[Numpy-discussion] suggestion for generalizing numpy functions

Darren Dale dsdale24@gmail....
Wed Jun 24 15:37:23 CDT 2009


On Wed, Jun 24, 2009 at 4:08 PM, Charles R Harris <charlesr.harris@gmail.com
> wrote:

> On Wed, Jun 24, 2009 at 1:49 PM, Darren Dale<dsdale24@gmail.com> wrote:
> > On Wed, Jun 24, 2009 at 3:37 PM, Charles R Harris
> > <charlesr.harris@gmail.com> wrote:
> >>
> >> On Wed, Jun 24, 2009 at 8:52 AM, Darren Dale<dsdale24@gmail.com> wrote:
> >> > On Wed, Jun 24, 2009 at 9:42 AM, Charles R Harris
> >> > <charlesr.harris@gmail.com> wrote:
> >> >>
> >> >> On Wed, Jun 24, 2009 at 7:08 AM, Darren Dale<dsdale24@gmail.com>
> wrote:
> >> >> > On Wed, May 27, 2009 at 11:30 AM, Darren Dale <dsdale24@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.
> >
>
> The timeline is open for discussion. A six month timeline would put it
> sometime in November but David might want it earlier for scipy 0.8. My
> guess would be sometime after Scipy-2009, late September at the
> earliest. But as I say, it is open for discussion. What schedule would
> you prefer?
>

I guess I'd like a shot at submitting this in time for 1.4, but I wouldn't
want to hold up the release. Late September should provide plenty of time.

Darren
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