Hi all, I have created a new prototype showcasing the new experimental DType and ufunc API here:
https://github.com/seberg/unitdtype
Aside from showing Units, this is also a good resource to get an idea
of how the new (including very complicated) DTypes will look like, as
well as how UFuncs should look like.
This time it is in C and much more full featured (although some
convenience methods are missing) -- expect more of those to be added
over time.
As of now, it requires a custom NumPy branch (I hope that goes away
quickly, but it may always be necessary as new things gets added).
It does use `unyt` under-the-hood in order to calculate conversion
factors and result units. [1]
The code should be able to do quite a lot of things, but of course is
not meant to be full-featured yet and bugs are likely if you do
stranger things.
A main thing is that a good chunk of ufuncs are now supported by
"reusing" NumPy's existing ufuncs:
* negative, positive
* add, subtract
* multiple, divide
* matmul
* fmod
* hypot (not sure if this is quite right)
* maximum, minimum, fmax, fmin
* comparisons (note that we never compare to 0 scalars, unlike some
implementations) – that also allows `np.min` and `np.max`.
* Logical ufuncs (via casting to boolean)
More common things that are definitely not support (as of now!):
* `sum` is clunky requiring `initial=QuantityScalar(0, correct_unit)`
to work. This is a problem with NumPy's design of the "identity",
and the (always known) fix is to improve the identity machinery in
NumPy. – I am looking into it, but as usual it requires more cleanup
elsewhere for a good solution.
* `prod` is more tricky, because its result unit would depend on the
number of elements. This (and gufuncs for such operations) would
require a specialized, new, API.
(I do not think this is hard, but I will not throw myself at it.)
* sorting/parititioning and many other functions. Those two can be
made to work easily (by using the old API, we should make a new one
but that is a different issue).
* `arange` and `linspace` each have their problems (linspace just
because the `scalar` is not really usable yet).
(again, nothing major, although `arange` could use a new machinery)
* printing is not great, this requires a cleanup of how NumPy prints...
* …
Cheers,
Sebastian
[1] Completely arbitrary and easily replaceable choice, I just found
how to get the conversion factors quickly in `unyt`. Plus, without
using a dev-version of `astropy`, `astropy.units` sometimes clashes
with NumPy `main`.
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