From allanhaldane at gmail.com  Fri Nov  1 11:51:46 2019
From: allanhaldane at gmail.com (Allan Haldane)
Date: Fri, 1 Nov 2019 11:51:46 -0400
Subject: [Numpy-discussion] argmax() indexes to value
In-Reply-To: <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
References: <3c8ed265-06bb-c527-f103-af827ee6597d@grinta.net>
 <CAG3t+pHA+hd=1AJW_63JhQQKfvh-0MDu-jMUOAXAqNWywnTt-Q@mail.gmail.com>
 <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
Message-ID: <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>

my thought was to try `take` or `take_along_axis`:

   ind = np.argmin(a, axis=1)
   np.take_along_axis(a, ind[:,None], axis=1)

But those functions tend to simply fall back to fancy indexing, and are
pretty slow. On my system plain fancy indexing is fastest:

>>> %timeit a[np.arange(N),ind]
1.58 ?s ? 18.1 ns per loop
>>> %timeit np.take_along_axis(a, ind[:,None], axis=1)
6.49 ?s ? 57.3 ns per loop
>>> %timeit np.min(a, axis=1)
9.51 ?s ? 64.1 ns per loop

Probably `take_along_axis` was designed with uses like yours in mind,
but it is not very optimized.

(I think numpy is lacking a category of efficient
indexing/search/reduction functions, like 'findfirst', 'groupby',
short-circuiting any_*/all_*/nonzero, the proposed oindex/vindex, better
gufunc broadcasting. There is slow but gradual infrastructure work
towards these, potentially).

Cheers,
Allan

On 10/30/19 11:31 PM, Daniele Nicolodi wrote:
> On 30/10/2019 19:10, Neal Becker wrote:
>> max(axis=1)?
> 
> Hi Neal,
> 
> I should have been more precise in stating the problem. Getting the
> values in the array for which I'm looking at the maxima is only one step
> in a more complex piece of code for which I need the indexes along the
> second axis of the array. I would like to avoid to have to iterate the
> array more than once.
> 
> Thank you!
> 
> Cheers,
> Dan
> 
> 
>> On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi <daniele at grinta.net
>> <mailto:daniele at grinta.net>> wrote:
>>
>>     Hello,
>>
>>     this is a very basic question, but I cannot find a satisfying answer.
>>     Assume a is a 2D array and that I get the index of the maximum value
>>     along the second dimension:
>>
>>     i = a.argmax(axis=1)
>>
>>     Is there a better way to get the value of the maximum array entries
>>     along the second axis other than:
>>
>>     v = a[np.arange(len(a)), i]
>>
>>     ??
>>
>>     Thank you.
>>
>>     Cheers,
>>     Daniele
>>     _______________________________________________
>>     NumPy-Discussion mailing list
>>     NumPy-Discussion at python.org <mailto:NumPy-Discussion at python.org>
>>     https://mail.python.org/mailman/listinfo/numpy-discussion
>>
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
> 
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
> 


From wieser.eric+numpy at gmail.com  Fri Nov  1 12:14:55 2019
From: wieser.eric+numpy at gmail.com (Eric Wieser)
Date: Fri, 1 Nov 2019 16:14:55 +0000
Subject: [Numpy-discussion] argmax() indexes to value
In-Reply-To: <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>
References: <3c8ed265-06bb-c527-f103-af827ee6597d@grinta.net>
 <CAG3t+pHA+hd=1AJW_63JhQQKfvh-0MDu-jMUOAXAqNWywnTt-Q@mail.gmail.com>
 <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
 <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>
Message-ID: <CAL1kJvDR6mdW5j6LpLhKjO8P-iOTuPWRSCGPdziJj4NuZcP-LA@mail.gmail.com>

> On my system plain fancy indexing is fastest

Hardly surprising, since take_along_axis is doing that under the hood,
after constructing the index for you :)

https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/shape_base.py#L58-L172

I deliberately didn't expose the internal function that constructs the
slice, since leaving it private frees us to move those functions to c or in
the distant future gufuncs.


On Fri, Nov 1, 2019, 15:54 Allan Haldane <allanhaldane at gmail.com> wrote:

> my thought was to try `take` or `take_along_axis`:
>
>    ind = np.argmin(a, axis=1)
>    np.take_along_axis(a, ind[:,None], axis=1)
>
> But those functions tend to simply fall back to fancy indexing, and are
> pretty slow. On my system plain fancy indexing is fastest:
>
> >>> %timeit a[np.arange(N),ind]
> 1.58 ?s ? 18.1 ns per loop
> >>> %timeit np.take_along_axis(a, ind[:,None], axis=1)
> 6.49 ?s ? 57.3 ns per loop
> >>> %timeit np.min(a, axis=1)
> 9.51 ?s ? 64.1 ns per loop
>
> Probably `take_along_axis` was designed with uses like yours in mind,
> but it is not very optimized.
>
> (I think numpy is lacking a category of efficient
> indexing/search/reduction functions, like 'findfirst', 'groupby',
> short-circuiting any_*/all_*/nonzero, the proposed oindex/vindex, better
> gufunc broadcasting. There is slow but gradual infrastructure work
> towards these, potentially).
>
> Cheers,
> Allan
>
> On 10/30/19 11:31 PM, Daniele Nicolodi wrote:
> > On 30/10/2019 19:10, Neal Becker wrote:
> >> max(axis=1)?
> >
> > Hi Neal,
> >
> > I should have been more precise in stating the problem. Getting the
> > values in the array for which I'm looking at the maxima is only one step
> > in a more complex piece of code for which I need the indexes along the
> > second axis of the array. I would like to avoid to have to iterate the
> > array more than once.
> >
> > Thank you!
> >
> > Cheers,
> > Dan
> >
> >
> >> On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi <daniele at grinta.net
> >> <mailto:daniele at grinta.net>> wrote:
> >>
> >>     Hello,
> >>
> >>     this is a very basic question, but I cannot find a satisfying
> answer.
> >>     Assume a is a 2D array and that I get the index of the maximum value
> >>     along the second dimension:
> >>
> >>     i = a.argmax(axis=1)
> >>
> >>     Is there a better way to get the value of the maximum array entries
> >>     along the second axis other than:
> >>
> >>     v = a[np.arange(len(a)), i]
> >>
> >>     ??
> >>
> >>     Thank you.
> >>
> >>     Cheers,
> >>     Daniele
> >>     _______________________________________________
> >>     NumPy-Discussion mailing list
> >>     NumPy-Discussion at python.org <mailto:NumPy-Discussion at python.org>
> >>     https://mail.python.org/mailman/listinfo/numpy-discussion
> >>
> >>
> >> _______________________________________________
> >> NumPy-Discussion mailing list
> >> NumPy-Discussion at python.org
> >> https://mail.python.org/mailman/listinfo/numpy-discussion
> >>
> >
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion at python.org
> > https://mail.python.org/mailman/listinfo/numpy-discussion
> >
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From daniele at grinta.net  Fri Nov  1 12:57:51 2019
From: daniele at grinta.net (Daniele Nicolodi)
Date: Fri, 1 Nov 2019 10:57:51 -0600
Subject: [Numpy-discussion] argmax() indexes to value
In-Reply-To: <CAHedU1SQvUcgwyBVpuSbxXV=TCcxW7Xa-kU7PnUzksyQ4JfmYQ@mail.gmail.com>
References: <3c8ed265-06bb-c527-f103-af827ee6597d@grinta.net>
 <CAG3t+pHA+hd=1AJW_63JhQQKfvh-0MDu-jMUOAXAqNWywnTt-Q@mail.gmail.com>
 <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
 <CAHedU1Q_NsjHzzuGYpoGAzfaiOQPArMypBDqmCui2Z86DBRuHQ@mail.gmail.com>
 <7ea8b98b-5c72-5475-025b-3b86292fec0b@grinta.net>
 <CAHedU1SQvUcgwyBVpuSbxXV=TCcxW7Xa-kU7PnUzksyQ4JfmYQ@mail.gmail.com>
Message-ID: <998b4840-efa6-22a4-4d08-98e15f8c516f@grinta.net>

On 31-10-2019 01:44, Elliot Hallmark wrote:
> Depends on how big your array is.? Numpy C code is 150x+ faster than
> python overhead. Fancy indexing can be expensive in my experience.?
> Without trying I'd guess arr[:, argmax(arr, axis=1)] does what you want,

It does not.

> but even if it is, try profiling the two and see.? I highly doubt such
> would be even 1% of your run time, but it depends on what your doing.?
> Part of python with numpy is slightly not caring about big O because
> trying to be clever is rarely worth it in my experience.

Why do you think I am asking for advice on how to do the complicated
thing?  If a 2x increase in the run time would have not mattered, I
would not have bothered. Don't you think?

I appreciate the effort spent guiding inexperienced users toward
pragmatic solutions and not over complicate their code. However, it is
disappointing to have very precise questions dismissed as "that is
complicated, thus you don't really want to do it".

Best,
Dan

> On Thu, Oct 31, 2019 at 12:35 AM Daniele Nicolodi <daniele at grinta.net
> <mailto:daniele at grinta.net>> wrote:
> 
>     On 30/10/2019 22:42, Elliot Hallmark wrote:
>     > I wouldn't be surprised at all if calling max in addition to argmax
>     > wasn't as fast or faster than indexing the array using argmax.
>     > Regardless, just use that then profile when you're done with the
>     > whole?thing and see if there's any gains to be made. Very likely
>     not here.
> 
>     Hi Elliot,
> 
>     how do you arrive at this conclusion? np.argmax() and np.max() are O(N)
>     while indexing is O(1) thus I don't see how you can conclude that
>     running both np.argmax() and np.max() on the input array is going to
>     incur in a small penalty compared to running np.argmax() and then
>     indexing.
> 
>     Cheers,
>     Dan
> 
> 
>     >
>     > -elliot
>     >
>     > On Wed, Oct 30, 2019, 10:32 PM Daniele Nicolodi
>     <daniele at grinta.net <mailto:daniele at grinta.net>
>     > <mailto:daniele at grinta.net <mailto:daniele at grinta.net>>> wrote:
>     >
>     >? ? ?On 30/10/2019 19:10, Neal Becker wrote:
>     >? ? ?> max(axis=1)?
>     >
>     >? ? ?Hi Neal,
>     >
>     >? ? ?I should have been more precise in stating the problem.
>     Getting the
>     >? ? ?values in the array for which I'm looking at the maxima is
>     only one step
>     >? ? ?in a more complex piece of code for which I need the indexes
>     along the
>     >? ? ?second axis of the array. I would like to avoid to have to
>     iterate the
>     >? ? ?array more than once.
>     >
>     >? ? ?Thank you!
>     >
>     >? ? ?Cheers,
>     >? ? ?Dan
>     >
>     >
>     >? ? ?> On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi
>     <daniele at grinta.net <mailto:daniele at grinta.net>
>     >? ? ?<mailto:daniele at grinta.net <mailto:daniele at grinta.net>>
>     >? ? ?> <mailto:daniele at grinta.net <mailto:daniele at grinta.net>
>     <mailto:daniele at grinta.net <mailto:daniele at grinta.net>>>> wrote:
>     >? ? ?>
>     >? ? ?>? ? ?Hello,
>     >? ? ?>
>     >? ? ?>? ? ?this is a very basic question, but I cannot find a
>     satisfying
>     >? ? ?answer.
>     >? ? ?>? ? ?Assume a is a 2D array and that I get the index of the
>     maximum
>     >? ? ?value
>     >? ? ?>? ? ?along the second dimension:
>     >? ? ?>
>     >? ? ?>? ? ?i = a.argmax(axis=1)
>     >? ? ?>
>     >? ? ?>? ? ?Is there a better way to get the value of the maximum array
>     >? ? ?entries
>     >? ? ?>? ? ?along the second axis other than:
>     >? ? ?>
>     >? ? ?>? ? ?v = a[np.arange(len(a)), i]
>     >? ? ?>
>     >? ? ?>? ? ???
>     >? ? ?>
>     >? ? ?>? ? ?Thank you.
>     >? ? ?>
>     >? ? ?>? ? ?Cheers,
>     >? ? ?>? ? ?Daniele
>     >? ? ?>? ? ?_______________________________________________
>     >? ? ?>? ? ?NumPy-Discussion mailing list
>     >? ? ?>? ? ?NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>
>     >? ? ?<mailto:NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>>
>     >? ? ?<mailto:NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>
>     >? ? ?<mailto:NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>>>
>     >? ? ?>? ? ?https://mail.python.org/mailman/listinfo/numpy-discussion
>     >? ? ?>
>     >? ? ?>
>     >? ? ?> _______________________________________________
>     >? ? ?> NumPy-Discussion mailing list
>     >? ? ?> NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>
>     <mailto:NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>>
>     >? ? ?> https://mail.python.org/mailman/listinfo/numpy-discussion
>     >? ? ?>
>     >
>     >? ? ?_______________________________________________
>     >? ? ?NumPy-Discussion mailing list
>     >? ? ?NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>
>     <mailto:NumPy-Discussion at python.org
>     <mailto:NumPy-Discussion at python.org>>
>     >? ? ?https://mail.python.org/mailman/listinfo/numpy-discussion
>     >
>     >
>     > _______________________________________________
>     > NumPy-Discussion mailing list
>     > NumPy-Discussion at python.org <mailto:NumPy-Discussion at python.org>
>     > https://mail.python.org/mailman/listinfo/numpy-discussion
>     >
> 
>     _______________________________________________
>     NumPy-Discussion mailing list
>     NumPy-Discussion at python.org <mailto:NumPy-Discussion at python.org>
>     https://mail.python.org/mailman/listinfo/numpy-discussion
> 
> 
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
> 


From daniele at grinta.net  Fri Nov  1 13:08:26 2019
From: daniele at grinta.net (Daniele Nicolodi)
Date: Fri, 1 Nov 2019 11:08:26 -0600
Subject: [Numpy-discussion] argmax() indexes to value
In-Reply-To: <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>
References: <3c8ed265-06bb-c527-f103-af827ee6597d@grinta.net>
 <CAG3t+pHA+hd=1AJW_63JhQQKfvh-0MDu-jMUOAXAqNWywnTt-Q@mail.gmail.com>
 <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
 <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>
Message-ID: <d34d0551-6356-d722-4acf-12e0f2ae84cf@grinta.net>

On 01-11-2019 09:51, Allan Haldane wrote:
> my thought was to try `take` or `take_along_axis`:
> 
>    ind = np.argmin(a, axis=1)
>    np.take_along_axis(a, ind[:,None], axis=1)
> 
> But those functions tend to simply fall back to fancy indexing, and are
> pretty slow. On my system plain fancy indexing is fastest:
> 
>>>> %timeit a[np.arange(N),ind]
> 1.58 ?s ? 18.1 ns per loop
>>>> %timeit np.take_along_axis(a, ind[:,None], axis=1)
> 6.49 ?s ? 57.3 ns per loop
>>>> %timeit np.min(a, axis=1)
> 9.51 ?s ? 64.1 ns per loop
> 
> Probably `take_along_axis` was designed with uses like yours in mind,
> but it is not very optimized.

Hi Allan,

after scanning the documentation once more I found `take_along_axis` and
was hoping that it implements some smart trick that does not involve
generating and indexing array, but apparently that is what it does.

Given the current numpy primitives, I don't see a way to optimize it
further and keep it generic. I think the direct fancy indexing is faster
in your case because of overhead in handling the generic case and not
because of algorithmic inefficiency (from the run times you report it
seems that your test array was fairly small).

Thank you.

