I added a few comments on the PR. The main comments of substance I had boil down to: - consistency with other programming languages/major frameworks (perhaps a few more "examples of consistency" for the new approach with others may help strengthen the arguments?)--I know JAX was mentioned, and their dtype promotion docs are quite nice - one thing I struggled with in deciding if some of the "new behaviors" were nicer was the tension between protecting from accidental overflow vs. a more "purist" view that types should be preserved more strictly; the latter would seem consistent with the "principle of least surprise" when moving from a typed language to NumPy work perhaps, though arguably slightly less user-friendly if naively doing some operations with a less formal view of typing (new Python user messing around with NumPy?)
On Mon, 21 Feb 2022 at 16:35, Sebastian Berg <sebast...@sipsolutions.net> wrote: > Hi all, > > NumPy has awkward behaviour when it comes to promotion with 0-D arrays, > and Python scalars. This is both a technical challenge (numpy needs to > inspect the values where it shouldn't), as well as surprising for > users. > > Roughly speaking, I have made a proposal under the 3 points: > * NumPy scalars and NumPy arrays always behave the same. > * A NumPy array always respects the dtype > * A Python scalar is "weak" so that uint8_arr + 3 returns a uint8_arr > > The NEP is here: > > https://25105-908607-gh.circle-artifacts.com/0/doc/neps/_build/html/nep-0050-scalar-promotion.html > > But please refer to the PR, since above may go away or get outdated: > https://github.com/numpy/numpy/pull/21103 > > > Note that I have not 100% made up my mind on these, because some > alternatives exist which may give a somewhat easier transition. > Because of this, this is a very early draft (expect large > changes/rewrite), but some feedback/input may go a long way to make > sure we keep moving on this project. > > For those aware of the issues, it probably makes sense to skip ahead to > the "Alternatives" section. I do expect that a large refactor/rewrite > will be necessary, but need some feedback to keep moving. > > > I had send the poll recently: > > https://discuss.scientific-python.org/t/poll-future-numpy-behavior-when-mixing-arrays-numpy-scalars-and-python-scalars/202 > > just to say, I have not completely ignored it, although (as expected) > the results do not give a very simple answer. Many agree with the > choices I made, but some also seem to prefer "strong" Python types, or > more special handling of NumPy scalars. > > > Please do not hesitate to give opinions! I am not sure we can find a > clear "obviously right" solution. Especially since there are tough > backwards compatibility choices (even if most users are likely not to > notice). So any input is appreciated. > > Cheers, > > Sebastian > > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: tyler.je.re...@gmail.com >
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