Hi matplotters, As any of you subscribed to the numpy-discussion list will have probably noticed, there's intense debate going on about how numpy can do a better job of handling missing data and masked arrays. Part of the problem is that we aren't actually sure what users need these features to do. There's one group who just wants R-style "missing data", and their needs are pretty straightforward -- they just want a magic value that indicates some data point doesn't actually exist. But it seems like there's also demand for a more "masked array"-like feature, similar to the current numpy.ma, where the mask is non-destructive and easily manipulable. No-one seems clear on who exactly this should work, though, and there's a lot of disagreement about what semantics make sense. (If you want more details, there's a wiki page summarizing some of this[1]).
Since you seem to be the biggest users of numpy.ma, it would be really helpful if you could explain how you actually use it, so we can make sure that whatever we do in numpy-land is actually useful to you! What does matplotlib use masked arrays for? Is it just a convenient way to keep an array and a boolean mask together in one object, or do you take advantage of more numpy.ma features? For example, do you ever: - unmask values? - create multiple arrays that share the same storage for their data, but have different masks? (i.e., creating a new array with new elements masked, but without actually allocating the memory for a full array copy) - use reduction operations on masked arrays? (e.g., np.sum(masked_arr)) - use binary operations on masked arrays? (e.g., masked_arr1 + masked_arr2) And while we're at it, any complaints about how numpy.ma works now, that a new version might do better? Thanks in advance, -- Nathaniel [1] https://github.com/njsmith/numpy/wiki/NA-discussion-status ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
