Hi, Here is another example.
To write pix2ang (and similar functions) to a ufunc, one may want to have implicit scalar broadcast on `nested` and `nsides` arguments. The function is described here: http://healpy.readthedocs.org/en/latest/generated/healpy.pixelfunc.pix2ang.html#healpy.pixelfunc.pix2ang Yu On Thu, Mar 17, 2016 at 2:04 AM, Travis Oliphant <tra...@continuum.io> wrote: > > > On Wed, Mar 16, 2016 at 3:07 PM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Wed, Mar 16, 2016 at 1:48 PM, Travis Oliphant <tra...@continuum.io> >> wrote: >> >>> >>> >>> 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. >>> >> >> The `@` operator doesn't allow that. >> >> >>> >>> 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. >>> >> >> Mixing array multiplications with scalar broadcasting is looking for >> trouble. Array multiplication needs strict dimensions and having stacked >> arrays and vectors was one of the prime objectives of gufuncs. Perhaps what >> we need is a more precise notation for broadcasting, maybe `*` or some such >> addition to the signaturs to indicate that scalar broadcasting is >> acceptable. >> > > I think that is a good idea. Let the user decide if scalar broadcasting > is acceptable for their function. > > Here is a simple concrete example where scalar broadcasting makes sense: > > A 1-d dot product (the core of np.inner) (k), (k) -> () > > A user would assume they could call this function with a scalar in either > argument and have it broadcast to a 1-d array. Of course, if both > arguments are scalars, then it doesn't make sense. > > Having a way for the user to allow scalar broadcasting seems sensible and > a nice compromise. > > -Travis > > > >> <snip> >> >> Chuck >> >> >> _______________________________________________ >> 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 > >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
