> print np.ma.convolve(a, b, propagate_mask=False) [1 2 3 2 2 2 3 2 1 -- 1 2 3 2 1] Allan

On 15 Oct. 2016, 5:23 AM +1100, Allan Haldane <allanhald...@gmail.com>, wrote:I think the possibilities that have been mentioned so far (here or in the PR) are: contagious contagious_mask propagate propagate_mask propagated `propogate_mask=False` seemed to imply that the mask would never be set, so Eric also suggested propagate_mask='any' or propagate_mask='all' I would be happy with 'propagated=False' as the name/default. As Eric pointed out, most MaskedArray functions like sum implicitly don't propagate, currently, so maybe we should do likewise here. Allan

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