Cheers,
Dan

From perimosocordiae at gmail.com  Fri Nov  1 14:31:46 2019
From: perimosocordiae at gmail.com (CJ Carey)
Date: Fri, 1 Nov 2019 14:31:46 -0400
Subject: [Numpy-discussion] argmax() indexes to value
In-Reply-To: <d34d0551-6356-d722-4acf-12e0f2ae84cf@grinta.net>
References: <3c8ed265-06bb-c527-f103-af827ee6597d@grinta.net>
 <CAG3t+pHA+hd=1AJW_63JhQQKfvh-0MDu-jMUOAXAqNWywnTt-Q@mail.gmail.com>
 <526001fa-32b9-a948-7101-e742ee1f0d7d@grinta.net>
 <9327350c-a24a-77ba-c8e5-3115567f6cab@gmail.com>
 <d34d0551-6356-d722-4acf-12e0f2ae84cf@grinta.net>
Message-ID: <CAEfGn+z7nybasEX02Nv398kt4rJ77Zq=SrJWXJUzUMUGrLv8bg@mail.gmail.com>

You could move some of the cost to index-creation time by converting the
per-row indices into flattened indices:

In [1]: a = np.random.random((5, 6))


In [2]: i = a.argmax(axis=1)


In [3]: a[np.arange(len(a)), i]

Out[3]: array([0.95774465, 0.90940106, 0.98025448, 0.97836906, 0.80483784])

In [4]: f = np.ravel_multi_index((np.arange(len(a)), i), a.shape)


In [5]: a.flat[f]

Out[5]: array([0.95774465, 0.90940106, 0.98025448, 0.97836906, 0.80483784])


I haven't benchmarked, but I suspect this will be faster if you're using
the same index multiple times.
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From pierre.augier at univ-grenoble-alpes.fr  Mon Nov  4 16:54:00 2019
From: pierre.augier at univ-grenoble-alpes.fr (PIERRE AUGIER)
Date: Mon, 4 Nov 2019 22:54:00 +0100 (CET)
Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
 accelerators
In-Reply-To: <mailman.28.1572624002.7908.numpy-discussion@python.org>
References: <mailman.28.1572624002.7908.numpy-discussion@python.org>
Message-ID: <367899200.8647963.1572904440461.JavaMail.zimbra@univ-grenoble-alpes.fr>

Dear Python-Numpy community,

Transonic is a pure Python package to easily accelerate modern Python-Numpy code with different accelerators (currently Cython, Pythran and Numba).

I'm trying to get some funding for this project. The related work would benefit in particular to Cython, Numba, Pythran and Xtensor.

To obtain this funding, we really need some feedback from some people knowing the subject of performance with Python-Numpy code.

That's one of the reason why we wrote this long and serious text on Transonic Vision: http://tiny.cc/transonic-vision. We describe some issues (perf for numerical kernels, incompatible accelerators, community split between experts and simple users, ...) and possible improvements.

Help would be very much appreciated.

Now a coding riddle:

import numpy as np
from transonic import jit

@jit(native=True, xsimd=True)
def fxfy(ft, fn, theta):
    sin_theta = np.sin(theta)
    cos_theta = np.cos(theta)
    fx = cos_theta * ft - sin_theta * fn
    fy = sin_theta * ft + cos_theta * fn
    return fx, fy

@jit(native=True, xsimd=True)
def fxfy_loops(ft, fn, theta):
    n0 = theta.size
    fx = np.empty_like(ft)
    fy = np.empty_like(fn)
    for index in range(n0):
        sin_theta = np.sin(theta[index])
        cos_theta = np.cos(theta[index])
        fx[index] = cos_theta * ft[index] - sin_theta * fn[index]
        fy[index] = sin_theta * ft[index] + cos_theta * fn[index]
    return fx, fy

How can be compared the performances of these functions with pure Numpy, Numba and Pythran ?

You can find out the answer in our note http://tiny.cc/transonic-vision :-)

Pierre

> Message: 1
> Date: Thu, 31 Oct 2019 21:16:06 +0100 (CET)
> From: PIERRE AUGIER <pierre.augier at univ-grenoble-alpes.fr>
> To: numpy-discussion at python.org
> Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
>	accelerators
> Message-ID:
>	<1080118635.5930814.1572552966711.JavaMail.zimbra at univ-grenoble-alpes.fr>
>	
> Content-Type: text/plain; charset=utf-8
> 
> Dear Python-Numpy community,
> 
> Few years ago I started to use a lot Python and Numpy for science. I'd like to
> thanks all people who contribute to this fantastic community.
> 
> I used a lot Cython, Pythran and Numba and for the FluidDyn project, we created
> Transonic, a pure Python package to easily accelerate modern Python-Numpy code
> with different accelerators. We wrote a long and serious text to explain why we
> think Transonic could have a positive impact on the scientific Python
> ecosystem.
> 
> Here it is: http://tiny.cc/transonic-vision
> 
> Feedback and discussions would be greatly appreciated!
> 
> Pierre
> 
> --
> Pierre Augier - CR CNRS                 http://www.legi.grenoble-inp.fr
> LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels
> BP53, 38041 Grenoble Cedex, France                tel:+33.4.56.52.86.16

From mikofski at berkeley.edu  Wed Nov  6 14:18:31 2019
From: mikofski at berkeley.edu (Dr. Mark Alexander Mikofski PhD)
Date: Wed, 6 Nov 2019 11:18:31 -0800
Subject: [Numpy-discussion] [ANN] pvfactors: a view-factor network model for
 bifacial PV by SunPower
Message-ID: <CAEqRcW35TTwNPPhX11cdDCt70SCVR0M+ZHKUAX-_7MQc-F+FnQ@mail.gmail.com>

SunPower has just released the latest version of pvfactors (v1.3.0), a
Python package for modeling the incident irradiance on bifacial
photovoltaic (PV) using the network radiosity model. Bifacial PV are solar
panels that can generate energy from both the front and back surfaces.

PyPI: https://pypi.org/project/pvfactors/
GitHub: https://github.com/SunPower/pvfactors/releases


Thanks SunPower!

-- 
Mark Mikofski, PhD (2005)
*Fiat Lux*
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From ralf.gommers at gmail.com  Wed Nov  6 23:49:08 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Wed, 6 Nov 2019 23:49:08 -0500
Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
 accelerators
In-Reply-To: <367899200.8647963.1572904440461.JavaMail.zimbra@univ-grenoble-alpes.fr>
References: <mailman.28.1572624002.7908.numpy-discussion@python.org>
 <367899200.8647963.1572904440461.JavaMail.zimbra@univ-grenoble-alpes.fr>
Message-ID: <CABL7CQhr6ps25Yrp_pAF8vWVLS3_2=Mw4bCOXb0H1dvU9AjTMw@mail.gmail.com>

On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER <
pierre.augier at univ-grenoble-alpes.fr> wrote:

> Dear Python-Numpy community,
>
> Transonic is a pure Python package to easily accelerate modern
> Python-Numpy code with different accelerators (currently Cython, Pythran
> and Numba).
>
> I'm trying to get some funding for this project. The related work would
> benefit in particular to Cython, Numba, Pythran and Xtensor.
>
> To obtain this funding, we really need some feedback from some people
> knowing the subject of performance with Python-Numpy code.
>
> That's one of the reason why we wrote this long and serious text on
> Transonic Vision: http://tiny.cc/transonic-vision. We describe some
> issues (perf for numerical kernels, incompatible accelerators, community
> split between experts and simple users, ...) and possible improvements.
>

Thanks Pierre, that's a very interesting vision paper.

In case you haven't seen it, there was a discussion on the pandas-dev
mailing list a couple of weeks ago about adopting Numba as a dependency
(and issues with that).

Your comment on my assessment from 1.5 years ago being a little unfair to
Pythran may be true - not sure it was at the time, but Pythran seems to
mature nicely.

The ability to switch between just-in-time and ahead-of-time compilation is
nice. One thing I noticed is that this actual switching is not completely
fluent: the jit and boost decorators have different signatures, and there's
no way to globally switch behavior (say with an env var, as for backend
selection).


> Help would be very much appreciated.
>

I'd be interested to help think about adoption and/or funding.

Cheers,
Ralf


>
> Now a coding riddle:
>
> import numpy as np
> from transonic import jit
>
> @jit(native=True, xsimd=True)
> def fxfy(ft, fn, theta):
>     sin_theta = np.sin(theta)
>     cos_theta = np.cos(theta)
>     fx = cos_theta * ft - sin_theta * fn
>     fy = sin_theta * ft + cos_theta * fn
>     return fx, fy
>
> @jit(native=True, xsimd=True)
> def fxfy_loops(ft, fn, theta):
>     n0 = theta.size
>     fx = np.empty_like(ft)
>     fy = np.empty_like(fn)
>     for index in range(n0):
>         sin_theta = np.sin(theta[index])
>         cos_theta = np.cos(theta[index])
>         fx[index] = cos_theta * ft[index] - sin_theta * fn[index]
>         fy[index] = sin_theta * ft[index] + cos_theta * fn[index]
>     return fx, fy
>
> How can be compared the performances of these functions with pure Numpy,
> Numba and Pythran ?
>
> You can find out the answer in our note http://tiny.cc/transonic-vision
> :-)
>
> Pierre
>
> > Message: 1
> > Date: Thu, 31 Oct 2019 21:16:06 +0100 (CET)
> > From: PIERRE AUGIER <pierre.augier at univ-grenoble-alpes.fr>
> > To: numpy-discussion at python.org
> > Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
> >       accelerators
> > Message-ID:
> >       <
> 1080118635.5930814.1572552966711.JavaMail.zimbra at univ-grenoble-alpes.fr>
> >
> > Content-Type: text/plain; charset=utf-8
> >
> > Dear Python-Numpy community,
> >
> > Few years ago I started to use a lot Python and Numpy for science. I'd
> like to
> > thanks all people who contribute to this fantastic community.
> >
> > I used a lot Cython, Pythran and Numba and for the FluidDyn project, we
> created
> > Transonic, a pure Python package to easily accelerate modern
> Python-Numpy code
> > with different accelerators. We wrote a long and serious text to explain
> why we
> > think Transonic could have a positive impact on the scientific Python
> > ecosystem.
> >
> > Here it is: http://tiny.cc/transonic-vision
> >
> > Feedback and discussions would be greatly appreciated!
> >
> > Pierre
> >
> > --
> > Pierre Augier - CR CNRS                 http://www.legi.grenoble-inp.fr
> > LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels
> > BP53, 38041 Grenoble Cedex, France                tel:+33.4.56.52.86.16
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From tyler.je.reddy at gmail.com  Sat Nov  9 17:10:15 2019
From: tyler.je.reddy at gmail.com (Tyler Reddy)
Date: Sat, 9 Nov 2019 15:10:15 -0700
Subject: [Numpy-discussion] ANN: SciPy 1.3.2
Message-ID: <CAHPuU_Z1t+zhYVB4YbTj+WTKjfomqxbLr968xVevb5G2QRPJOQ@mail.gmail.com>

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

Hi all,

On behalf of the SciPy development team I'm pleased to announce
the release of SciPy 1.3.2, a maintenance and bug fix release that
adds support for Python 3.8.

Sources and binary wheels can be found at:
https://pypi.org/project/scipy/
and at: https://github.com/scipy/scipy/releases/tag/v1.3.2

One of a few ways to install this release with pip:

pip install scipy==1.3.2

==========================
SciPy 1.3.2 Release Notes
==========================

SciPy 1.3.2 is a bug-fix and maintenance release that adds
support for Python 3.8.

Authors
=======

* CJ Carey
* Dany Vohl
* Martin Gauch +
* Ralf Gommers
* Matt Haberland
* Eric Larson
* Nikolay Mayorov
* Sam McCormack +
* Andrew Nelson
* Tyler Reddy
* Pauli Virtanen
* Huize Wang +
* Warren Weckesser
* Joseph Weston +

A total of 14 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully
complete.

Issues closed for 1.3.2
-------------------------------

* `#4915 <https://github.com/scipy/scipy/issues/4915>`__: Bug in
unique_roots in scipy.signal.signaltools.py for roots...
* `#5161 <https://github.com/scipy/scipy/issues/5161>`__: Optimizers
reporting success when the minimum is NaN
* `#5546 <https://github.com/scipy/scipy/issues/5546>`__: ValueError raised
if scipy.sparse.linalg.expm recieves array...
* `#10124 <https://github.com/scipy/scipy/issues/10124>`__:
linprog(method='revised simplex') doctest bug
* `#10609 <https://github.com/scipy/scipy/issues/10609>`__: Graph shortest
path with Floyd-Warshall removes explicit zeros.
* `#10658 <https://github.com/scipy/scipy/issues/10658>`__: BUG: stats:
Formula for the variance of the noncentral F distribution...
* `#10695 <https://github.com/scipy/scipy/issues/10695>`__: BUG:
Assignation issues in csr_matrix with fancy indexing
* `#10846 <https://github.com/scipy/scipy/issues/10846>`__: root_scalar
fails when passed a function wrapped with functools.lru_cache
* `#10902 <https://github.com/scipy/scipy/issues/10902>`__: CI: travis osx
build failure
* `#10967 <https://github.com/scipy/scipy/issues/10967>`__: macOS build
failure in SuperLU on maintenance/1.3.x
* `#10976 <https://github.com/scipy/scipy/issues/10976>`__: Typo in
sp.stats.wilcoxon docstring


Pull requests for 1.3.2
------------------------------

* `#10498 <https://github.com/scipy/scipy/pull/10498>`__: TST: optimize:
fixed \`linprog\` \`"disp": True\` bug
* `#10536 <https://github.com/scipy/scipy/pull/10536>`__: CI: add 3.8-dev
to travis
* `#10671 <https://github.com/scipy/scipy/pull/10671>`__: BUG: stats: Fix
the formula for the variance of the noncentral...
* `#10693 <https://github.com/scipy/scipy/pull/10693>`__: BUG:
ScalarFunction stores original array
* `#10700 <https://github.com/scipy/scipy/pull/10700>`__: BUG: sparse:
Loosen checks on sparse fancy assignment
* `#10709 <https://github.com/scipy/scipy/pull/10709>`__: BUG: Fix
floyd_warshall to support zero-weight edges
* `#10756 <https://github.com/scipy/scipy/pull/10756>`__: BUG: optimize:
ensure solvers exit with success=False for nan...
* `#10833 <https://github.com/scipy/scipy/pull/10833>`__: BUG: Fix
subspace_angles for complex values
* `#10882 <https://github.com/scipy/scipy/pull/10882>`__: BUG:
sparse/arpack: fix incorrect code for complex hermitian...
* `#10891 <https://github.com/scipy/scipy/pull/10891>`__: BUG: make
C-implemented root finders work with functools.lru_cache
* `#10906 <https://github.com/scipy/scipy/pull/10906>`__: BUG:
sparse/linalg: fix expm for np.matrix inputs
* `#10917 <https://github.com/scipy/scipy/pull/10917>`__: CI: fix travis
osx CI
* `#10930 <https://github.com/scipy/scipy/pull/10930>`__: MAINT: Updates
for 3.8
* `#10938 <https://github.com/scipy/scipy/pull/10938>`__: MAINT: Add Python
3.8 to pyproject.toml
* `#10943 <https://github.com/scipy/scipy/pull/10943>`__: BLD: update
Cython version to 0.29.13
* `#10961 <https://github.com/scipy/scipy/pull/10961>`__: BUG: Fix
signal.unique_roots
* `#10971 <https://github.com/scipy/scipy/pull/10971>`__: MAINT: use 3.8
stable in CI
* `#10977 <https://github.com/scipy/scipy/pull/10977>`__: DOC: Fix typo in
sp.stats.wilcoxon docsting
* `#11025 <https://github.com/scipy/scipy/pull/11025>`__: Update
_peak_finding.py

