[Numpy-discussion] NEP 42 status

[Lee Johnston]

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 <sebastian at 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
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