On 06/29/2011 09:32 AM, Matthew Brett wrote: > Hi, > [...] > > Clearly there are some overlaps between what masked arrays are trying > to achieve and what Rs NA mechanisms are trying to achieve. Are they > really similar enough that they should function using the same API? > And if so, won't that be confusing? I think that's the question > that's being asked.
And I think the answer is "no". No more confusing to people coming from R to numpy than views already are--with or without the NEP--and not *requiring* people to use any NA-related functionality beyond what they are used to from R. My understanding of the NEP is that it directly yields an API closely matching that of R, but with the opportunity, via views, to do more with less work, if one so desires. The present masked array module could be made more efficient if the NEP is implemented; regardless of whether this is done, the masked array module is not about to vanish, so anyone wanting precisely the masked array API will have it; and others remain free to ignore it (except for those of us involved in developing libraries such as matplotlib, which will have to support all variations of the new API along with the already-supported masked arrays). In addition, for new code, the full-blown masked array module may not be needed. A convenience it adds, however, is the automatic masking of invalid values: In [1]: np.ma.log(-1) Out[1]: masked I'm sure this horrifies some, but there are times and places where it is a genuine convenience, and preferable to having to use a separate operation to replace nan or inf with NA or whatever it ends up being. If np.seterr were extended to allow such automatic masking as an option, then the need for a separate masked array module would shrink further. I wouldn't mind having to use an explicit kwarg for ignoring NA in reduction methods. Eric > > See you, > > Matthew > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