Checksums
=========

MD5
~~~

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From charlesr.harris at gmail.com  Sat Nov  9 19:52:17 2019
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sat, 9 Nov 2019 17:52:17 -0700
Subject: [Numpy-discussion] ANN: SciPy 1.3.2
In-Reply-To: <CAHPuU_Z1t+zhYVB4YbTj+WTKjfomqxbLr968xVevb5G2QRPJOQ@mail.gmail.com>
References: <CAHPuU_Z1t+zhYVB4YbTj+WTKjfomqxbLr968xVevb5G2QRPJOQ@mail.gmail.com>
Message-ID: <CAB6mnxL2xMihjuJ4jL9qOOcvTcV6SEYhY01CMS+LemZA3sF7Kg@mail.gmail.com>

On Sat, Nov 9, 2019 at 3:11 PM Tyler Reddy <tyler.je.reddy at gmail.com> wrote:

> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA256
>
> Hi all,
>
> On behalf of the SciPy development team I'm pleased to announce
> the release of SciPy 1.3.2, a maintenance and bug fix release that
> adds support for Python 3.8.
>
>
<snip>

Good job... Chuck

>
>
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From charlesr.harris at gmail.com  Sun Nov 10 21:32:41 2019
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sun, 10 Nov 2019 19:32:41 -0700
Subject: [Numpy-discussion] NumPy 1.17.4 release.
Message-ID: <CAB6mnx+446Kpt_HyJ0MHNEWmGkp6+AAAePQACc-Lnr2Fk67rjg@mail.gmail.com>

Hi All,

On behalf of the NumPy team I am pleased to announce that NumPy 1.17.4 has
been released. This is a bugfix release. The Python versions supported in
this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support
and OpenBLAS >= 3.7 to avoid wrong results on the Skylake architecture. The
NumPy Wheels for this release can be downloaded from PyPI
<https://pypi.org/project/numpy/1.17.4/>, source archives and release notes
are available from Github
<https://github.com/numpy/numpy/releases/tag/v1.17.4>.

*Highlights*

   - Fixed `random.random_integers` biased generation of 8 and 16 bit
   integers.
   - Fixed `np.einsum` regression on Power9 and z/Linux.
   - Fixed histogram problem with signed integer arrays

*Contributors*

A total of 5 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Charles Harris
   - Chris Burr +
   - Matti Picus
   - Qiming Sun +
   - Warren Weckesser


*Pull requests merged*
A total of 8 pull requests were merged for this release.

   - gh-14758: BLD: Declare support for python 3.8
   - gh-14781: BUG: Random: biased samples from integers() with 8 or 16
   bit...
   - gh-14851: BUG: Fix `_ctypes` class circular reference. (gh-13808)
   - gh-14852: BLD: Add 'apt update' to shippable
   - gh-14855: BUG: Fix `np.einsum` errors on Power9 Linux and z/Linux
   - gh-14857: BUG: Fix histogram problem with signed integer arrays.
   - gh-14858: BLD: Prevent -flto from optimising long double
   representation...
   - gh-14866: MAINT: move buffer.h -> npy_buffer.h to avoid conflicts


Cheers,

Charles Harris
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From matti.picus at gmail.com  Mon Nov 11 19:40:03 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Mon, 11 Nov 2019 17:40:03 -0700
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for NumPy
Message-ID: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>

Apple has dropped support for Accelerate. It has bugs that have not been 
fixed, and is closed source so we cannot fix them ourselves. We have 
been getting a handful of reports from users who end up building NumPy 
on macOS, and inadvertently link to Accelerate, then end up with wrong 
linalg results. In PR 14880 https://github.com/numpy/numpy/pull/14880 I 
propose to disallow finding it when building NumPy. At this time it will 
remain in distutils as one of the backends to support users, but how do 
people feel about a future PR to totally remove it?


Matti


From insertinterestingnamehere at gmail.com  Mon Nov 11 21:27:26 2019
From: insertinterestingnamehere at gmail.com (Ian Henriksen)
Date: Mon, 11 Nov 2019 20:27:26 -0600
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for
 NumPy
In-Reply-To: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
References: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
Message-ID: <CAPWbfpQvYif9iai9tLoXCBBDVGdKrLmFiazN9Th6AKmTJt8m7w@mail.gmail.com>

Extra data point here: SciPy already dropped support for Accelerate as of
version 1.2.0.

Best,

Ian Henriksen


On Mon, Nov 11, 2019 at 6:40 PM Matti Picus <matti.picus at gmail.com> wrote:

> Apple has dropped support for Accelerate. It has bugs that have not been
> fixed, and is closed source so we cannot fix them ourselves. We have
> been getting a handful of reports from users who end up building NumPy
> on macOS, and inadvertently link to Accelerate, then end up with wrong
> linalg results. In PR 14880 https://github.com/numpy/numpy/pull/14880 I
> propose to disallow finding it when building NumPy. At this time it will
> remain in distutils as one of the backends to support users, but how do
> people feel about a future PR to totally remove it?
>
>
> Matti
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From derek at astro.physik.uni-goettingen.de  Tue Nov 12 09:30:24 2019
From: derek at astro.physik.uni-goettingen.de (Derek Homeier)
Date: Tue, 12 Nov 2019 15:30:24 +0100
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for
 NumPy
In-Reply-To: <CAPWbfpQvYif9iai9tLoXCBBDVGdKrLmFiazN9Th6AKmTJt8m7w@mail.gmail.com>
References: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
 <CAPWbfpQvYif9iai9tLoXCBBDVGdKrLmFiazN9Th6AKmTJt8m7w@mail.gmail.com>
Message-ID: <62767AE8-54E4-4762-B128-F4A19922B342@astro.physik.uni-goettingen.de>

On 12 Nov 2019, at 3:27 am, Ian Henriksen <insertinterestingnamehere at gmail.com> wrote:
> 
> Extra data point here: SciPy already dropped support for Accelerate as of version 1.2.0.
> 
> Best,
> 
> Ian Henriksen
> 
> 
> On Mon, Nov 11, 2019 at 6:40 PM Matti Picus <matti.picus at gmail.com> wrote:
> Apple has dropped support for Accelerate. It has bugs that have not been 
> fixed, and is closed source so we cannot fix them ourselves. We have 
> been getting a handful of reports from users who end up building NumPy 
> on macOS, and inadvertently link to Accelerate, then end up with wrong 
> linalg results. In PR 14880 https://github.com/numpy/numpy/pull/14880 I 
> propose to disallow finding it when building NumPy. At this time it will 
> remain in distutils as one of the backends to support users, but how do 
> people feel about a future PR to totally remove it?

+1 from this side - when switching the packaged version of Scipy to OpenBLAS
(already taking Numpy along the way) I noticed barely any performance penalties
(if - with some benchmarks - it wasn?t actually faster than Accelerate).

Cheers,
				Derek


From reed at cs.unc.edu  Tue Nov 12 12:35:39 2019
From: reed at cs.unc.edu (Andrew Reed)
Date: Tue, 12 Nov 2019 12:35:39 -0500
Subject: [Numpy-discussion] Code Review for the Two-Sided Geometric
 Distribution - PR 14890
Message-ID: <CAL7O2ZvOON=xX5OhmR3sOn9z8DEfWi2nFdQ+VHkafQQCuwh-cA@mail.gmail.com>

 All,

I created a PR for the "two-sided geometric distribution":
https://github.com/numpy/numpy/pull/14890

This is my first attempt at contributing to any sort of open source
project, and I can already see that some of the post-commit checks have
failed.  I will try to dig into those.  Also, I was unable to get Sphinx to
build a reference to my added function, so I have not been able to verify
if the docstring renders properly.

Additional Notes:
(1) The first paper that I reference presented this distribution as the
"discrete Laplace distribution."  This is also how the R programming
language refers to this distribution.  SciPy, however, appears to use a
different distribution for what it calls the "discrete Laplacian
distribution."  Personally, I feel that referring to the version in my PR
as the "two-sided geometric distribution" better conveys how it works.

(2) I describe the parameter 'alpha' as the distribution's "rate of
decay."  I can see, though, that this may be an inaccurate way to describe
'alpha' since it doesn't work exactly like a "rate of decay" in the Physics
sense.  Perhaps I should have referred to it as the "distribution's
constant ratio."  Either way, I think the documentation should be clear
enough that a user would not confuse 'alpha' with 'p' from the geometric
function (caveat: once the distribution moves away from zero, alpha works
like 1 - p).

Respectfully,
Andrew Reed
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From pierre.augier at univ-grenoble-alpes.fr  Tue Nov 12 16:00:18 2019
From: pierre.augier at univ-grenoble-alpes.fr (PIERRE AUGIER)
Date: Tue, 12 Nov 2019 22:00:18 +0100 (CET)
Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
 accelerators
In-Reply-To: <mailman.27.1573146004.2367.numpy-discussion@python.org>
References: <mailman.27.1573146004.2367.numpy-discussion@python.org>
Message-ID: <134533761.17765043.1573592418664.JavaMail.zimbra@univ-grenoble-alpes.fr>


> Date: Wed, 6 Nov 2019 23:49:08 -0500
> From: Ralf Gommers <ralf.gommers at gmail.com>
> To: Discussion of Numerical Python <numpy-discussion at python.org>
> Subject: Re: [Numpy-discussion] Transonic Vision: unifying
>	Python-Numpy accelerators
> Message-ID:
>	<CABL7CQhr6ps25Yrp_pAF8vWVLS3_2=Mw4bCOXb0H1dvU9AjTMw at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
> 
> On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER <
> pierre.augier at univ-grenoble-alpes.fr> wrote:
> 
>> Dear Python-Numpy community,
>>
>> Transonic is a pure Python package to easily accelerate modern
>> Python-Numpy code with different accelerators (currently Cython, Pythran
>> and Numba).
>>
>> I'm trying to get some funding for this project. The related work would
>> benefit in particular to Cython, Numba, Pythran and Xtensor.
>>
>> To obtain this funding, we really need some feedback from some people
>> knowing the subject of performance with Python-Numpy code.
>>
>> That's one of the reason why we wrote this long and serious text on
>> Transonic Vision: http://tiny.cc/transonic-vision. We describe some
>> issues (perf for numerical kernels, incompatible accelerators, community
>> split between experts and simple users, ...) and possible improvements.
>>
> 
> Thanks Pierre, that's a very interesting vision paper.

Thanks Ralf for this kind and interesting reply!

> 
> In case you haven't seen it, there was a discussion on the pandas-dev
> mailing list a couple of weeks ago about adopting Numba as a dependency
> (and issues with that).
> 
> Your comment on my assessment from 1.5 years ago being a little unfair to
> Pythran may be true - not sure it was at the time, but Pythran seems to
> mature nicely.
> 
> The ability to switch between just-in-time and ahead-of-time compilation is
> nice. One thing I noticed is that this actual switching is not completely
> fluent: the jit and boost decorators have different signatures, and there's
> no way to globally switch behavior (say with an env var, as for backend
> selection).

Yes, it seems evident now but I forgot to update the jit decorators when I was working on the boost decorator.  
My first "targets" for Transonic are packages for which the ahead-of-time mode seems more adapted.

This incompatibility between the 2 main decorators used in Transonic will soon be fixed!

Regarding the way to globally switch behavior, I'll open a dedicated issue.

>> Help would be very much appreciated.
>>
> 
> I'd be interested to help think about adoption and/or funding.
> 
> Cheers,
> Ralf
>

As you've seen with the jit/boost incompatibility, I guess API design would be better if people knowing the subject could be included in some discussions.

For example, I had to design the Python API for type declaration of arrays (see https://transonic.readthedocs.io/en/latest/generated/transonic.typing.html) since I didn't find anything adapted. My implementation is not great neither since types in transonic.typing and in `typing` are right now not compatible ! (However, it won't be difficult to fix that)

Another API design that needs to be thought is about user-defined types in Transonic. This is for future because Pythran does not currently support that, but I think we will have to be able to support kind of dataclass, something like the equivalent of C struct (corresponding to Cython `cdef class` and Numba `jitclass`).

A more theoretical subject that would be interesting to investigate is about the subset of Python-Numpy that can and should be implemented by accelerators. For example, I think a function having different branches with different types for the returned objects depending of runtime values cannot be rewritten as efficient modern C++.

If you know people potentially interested to discuss about these subjects, please tell me.

Cheers,
Pierre


From ralf.gommers at gmail.com  Tue Nov 12 17:42:02 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 12 Nov 2019 14:42:02 -0800
Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
 accelerators
In-Reply-To: <134533761.17765043.1573592418664.JavaMail.zimbra@univ-grenoble-alpes.fr>
References: <mailman.27.1573146004.2367.numpy-discussion@python.org>
 <134533761.17765043.1573592418664.JavaMail.zimbra@univ-grenoble-alpes.fr>
Message-ID: <CABL7CQh78SQLbuH__WbuC=kMZ3_kcKn4KH4d1b-R-CHrgYPmAw@mail.gmail.com>

On Tue, Nov 12, 2019 at 1:09 PM PIERRE AUGIER <
pierre.augier at univ-grenoble-alpes.fr> wrote:

>
> > Date: Wed, 6 Nov 2019 23:49:08 -0500
> > From: Ralf Gommers <ralf.gommers at gmail.com>
> > To: Discussion of Numerical Python <numpy-discussion at python.org>
> > Subject: Re: [Numpy-discussion] Transonic Vision: unifying
> >       Python-Numpy accelerators
> > Message-ID:
> >       <CABL7CQhr6ps25Yrp_pAF8vWVLS3_2=
> Mw4bCOXb0H1dvU9AjTMw at mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> > On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER <
> > pierre.augier at univ-grenoble-alpes.fr> wrote:
> >
> >> Dear Python-Numpy community,
> >>
> >> Transonic is a pure Python package to easily accelerate modern
> >> Python-Numpy code with different accelerators (currently Cython, Pythran
> >> and Numba).
> >>
> >> I'm trying to get some funding for this project. The related work would
> >> benefit in particular to Cython, Numba, Pythran and Xtensor.
> >>
> >> To obtain this funding, we really need some feedback from some people
> >> knowing the subject of performance with Python-Numpy code.
> >>
> >> That's one of the reason why we wrote this long and serious text on
> >> Transonic Vision: http://tiny.cc/transonic-vision. We describe some
> >> issues (perf for numerical kernels, incompatible accelerators, community
> >> split between experts and simple users, ...) and possible improvements.
> >>
> >
> > Thanks Pierre, that's a very interesting vision paper.
>
> Thanks Ralf for this kind and interesting reply!
>
> >
> > In case you haven't seen it, there was a discussion on the pandas-dev
> > mailing list a couple of weeks ago about adopting Numba as a dependency
> > (and issues with that).
> >
> > Your comment on my assessment from 1.5 years ago being a little unfair to
> > Pythran may be true - not sure it was at the time, but Pythran seems to
> > mature nicely.
> >
> > The ability to switch between just-in-time and ahead-of-time compilation
> is
> > nice. One thing I noticed is that this actual switching is not completely
> > fluent: the jit and boost decorators have different signatures, and
> there's
> > no way to globally switch behavior (say with an env var, as for backend
> > selection).
>
> Yes, it seems evident now but I forgot to update the jit decorators when I
> was working on the boost decorator.
> My first "targets" for Transonic are packages for which the ahead-of-time
> mode seems more adapted.
>
> This incompatibility between the 2 main decorators used in Transonic will
> soon be fixed!
>
> Regarding the way to globally switch behavior, I'll open a dedicated issue.
>
> >> Help would be very much appreciated.
> >>
> >
> > I'd be interested to help think about adoption and/or funding.
