Hi all,
I am curious about the correct argument for "normalizing" and
forwarding arguments in `__array_function__` and `__array_ufunc__`. I
have listed the three rules as I understand how the "right" way is
below.
Mainly this is just to write down the rules that I think we should aim
for in case
numpy 1.19.3 installs fine.
numpy 1.19.4 appears to install but does not work.
(Details below. The supplied tinyurl appears relevant.)
Alan Isaac
PS test> python38 -m pip install -U numpy
Collecting numpy
Using cached numpy-1.19.4-cp38-cp38-win_amd64.whl (13.0 MB)
Installing collected packages:
Yes this is known and we are waiting MS to roll out a solution for this.
Here are more details
https://developercommunity2.visualstudio.com/t/fmod-after-an-update-to-windows-2004-is-causing-a/1207405
On Thu, Dec 3, 2020 at 12:57 AM Alan G. Isaac wrote:
> numpy 1.19.3 installs fine.
> numpy 1.19.
On Thu, 2020-12-03 at 01:13 +0100, Ilhan Polat wrote:
> Yes this is known and we are waiting MS to roll out a solution for
> this.
> Here are more details
> https://developercommunity2.visualstudio.com/t/fmod-after-an-update-to-windows-2004-is-causing-a/1207405
I think one workaround was `pip ins
If I want to provide the "out" kwarg to, for example, a reduce ufunc then I
need to know the shape of the output given the other set of inputs. Is
there a utility function to take these arguments and just compute what the
shape of the output is going to be without actually computing the result?
W
Hi Sebastian,
Looking at these three rules, they all seem to stem from one simple question:
do we desire for a single code snippet to be runnable on multiple array
implementations?
On Wed, Dec 2, 2020, at 15:34, Sebastian Berg wrote:
> 1. If an argument is invalid in NumPy it is considered and
