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But please refer to the PR, since above may go away or get outdated: https://github.com/numpy/numpy/pull/21103 Note that I have not 100% made up my mind on these, because some alternatives exist which may give a somewhat easier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
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