Ah, semantics... On Jul 6, 2011, at 5:40 PM, Mark Wiebe wrote: > > NA (Not Available) > A placeholder for a value which is unknown to computations. That > value may be temporarily hidden with a mask, may have been lost > due to hard drive corruption, or gone for any number of reasons. > This is the same as NA in the R project.
I have a problem with 'temporarily hidden with a mask'. In my mind, the concept of NA carries a notion of perennation. The data is just not available, just as a NaN is just not a number. > IGNORE (Skip/Ignore) > A placeholder which should be treated by computations as if no value does > or could exist there. For sums, this means act as if the value > were zero, and for products, this means act as if the value were one. > It's as if the array were compressed in some fashion to not include > that element. A data temporarily hidden by a mask becomes np.IGNORE. > bitpattern > A technique for implementing either NA or IGNORE, where a particular > set of bit patterns are chosen from all the possible bit patterns of the > value's data type to signal that the element is NA or IGNORE. > > mask > A technique for implementing either NA or IGNORE, where a > boolean or enum array parallel to the data array is used to signal > which elements are NA or IGNORE. > > numpy.ma > The existing implementation of a particular form of masked arrays, > which is part of the NumPy codebase. OK with that. > > The most important distinctions I'm trying to draw are: > > 1) NA vs IGNORE and bitpattern vs mask are completely independent. Any > combination of NA as bitpattern, NA as mask, IGNORE as bitpattern, and IGNORE > as mask are reasonable. OK with that. > 2) The idea of masking and the numpy.ma implementation are different. The > numpy.ma object makes particular choices about how to interpret the mask, but > while backwards compatibility is important, a fresh evaluation of all the > design choices going into a mask implementation is worthwhile. Indeed. _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
