[Numpy-discussion] feedback request: proposal to add masks to the core ndarray
Thu Jun 23 19:21:44 CDT 2011
On Thu, Jun 23, 2011 at 7:00 PM, Nathaniel Smith <email@example.com> wrote:
> On Thu, Jun 23, 2011 at 2:44 PM, Robert Kern <firstname.lastname@example.org>
> > On Thu, Jun 23, 2011 at 15:53, Mark Wiebe <email@example.com> wrote:
> >> Enthought has asked me to look into the "missing data" problem and how
> >> could treat it better. I've considered the different ideas of adding
> >> variants with a special signal value and masked arrays, and concluded
> >> adding masks to the core ndarray appears is the best way to deal with
> >> problem in general.
> >> I've written a NEP that proposes a particular design, viewable here:
> >> 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
> >> 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
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
> 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
This would require fixing the issue where ufunc inner loops can't
> actually access the dtype object, but we should fix that anyway :-).
> -- Nathaniel
> NumPy-Discussion mailing list
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