On Wed, Jun 29, 2011 at 4:21 PM, Eric Firing <efir...@hawaii.edu> wrote:
> 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. > I added a mechanism to support this idea with the NA dtypes approach, spelled 'NA[f8,InfNan]'. Here, all Infs and NaNs are treated as NA by the system. -Mark 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 >
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