> >
> > Cheers,
> > Ralf
> >
>
> As you've seen with the jit/boost incompatibility, I guess API design
> would be better if people knowing the subject could be included in some
> discussions.
>
> For example, I had to design the Python API for type declaration of arrays
> (see
> https://transonic.readthedocs.io/en/latest/generated/transonic.typing.html)
> since I didn't find anything adapted. My implementation is not great
> neither since types in transonic.typing and in `typing` are right now not
> compatible ! (However, it won't be difficult to fix that)
>
> Another API design that needs to be thought is about user-defined types in
> Transonic. This is for future because Pythran does not currently support
> that, but I think we will have to be able to support kind of dataclass,
> something like the equivalent of C struct (corresponding to Cython `cdef
> class` and Numba `jitclass`).
>
> A more theoretical subject that would be interesting to investigate is
> about the subset of Python-Numpy that can and should be implemented by
> accelerators.


This is indeed interesting, I've been thinking about this a lot and have a
very rough first cut at what should be included (
https://github.com/Quansight-Labs/rnumpy). That should be redone next year
with a better basis for decision-making of what should and should not be in
it.

For example, I think a function having different branches with different
> types for the returned objects depending of runtime values cannot be
> rewritten as efficient modern C++.
>

Agreed. That's anyway due to sub-optimal design decisions long ago in most
cases.

Cheers,
Ralf


> If you know people potentially interested to discuss about these subjects,
> please tell me.
>
> Cheers,
> Pierre
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From dencheva at stsci.edu  Wed Nov 13 09:50:55 2019
From: dencheva at stsci.edu (Nadia Dencheva)
Date: Wed, 13 Nov 2019 14:50:55 +0000
Subject: [Numpy-discussion] [JOB] Software Engineer position for open
 source/development projects
Message-ID: <8ed098ed05064660b655f073f2a99ee4@stsci.edu>

Who we are:


The Data Analysis Tools Branch (DATB) at the Space Telescope Science Institute (STScI) in Baltimore, MD, has an opening for a software engineer to work on community developed projects like astropy<https://github.com/astropy/astropy>, asdf<https://github.com/spacetelescope/asdf>, etc. DATB develops open source data-analysis tools for astronomy though some of them are general and used by other sciences. Contributions to numpy/scipy are possible.

Skillset

We are looking for someone with the following skillset

  *   Proficient in Python and C/C++ .
  *   Comfortable developing primarily in/for Linux/Unix environment.
  *   Experience with software library development and library API design
  *   Experience writing developer and user documentation.
  *   Fluent with continuous integration, Git and common developer tools.
  *   Skilled in testing tools
  *   Familiarity with both binary data representation in CPU memory and with data serialization standards such as YAML, JSON and XML

How to apply

Please apply online at
https://recruiting2.ultipro.com/SPA1004AURA/JobBoard/93330e50-7b3a-4ba8-94f2-6f32360aa4e1/OpportunityDetail?opportunityId=158b84d3-58f9-4e62-8c57-30a64a127275

Starting date
This position is available for immediate start.



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From matti.picus at gmail.com  Wed Nov 13 13:12:11 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Wed, 13 Nov 2019 11:12:11 -0700
Subject: [Numpy-discussion] Numpy community meeting TODAY !!!
Message-ID: <e50a6721-ab6f-40ea-e016-189b2741be41@gmail.com>

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From ralf.gommers at gmail.com  Thu Nov 14 18:42:15 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Thu, 14 Nov 2019 15:42:15 -0800
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
Message-ID: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>

Hi all,

I'm very pleased to announce that NumPy and OpenBLAS have received a joint
grant for $195,000 from the Chan Zuckerberg Initiative.

In summary, this grant is for high-level documentation, website development
and graphic design, governance activities and community building for NumPy,
and for technical work on OpenBLAS (which is one of NumPy's key
dependencies). I wrote a blog post on what this grant is about and some
background at [1], and published the full proposal at [2]. The program
managers wrote a blog post about the whole program [3] which is also well
worth reading.

I'm looking forward to what we'll be able to do with this grant. The work
is planned to start quite soon, Dec 1st, and run for one year.

Questions and ideas on any aspect of this grant are very welcome!

Cheers,
Ralf

[1] https://labs.quansight.org/blog/2019/11/numpy-openblas-CZI-grant/
[2]
https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167
[3]
https://medium.com/@cziscience/the-invisible-foundations-of-biomedicine-4ab7f8d4f5dd
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From charlesr.harris at gmail.com  Thu Nov 14 20:44:42 2019
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Thu, 14 Nov 2019 18:44:42 -0700
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
In-Reply-To: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
References: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
Message-ID: <CAB6mnx+2VG5r4s=Nrc2qhnyd2tz5-dP4LbsCMnHWGjA7RQw6Lg@mail.gmail.com>

On Thu, Nov 14, 2019 at 4:42 PM Ralf Gommers <ralf.gommers at gmail.com> wrote:

> Hi all,
>
> I'm very pleased to announce that NumPy and OpenBLAS have received a joint
> grant for $195,000 from the Chan Zuckerberg Initiative.
>
> In summary, this grant is for high-level documentation, website
> development and graphic design, governance activities and community
> building for NumPy, and for technical work on OpenBLAS (which is one of
> NumPy's key dependencies). I wrote a blog post on what this grant is about
> and some background at [1], and published the full proposal at [2]. The
> program managers wrote a blog post about the whole program [3] which is
> also well worth reading.
>
> I'm looking forward to what we'll be able to do with this grant. The work
> is planned to start quite soon, Dec 1st, and run for one year.
>
> Questions and ideas on any aspect of this grant are very welcome!
>
> Cheers,
> Ralf
>
> [1] https://labs.quansight.org/blog/2019/11/numpy-openblas-CZI-grant/
> [2]
> https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167
> [3]
> https://medium.com/@cziscience/the-invisible-foundations-of-biomedicine-4ab7f8d4f5dd
>
>
Nice. The distribution of funded projects across institutions was
interesting and I was a bit surprised that Harvard/MIT/Stanford didn't have
more representation. I'm glad to see OpenBLAS get some help.  We should
also begin thinking about quad precision linear algebra at some point,
although double precision does the job for most things.

Chuck
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From stefanv at berkeley.edu  Fri Nov 15 00:34:17 2019
From: stefanv at berkeley.edu (Stefan van der Walt)
Date: Thu, 14 Nov 2019 21:34:17 -0800
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
In-Reply-To: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
References: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
Message-ID: <ae9f0770-7cee-4edb-8831-17e1df3f01c5@www.fastmail.com>

Hi Ralf,

On Thu, Nov 14, 2019, at 15:42, Ralf Gommers wrote:
> I'm very pleased to announce that NumPy and OpenBLAS have received a joint grant for $195,000 from the Chan Zuckerberg Initiative.

Congratulations on receiving this grant! I am very happy to see more funded involvement in NumPy, and am pleased that you will get to focus some time on increasing participation and diversifying roles. It is absolutely crucial that we succeed in this if we want to remain a healthy project---so thank you for all your hard work on that front.

Best regards,
St?fan


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From andyfaff at gmail.com  Fri Nov 15 02:04:55 2019
From: andyfaff at gmail.com (Andrew Nelson)
Date: Fri, 15 Nov 2019 15:04:55 +0800
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
In-Reply-To: <ae9f0770-7cee-4edb-8831-17e1df3f01c5@www.fastmail.com>
References: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
 <ae9f0770-7cee-4edb-8831-17e1df3f01c5@www.fastmail.com>
Message-ID: <CAAbtOZdS74-AiLw0dLBtAL2k=afLhPZ-CDpVawJF-8KYW2pqFQ@mail.gmail.com>

Well done, that's a sizeable grant and has the ability to really move
things along.

On Fri, 15 Nov 2019, 13:35 Stefan van der Walt, <stefanv at berkeley.edu>
wrote:

> Hi Ralf,
>
> On Thu, Nov 14, 2019, at 15:42, Ralf Gommers wrote:
>
> I'm very pleased to announce that NumPy and OpenBLAS have received a joint
> grant for $195,000 from the Chan Zuckerberg Initiative.
>
>
> Congratulations on receiving this grant!  I am very happy to see more
> funded involvement in NumPy, and am pleased that you will get to focus some
> time on increasing participation and diversifying roles.  It is absolutely
> crucial that we succeed in this if we want to remain a healthy project---so
> thank you for all your hard work on that front.
>
> Best regards,
> St?fan
>
>
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From matti.picus at gmail.com  Fri Nov 15 08:27:26 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Fri, 15 Nov 2019 06:27:26 -0700
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for
 NumPy
In-Reply-To: <81512cf9-2f18-5027-4d17-da67aac8ecaa@gmail.com>
References: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
 <CAH6Pt5o6owJAQVFNtjMcSfnYp5wrstgAaZ3Z7fowo_tJ-N5PJg@mail.gmail.com>
 <81512cf9-2f18-5027-4d17-da67aac8ecaa@gmail.com>
Message-ID: <6b94f077-d0fa-83a9-5286-b8f4a46d3fd0@gmail.com>

    On Tue, Nov 12, 2019 at 12:41 AM Matti Picus <matti.picus at gmail.com>
    wrote:

    Apple has dropped support for Accelerate. It has bugs that have not been
    fixed, and is closed source so we cannot fix them ourselves. We have
    been getting a handful of reports from users who end up building NumPy
    on macOS, and inadvertently link to Accelerate, then end up with wrong
    linalg results. In PR 14880https://github.com/numpy/numpy/pull/14880  I
    propose to disallow finding it when building NumPy. At this time it will
    remain in distutils as one of the backends to support users, but how do
    people feel about a future PR to totally remove it?

Someone pointed out that Apple has not officially dropped support as far as it can be determined. Sorry for the bad information. However, I still stand by the "has bugs that have not been fixed, and is closed source". An alternative to dropping automatic support for it would be to find a channel for engaging with Apple to report and fix the bugs.

Matti


From ralf.gommers at gmail.com  Fri Nov 15 09:39:14 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 15 Nov 2019 06:39:14 -0800
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for
 NumPy
In-Reply-To: <6b94f077-d0fa-83a9-5286-b8f4a46d3fd0@gmail.com>
References: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
 <CAH6Pt5o6owJAQVFNtjMcSfnYp5wrstgAaZ3Z7fowo_tJ-N5PJg@mail.gmail.com>
 <81512cf9-2f18-5027-4d17-da67aac8ecaa@gmail.com>
 <6b94f077-d0fa-83a9-5286-b8f4a46d3fd0@gmail.com>
Message-ID: <CABL7CQgWPYwapcc1huh65qM=NxmSh-Gfgdo_c-2jUm1w+qRDTA@mail.gmail.com>

On Fri, Nov 15, 2019 at 5:27 AM Matti Picus <matti.picus at gmail.com> wrote:

>     On Tue, Nov 12, 2019 at 12:41 AM Matti Picus <matti.picus at gmail.com>
>     wrote:
>
>     Apple has dropped support for Accelerate. It has bugs that have not
> been
>     fixed, and is closed source so we cannot fix them ourselves. We have
>     been getting a handful of reports from users who end up building NumPy
>     on macOS, and inadvertently link to Accelerate, then end up with wrong
>     linalg results. In PR 14880https://github.com/numpy/numpy/pull/14880
> I
>     propose to disallow finding it when building NumPy. At this time it
> will
>     remain in distutils as one of the backends to support users, but how do
>     people feel about a future PR to totally remove it?
>
> Someone pointed out that Apple has not officially dropped support as far
> as it can be determined. Sorry for the bad information. However, I still
> stand by the "has bugs that have not been fixed, and is closed source". An
> alternative to dropping automatic support for it would be to find a channel
> for engaging with Apple to report and fix the bugs.
>

That's been tried, repeatedly. I would suggest not to spend time on that.
Apple knows, they have just decided it's not important to them.

Spending time on contributing to either OpenBLAS or BLIS/libFLAME seems
like a more useful activity.

Cheers,
Ralf
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From ilhanpolat at gmail.com  Fri Nov 15 09:55:39 2019
From: ilhanpolat at gmail.com (Ilhan Polat)
Date: Fri, 15 Nov 2019 15:55:39 +0100
Subject: [Numpy-discussion] Disallow Accelerate as a LAPACK backend for
 NumPy
In-Reply-To: <CABL7CQgWPYwapcc1huh65qM=NxmSh-Gfgdo_c-2jUm1w+qRDTA@mail.gmail.com>
References: <0c374fc6-50ed-625f-37c3-f96460e3046a@gmail.com>
 <CAH6Pt5o6owJAQVFNtjMcSfnYp5wrstgAaZ3Z7fowo_tJ-N5PJg@mail.gmail.com>
 <81512cf9-2f18-5027-4d17-da67aac8ecaa@gmail.com>
 <6b94f077-d0fa-83a9-5286-b8f4a46d3fd0@gmail.com>
 <CABL7CQgWPYwapcc1huh65qM=NxmSh-Gfgdo_c-2jUm1w+qRDTA@mail.gmail.com>
Message-ID: <CAEBuzr8UyvfxetNHzHZc_0qvSGpEmWwsZeCLtkZsoyZJf-Hw1Q@mail.gmail.com>

We have a wiki page with all the details on scipy repo for the rationale of
why we wanted to drop it. There is no need to discuss further about the
situation of Accelerate.

On Fri, Nov 15, 2019, 15:41 Ralf Gommers <ralf.gommers at gmail.com> wrote:

>
>
> On Fri, Nov 15, 2019 at 5:27 AM Matti Picus <matti.picus at gmail.com> wrote:
>
>>     On Tue, Nov 12, 2019 at 12:41 AM Matti Picus <matti.picus at gmail.com>
>>     wrote:
>>
>>     Apple has dropped support for Accelerate. It has bugs that have not
>> been
>>     fixed, and is closed source so we cannot fix them ourselves. We have
>>     been getting a handful of reports from users who end up building NumPy
>>     on macOS, and inadvertently link to Accelerate, then end up with wrong
>>     linalg results. In PR 14880https://github.com/numpy/numpy/pull/14880
>> I
>>     propose to disallow finding it when building NumPy. At this time it
>> will
>>     remain in distutils as one of the backends to support users, but how
>> do
>>     people feel about a future PR to totally remove it?
>>
>> Someone pointed out that Apple has not officially dropped support as far
>> as it can be determined. Sorry for the bad information. However, I still
>> stand by the "has bugs that have not been fixed, and is closed source". An
>> alternative to dropping automatic support for it would be to find a channel
>> for engaging with Apple to report and fix the bugs.
>>
>
> That's been tried, repeatedly. I would suggest not to spend time on that.
> Apple knows, they have just decided it's not important to them.
>
> Spending time on contributing to either OpenBLAS or BLIS/libFLAME seems
> like a more useful activity.
>
> Cheers,
> Ralf
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From marcelo.gosling at gmail.com  Fri Nov 15 11:59:57 2019
From: marcelo.gosling at gmail.com (Marcelo Gasparian Gosling)
Date: Fri, 15 Nov 2019 13:59:57 -0300
Subject: [Numpy-discussion] State of numpy-financial
Message-ID: <CAK_EOBVtghffR6mDje7wNoDdfawmLdy7RqAALeMYMbVsNqLguw@mail.gmail.com>

Hi everyone!

So, np-financial is being phased out of mainline Numpy, right? I have a
patch for the IRR function that I'd like to submit, is it just as easy as
making a PR on Github?

Cheers,

Marcelo
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From warren.weckesser at gmail.com  Fri Nov 15 12:11:49 2019
