Hi all,
On behalf of the SciPy development team I'm pleased to announce the release
of SciPy 1.7.2, which is a bug fix release that includes wheels for Python
3.10 on many platforms. Many thanks to upstream developers in the ecosystem
for their assistance in making this possible.
Sources and
I agree with the argument to revert. Consistency among libraries should be near
the top of the list of priorities, as should performance. Whether the new
behaviour "makes more sense", meanwhile, is debatable.
On Fri, 5 Nov 2021, at 4:08 PM, Ralf Gommers wrote:
>
>
> On Mon, Aug 2, 2021 at
On Mon, Aug 2, 2021 at 7:49 PM Ralf Gommers wrote:
>
>
> On Mon, Aug 2, 2021 at 7:04 PM Sebastian Berg
> wrote:
>
>> Hi all,
>>
>> In NumPy 1.21, the output of `np.unique` changed in the presence of
>> multiple NaNs. Previously, all NaNs were returned when we now only
>> return one (all NaNs
They are meant to be optimized. Any contribution to improve them further is
more than welcome.
Raghuveer
-Original Message-
From: Noah Goldstein
Sent: Thursday, November 4, 2021 10:46 AM
To: numpy-discussion@python.org
Subject: [Numpy-discussion] [RFC] - numpy/SVML appears to be
Hi all,
Using `np.ndenumerate` on masked arrays gives unexpected results. For instance,
next(np.ndenumerate(np.ma.masked_all((
does *not* equal `((), np.ma.masked)`, but instead contains whatever
value happens to be in the uninitialized data part of the masked
array.
Even better than
The numpy SVML library: https://github.com/numpy/SVML
appears to be poorly optimized. Since its just the raw assembly dump
this also makes it quite difficult to improve (with either a better compiler
or by hand).
Some of the glaring issues are:
1. register allocation / spilling
2. rodata layouts
