[Numpy-discussion] Changes to generalized ufunc core dimension checking

[Travis Oliphant]

On Wed, Mar 16, 2016 at 12:55 PM, Nathaniel Smith <njs at pobox.com> wrote:

> Hi Travis,
>
> On Mar 16, 2016 9:52 AM, "Travis Oliphant" <travis at continuum.io> wrote:
> >
> > Hi everyone,
> >
> > Can you help me understand why the stricter changes to generalized ufunc
> argument checking no now longer allows scalars to be interpreted as 1-d
> arrays in the core-dimensions?
> >
> > Is there a way to specify in the core-signature that scalars should be
> allowed and interpreted in those cases as an array with all the elements
> the same?   This seems like an important feature.
>
> Can you share some example of when this is useful?
>

Being able to implicitly broadcast scalars to arrays is the core-function
of broadcasting.    This is still very useful when you have a core-kernel
an want to pass in a scalar for many of the arguments.   It seems that at
least in that case, automatic broadcasting should be allowed --- as it
seems clear what is meant.

While you can use the broadcast* features to get the same effect with the
current code-base, this is not intuitive to a user who is used to having
scalars interpreted as arrays in other NumPy operations.

It used to automatically happen and a few people depended on it in several
companies and so the 1.10 release broke their code.

I can appreciate that in the general case, allowing arbitrary broadcasting
on the internal core dimensions can create confusion.  But, scalar
broadcasting still makes sense.


A better workaround would be to use one of the np.broadcast* functions to
> request exactly the broadcasting you want and make an arr2-sized view of
> the scalar. In this case where you presumably (?) want to allow the last
> two arguments to be broadcast against each other arbitrarily:
>
> arr2, arr3 = np.broadcast_arrays(arr2, scalar)
> myufunc(arr1, arr2, arr3)
>
> A little wordier than implicit broadcasting, but not as bad as manually
> creating an array, and like implicit broadcasting the memory overhead is
> O(1) instead of O(size).
>

Thanks for the pointer (after I wrote the email this solution also occured
to me).       I think adding back automatic broadcasting for the scalar
case makes a lot of sense as well, however.   What do people think of that?

Also adding this example to the documentation as a work-around for people
whose code breaks with the new changes.

Thanks,

-Travis




> -n
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-- 

*Travis Oliphant, PhD*
*Co-founder and CEO*


@teoliphant
512-222-5440
http://www.continuum.io
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