[Numpy-discussion] Behavior of __array_wrap__?

[Brian Granger]

Travis,

Thanks.  Here is the text from the numpybook that was confusing me:

>From section 9.1.2 on ufuncs:

The ufuncs can also all take output arguments. The output will be cast if
necessary to the provided output array. If a class with an array method is used
for the output, results will be written to the ob ject returned by
array . Then,
if the class also has an array wrap method, the returned ndarray result will be
passed to that method just before passing control back to the caller.

I can easily work around this for now though, so it is not a problem.

Cheers,

Brian

On Wed, Apr 2, 2008 at 10:20 AM, Travis E. Oliphant
<oliphant at enthought.com> wrote:
> Brian Granger wrote:
>  > Hi,
>  >
>  > I am creating a custom array type (distributed memory arrays -
>  > DistArray) and I am using the __array__ and __array_wrap__ methods and
>  > __array_priority__ attribute to get these arrays to work with numpy's
>  > ufuncs.  Things are working fine when I call a ufunc like this:
>  >
>  > # This works fine (c comes back as an DistArray)
>  > a = DistArray(10)
>  > b = DistArray(10)
>  > c = np.add(a, b)
>  >
>  > But, when you pass in an ndarray as the return array, the
>  > __array_wrap__ doesn't get called:
>  >
>
>  That is true.  Currently, the output argument must be an ndarray,
>  because the idea is to save memory.
>
>  If you have an object that uses __array_wrap__ to channel the output
>  back into your object's memory, then there is no benefit to the output
>  value.
>
>  It could be possible to allow additional output arguments that are not
>  ndarrays to be syntactic sugar for __array_wrap__, but this has not been
>  done and if the documentation led you to believe that it was possible,
>  then the docs need to be updated.
>
>  Best regards,
>
>  -Travis O.
>
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