I am willing to wait for PR #18398 as I am mainly interested at this point in the process of developing a new DType and then array coercion and casting.
Does _rational_tests.c.src <https://github.com/numpy/numpy/blob/main/numpy/core/src/umath/_rational_tests.c.src> illustrate the new DType? On Tue, Mar 16, 2021 at 4:11 PM Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Tue, 2021-03-16 at 13:17 -0500, Lee Johnston wrote: > > Is the work on NEP 42 custom DTypes far enough along to experiment > > with? > > > > TL;DR: Its not quite ready, but if we work together I think we could > experiment a fair bit. Mainly ufuncs are still limited (though not > quite completely missing). The main problem is that we need to find a > way to expose the currently private API. > > I would be happy to discuss this also in a call. > > > ** The long story: ** > > There is one more PR related to casting, for which merge should be > around the corner. And which would bring a lot bang to such an > experiment: > > https://github.com/numpy/numpy/pull/18398 > > > At that point, the new machinery supports (or is used for): > > * Array-coercion: `np.array([your_scalar])` or > `np.array([1], dtype=your_dtype)`. > > * Casting (practically full support). > > * UFuncs do not quite work. But short of writing `np.add(arr1, arr2)` > with your DType involved, you can try a whole lot. (see below) > > * Promotion `np.result_type` should work very soon, but probably isn't > is not very relevant anyway until ufuncs are fully implemented. > > That should allow you to do a lot of good experimentation, but due to > the ufunc limitation, maybe not well on "existing" python code. > > > The long story about limitations is: > > We are missing exposure of the new public API. I think I should be > able to provide a solution for this pretty quickly, but it might > require working of a NumPy branch. (I will write another email about > it, hopefully we can find a better solution.) > > > Limitations for UFuncs: UFuncs are the next big project, so to try it > fully you will need some patience, unfortunately. > > But, there is some good news! You can write most of the "ufunc" > already, you just can't "register" it. > So what I can already offer you is a "DType-specific UFunc", e.g.: > > unit_dtype_multiply(np.array([1.], dtype=Float64UnitDType("m")), > np.array([2.], dtype=Float64UnitDtype("s"))) > > And get out `np.array([2.], dtype=Float64UnitDtype("m s"))`. > > But you can't write `np.multiple(arr1, arr2)` or `arr1 * arr2` yet. > Both registration and "promotion" logic are missing. > > I admit promotion may be one of the trickiest things, but trying this a > bit might help with getting a clearer picture for promotion as well. > > > The main last limitation is that I did not replace or create "fallback" > solutions and/or replacement for the legacy `dtype->f-><slots>` yet. > This is not a serious limitation for experimentation, though. It might > even make sense to keep some of them around and replace them slowly. > > > And of course, all the small issues/limitations that are not fixed > because nobody tried yet... > > > > I hope this doesn't scare you away, or at least not for long :/. It > could be very useful to start experimentation soon to push things > forward a bit quicker. And I really want to have at least an > experimental version in NumPy 1.21. > > Cheers, > > Sebastian > > > > Lee > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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