[Numpy-discussion] replacing the mechanism for dispatching ufuncs

Mark Wiebe mwwiebe@gmail....
Tue Jun 21 16:43:13 CDT 2011


On Tue, Jun 21, 2011 at 1:28 PM, Charles R Harris <charlesr.harris@gmail.com
> wrote:

>
>
> On Tue, Jun 21, 2011 at 11:57 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>
>> On Tue, Jun 21, 2011 at 12:36 PM, Charles R Harris <
>> charlesr.harris@gmail.com> wrote:
>>
>>>
>>>
>>> On Mon, Jun 20, 2011 at 12:32 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>>>
>>>> NumPy has a mechanism built in to allow subclasses to adjust or override
>>>> aspects of the ufunc behavior. While this goal is important, this mechanism
>>>> only allows for very limited customization, making for instance the masked
>>>> arrays unable to work with the native ufuncs in a full and proper way. I
>>>> would like to deprecate the current mechanism, in particular
>>>> __array_prepare__ and __array_wrap__, and introduce a new method I will
>>>> describe below. If you've ever used these mechanisms, please review this
>>>> design to see if it meets your needs.
>>>>
>>>>
>>>>
>>> The current approach is at a dead end, so something better needs to be
>>> done.
>>>
>>>
>>>> Any class type which would like to override its behavior in ufuncs would
>>>> define a method called _numpy_ufunc_, and optionally an attribute
>>>> __array_priority__ as can already be done. The class which wins the priority
>>>> battle gets its _numpy_ufunc_ function called as follows:
>>>>
>>>> return arr._numpy_ufunc_(current_ufunc, *args, **kwargs)
>>>>
>>>>
>>>> To support this overloading, the ufunc would get a new support method,
>>>> result_type, and there would be a new global function, broadcast_empty_like.
>>>>
>>>> The function ufunc.empty_like behaves like the global np.result_type,
>>>> but produces the output type or a tuple of output types specific to the
>>>> ufunc, which may follow a different convention than regular arithmetic type
>>>> promotion. This allows for a class to create an output array of the correct
>>>> type to pass to the ufunc if it needs to be different than the default.
>>>>
>>>> The function broadcast_empty_like is just like empty_like, but takes a
>>>> list or tuple of arrays which are to be broadcast together for producing the
>>>> output, instead of just one.
>>>>
>>>>
>>> How does the ufunc get called so it doesn't get caught in an endless
>>> loop? I like the proposed method if it can also be used for classes that
>>> don't subclass ndarray. Masked array, for instance, should probably not
>>> subclass ndarray.
>>>
>>
>> The function being called needs to ensure this, either by extracting a raw
>> ndarray from instances of its class, or adding a 'subok = False' parameter
>> to the kwargs. Supporting objects that aren't ndarray subclasses is one of
>> the purposes for this approach, and neither of my two example cases
>> subclassed ndarray.
>>
>>
> Sounds good. Many of the current uses of __array_wrap__ that I am aware of
> are in the wrappers in the linalg module and don't go through the ufunc
> machinery. How would that be handled?
>

Those could stay as they are, and just the ufunc usage of __array_wrap__ can
be deprecated. For classes which currently use __array_wrap__, they would
just need to also implement _numpy_ufunc_ to eliminate any deprecation
messages.

-Mark

<snip>
>
> Chuck
>
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