[Numpy-discussion] PR hasn't been responded to in 2 weeks

[matteo_luigi_raso]

I made a PR (#22255) that received some attention, but has been ignored for 2 
weeks now. I commented on the PR 5 days ago, but there was still no response.

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[Numpy-discussion] Re: PR hasn't been responded to in 2 weeks

[Matti Picus]

The idea of using fft in polynomial.polymul in the PR got a cool 
reception. It is not clear if we should add this to NumPy or refer users 
to other packages.  Maybe the mailing list would be a forum to discuss 
the need for the enhancement. It should have at least one other champion 
in order to move forward.



The reviewers also asked you to consider a different interface 
(method=), I did not see a response to that suggestion.


Matti


On 28/9/22 08:23, matteo_luigi_raso wrote:
I made a PR (#22255) that received some attention, but has been 
ignored for 2 weeks now. I commented on the PR 5 days ago, but there 
was still no response.


Sent with Proton Mail  secure email.

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[Numpy-discussion] Re: PR hasn't been responded to in 2 weeks

[Stefan van der Walt]

On Wed, Sep 28, 2022, at 08:01, Matti Picus wrote:
> The reviewers also asked you to consider a different interface 
> (method=), I did not see a response to that suggestion.

I think that interface suggestion makes sense. Since there seemed to be some 
confusion in the comments, I'll summarize here:

Polynomials, as Chuck pointed out on the PR, typically do not have that many 
coefficients, so method='direct' would remain the default. Adding method='fft' 
becomes an optionally enabled feature.

Stéfan
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[Numpy-discussion] Re: PR hasn't been responded to in 2 weeks

[Inessa Pawson]

Hi, Matteo!
Thank you for the ping. The NumPy core developer team will make a decision on 
how to move forward with your pull request at the next triage meeting on 
October 5th. It would be great if you could join us at the meeting.

Cheers,
Inessa

Inessa Pawson
Contributor Experience Lead | NumPy
https://numpy.org/
GitHub: inessapawson
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[Numpy-discussion] NEP 50 and integers (e.g. uint8 together -1)

[Sebastian Berg]

Hi all,

In NEP 50 (https://numpy.org/neps/nep-0050-scalar-promotion.html) my
current proposal is that the following:

np.array([1, 2], dtype="uint8") + (-1)

which currently returns an "int16" array must raise an error.  This is
because we want to return a uint8 array but the -1 cannot be
represented well by -1.
The same should also happen for a value of 300 (rather than -1).

My main question is not about making this an error though.  The
question is whether the following two cases should also error:

uint8_arr = np.array([1, 2], dtype="uint8")
uint8_arr[0] = -1

or:

np.array([-1], dtype=np.uint8)

(In practice these currently give the maximum integer with well defined
integer overflow.)

Note that the call `np.uint8(-1)` could be a special case here!

The reason I ask is that my PR:

https://github.com/numpy/numpy/pull/21875

currently introduces the new error without changing the other cases and
this requires adding a full new conversion path.  If we want to change
that anyway (or keep things aligned), I could simplify the logic in the
PR.

Cheers,

Sebastian

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[Numpy-discussion] Re: NEP 50 and integers (e.g. uint8 together -1)

[Stefan van der Walt]

Hi Sebastian,

On Wed, Sep 28, 2022, at 12:11, Sebastian Berg wrote:
> np.array([1, 2], dtype="uint8") + (-1)
>
> which currently returns an "int16" array must raise an error.  This is
> because we want to return a uint8 array but the -1 cannot be
> represented well by -1.

Did you mean: the -1 is not representable in uint8?

Since -1 cannot cast to uint8, and since we cannot look at the value, we cannot 
determine a suitable minimal output dtype for x - 1.

Stéfan
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