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Fri, 15 Nov 2019 12:11:49 -0500
Subject: [Numpy-discussion] State of numpy-financial
In-Reply-To: <CAK_EOBVtghffR6mDje7wNoDdfawmLdy7RqAALeMYMbVsNqLguw@mail.gmail.com>
References: <CAK_EOBVtghffR6mDje7wNoDdfawmLdy7RqAALeMYMbVsNqLguw@mail.gmail.com>
Message-ID: <CAGzF1uf0WSjMCykEvM75V8V0LphL2OCuz6=x1u5EtBHj3Vj_Jg@mail.gmail.com>

On 11/15/19, Marcelo Gasparian Gosling <marcelo.gosling at gmail.com> wrote:
> Hi everyone!
>
> So, np-financial is being phased out of mainline Numpy, right? I have a
> patch for the IRR function that I'd like to submit, is it just as easy as
> making a PR on Github?


Hi Marcelo,

The new home for the NumPy financial functions is
https://github.com/numpy/numpy-financial

Pull requests are welcome!

Warren


>
> Cheers,
>
> Marcelo
>

From evgeny.burovskiy at gmail.com  Fri Nov 15 13:35:14 2019
From: evgeny.burovskiy at gmail.com (Evgeni Burovski)
Date: Fri, 15 Nov 2019 21:35:14 +0300
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
In-Reply-To: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
References: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
Message-ID: <CAMRo0is22KjNDtGkuaYPnt8pD-G34jNP-KVNnDRPq03AMjNmaQ@mail.gmail.com>

Congratulations!

??, 15 ????. 2019 ?., 2:42 Ralf Gommers <ralf.gommers at gmail.com>:

> Hi all,
>
> I'm very pleased to announce that NumPy and OpenBLAS have received a joint
> grant for $195,000 from the Chan Zuckerberg Initiative.
>
> In summary, this grant is for high-level documentation, website
> development and graphic design, governance activities and community
> building for NumPy, and for technical work on OpenBLAS (which is one of
> NumPy's key dependencies). I wrote a blog post on what this grant is about
> and some background at [1], and published the full proposal at [2]. The
> program managers wrote a blog post about the whole program [3] which is
> also well worth reading.
>
> I'm looking forward to what we'll be able to do with this grant. The work
> is planned to start quite soon, Dec 1st, and run for one year.
>
> Questions and ideas on any aspect of this grant are very welcome!
>
> Cheers,
> Ralf
>
> [1] https://labs.quansight.org/blog/2019/11/numpy-openblas-CZI-grant/
> [2]
> https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167
> [3]
> https://medium.com/@cziscience/the-invisible-foundations-of-biomedicine-4ab7f8d4f5dd
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From charlesr.harris at gmail.com  Fri Nov 15 13:50:01 2019
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Fri, 15 Nov 2019 11:50:01 -0700
Subject: [Numpy-discussion] Cleanup of travisCI tests.
Message-ID: <CAB6mnxJHSNOFn-=ipBL38s6sY-a634Zc9uUbUeEBuVFfXbiiZA@mail.gmail.com>

Hi All,

I think there are some travisCI tests that we can eliminate, see  tests
<https://travis-ci.org/numpy/numpy/builds/612498337?utm_source=github_status&utm_medium=notification>
for
the current proposed set. I think we can eliminate the following

INSTALL_PICKLE5=1  # Python3.8 has this
NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0

And possibly one or both of

NPY_RELAXED_STRIDES_CHECKING=0
NPY_RELAXED_STRIDES_CHECKING_DEBUG=1

Thoughts?

Chuck
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From ralf.gommers at gmail.com  Fri Nov 15 18:17:38 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 15 Nov 2019 15:17:38 -0800
Subject: [Numpy-discussion] welcome Shaloo Shalini to the NumPy team
Message-ID: <CABL7CQg0QW-xqdqN9xFE82BE9-b5n-_HtEOeoxU6Vo8gKvMZaw@mail.gmail.com>

Hi all,

Please welcome Shaloo Shalini to the NumPy team. Shaloo is an
experienced technology
marketing consultant with a passion for open source. She has been
contributing graphical content, information mapping and technical writing
since the summer. Most of that still needs to be integrated into the docs,
the website, and the user survey that Inessa is designing, but I'm really
excited about it. Nice examples are
https://github.com/numpy/numpy-surveys/pull/3 and
https://github.com/numpy/numpy.org/pull/23.

The NumPy Steering Council has decided to use part of our new Tidelift
income to contract Shaloo part-time for the next three months to help us
create content for the new website and create an information map that will
aid us in, e.g., restructuring the documentation and consolidating
duplicate content. Both of these activities will be very helpful in our
efforts to make NumPy more accessible and serve especially beginning users
and contributors better.

I'm really looking forward to Shaloo's continued contributions!

Cheers,
Ralf
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From shoyer at gmail.com  Fri Nov 15 19:25:23 2019
From: shoyer at gmail.com (Stephan Hoyer)
Date: Fri, 15 Nov 2019 19:25:23 -0500
Subject: [Numpy-discussion] Cleanup of travisCI tests.
In-Reply-To: <CAB6mnxJHSNOFn-=ipBL38s6sY-a634Zc9uUbUeEBuVFfXbiiZA@mail.gmail.com>
References: <CAB6mnxJHSNOFn-=ipBL38s6sY-a634Zc9uUbUeEBuVFfXbiiZA@mail.gmail.com>
Message-ID: <CAEQ_Tvfn_poc3kAgXmJoP8jL0OOrRSHM0U=FDpYbTHDu27-5vQ@mail.gmail.com>

We still support explicitly opting-out of __array_function__, so I think we
should still keep that test configuration.

On Fri, Nov 15, 2019 at 1:50 PM Charles R Harris <charlesr.harris at gmail.com>
wrote:

> Hi All,
>
> I think there are some travisCI tests that we can eliminate, see  tests
> <https://travis-ci.org/numpy/numpy/builds/612498337?utm_source=github_status&utm_medium=notification> for
> the current proposed set. I think we can eliminate the following
>
> INSTALL_PICKLE5=1  # Python3.8 has this
> NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0
>
> And possibly one or both of
>
> NPY_RELAXED_STRIDES_CHECKING=0
> NPY_RELAXED_STRIDES_CHECKING_DEBUG=1
>
> Thoughts?
>
> Chuck
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From ralf.gommers at gmail.com  Fri Nov 15 20:25:39 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 15 Nov 2019 17:25:39 -0800
Subject: [Numpy-discussion] a new grant for NumPy and OpenBLAS!
In-Reply-To: <ae9f0770-7cee-4edb-8831-17e1df3f01c5@www.fastmail.com>
References: <CABL7CQh6fV+bVHMq=gZhPywNKaOFoy7o+-zDj8mquHnj=DPWOA@mail.gmail.com>
 <ae9f0770-7cee-4edb-8831-17e1df3f01c5@www.fastmail.com>
Message-ID: <CABL7CQhq69k52Xs7gxq5kA1w1k-K7HAS8a=ESuoyf2rkb=YRCA@mail.gmail.com>

On Thu, Nov 14, 2019 at 9:35 PM Stefan van der Walt <stefanv at berkeley.edu>
wrote:

> Hi Ralf,
>
> On Thu, Nov 14, 2019, at 15:42, Ralf Gommers wrote:
>
> I'm very pleased to announce that NumPy and OpenBLAS have received a joint
> grant for $195,000 from the Chan Zuckerberg Initiative.
>
>
> Congratulations on receiving this grant!  I am very happy to see more
> funded involvement in NumPy, and am pleased that you will get to focus some
> time on increasing participation and diversifying roles.  It is absolutely
> crucial that we succeed in this if we want to remain a healthy project---so
> thank you for all your hard work on that front.
>

Thanks St?fan and everyone else!

Cheers,
Ralf
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From matti.picus at gmail.com  Sat Nov 16 16:42:48 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Sat, 16 Nov 2019 14:42:48 -0700
Subject: [Numpy-discussion] "Proposal to accept NEP 34: Disallow inferring
 dtype=object from sequence
Message-ID: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>

I propose to move the NEP https://numpy.org/neps/nep-0034.html from 
"draft" to "accepted" status. There were no objections (actually there 
were no responses at all) to the mail proposing the NEP 
https://mail.python.org/pipermail/numpy-discussion/2019-October/080200.html.


PR 14794 https://github.com/numpy/numpy/pull/14794 is an implementation 
of the first step, deprecating current automatic detection,


f there are no substantive objections within 7 days from this email, 
then the NEP will be accepted; see NEP 0 for more details.


Matti


From jni at fastmail.com  Sun Nov 17 21:29:18 2019
From: jni at fastmail.com (Juan Nunez-Iglesias)
Date: Mon, 18 Nov 2019 13:29:18 +1100
Subject: [Numpy-discussion] "Proposal to accept NEP 34: Disallow
 inferring dtype=object from sequence
In-Reply-To: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>
References: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>
Message-ID: <D40E44FB-1AAD-4BEC-BF24-DE3249868158@fastmail.com>

For what it?s worth, I support this NEP. I have indeed seen this mistake often when teaching NumPy. 

> On 17 Nov 2019, at 8:43 am, Matti Picus <matti.picus at gmail.com> wrote:
> 
> ?I propose to move the NEP https://numpy.org/neps/nep-0034.html from "draft" to "accepted" status. There were no objections (actually there were no responses at all) to the mail proposing the NEP https://mail.python.org/pipermail/numpy-discussion/2019-October/080200.html.
> 
> 
> PR 14794 https://github.com/numpy/numpy/pull/14794 is an implementation of the first step, deprecating current automatic detection,
> 
> 
> f there are no substantive objections within 7 days from this email, then the NEP will be accepted; see NEP 0 for more details.
> 
> 
> Matti
> 
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion


From marcelo.gosling at gmail.com  Mon Nov 18 05:46:31 2019
From: marcelo.gosling at gmail.com (Marcelo Gasparian Gosling)
Date: Mon, 18 Nov 2019 07:46:31 -0300
Subject: [Numpy-discussion] "Proposal to accept NEP 34: Disallow
 inferring dtype=object from sequence
In-Reply-To: <D40E44FB-1AAD-4BEC-BF24-DE3249868158@fastmail.com>
References: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>
 <D40E44FB-1AAD-4BEC-BF24-DE3249868158@fastmail.com>
Message-ID: <CAK_EOBWCTqnZvjKwHnb+z+=jSoNC5RWyi-fXLx3iYRFgnwZtag@mail.gmail.com>

I also support this. Small price in edge case usability for a lot less
shooting yourself in the foot

On Sun, Nov 17, 2019 at 11:29 PM Juan Nunez-Iglesias <jni at fastmail.com>
wrote:

> For what it?s worth, I support this NEP. I have indeed seen this mistake
> often when teaching NumPy.
>
> > On 17 Nov 2019, at 8:43 am, Matti Picus <matti.picus at gmail.com> wrote:
> >
> > ?I propose to move the NEP https://numpy.org/neps/nep-0034.html from
> "draft" to "accepted" status. There were no objections (actually there were
> no responses at all) to the mail proposing the NEP
> https://mail.python.org/pipermail/numpy-discussion/2019-October/080200.html
> .
> >
> >
> > PR 14794 https://github.com/numpy/numpy/pull/14794 is an implementation
> of the first step, deprecating current automatic detection,
> >
> >
> > f there are no substantive objections within 7 days from this email,
> then the NEP will be accepted; see NEP 0 for more details.
> >
> >
> > Matti
> >
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion at python.org
> > https://mail.python.org/mailman/listinfo/numpy-discussion
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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From stefanv at berkeley.edu  Mon Nov 18 19:59:24 2019
From: stefanv at berkeley.edu (Stefan van der Walt)
Date: Mon, 18 Nov 2019 16:59:24 -0800
Subject: [Numpy-discussion] Sprint, this Friday/Saturday (22/23 November)
Message-ID: <5d0982e7-5b9f-486e-8d04-ab5b12289823@www.fastmail.com>

Hi everyone,

This is a reminder of the NumPy sprint that will be happening in Berkeley this Friday and Saturday, 22 and 23 November.  The schedule for the sprint is at:

https://hackmd.io/@GicvKC4NRbK9X9ElJTixwg/ryEPQyprr

Best regards,
St?fan

From matti.picus at gmail.com  Tue Nov 19 07:35:03 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Tue, 19 Nov 2019 05:35:03 -0700
Subject: [Numpy-discussion] NumPy community meeting Wed Nov 20
Message-ID: <a8a98743-f721-1424-ea40-33d9d59126f0@gmail.com>

There will be a NumPy Community meeting Wednesday Nov 20 at 11 am 
Pacific Time. Everyone is invited to join in and edit the 
work-in-progress meeting topics and notes:
https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both

Matti

From ralf.gommers at gmail.com  Tue Nov 19 14:27:15 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 19 Nov 2019 11:27:15 -0800
Subject: [Numpy-discussion] website repo change
Message-ID: <CABL7CQj42eK1x_JSjQhK-YjRB_rFHTbKXW8txbW159xSxtAcqw@mail.gmail.com>

Hi all,

I plan to change the master branch of the website repository to contain the
new Hugo site rather than the old Sphinx site, later today. I will help
with migrating git branches if anyone needs such help.

Aside: two longer messages on this topic got blocked by some weird new
Gmail spam filter (abusix), so keeping this short. If this has happened to
anyone else recently, that would be good to know.

Cheers,
Ralf
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From stefanv at berkeley.edu  Tue Nov 19 20:37:20 2019
From: stefanv at berkeley.edu (Stefan van der Walt)
Date: Tue, 19 Nov 2019 17:37:20 -0800
Subject: [Numpy-discussion] welcome Shaloo Shalini to the NumPy team
In-Reply-To: <CABL7CQg0QW-xqdqN9xFE82BE9-b5n-_HtEOeoxU6Vo8gKvMZaw@mail.gmail.com>
References: <CABL7CQg0QW-xqdqN9xFE82BE9-b5n-_HtEOeoxU6Vo8gKvMZaw@mail.gmail.com>
Message-ID: <8d337cd8-fda2-4c9d-9e7b-6bc2f50caf9c@www.fastmail.com>

On Fri, Nov 15, 2019, at 15:17, Ralf Gommers wrote:
> Please welcome Shaloo Shalini to the NumPy team. Shaloo is an experienced technology marketing consultant with a passion for open source. She has been contributing graphical content, information mapping and technical writing since the summer. Most of that still needs to be integrated into the docs, the website, and the user survey that Inessa is designing, but I'm really excited about it. Nice examples are https://github.com/numpy/numpy-surveys/pull/3 and https://github.com/numpy/numpy.org/pull/23.

Shaloo, welcome to the NumPy team! The work you are doing on communicating, visually and with words, is both important and potentially highly impactful. We look forward to working with you!

Best regards,
St?fan
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From tyler.je.reddy at gmail.com  Fri Nov 22 00:32:08 2019
From: tyler.je.reddy at gmail.com (Tyler Reddy)
Date: Thu, 21 Nov 2019 22:32:08 -0700
Subject: [Numpy-discussion] ANN: SciPy 1.4.0rc1 -- please test
Message-ID: <CAHPuU_bjFjPCbVhyMMBmzmY6WTAD24z5LQUZ1HOwZysCTyJtLA@mail.gmail.com>

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

Hi all,

On behalf of the SciPy development team I'm pleased to announce
the release candidate SciPy 1.4.0rc1. Please help us test this pre-release.

Sources and binary wheels can be found at:
https://pypi.org/project/scipy/
and at:
https://github.com/scipy/scipy/releases/tag/v1.4.0rc1

One of a few ways to install the release candidate with pip:

pip install scipy==1.4.0rc1

==========================
SciPy 1.4.0 Release Notes
==========================

Note: Scipy 1.4.0 is not released yet!

SciPy 1.4.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
1.4.x branch, and on adding new features on the master branch.

This release requires Python 3.5+ and NumPy >=1.13.3 (for Python 3.5, 3.6),
>=1.14.5 (for Python 3.7), >= 1.17.3 (for Python 3.8)

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release
----------------------------------

 - a new submodule, `scipy.fft`, now supersedes `scipy.fftpack`; this
  means support for ``long double`` transforms, faster multi-dimensional
  transforms, improved algorithm time complexity, release of the global
  intepreter lock, and control over threading behavior
 - support for ``pydata/sparse`` arrays in `scipy.sparse.linalg`
 - substantial improvement to the documentation and functionality of
  several `scipy.special` functions, and some new additions
 - the generalized inverse Gaussian distribution has been added to
  `scipy.stats`
 - an implementation of the Edmonds-Karp algorithm in
  `scipy.sparse.csgraph.maximum_flow`
 - `scipy.spatial.SphericalVoronoi` now supports n-dimensional input,
   has linear memory complexity, improved performance, and
