Thanks for these notes. Just a couple of thoughts as I looked over these notes.
On Tue, Jul 5, 2011 at 6:46 PM, Christopher Jordan-Squire <[email protected]>wrote: > 3. Using IGNORE to signal a jagged array. e.g., [ [1, 2, IGNORE], [IGNORE, > 3, 4] ] should behave exactly the same as [ [1 , 2] , [3 , 4] ]. Though this > leaves open how [1, 2, IGNORE] + [3 , 4] should behave. > > I don't think there is any confusion about that particular case. Even when using the IGNORE semantics, numpy broadcasting rules are still in play. This particular case should throw an exception. > Because of these different uses of IGNORE, it doesn't have as clear a > theoretical interpretation as NA. (For instance, what is IGNORE+3, IGNORE*3, > or IGNORE | True?) > > I think we were more referring to matrix operations like dot products. Element-by-element operations should still behave the same as NA. Scalar operations should return IGNORE. HOW DOES THIS RELATE TO THE CURRENT MASKED ARRAY? > > ---------------------------------------------------------------------------------------------------- > > Everyone seems to agree they'd love it if this could encompass all current > use cases of the numpy.ma arrays, so numpy.ma arrays could be deprecated. > (However they wouldn't be eliminated for several years, even in the most > optimistic scenarios.) > > This is going to be a very tricky thing to handle and it is going to require coordination and agreements among many of the third-party toolkits like scipy and matplotlib. In addition to these notes (unless I missed it), Nathaniel pointed out that with the ufunc where= parameter feature and the ufunc wrapper, we have the potential to greatly improve the codebase of numpy.ma as it stands. Potentially mitigating the need for moving more of numpy.ma into the core, and to focus more on NA. While I am not 100% on board with this idea, I can definitely see the potential for this path. Thanks everybody for the productive chat! Ben Root
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