[Numpy-discussion] feedback request: proposal to add masks to the core ndarray
Charles R Harris
Thu Jun 23 19:31:03 CDT 2011
On Thu, Jun 23, 2011 at 6:21 PM, Mark Wiebe <firstname.lastname@example.org> wrote:
> 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 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
> 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
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!
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion