On Thu, Jun 23, 2011 at 6:21 PM, Mark Wiebe <mwwi...@gmail.com> wrote:
> 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. > Yes, but that assumes it is appended to the existing types in the dtype individually instead of the dtype as a whole. The dtype with mask could just indicate a shadow array, an alpha channel if you will, that is essentially what you are already doing but just probide a different place to track it. > > 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! > > Chuck > >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