   supports single-hemisphere generators


New features
============

Infrastructure
-------------------
Documentation can now be built with ``runtests.py --doc``

A ``Dockerfile`` is now available in the ``scipy/scipy-dev`` repository to
facilitate getting started with SciPy development.

`scipy.constants` improvements
--------------------------------------------
`scipy.constants` has been updated with the CODATA 2018 constants.

`scipy.fft` added
-----------------------
`scipy.fft` is a new submodule that supersedes the `scipy.fftpack`
submodule.
For the most part, this is a drop-in replacement for ``numpy.fft`` and
`scipy.fftpack` alike. With some important differences, `scipy.fft`:
 - uses NumPy's conventions for real transforms (``rfft``). This means the
return value is a complex array, half the size of the full ``fft`` output.
This is different from the output of ``fftpack`` which returned a real
array
representing complex components packed together.
 - the inverse real to real transforms (``idct`` and ``idst``) are
normalized
for ``norm=None`` in thesame way as ``ifft``. This means the identity
``idct(dct(x)) == x`` is now ``True`` for all norm modes.
 - does not include the convolutions or pseudo-differential operators
from ``fftpack``.

This submodule is based on the ``pypocketfft`` library, developed by the
author of ``pocketfft`` which was recently adopted by NumPy as well.
``pypocketfft`` offers a number of advantages over fortran ``FFTPACK``:
 - support for long double (``np.longfloat``) precision transforms.
 - faster multi-dimensional transforms using vectorisation
 - Bluestein?s algorithm removes the worst-case ``O(n^2)`` complexity of
``FFTPACK``
 - the global interpreter lock (``GIL``) is released during transforms
 - optional multithreading of multi-dimensional transforms via the
``workers``
argument

Note that `scipy.fftpack` has not been deprecated and will continue to be
maintained but is now considered legacy. New code is recommended to use
`scipy.fft` instead, where possible.

`scipy.fftpack` improvements
----------------------------------------
`scipy.fftpack` now uses pypocketfft to perform its FFTs, offering the same
speed and accuracy benefits listed for scipy.fft above but without the
improved API.

`scipy.integrate` improvements
-------------------------------------------

The function `scipy.integrate.solve_ivp` now has an ``args`` argument.
This allows the user-defined functions passed to the function to have
additional parameters without having to create wrapper functions or
lambda expressions for them.

`scipy.integrate.solve_ivp` can now return a ``y_events`` attribute
representing the solution of the ODE at event times

New ``OdeSolver`` is implemented --- ``DOP853``. This is a high-order
explicit
Runge-Kutta method originally implemented in Fortran. Now we provide a pure
Python implementation usable through ``solve_ivp`` with all its features.

`scipy.integrate.quad` provides better user feedback when break points are
specified with a weighted integrand.

`scipy.integrate.quad_vec` is now available for general purpose integration
of vector-valued functions


`scipy.interpolate` improvements
---------------------------------------------
`scipy.interpolate.pade` now handles complex input data gracefully

`scipy.interpolate.Rbf` can now interpolate multi-dimensional functions

`scipy.io` improvements
---------------------------------

`scipy.io.wavfile.read` can now read data from a `WAV` file that has a
malformed header, similar to other modern `WAV` file parsers

`scipy.io.FortranFile` now has an expanded set of available ``Exception``
classes for handling poorly-formatted files


`scipy.linalg` improvements
--------------------------------------
The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for complex-valued matrices. Before this, the function only returned
correct values for real-valued matrices.

New boolean keyword argument ``check_finite`` for `scipy.linalg.norm`;
whether
to check that the input matrix contains only finite numbers. Disabling may
give a performance gain, but may result in problems (crashes,
non-termination)
if the inputs do contain infinities or NaNs.

`scipy.linalg.solve_triangular` has improved performance for a C-ordered
triangular matrix

``LAPACK`` wrappers have been added for ``?geequ``, ``?geequb``,
``?syequb``,
and ``?heequb``

Some performance improvements may be observed due to an internal
optimization
in operations involving LAPACK routines via ``_compute_lwork``. This is
particularly true for operations on small arrays.

Block ``QR`` wrappers are now available in `scipy.linalg.lapack`


`scipy.ndimage` improvements
------------------------------------------


`scipy.optimize` improvements
------------------------------------------
It is now possible to use linear and non-linear constraints with
`scipy.optimize.differential_evolution`.

`scipy.optimize.linear_sum_assignment` has been re-written in C++ to
improve
performance, and now allows input costs to be infinite.

A ``ScalarFunction.fun_and_grad`` method was added for convenient
simultaneous
retrieval of a function and gradient evaluation

`scipy.optimize.minimize` ``BFGS`` method has improved performance by
avoiding
duplicate evaluations in some cases

Better user feedback is provided when an objective function returns an array
instead of a scalar.


`scipy.signal` improvements
----------------------------------------

Added a new function to calculate convolution using the overlap-add method,
named `scipy.signal.oaconvolve`. Like `scipy.signal.fftconvolve`, this
function supports specifying dimensions along which to do the convolution.

`scipy.signal.cwt` now supports complex wavelets.

The implementation of ``choose_conv_method`` has been updated to reflect
the
new FFT implementation. In addition, the performance has been significantly
improved (with rather drastic improvements in edge cases).

The function ``upfirdn`` now has a ``mode`` keyword argument that can be
used
to select the signal extension mode used at the signal boundaries. These
modes
are also available for use in ``resample_poly`` via a newly added
``padtype``
argument.

`scipy.signal.sosfilt` now benefits from Cython code for improved
performance

`scipy.signal.resample` should be more efficient by leveraging ``rfft`` when
possible

`scipy.sparse` improvements
-----------------------------------------
It is now possible to use the LOBPCG method in `scipy.sparse.linalg.svds`.

`scipy.sparse.linalg.LinearOperator` now supports the operation ``rmatmat``
for adjoint matrix-matrix multiplication, in addition to ``rmatvec``.

Multiple stability updates enable float32 support in the LOBPCG eigenvalue
solver for symmetric and Hermitian eigenvalues problems in
``scipy.sparse.linalg.lobpcg``.

A solver for the maximum flow problem has been added as
`scipy.sparse.csgraph.maximum_flow`.

`scipy.sparse.csgraph.maximum_bipartite_matching` now allows non-square
inputs,
no longer requires a perfect matching to exist, and has improved
performance.

`scipy.sparse.lil_matrix` conversions now perform better in some scenarios

Basic support is available for ``pydata/sparse`` arrays in
`scipy.sparse.linalg`

`scipy.sparse.linalg.spsolve_triangular` now supports the ``unit_diagonal``
argument to improve call signature similarity with its dense counterpart,
`scipy.linalg.solve_triangular`

``assertAlmostEqual`` may now be used with sparse matrices, which have added
support for ``__round__``

`scipy.spatial` improvements
-----------------------------------------
The bundled Qhull library was upgraded to version 2019.1, fixing several
issues. Scipy-specific patches are no longer applied to it.

`scipy.spatial.SphericalVoronoi` now has linear memory complexity, improved
performance, and supports single-hemisphere generators. Support has also
been
added for handling generators that lie on a great circle arc (geodesic
input)
and for generators in n-dimensions.

`scipy.spatial.transform.Rotation` now includes functions for calculation
of a
mean rotation, generation of the 3D rotation groups, and reduction of
rotations
with rotational symmetries.

`scipy.spatial.transform.Slerp` is now callable with a scalar argument

`scipy.spatial.voronoi_plot_2d` now supports furthest site Voronoi diagrams

`scipy.spatial.Delaunay` and `scipy.spatial.Voronoi` now have attributes
for tracking whether they are furthest site diagrams

`scipy.special` improvements
-----------------------------------------
The Voigt profile has been added as `scipy.special.voigt_profile`.

A real dispatch has been added for the Wright Omega function
(`scipy.special.wrightomega`).

The analytic continuation of the Riemann zeta function has been added. (The
Riemann zeta function is the one-argument variant of `scipy.special.zeta`.)

The complete elliptic integral of the first kind (`scipy.special.ellipk`)
is
now available in `scipy.special.cython_special`.

The accuracy of `scipy.special.hyp1f1` for real arguments has been improved.

The documentation of many functions has been improved.

`scipy.stats` improvements
--------------------------------------
`scipy.stats.multiscale_graphcorr` added as an independence test that
operates on high dimensional and nonlinear data sets. It has higher
statistical
power than other `scipy.stats` tests while being the only one that operates
on
multivariate data.
The generalized inverse Gaussian distribution (`scipy.stats.geninvgauss`)
has
been added.

It is now possible to efficiently reuse `scipy.stats.binned_statistic_dd`
with new values by providing the result of a previous call to the function.

`scipy.stats.hmean` now handles input with zeros more gracefully.

The beta-binomial distribution is now available in `scipy.stats.betabinom`.

`scipy.stats.zscore`, `scipy.stats.circmean`, `scipy.stats.circstd`, and
`scipy.stats.circvar` now support the ``nan_policy`` argument for enhanced
handling of ``NaN`` values

`scipy.stats.entropy` now accepts an ``axis`` argument

`scipy.stats.gaussian_kde.resample` now accepts a ``seed`` argument to
empower
reproducibility

`scipy.stats.multiscale_graphcorr` has been added for calculation of the
multiscale graph correlation (MGC) test statistic

`scipy.stats.kendalltau` performance has improved, especially for large
inputs,
due to improved cache usage

`scipy.stats.truncnorm` distribution has been rewritten to support much
wider
tails


Deprecated features
===================

`scipy` deprecations
-----------------------------
Support for NumPy functions exposed via the root SciPy namespace is
deprecated
and will be removed in 2.0.0. For example, if you use ``scipy.rand`` or
``scipy.diag``, you should change your code to directly use
``numpy.random.default_rng`` or ``numpy.diag``, respectively.
They remain available in the currently continuing Scipy 1.x release series.

The exception to this rule is using ``scipy.fft`` as a function --
:mod:`scipy.fft` is now meant to be used only as a module, so the ability to
call ``scipy.fft(...)`` will be removed in SciPy 1.5.0.

In `scipy.spatial.Rotation` methods ``from_dcm``, ``as_dcm`` were renamed
to
``from_matrix``, ``as_matrix`` respectively. The old names will be removed
in
SciPy 1.6.0.

Backwards incompatible changes
==============================

`scipy.special` changes
----------------------------------
The deprecated functions ``hyp2f0``, ``hyp1f2``, and ``hyp3f0`` have been
removed.

The deprecated function ``bessel_diff_formula`` has been removed.

The function ``i0`` is no longer registered with ``numpy.dual``, so that
``numpy.dual.i0`` will unconditionally refer to the NumPy version
regardless
of whether `scipy.special` is imported.

The function ``expn`` has been changed to return ``nan`` outside of its
domain of definition (``x, n < 0``) instead of ``inf``.

`scipy.sparse` changes
---------------------------------
Sparse matrix reshape now raises an error if shape is not two-dimensional,
rather than guessing what was meant. The behavior is now the same as before
SciPy 1.1.0.


`scipy.spatial` changes
---------------------------------
The default behavior of the ``match_vectors`` method of
`scipy.spatial.transform.Rotation` was changed for input vectors
that are not normalized and not of equal lengths.
Previously, such vectors would be normalized within the method.
Now, the calculated rotation takes the vector length into account, longer
vectors will have a larger weight. For more details, see
https://github.com/scipy/scipy/issues/10968.

`scipy.signal` changes
--------------------------------
`scipy.signal.resample` behavior for length-1 signal inputs has been
fixed to output a constant (DC) value rather than an impulse, consistent
with
the assumption of signal periodicity in the FFT method.

`scipy.signal.cwt` now performs complex conjugation and time-reversal of
wavelet data, which is a backwards-incompatible bugfix for
time-asymmetric wavelets.

`scipy.stats` changes
------------------------------
`scipy.stats.loguniform` added with better documentation as (an alias for
``scipy.stats.reciprocal``). ``loguniform`` generates random variables
that are equally likely in the log space; e.g., ``1``, ``10`` and ``100``
are all equally likely if ``loguniform(10 ** 0, 10 ** 2).rvs()`` is used.


Other changes
=============
The ``LSODA`` method of `scipy.integrate.solve_ivp` now correctly detects
stiff
problems.

`scipy.spatial.cKDTree` now accepts and correctly handles empty input data

`scipy.stats.binned_statistic_dd` now calculates the standard deviation
statistic in a numerically stable way.

`scipy.stats.binned_statistic_dd` now throws an error if the input data
contains either ``np.nan`` or ``np.inf``. Similarly, in `scipy.stats` now
all
continuous distributions' ``.fit()`` methods throw an error if the input
data
contain any instance of either ``np.nan`` or ``np.inf``.


Authors
=======

* @endolith
* Abhinav +
* Anne Archibald
* ashwinpathak20nov1996 +
* Danilo Augusto +
* Nelson Auner +
* aypiggott +
* Christoph Baumgarten
* Peter Bell
* Sebastian Berg
* Arman Bilge +
* Benedikt Boecking +
* Christoph Boeddeker +
* Daniel Bunting
* Evgeni Burovski
* Angeline Burrell +
* Angeline G. Burrell +
* CJ Carey
* Carlos Ramos Carre?o +
* Mak Sze Chun +
* Malayaja Chutani +
* Christian Clauss +
* Jonathan Conroy +
* Stephen P Cook +
* Dylan Cutler +
* Anirudh Dagar +
* Aidan Dang +
* dankleeman +
* Brandon David +
* Tyler Dawson +
* Dieter Werthm?ller
* Joe Driscoll +
* Jakub Dyczek +
* D?vid Bodn?r
* Fletcher Easton +
* Stefan Endres
* etienne +
* Johann Faouzi
* Yu Feng
* Isuru Fernando +
* Matthew H Flamm
* Martin Gauch +
* Gabriel Gerlero +
* Ralf Gommers
* Chris Gorgolewski +
* Domen Gorjup +
* Edouard Goudenhoofdt +
* Jan Gwinner +
* Maja Gwozdz +
* Matt Haberland
* hadshirt +
* Pierre Haessig +
* David Hagen
* Charles Harris
* Gina Helfrich +
* Alex Henrie +
* Francisco J. Hernandez Heras +
* Andreas Hilboll
* Lindsey Hiltner
* Thomas Hisch
* Min ho Kim +
* Gert-Ludwig Ingold
* jakobjakobson13 +
* Todd Jennings
* He Jia
* Muhammad Firmansyah Kasim +
* Andrew Knyazev +
* Holger Kohr +
* Mateusz Konieczny +
* Krzysztof Pi?ro +
* Philipp Lang +
* Peter Mahler Larsen +
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Chelsea Liu +
* Jesse Livezey
* Peter Lysakovski +
* Jason Manley +
* Michael Marien +
* Nikolay Mayorov
* G. D. McBain +
* Sam McCormack +
* Melissa Weber Mendon?a +
* Kevin Michel +
* mikeWShef +
* Sturla Molden
* Eric Moore
* Peyton Murray +
* Andrew Nelson
* Clement Ng +
* Juan Nunez-Iglesias
* Renee Otten +
* Kellie Ottoboni +
* Ayappan P
* Sambit Panda +
* Tapasweni Pathak +
* Oleksandr Pavlyk
* Fabian Pedregosa
* Petar Mlinari?
* Matti Picus
* Marcel Plch +
* Christoph Pohl +
* Ilhan Polat
* Siddhesh Poyarekar +
* Ioannis Prapas +
* James Alan Preiss +
* Yisheng Qiu +
* Eric Quintero
* Bharat Raghunathan +
* Tyler Reddy
* Joscha Reimer
