[Numpy-discussion] suggestion for generalizing numpy functions

[Darren Dale]

On Wed, Jun 24, 2009 at 3:37 PM, Charles R Harris <charlesr.harris at gmail.com
> wrote:

> On Wed, Jun 24, 2009 at 8:52 AM, Darren Dale<dsdale24 at gmail.com> wrote:
> > On Wed, Jun 24, 2009 at 9:42 AM, Charles R Harris
> > <charlesr.harris at gmail.com> wrote:
> >>
> >> On Wed, Jun 24, 2009 at 7:08 AM, Darren Dale<dsdale24 at gmail.com> wrote:
> >> > On Wed, May 27, 2009 at 11:30 AM, Darren Dale <dsdale24 at gmail.com>
> >> > wrote:
> >> >>
> >> >> Now that numpy-1.3 has been released, I was hoping I could engage the
> >> >> numpy developers and community concerning my suggestion to improve
> the
> >> >> ufunc
> >> >> wrapping mechanism. Currently, ufuncs call, on the way out, the
> >> >> __array_wrap__ method of the input array with the highest
> >> >> __array_priority__.
> >> >>
> >> >> There are use cases, like masked arrays or arrays with units, where
> it
> >> >> is
> >> >> imperative to run some code on the way in to the ufunc as well.
> >> >> MaskedArrays
> >> >> do this by reimplementing or wrapping ufuncs, but this approach puts
> >> >> some
> >> >> pretty severe constraints on subclassing. For example, in my
> Quantities
> >> >> package I have a Quantity object that derives from ndarray. It has
> been
> >> >> suggested that in order to make ufuncs work with Quantity, I should
> >> >> wrap
> >> >> numpy's built-in ufuncs. But I intend to make a MaskedQuantity object
> >> >> as
> >> >> well, deriving from MaskedArray, and would therefore have to wrap the
> >> >> MaskedArray ufuncs as well.
> >> >>
> >> >> If ufuncs would simply call a method both on the way in and on the
> way
> >> >> out, I think this would go a long way to improving this situation. I
> >> >> whipped
> >> >> up a simple proof of concept and posted it in this thread a while
> back.
> >> >> For
> >> >> example, a MaskedQuantity would implement a method like __gfunc_pre__
> >> >> to
> >> >> check the validity of the units operation etc, and would then call
> >> >> MaskedArray.__gfunc_pre__ (if defined) to determine the domain etc.
> >> >> __gfunc_pre__ would return a dict containing any metadata the
> >> >> subclasses
> >> >> wish to provide based on the inputs, and that dict would be passed
> >> >> along
> >> >> with the inputs, output and context to __gfunc_post__, so
> >> >> postprocessing can
> >> >> be done (__gfunc_post__ replacing __array_wrap__).
> >> >>
> >> >> Of course, packages like MaskedArray may still wish to reimplement
> >> >> ufuncs,
> >> >> like Eric Firing is investigating right now. The point is that
> classes
> >> >> that
> >> >> dont care about the implementation of ufuncs, that only need to
> provide
> >> >> metadata based on the inputs and the output, can do so using this
> >> >> mechanism
> >> >> and can build upon other specialized arrays.
> >> >>
> >> >> I would really appreciate input from numpy developers and other
> >> >> interested
> >> >> parties. I would like to continue developing the Quantities package
> >> >> this
> >> >> summer, and have been approached by numerous people interested in
> using
> >> >> Quantities with sage, sympy, matplotlib. But I would prefer to
> improve
> >> >> the
> >> >> ufunc mechanism (or establish that there is no interest among the
> >> >> community
> >> >> to do so) so I can improve the package (or limit its scope) before
> >> >> making an
> >> >> official announcement.
> >> >
> >> > There was some discussion of this proposal to allow better interaction
> >> > of
> >> > ufuncs with ndarray subclasses in another thread (Plans for
> numpy-1.4.0
> >> > and
> >> > scipy-0.8.0) and the comments were encouraging. I have been trying to
> >> > gather
> >> > feedback as to whether the numpy devs were receptive to the idea, and
> it
> >> > seems the answer is tentatively yes, although there were questions
> about
> >> > who
> >> > would actually write the code. I guess I have not made clear that I
> >> > intend
> >> > to write the implementation and tests. I gained some familiarity with
> >> > the
> >> > relevant code while squashing a few bugs for numpy-1.3, but it would
> be
> >> > helpful if someone else who is familiar with the existing
> __array_wrap__
> >> > machinery would be willing to discuss this proposal in more detail and
> >> > offer
> >> > constructive criticism along the way. Is anyone willing?
> >> >
> >>
> >> I think Travis would be the only one familiar with that code and that
> >> would be from a couple of years back when he wrote it. Most of us have
> >> followed the same route as yourself, finding our way into the code by
> >> squashing bugs.
> >>
> >
> > Do you mean that you would require Travis to sign off on the
> implementation
> > (assuming he would agree to review my work)? I would really like to avoid
> a
> > situation where I invest the time and then the code bitrots because I
> can't
> > find a route to committing it to svn.
> >
>
> No, just that Travis would know the most about that subsystem if you
> are looking for help. I and others here can look over the code and
> commit it without Travis signing off on it. You could ask for commit
> privileges yourself. The important thing is having some tests and an
> agreement that the interface is appropriate. Pierre also seems
> interested in the functionality so it would be useful for him to say
> that it serves his needs also.
>

Ok, I'll start working on it then. Any idea what you are targeting for
numpy-1.4? Scipy-2009, or much earlier? I'd like to gauge how to budget my
time.

Darren
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