On Thu, Jun 23, 2011 at 7:00 PM, Nathaniel Smith <n...@pobox.com> wrote:
> On Thu, Jun 23, 2011 at 2:44 PM, Robert Kern <robert.k...@gmail.com> > wrote: > > On Thu, Jun 23, 2011 at 15:53, Mark Wiebe <mwwi...@gmail.com> wrote: > >> Enthought has asked me to look into the "missing data" problem and how > NumPy > >> could treat it better. I've considered the different ideas of adding > dtype > >> variants with a special signal value and masked arrays, and concluded > that > >> adding masks to the core ndarray appears is the best way to deal with > the > >> problem in general. > >> I've written a NEP that proposes a particular design, viewable here: > >> > https://github.com/m-paradox/numpy/blob/cmaskedarray/doc/neps/c-masked-array.rst > >> There are some questions at the bottom of the NEP which definitely need > >> discussion to find the best design choices. Please read, and let me know > of > >> all the errors and gaps you find in the document. > > > > One thing that could use more explanation is how your proposal > > improves on the status quo, i.e. numpy.ma. As far as I can see, you > > are mostly just shuffling around the functionality that already > > exists. There has been a continual desire for something like R's NA > > values by people who are very familiar with both R and numpy's masked > > arrays. Both have their uses, and as Nathaniel points out, R's > > approach seems to be very well-liked by a lot of users. In essence, > > *that's* the "missing data problem" that you were charged with: making > > happy the users who are currently dissatisfied with masked arrays. It > > doesn't seem to me that moving the functionality from numpy.ma to > > numpy.ndarray resolves any of their issues. > > Speaking as a user who's avoided numpy.ma, it wasn't actually because > of the behavior I pointed out (I never got far enough to notice it), > but because I got the distinct impression that it was a "second-class > citizen" in numpy-land. I don't know if that's true. But I wasn't sure > how solidly things like interactions between numpy and masked arrays > worked, or how , and it seemed like it had more niche uses. So it just > seemed like more hassle than it was worth for my purposes. Moving it > into the core and making it really solid *would* address these > issues... > These are definitely things I'm trying to address. It does have to be solid, though. It occurs to me on further thought > that one major advantage of having first-class "NA" values is that it > preserves the standard looping idioms: > > for i in xrange(len(x)): > x[i] = np.log(x[i]) > > According to the current proposal, this will blow up, but np.log(x) > will work. That seems suboptimal to me. > This boils down to the choice between None and a zero-dimensional array as the return value of 'x[i]'. This, and the desire that 'x[i] == x[i]' should be False if it's a masked value have convinced me that a zero-dimensional array is the way to go, and your example will work with this choice. > > I do find the argument that we want a general solution compelling. I > suppose we could have a magic "NA" value in Python-land which > magically triggers fiddling with the mask when assigned to numpy > arrays. > > It's should also be possible to accomplish a general solution at the > dtype level. We could have a 'dtype factory' used like: > np.zeros(10, dtype=np.maybe(float)) > where np.maybe(x) returns a new dtype whose storage size is x.itemsize > + 1, where the extra byte is used to store missingness information. > (There might be some annoying alignment issues to deal with.) Then for > each ufunc we define a handler for the maybe dtype (or add a > special-case to the ufunc dispatch machinery) that checks the > missingness value and then dispatches to the ordinary ufunc handler > for the wrapped dtype. > The 'dtype factory' idea builds on the way I've structured datetime as a parameterized type, but the thing that kills it for me is the alignment problems of 'x.itemsize + 1'. Having the mask in a separate memory block is a lot better than having to store 16 bytes for an 8-byte int to preserve the alignment. This would require fixing the issue where ufunc inner loops can't > actually access the dtype object, but we should fix that anyway :-). > Certainly true! -Mark > > -- Nathaniel > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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