* Antonio Horta Ribeiro
* Lucas Roberts
* rtshort +
* Josua Sassen
* Kevin Sheppard
* Scott Sievert
* Leo Singer
* Kai Striega
* S?ren Fuglede J?rgensen
* tborisow +
* ?tienne Tremblay +
* tuxcell +
* Miguel de Val-Borro
* Andrew Valentine +
* Hugo van Kemenade
* Paul van Mulbregt
* Sebastiano Vigna
* Pauli Virtanen
* Dany Vohl +
* Ben Walsh +
* Huize Wang +
* Warren Weckesser
* Anreas Weh +
* Joseph Weston +
* Adrian Wijaya +
* Timothy Willard +
* Josh Wilson
* Kentaro Yamamoto +
* Dave Zbarsky +

A total of 141 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully
complete.


Issues closed for 1.4.0
---------------------------------

* `#1255 <https://github.com/scipy/scipy/issues/1255>`__: maxiter broken
for Scipy.sparse.linalg gmres, in addition to...
* `#1301 <https://github.com/scipy/scipy/issues/1301>`__: consolidate
multipack.h from interpolate and integrate packages...
* `#1739 <https://github.com/scipy/scipy/issues/1739>`__: Single precision
FFT insufficiently accurate. (Trac #1212)
* `#1795 <https://github.com/scipy/scipy/issues/1795>`__: stats
test_distributions.py: replace old fuzz tests (Trac #1269)
* `#2233 <https://github.com/scipy/scipy/issues/2233>`__: fftpack segfault
with big arrays (Trac #1714)
* `#2434 <https://github.com/scipy/scipy/issues/2434>`__: rmatmat and the
sophistication of linear operator objects
* `#2477 <https://github.com/scipy/scipy/issues/2477>`__:
stats.truncnorm.rvs() does not give symmetric results for negative...
* `#2629 <https://github.com/scipy/scipy/issues/2629>`__: FFTpack is
unacceptably slow on non power of 2
* `#2883 <https://github.com/scipy/scipy/issues/2883>`__: UnboundLocalError
in scipy.interpolate.splrep
* `#2956 <https://github.com/scipy/scipy/issues/2956>`__: Feature Request:
axis argument for stats.entropy function
* `#3528 <https://github.com/scipy/scipy/issues/3528>`__: Segfault on
test_djbfft (possibly MKL-related?)
* `#3793 <https://github.com/scipy/scipy/issues/3793>`__: cwt should also
return complex array
* `#4464 <https://github.com/scipy/scipy/issues/4464>`__: TST:
residue/residuez/invres/invresz don't have any tests
* `#4561 <https://github.com/scipy/scipy/issues/4561>`__: BUG: tf filter
trailing and leading zeros in residuez
* `#4669 <https://github.com/scipy/scipy/issues/4669>`__: Rewrite sosfilt
to make a single loop over the input?
* `#5040 <https://github.com/scipy/scipy/issues/5040>`__: BUG: Empty data
handling of (c)KDTrees
* `#5112 <https://github.com/scipy/scipy/issues/5112>`__: boxcox transform
edge cases could use more care
* `#5441 <https://github.com/scipy/scipy/issues/5441>`__: scipy.stats.ncx2
fails for nc=0
* `#5502 <https://github.com/scipy/scipy/issues/5502>`__: args keyword not
handled in optimize.curve_fit
* `#6484 <https://github.com/scipy/scipy/issues/6484>`__: Qhull
segmentation fault
* `#6900 <https://github.com/scipy/scipy/issues/6900>`__:
linear_sum_assignment with infinite weights
* `#6966 <https://github.com/scipy/scipy/issues/6966>`__: Hypergeometric
Functions documentation is lacking
* `#6999 <https://github.com/scipy/scipy/issues/6999>`__: possible false
positive corruption check in compressed loadmat()
* `#7018 <https://github.com/scipy/scipy/issues/7018>`__: ydata that needs
broadcasting renders curve_fit unable to compute...
* `#7140 <https://github.com/scipy/scipy/issues/7140>`__: trouble with
documentation for windows
* `#7327 <https://github.com/scipy/scipy/issues/7327>`__:
interpolate.ndgriddata.griddata causes Python to crash rather...
* `#7396 <https://github.com/scipy/scipy/issues/7396>`__:
MatrixLinearOperator implements _adjoint(), but not _transpose()
* `#7400 <https://github.com/scipy/scipy/issues/7400>`__: BUG(?): special:
factorial and factorial2 return a 0-dimensional...
* `#7434 <https://github.com/scipy/scipy/issues/7434>`__: Testing of
scipy.stats continuous distributions misses 25 distributions
* `#7491 <https://github.com/scipy/scipy/issues/7491>`__: Several
scipy.stats distributions (fisk, burr, burr12, f) return...
* `#7759 <https://github.com/scipy/scipy/issues/7759>`__: Overflow in
stats.kruskal for large samples
* `#7906 <https://github.com/scipy/scipy/issues/7906>`__: Wrong result from
scipy.interpolate.UnivariateSpline.integral...
* `#8165 <https://github.com/scipy/scipy/issues/8165>`__: ENH: match
functionality of R for hmean
* `#8417 <https://github.com/scipy/scipy/issues/8417>`__:
optimimze.minimize(method='L-BFGS-B', options={'disp': True})...
* `#8535 <https://github.com/scipy/scipy/issues/8535>`__: Strictly
increasing requirement in UnivariateSpline
* `#8815 <https://github.com/scipy/scipy/issues/8815>`__: [BUG] GMRES:
number of iteration is only increased if callback...
* `#9207 <https://github.com/scipy/scipy/issues/9207>`__:
scipy.linalg.solve_triangular speed after scipy.linalg.lu_factor
* `#9275 <https://github.com/scipy/scipy/issues/9275>`__: new feature:
adding LOBPCG solver in svds in addition to ARPACK
* `#9403 <https://github.com/scipy/scipy/issues/9403>`__: range of
truncnorm.logpdf could be extended
* `#9429 <https://github.com/scipy/scipy/issues/9429>`__: gaussian_kde not
working with numpy matrix
* `#9515 <https://github.com/scipy/scipy/issues/9515>`__: ndimage
implementation relies on undefined behavior
* `#9643 <https://github.com/scipy/scipy/issues/9643>`__: arpack returns
singular values in ascending order
* `#9669 <https://github.com/scipy/scipy/issues/9669>`__: DOC:
matthew-brett/build-openblas has been retired
* `#9852 <https://github.com/scipy/scipy/issues/9852>`__:
scipy.spatial.ConvexHull exit with code 134, free(): invalid...
* `#9902 <https://github.com/scipy/scipy/issues/9902>`__:
scipy.stats.truncnorm second moment may be wrong
* `#9943 <https://github.com/scipy/scipy/issues/9943>`__: Custom sampling
methods in shgo do not work
* `#9947 <https://github.com/scipy/scipy/issues/9947>`__: DOC: Incorrect
documentation for \`nan_policy='propagate\` in...
* `#9994 <https://github.com/scipy/scipy/issues/9994>`__: BUG: sparse:
reshape method allows a shape containing an arbitrary...
* `#10036 <https://github.com/scipy/scipy/issues/10036>`__: Official Nelder
mead tutorial uses xtol instead of xatol, which...
* `#10078 <https://github.com/scipy/scipy/issues/10078>`__: possible to get
a better error message when objective function...
* `#10092 <https://github.com/scipy/scipy/issues/10092>`__: overflow in
truncnorm.rvs
* `#10121 <https://github.com/scipy/scipy/issues/10121>`__: A little
spelling mistake
* `#10126 <https://github.com/scipy/scipy/issues/10126>`__: inaccurate std
implementation in binned_statistic
* `#10161 <https://github.com/scipy/scipy/issues/10161>`__: Error in
documentation scipy.special.modstruve
* `#10195 <https://github.com/scipy/scipy/issues/10195>`__: Derivative of
spline with 'const' extrapolation is also extrapolted...
* `#10206 <https://github.com/scipy/scipy/issues/10206>`__: sparse matrices
indexing with scipy 1.3
* `#10236 <https://github.com/scipy/scipy/issues/10236>`__: Non-descriptive
error on type mismatch for functions of scipy.optimize...
* `#10258 <https://github.com/scipy/scipy/issues/10258>`__: LOBPCG
convergence failure if guess provided
* `#10262 <https://github.com/scipy/scipy/issues/10262>`__: distance matrix
lacks dtype checks / warnings
* `#10271 <https://github.com/scipy/scipy/issues/10271>`__: BUG: optimize
failure on wheels
* `#10277 <https://github.com/scipy/scipy/issues/10277>`__:
scipy.special.zeta(0) = NAN
* `#10292 <https://github.com/scipy/scipy/issues/10292>`__: DOC/REL: Some
sections of the release notes are not nested correctly.
* `#10300 <https://github.com/scipy/scipy/issues/10300>`__:
scipy.stats.rv_continuous.fit throws empty RuntimeError when...
* `#10319 <https://github.com/scipy/scipy/issues/10319>`__: events in
scipy.integrate.solve_ivp: How do I setup an events...
* `#10323 <https://github.com/scipy/scipy/issues/10323>`__: Adding more
low-level LAPACK wrappers
* `#10360 <https://github.com/scipy/scipy/issues/10360>`__: firwin2
inadvertently modifies input and may result in undefined...
* `#10388 <https://github.com/scipy/scipy/issues/10388>`__: BLD:
TestHerd::test_hetrd core dumps with Python-dbg
* `#10395 <https://github.com/scipy/scipy/issues/10395>`__: Remove warning
about output shape of zoom
* `#10403 <https://github.com/scipy/scipy/issues/10403>`__: DOC:
scipy.signal.resample ignores t parameter
* `#10421 <https://github.com/scipy/scipy/issues/10421>`__: Yeo-Johnson
power transformation fails with integer input data
* `#10422 <https://github.com/scipy/scipy/issues/10422>`__: BUG: scipy.fft
does not support multiprocessing
* `#10427 <https://github.com/scipy/scipy/issues/10427>`__: ENH: convolve
numbers should be updated
* `#10444 <https://github.com/scipy/scipy/issues/10444>`__: BUG:
scipy.spatial.transform.Rotation.match_vectors returns improper...
* `#10488 <https://github.com/scipy/scipy/issues/10488>`__: ENH: DCTs/DSTs
for scipy.fft
* `#10501 <https://github.com/scipy/scipy/issues/10501>`__: BUG:
scipy.spatial.HalfspaceIntersection works incorrectly
* `#10514 <https://github.com/scipy/scipy/issues/10514>`__: BUG: cKDTree
GIL handling is incorrect
* `#10535 <https://github.com/scipy/scipy/issues/10535>`__: TST: master
branch CI failures
* `#10588 <https://github.com/scipy/scipy/issues/10588>`__: scipy.fft and
numpy.fft inconsistency when axes=None and shape...
* `#10628 <https://github.com/scipy/scipy/issues/10628>`__: Scipy
python>3.6 Windows wheels don't ship msvcp\*.dll
* `#10733 <https://github.com/scipy/scipy/issues/10733>`__: DOC/BUG:
min_only result does not match documentation
* `#10775 <https://github.com/scipy/scipy/issues/10775>`__:
UnboundLocalError in Radau when given a NaN
* `#10835 <https://github.com/scipy/scipy/issues/10835>`__: io.wavfile.read
unnecessarily raises an error for a bad wav header
* `#10838 <https://github.com/scipy/scipy/issues/10838>`__: Error in
documentation for scipy.linalg.lu_factor
* `#10875 <https://github.com/scipy/scipy/issues/10875>`__: DOC: Graphical
guides (using TikZ)
* `#10880 <https://github.com/scipy/scipy/issues/10880>`__: setting verbose
> 2 in minimize with trust-constr method leads...
* `#10887 <https://github.com/scipy/scipy/issues/10887>`__:
scipy.signal.signaltools._fftconv_faster has incorrect estimates
* `#10948 <https://github.com/scipy/scipy/issues/10948>`__: gammainc(0,x) =
nan but should be 1, gammaincc(0,x) = nan but...
* `#10952 <https://github.com/scipy/scipy/issues/10952>`__:
TestQRdelete_F.test_delete_last_p_col test failure
* `#10968 <https://github.com/scipy/scipy/issues/10968>`__: API: Change
normalized=False to normalize=True in Rotation
* `#10987 <https://github.com/scipy/scipy/issues/10987>`__: Memory leak in
shgo triangulation
* `#10991 <https://github.com/scipy/scipy/issues/10991>`__: Error running
openBlas probably missing a step
* `#11033 <https://github.com/scipy/scipy/issues/11033>`__: deadlock on osx
for python 3.8
* `#11041 <https://github.com/scipy/scipy/issues/11041>`__: Test failure in
wheel builds for TestTf2zpk.test_simple
* `#11089 <https://github.com/scipy/scipy/issues/11089>`__: Regression in
scipy.stats where distribution will not accept loc and scale parameters

Pull requests for 1.4.0
-------------------------------

* `#4591 <https://github.com/scipy/scipy/pull/4591>`__: BUG, TST: Several
issues with scipy.signal.residue
* `#6629 <https://github.com/scipy/scipy/pull/6629>`__: ENH: sparse:
canonicalize on initialization
* `#7076 <https://github.com/scipy/scipy/pull/7076>`__: ENH: add complex
wavelet support to scipy.signal.cwt.
* `#8681 <https://github.com/scipy/scipy/pull/8681>`__: ENH add generalized
inverse Gaussian distribution to scipy.stats
* `#9064 <https://github.com/scipy/scipy/pull/9064>`__: BUG/ENH: Added
default _transpose into LinearOperator. Fixes...
* `#9215 <https://github.com/scipy/scipy/pull/9215>`__: ENH: Rbf
interpolation of large multi-dimensional data
* `#9311 <https://github.com/scipy/scipy/pull/9311>`__: ENH: Added voigt in
scipy.special.
* `#9642 <https://github.com/scipy/scipy/pull/9642>`__: ENH: integrate:
quad() for vector-valued functions
* `#9679 <https://github.com/scipy/scipy/pull/9679>`__: DOC: expand
docstring of exponweib distribution
* `#9684 <https://github.com/scipy/scipy/pull/9684>`__: TST: add ppc64le ci
testing
* `#9800 <https://github.com/scipy/scipy/pull/9800>`__: WIP : ENH:
Refactored _hungarian.py for speed and added a minimize/maximize?
* `#9847 <https://github.com/scipy/scipy/pull/9847>`__: DOC: Change
integrate tutorial to use solve_ivp instead of odeint
* `#9876 <https://github.com/scipy/scipy/pull/9876>`__: ENH: Use rfft when
possible in resampling
* `#9998 <https://github.com/scipy/scipy/pull/9998>`__: BUG: Do not remove
1s when calling sparse: reshape method #9994
* `#10002 <https://github.com/scipy/scipy/pull/10002>`__: ENH: adds
constraints for differential evolution
* `#10098 <https://github.com/scipy/scipy/pull/10098>`__: ENH: integrate:
add args argument to solve_ivp.
* `#10099 <https://github.com/scipy/scipy/pull/10099>`__: DOC: Add missing
docs for linprog unknown_options
* `#10104 <https://github.com/scipy/scipy/pull/10104>`__: BUG: Rewrite of
stats.truncnorm distribution.
* `#10105 <https://github.com/scipy/scipy/pull/10105>`__: MAINT improve
efficiency of rvs_ratio_uniforms in scipy.stats
* `#10107 <https://github.com/scipy/scipy/pull/10107>`__: TST:
dual_annealing set seed
* `#10108 <https://github.com/scipy/scipy/pull/10108>`__: ENH: stats:
improve kendall_tau cache usage
* `#10110 <https://github.com/scipy/scipy/pull/10110>`__: MAINT: _lib: Fix
a build warning.
* `#10114 <https://github.com/scipy/scipy/pull/10114>`__: FIX: only print
bounds when supported by minimizer (shgo)
* `#10115 <https://github.com/scipy/scipy/pull/10115>`__: TST: Add a test
with an almost singular design matrix for lsq_linear
* `#10118 <https://github.com/scipy/scipy/pull/10118>`__: MAINT: fix rdist
methods in scipy.stats
* `#10119 <https://github.com/scipy/scipy/pull/10119>`__: MAINT: improve
rvs of randint in scipy.stats
* `#10127 <https://github.com/scipy/scipy/pull/10127>`__: Fix typo in
record array field name (spatial-ckdtree-sparse_distance?
* `#10130 <https://github.com/scipy/scipy/pull/10130>`__: MAINT: ndimage:
Fix some compiler warnings.
* `#10131 <https://github.com/scipy/scipy/pull/10131>`__: DOC: Note the
solve_ivp args enhancement in the 1.4.0 release...
* `#10133 <https://github.com/scipy/scipy/pull/10133>`__: MAINT: add rvs
for semicircular in scipy.stats
* `#10138 <https://github.com/scipy/scipy/pull/10138>`__: BUG: special:
Invalid arguments to ellip_harm can crash Python.
* `#10139 <https://github.com/scipy/scipy/pull/10139>`__: MAINT: spatial:
Fix some compiler warnings in the file distance_wrap.c.
* `#10140 <https://github.com/scipy/scipy/pull/10140>`__: ENH: add handling
of NaN in RuntimeWarning except clause
* `#10142 <https://github.com/scipy/scipy/pull/10142>`__: DOC: return value
of scipy.special.comb
* `#10143 <https://github.com/scipy/scipy/pull/10143>`__: MAINT: Loosen
linprog tol
* `#10152 <https://github.com/scipy/scipy/pull/10152>`__: BUG: Fix custom
sampling input for shgo, add unittest
* `#10154 <https://github.com/scipy/scipy/pull/10154>`__: MAINT: add
moments and improve doc of mielke in scipy.stats
* `#10158 <https://github.com/scipy/scipy/pull/10158>`__: Issue #6999: read
zlib checksum before checking bytes read.
* `#10166 <https://github.com/scipy/scipy/pull/10166>`__: BUG: Correctly
handle broadcasted ydata in curve_fit pcov computation.
* `#10167 <https://github.com/scipy/scipy/pull/10167>`__: DOC: special: Add
missing factor of \`i\` to \`modstruve\` docstring
* `#10168 <https://github.com/scipy/scipy/pull/10168>`__: MAINT: stats: Fix
an incorrect comment.
* `#10169 <https://github.com/scipy/scipy/pull/10169>`__: ENH: optimize:
Clarify error when objective function returns...
* `#10172 <https://github.com/scipy/scipy/pull/10172>`__: DEV: Run tests in
