On January 11, 2022, Sebastian Berg wrote:
> On Mon, 2022-01-10 at 15:25 -0800, Stephan Hoyer wrote:
> > There are no C-level APIs for __array_function__ or __array_ufunc__,
> > so
> > yes, at a high-level Python methods will be invoked by NumPy.
> >
> > That said, NumPy's logic for handling __ar
On Mon, 2022-01-10 at 15:25 -0800, Stephan Hoyer wrote:
> There are no C-level APIs for __array_function__ or __array_ufunc__,
> so
> yes, at a high-level Python methods will be invoked by NumPy.
>
> That said, NumPy's logic for handling __array_function__ and
> __array_ufunc__ methods is written
There are no C-level APIs for __array_function__ or __array_ufunc__, so
yes, at a high-level Python methods will be invoked by NumPy.
That said, NumPy's logic for handling __array_function__ and
__array_ufunc__ methods is written in highly optimized C. If you wrote your
own __array_function__ and
Hi all,
I am a long time user of astropy.units, which allows one to define
quantities with physical units as follows:
>>> from astropy import units as u
>>> 10 << u.cm
>>> np.sqrt(4 << u.m ** 2)
>>> ([1, 1, 0] << u.m) @ ([0, 10, 20] << u.cm / u.s)
>>> (([1, 1, 0] << u.m) * ([0, 10, 20] << u.cm
On Mon, Jan 10, 2022 at 7:15 AM Hameer Abbasi
wrote:
> Hello all.
>
> I believe that over the years there were multiple proposals to replace the
> linspace formula start + n *(stop - start) / (npoints - 1) with a * start +
> b * end with a, b linearly spaced between 0 and 1 with npoints. Concrete
Hello,
perhaps it would be best to have an issue about this on github?
It might be worth pointing out that the original problem triggers a
floating point error that can be caught and handled via errstate. This
might be used either in linspace itself, or, if you think this rare
problem is like
Hello all.
I believe that over the years there were multiple proposals to replace the
linspace formula start + n *(stop - start) / (npoints - 1) with a * start + b *
end with a, b linearly spaced between 0 and 1 with npoints. Concretely, a = n /
(npoints - 1), b = 1 - a. Here, 0 <= n < npoints.
I see what you mean, there is, however, some inconsistency on how this is
handled, and it's not entirely intuitive
```
_type=np.int8
N=8
np.linspace(
start=np.iinfo(_type).min,
stop=np.iinfo(_type).max,
num=N,
dtype=_type,
)
=>array([-128, -92, -56, -19, 17, 54, 90,
