On Wed, Dec 2, 2020 at 3:39 PM Sebastian Berg
wrote:
> 1. If an argument is invalid in NumPy it is considered and error.
>For example:
>
>np.log(arr, my_weird_argument=True)
>
>is always an error even if the `__array_function__` implementation
>of `arr` would support it.
>
On Sat, Dec 5, 2020 at 9:24 PM Mark Harfouche
wrote:
> If the answer is to deprecate
>
> np.int(1) == int(1)
>
> then one can add a warning to the __init__ of the np.int class, but
> continue to subclass the python int class.
>
> It just doesn’t seem worthwhile to to stop people from using dtype=
I guess if the answer is to stop people from
from numpy import *
there is a good fix for that doesn’t involve deprecating dtype=np.int.
If the answer is to deprecate
np.int(1) == int(1)
then one can add a warning to the __init__ of the np.int class, but
continue to subclass the python int clas
On Sat, Dec 5, 2020 at 4:31 PM Juan Nunez-Iglesias wrote:
> Hi all,
>
> At the prodding [1] of Sebastian, I’m starting a discussion on the
> decision to deprecate np.{bool,float,int}. This deprecation broke our
> prerelease testing in scikit-image (which, hooray for rcs!), and resulted
> in a lar
On Sun, Dec 6, 2020 at 12:31 AM Juan Nunez-Iglesias wrote:
>
> Hi all,
>
> At the prodding [1] of Sebastian, I’m starting a discussion on the decision
> to deprecate np.{bool,float,int}. This deprecation broke our prerelease
> testing in scikit-image (which, hooray for rcs!), and resulted in a l
Hi all,
At the prodding [1] of Sebastian, I’m starting a discussion on the decision to
deprecate np.{bool,float,int}. This deprecation broke our prerelease testing in
scikit-image (which, hooray for rcs!), and resulted in a large amount of code
churn to fix [2].
To be honest, I do think *some*
