On Wed, Mar 16, 2016 at 12:55 PM, Nathaniel Smith <n...@pobox.com> wrote:
> Hi Travis, > > On Mar 16, 2016 9:52 AM, "Travis Oliphant" <tra...@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 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- *Travis Oliphant, PhD* *Co-founder and CEO* @teoliphant 512-222-5440 http://www.continuum.io
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
