Re Ralf's question:
> Can you quantify the precision improvement (approximately)?
On one level you'll get a large decrease in relative error around the
zeros of the sinc function because argument reduction is being done by
a number which is exactly representable in double precision (i.e. the
numb
On Wed, May 22, 2019 at 7:14 PM Marten van Kerkwijk <
m.h.vankerkw...@gmail.com> wrote:
> On a more general note, if we change to a ufunc, it will get us stuck with
> sinc being the normalized version, where the units of the input have to be
> in the half-cycles preferred by signal-processing peop
On a more general note, if we change to a ufunc, it will get us stuck with
sinc being the normalized version, where the units of the input have to be
in the half-cycles preferred by signal-processing people rather than the
radians preferred by mathematicians.
In this respect, note that there is an
> Otherwise, there should
>>> be no change except additional features of ufuncs and the move to a C
>>> implementation.
>>>
>>
> I see this is one of the functions that uses asanyarray, so what about
> impact on subclass behavior?
>
So, subclasses are passed on, as they are in ufuncs. In general,
> If we want to keep an "off" switch we might want to add some sort of API
> for exposing whether NumPy is using __array_function__ or not. Maybe
> numpy.__experimental_array_function_enabled__ = True, so you can just test
> `hasattr(numpy, '__experimental_array_function_enabled__')`? This is
> ass
On Wed, May 22, 2019 at 2:00 PM Ralf Gommers wrote:
>
>
> On Wed, May 22, 2019 at 7:34 PM Nathan Goldbaum
> wrote:
>
>> It might be worth using BigQuery to search the github repository public
>> dataset for usages of np.sinc with keyword arguments.
>>
>
> We spent some effort at Quansight to try
On Wed, May 22, 2019 at 2:36 PM Ralf Gommers wrote:
> I would still like to turn on __array_function__ in NumPy 1.17. At least,
>>> let's try that for the release candidate and see how it goes.
>>>
>>
> I agree. I'd actually suggest flipping the switch asap and see if it
> causes any issues for p
On Wed, May 22, 2019 at 9:46 PM Marten van Kerkwijk <
m.h.vankerkw...@gmail.com> wrote:
> Hi Stephan,
>
> I'm quite happy with the idea of turning on __array_function__ but
> postponing any formal solution to getting into the wrapped routines (i.e.,
> one can use __wrapped__, but it is an implemen
On Wed, May 22, 2019 at 7:34 PM Nathan Goldbaum
wrote:
> It might be worth using BigQuery to search the github repository public
> dataset for usages of np.sinc with keyword arguments.
>
We spent some effort at Quansight to try different approaches to this.
BigQuery turns out to be suboptimal, p
Hi Stephan,
I'm quite happy with the idea of turning on __array_function__ but
postponing any formal solution to getting into the wrapped routines (i.e.,
one can use __wrapped__, but it is an implementation detail that is not
documented and comes with absolutely no guarantees).
That way, 1.17 wil
Hi all,
On Tue, 21 May 2019 10:06:30 -0700, Tyler Reddy wrote:
> Hi,
>
> Starting from this week, the community meetings will be at a new time (11
> am Pacific Time) and on a new meeting platform (see the linked doc).
>
> Anyone is free to join and edit the work-in-progress meeting notes:
> http
It might be worth using BigQuery to search the github repository public
dataset for usages of np.sinc with keyword arguments.
On Wed, May 22, 2019 at 1:05 PM Sebastian Berg
wrote:
> Hi all,
>
> there is an open PR (https://github.com/numpy/numpy/pull/12924) to
> convert `np.sinc` into a ufunc. S
Hi all,
there is an open PR (https://github.com/numpy/numpy/pull/12924) to
convert `np.sinc` into a ufunc. Since it should improve general
precision in `np.sinc`, I thought we could try to move that forward a
bit. We check whether this is worth it or not in the end.
However, it would also change
Thanks for raising these concerns.
The full implications of my recent __skip_array_function__ proposal are
only now becoming evident to me now, looking at it's use in GH-13585.
Guaranteeing that it does not expand NumPy's API surface seems hard to
achieve without pervasive use of __skip_array_func
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