On Sun, Jun 3, 2018 at 8:22 PM Ralf Gommers wrote:
> It may be worth having a look at test suites for scipy, statsmodels,
> scikit-learn, etc. and estimate how much work this NEP causes those
> projects. If the devs of those packages are forced to do large scale
> migrations from RandomState to S
On Mon, Jun 4, 2018 at 2:55 AM Kevin Sheppard
wrote:
> I’m not sure if this is within the scope of the NEP or an implementation
> detail, but I think a new PRNG should use platform independent integer
> types rather than depending on the platform’s choice of 64-bit data model.
> This should be en
PEP-574 isn't on the roadmap (yet!), but I think we would clearly welcome
it. Like all NumPy improvements, it would need to implemented by an
interested party.
On Mon, Jun 4, 2018 at 1:52 AM Antoine Pitrou wrote:
>
> Hi,
>
> Do you plan to consider trying to add PEP 574 / pickle5 support? There's
I agree that second rounds of overloads have to be left to the implementers
of `__array_function__` - obviously, though, we should be sure that these
rounds are rarely necessary... The link posted by Stephan [1] has some
decent discussion for `__array_ufunc__` about when an override should
re-call
Hi Stephan,
Another potential consideration in favor of NotImplementedButCoercible is
> for subclassing: we could use it to write the default implementations of
> ndarray.__array_ufunc__ and ndarray.__array_function__, e.g.,
>
> class ndarray:
> def __array_ufunc__(self, *args, **kwargs):
>
Should there be discussion of typing (pep-484) or abstract base classes in
this nep? Are there any requirements on the result returned by
__array_function__?
On Mon, Jun 4, 2018, 2:20 AM Stephan Hoyer wrote:
>
> On Sun, Jun 3, 2018 at 9:54 PM Hameer Abbasi
> wrote:
>
>> Mixed return values of
On Mon, Jun 4, 2018 at 2:22 AM, Robert Kern wrote:
> On Sun, Jun 3, 2018 at 10:27 PM wrote:
>
>>
>>
>> On Mon, Jun 4, 2018 at 12:53 AM, Stephan Hoyer wrote:
>>
>>> On Sun, Jun 3, 2018 at 8:22 PM Ralf Gommers
>>> wrote:
>>>
It may be worth having a look at test suites for scipy, statsmodel
I’m not sure if this is within the scope of the NEP or an implementation
detail, but I think a new PRNG should use platform independent integer types
rather than depending on the platform’s choice of 64-bit data model. This
should be enough to ensure that any integer distribution that only uses
Hi,
Do you plan to consider trying to add PEP 574 / pickle5 support? There's
an implementation ready (and a PyPI backport) that you can play with.
https://www.python.org/dev/peps/pep-0574/
PEP 574 implicits targets Numpy arrays as one of its primary producers,
since Numpy arrays is how large sc