parallel when --parallel flag is passed to...
* `#10173 <https://github.com/scipy/scipy/pull/10173>`__: ENH: Implement
DOP853 ODE integrator
* `#10176 <https://github.com/scipy/scipy/pull/10176>`__: Fixed typo
* `#10182 <https://github.com/scipy/scipy/pull/10182>`__: TST: fix test
issue for stats.pearsonr
* `#10184 <https://github.com/scipy/scipy/pull/10184>`__: MAINT: stats:
Simplify zmap and zscore (we can use keepdims now).
* `#10191 <https://github.com/scipy/scipy/pull/10191>`__: DOC: fix a
formatting issue in the scipy.spatial module docstring.
* `#10193 <https://github.com/scipy/scipy/pull/10193>`__: DOC: Updated
docstring for optimize.nnls
* `#10198 <https://github.com/scipy/scipy/pull/10198>`__: DOC, ENH:
special: Make \`hyp2f1\` references more specific
* `#10202 <https://github.com/scipy/scipy/pull/10202>`__: DOC: Format DST
and DCT definitions as latex equations
* `#10207 <https://github.com/scipy/scipy/pull/10207>`__: BUG: Compressed
matrix indexing should return a scalar
* `#10210 <https://github.com/scipy/scipy/pull/10210>`__: DOC: Update docs
for connection='weak' in connected_components
* `#10225 <https://github.com/scipy/scipy/pull/10225>`__: DOC: Clarify new
interfaces for legacy functions in 'optimize'
* `#10231 <https://github.com/scipy/scipy/pull/10231>`__: DOC, MAINT: gpg2
updates to release docs / pavement
* `#10235 <https://github.com/scipy/scipy/pull/10235>`__: LICENSE: split
license file in standard BSD 3-clause and bundled.
* `#10238 <https://github.com/scipy/scipy/pull/10238>`__: ENH: Add new
scipy.fft module using pocketfft
* `#10243 <https://github.com/scipy/scipy/pull/10243>`__: BUG: fix ARFF
reader regression with quoted values.
* `#10248 <https://github.com/scipy/scipy/pull/10248>`__: DOC: update
README file
* `#10255 <https://github.com/scipy/scipy/pull/10255>`__: CI: bump OpenBLAS
to match wheels
* `#10264 <https://github.com/scipy/scipy/pull/10264>`__: TST: add tests
for stats.tvar with unflattened arrays
* `#10280 <https://github.com/scipy/scipy/pull/10280>`__: MAINT: stats: Use
a constant value for sqrt(2/PI).
* `#10286 <https://github.com/scipy/scipy/pull/10286>`__: Development
Documentation Overhaul
* `#10290 <https://github.com/scipy/scipy/pull/10290>`__: MAINT: Deprecate
NumPy functions in SciPy root
* `#10291 <https://github.com/scipy/scipy/pull/10291>`__: FIX: Avoid
importing xdist when checking for availability
* `#10295 <https://github.com/scipy/scipy/pull/10295>`__: Disable
deprecated Numpy API in __odrpack.c
* `#10296 <https://github.com/scipy/scipy/pull/10296>`__: ENH: C++
extension for linear assignment problem
* `#10298 <https://github.com/scipy/scipy/pull/10298>`__: ENH: Made pade
function work with complex inputs
* `#10301 <https://github.com/scipy/scipy/pull/10301>`__: DOC: Fix critical
value significance levels in stats.anderson_ksamp
* `#10307 <https://github.com/scipy/scipy/pull/10307>`__: Minkowski
Distance Type Fix (issue #10262)
* `#10309 <https://github.com/scipy/scipy/pull/10309>`__: BUG: Pass
jac=None directly to lsoda
* `#10310 <https://github.com/scipy/scipy/pull/10310>`__: BUG: interpolate:
UnivariateSpline.derivative.ext is 'zeros'...
* `#10312 <https://github.com/scipy/scipy/pull/10312>`__: FIX: Fixing a
typo in a comment
* `#10314 <https://github.com/scipy/scipy/pull/10314>`__: scipy.spatial
enhancement request
* `#10315 <https://github.com/scipy/scipy/pull/10315>`__: DOC: Update
integration tutorial to solve_ivp
* `#10318 <https://github.com/scipy/scipy/pull/10318>`__: DOC: update the
example for PPoly.solve
* `#10333 <https://github.com/scipy/scipy/pull/10333>`__: TST: add tests
for stats.tvar with unflattened arrays
* `#10334 <https://github.com/scipy/scipy/pull/10334>`__: MAINT: special:
Remove deprecated \`hyp2f0\`, \`hyp1f2\`, and...
* `#10336 <https://github.com/scipy/scipy/pull/10336>`__: BUG:
linalg/interpolative: fix interp_decomp modifying input
* `#10341 <https://github.com/scipy/scipy/pull/10341>`__: BUG:
sparse.linalg/gmres: deprecate effect of callback on semantics...
* `#10344 <https://github.com/scipy/scipy/pull/10344>`__: DOC: improve
wording of mathematical formulation
* `#10345 <https://github.com/scipy/scipy/pull/10345>`__: ENH: Tiled QR
wrappers for scipy.linalg.lapack
* `#10350 <https://github.com/scipy/scipy/pull/10350>`__: MAINT: linalg:
Use the new fft subpackage in linalg.dft test...
* `#10351 <https://github.com/scipy/scipy/pull/10351>`__: BUG: Fix unstable
standard deviation calculation in histogram
* `#10353 <https://github.com/scipy/scipy/pull/10353>`__: Bug:
interpolate.NearestNDInterpolator (issue #10352)
* `#10357 <https://github.com/scipy/scipy/pull/10357>`__: DOC: linalg:
Refer to scipy.fft.fft (not fftpack) in the dft...
* `#10359 <https://github.com/scipy/scipy/pull/10359>`__: DOC: Update
roadmap now scipy.fft has been merged
* `#10361 <https://github.com/scipy/scipy/pull/10361>`__: ENH: Prefer
scipy.fft to scipy.fftpack in scipy.signal
* `#10371 <https://github.com/scipy/scipy/pull/10371>`__: DOC: Tweaks to
fft documentation
* `#10372 <https://github.com/scipy/scipy/pull/10372>`__: DOC: Fix typos
* `#10377 <https://github.com/scipy/scipy/pull/10377>`__: TST, MAINT:
adjustments for pytest 5.0
* `#10378 <https://github.com/scipy/scipy/pull/10378>`__: ENH: _lib: allow
new np.random.Generator in check_random_state
* `#10379 <https://github.com/scipy/scipy/pull/10379>`__: BUG: sparse: set
writeability to be forward-compatible with numpy>=1.17
* `#10381 <https://github.com/scipy/scipy/pull/10381>`__: BUG: Fixes
gh-7491, pdf at x=0 of fisk/burr/burr12/f distributions.
* `#10387 <https://github.com/scipy/scipy/pull/10387>`__: ENH:
optimize/bfgs: don't evaluate twice at initial point for...
* `#10392 <https://github.com/scipy/scipy/pull/10392>`__: [DOC] Add an
example for _binned_statistic_dd
* `#10396 <https://github.com/scipy/scipy/pull/10396>`__: Remove warning
about output shape of zoom
* `#10397 <https://github.com/scipy/scipy/pull/10397>`__: ENH: Add
check_finite to sp.linalg.norm
* `#10399 <https://github.com/scipy/scipy/pull/10399>`__: ENH: Add
__round__ method to sparse matrix
* `#10407 <https://github.com/scipy/scipy/pull/10407>`__: MAINT: drop
pybind11 from install_requires, it's only build-time...
* `#10408 <https://github.com/scipy/scipy/pull/10408>`__: TST: use
pytest.raises, not numpy assert_raises
* `#10409 <https://github.com/scipy/scipy/pull/10409>`__: CI: uninstall
nose on Travis
* `#10410 <https://github.com/scipy/scipy/pull/10410>`__: [ENH] ncx2
dispatch to chi2 when nc=0
* `#10411 <https://github.com/scipy/scipy/pull/10411>`__: TST: optimize:
test should use assert_allclose for fp comparisons
* `#10414 <https://github.com/scipy/scipy/pull/10414>`__: DOC: add pybind11
to the other part of quickstart guides
* `#10417 <https://github.com/scipy/scipy/pull/10417>`__: DOC: special:
don't mark non-ufuncs with a \`[+]\`
* `#10423 <https://github.com/scipy/scipy/pull/10423>`__: FIX: Use
pybind11::isinstace to check array dtypes
* `#10424 <https://github.com/scipy/scipy/pull/10424>`__: DOC: add doctest
example for binary data for ttest_ind_from_stats
* `#10425 <https://github.com/scipy/scipy/pull/10425>`__: ENH: Add missing
Hermitian transforms to scipy.fft
* `#10426 <https://github.com/scipy/scipy/pull/10426>`__: MAINT: Fix doc
build bugs
* `#10431 <https://github.com/scipy/scipy/pull/10431>`__: Update numpy
version for AIX
* `#10433 <https://github.com/scipy/scipy/pull/10433>`__: MAINT: Minor
fixes for the stats
* `#10434 <https://github.com/scipy/scipy/pull/10434>`__: BUG: special:
make \`ndtri\` return NaN outside domain of definition
* `#10435 <https://github.com/scipy/scipy/pull/10435>`__: BUG: Allow
integer input data in scipy.stats.yeojohnson
* `#10438 <https://github.com/scipy/scipy/pull/10438>`__: [DOC] Add example
for kurtosis
* `#10440 <https://github.com/scipy/scipy/pull/10440>`__: ENH: special:
make \`ellipk\` a ufunc
* `#10443 <https://github.com/scipy/scipy/pull/10443>`__: MAINT: ndimage:
malloc fail check
* `#10447 <https://github.com/scipy/scipy/pull/10447>`__: BLD: Divert
output from test compiles into a temporary directory
* `#10451 <https://github.com/scipy/scipy/pull/10451>`__: MAINT: signal:
malloc fail check
* `#10455 <https://github.com/scipy/scipy/pull/10455>`__: BUG: special: fix
values of \`hyperu\` for negative \`x\`
* `#10456 <https://github.com/scipy/scipy/pull/10456>`__: DOC: Added
comment clarifying the call for dcsrch.f in lbfgsb.f
* `#10457 <https://github.com/scipy/scipy/pull/10457>`__: BUG: Allow
ckdtree to accept empty data input
* `#10459 <https://github.com/scipy/scipy/pull/10459>`__: BUG:TST: Compute
lwork safely
* `#10460 <https://github.com/scipy/scipy/pull/10460>`__: [DOC] Add example
to entropy
* `#10461 <https://github.com/scipy/scipy/pull/10461>`__: DOC: Quickstart
Guide updates
* `#10462 <https://github.com/scipy/scipy/pull/10462>`__: TST: special:
only show max atol/rtol for test points that failed
* `#10465 <https://github.com/scipy/scipy/pull/10465>`__: BUG: Correctly
align fft inputs
* `#10467 <https://github.com/scipy/scipy/pull/10467>`__: ENH: lower-memory
duplicate generator checking in spatial.SphericalVoronoi
* `#10470 <https://github.com/scipy/scipy/pull/10470>`__: ENH: Normalise
the inverse DCT/DST in scipy.fft
* `#10472 <https://github.com/scipy/scipy/pull/10472>`__: BENCH: adjust
timeout for slow setup_cache
* `#10475 <https://github.com/scipy/scipy/pull/10475>`__: CI: include
python debug for Travis-ci
* `#10476 <https://github.com/scipy/scipy/pull/10476>`__: TST: special: use
\`__tracebackhide__\` to get better error messages
* `#10477 <https://github.com/scipy/scipy/pull/10477>`__: ENH: faster
region building in spatial.SphericalVoronoi
* `#10479 <https://github.com/scipy/scipy/pull/10479>`__: BUG: stats: Fix a
few issues with the distributions' fit method.
* `#10480 <https://github.com/scipy/scipy/pull/10480>`__: Add RuntimeError
in _distn_infrastructure.py in fit() method
* `#10481 <https://github.com/scipy/scipy/pull/10481>`__: BENCH, MAINT:
wheel_cache_size has been renamed build_cache_size
* `#10494 <https://github.com/scipy/scipy/pull/10494>`__: ENH: faster
circumcenter calculation in spatial.SphericalVoronoi
* `#10500 <https://github.com/scipy/scipy/pull/10500>`__: Splrep
_curfit_cache global variable bugfix
* `#10503 <https://github.com/scipy/scipy/pull/10503>`__: BUG:
spatial/qhull: get HalfspaceIntersection.dual_points from...
* `#10506 <https://github.com/scipy/scipy/pull/10506>`__: DOC: interp2d,
note nearest neighbor extrapolation
* `#10507 <https://github.com/scipy/scipy/pull/10507>`__: MAINT: Remove
fortran fftpack library in favour of pypocketfft
* `#10508 <https://github.com/scipy/scipy/pull/10508>`__: TST: fix a bug in
the circular import test.
* `#10509 <https://github.com/scipy/scipy/pull/10509>`__: MAINT: Set up
_build_utils as subpackage
* `#10516 <https://github.com/scipy/scipy/pull/10516>`__: BUG: Use nogil
contexts in cKDTree
* `#10517 <https://github.com/scipy/scipy/pull/10517>`__: ENH: fftconvolve
should not FFT broadcastable axes
* `#10518 <https://github.com/scipy/scipy/pull/10518>`__: ENH: Speedup
fftconvolve
* `#10520 <https://github.com/scipy/scipy/pull/10520>`__: DOC: Proper .rst
formatting for deprecated features and Backwards...
* `#10523 <https://github.com/scipy/scipy/pull/10523>`__: DOC: Improve
scipy.signal.resample documentation
* `#10524 <https://github.com/scipy/scipy/pull/10524>`__: ENH: Add MGC to
scipy.stats
* `#10525 <https://github.com/scipy/scipy/pull/10525>`__: [ENH] ncx2.ppf
dispatch to chi2 when nc=0
* `#10526 <https://github.com/scipy/scipy/pull/10526>`__: DOC: clarify
laplacian normalization
* `#10528 <https://github.com/scipy/scipy/pull/10528>`__: API: Rename
scipy.fft DCT/DST shape argument to s
* `#10531 <https://github.com/scipy/scipy/pull/10531>`__: BUG: fixed
improper rotations in spatial.transform.rotation.match_vectors
* `#10533 <https://github.com/scipy/scipy/pull/10533>`__: [DOC] Add example
for winsorize function
* `#10539 <https://github.com/scipy/scipy/pull/10539>`__: MAINT: special:
don't register \`i0\` with \`numpy.dual\`
* `#10540 <https://github.com/scipy/scipy/pull/10540>`__: MAINT: Fix Travis
and Circle
* `#10542 <https://github.com/scipy/scipy/pull/10542>`__: MAINT:
interpolate: use cython_lapack
* `#10547 <https://github.com/scipy/scipy/pull/10547>`__: Feature request.
Add furthest site Voronoi diagrams to scipy.spatial.plotutils.
* `#10549 <https://github.com/scipy/scipy/pull/10549>`__: [BUG] Fix bug in
trimr when inclusive=False
* `#10552 <https://github.com/scipy/scipy/pull/10552>`__: add
scipy.signal.upfirdn signal extension modes
* `#10555 <https://github.com/scipy/scipy/pull/10555>`__: MAINT: special:
move \`c_misc\` into Cephes
* `#10556 <https://github.com/scipy/scipy/pull/10556>`__: [DOC] Add example
for trima
* `#10562 <https://github.com/scipy/scipy/pull/10562>`__: [DOC] Fix triple
string fo trimmed so that __doc__ can show...
* `#10563 <https://github.com/scipy/scipy/pull/10563>`__: improve
least_squares error msg for mismatched shape
* `#10564 <https://github.com/scipy/scipy/pull/10564>`__: ENH: linalg:
memoize get_lapack/blas_funcs to speed it up
* `#10566 <https://github.com/scipy/scipy/pull/10566>`__: ENH: add
implementation of solver for the maximum flow problem
* `#10567 <https://github.com/scipy/scipy/pull/10567>`__: BUG: spatial: use
c++11 construct for getting start of vector...
* `#10568 <https://github.com/scipy/scipy/pull/10568>`__: DOC: special:
small tweaks to the \`zetac\` docstring
* `#10571 <https://github.com/scipy/scipy/pull/10571>`__: [ENH]
Gaussian_kde can accept matrix dataset
* `#10574 <https://github.com/scipy/scipy/pull/10574>`__: ENH: linalg:
speed up _compute_lwork by avoiding numpy constructs
* `#10582 <https://github.com/scipy/scipy/pull/10582>`__: Fix typos with
typos in bundled libraries reverted
* `#10583 <https://github.com/scipy/scipy/pull/10583>`__: ENH: special: add
the analytic continuation of Riemann zeta
* `#10584 <https://github.com/scipy/scipy/pull/10584>`__: MAINT: special:
clean up \`special.__all__\`
* `#10586 <https://github.com/scipy/scipy/pull/10586>`__: BUG:
multidimensional scipy.fft functions should accept 's' rather...
* `#10587 <https://github.com/scipy/scipy/pull/10587>`__: BUG:
integrate/lsoda: never abort run, set error istate instead
* `#10594 <https://github.com/scipy/scipy/pull/10594>`__: API: Replicate
numpy's fftn behaviour when s is given but not...
* `#10599 <https://github.com/scipy/scipy/pull/10599>`__: DOC: dev: update
documentation vs. github pull request workflow...
* `#10603 <https://github.com/scipy/scipy/pull/10603>`__: MAINT: installer
scripts removed
* `#10604 <https://github.com/scipy/scipy/pull/10604>`__: MAINT: Change
c\*np.ones(...) to np.full(..., c, ...) in many...
* `#10608 <https://github.com/scipy/scipy/pull/10608>`__: Univariate
splines should require x to be strictly increasing...
* `#10613 <https://github.com/scipy/scipy/pull/10613>`__: ENH: Add seed
option for gaussian_kde.resample
* `#10614 <https://github.com/scipy/scipy/pull/10614>`__: ENH: Add parallel
computation to scipy.fft
* `#10615 <https://github.com/scipy/scipy/pull/10615>`__: MAINT:
interpolate: remove unused header file
* `#10616 <https://github.com/scipy/scipy/pull/10616>`__: MAINT: Clean up
32-bit platform xfail markers
* `#10618 <https://github.com/scipy/scipy/pull/10618>`__: BENCH: Added
'trust-constr' to minimize benchmarks
* `#10621 <https://github.com/scipy/scipy/pull/10621>`__: [MRG] multiple
stability updates in lobpcg
* `#10622 <https://github.com/scipy/scipy/pull/10622>`__: MAINT: forward
port 1.3.1 release notes
* `#10624 <https://github.com/scipy/scipy/pull/10624>`__: DOC: stats: Fix
spelling of 'support'.
* `#10627 <https://github.com/scipy/scipy/pull/10627>`__: DOC: stats: Add
references for the alpha distribution.
* `#10629 <https://github.com/scipy/scipy/pull/10629>`__: MAINT: special:
avoid overflow longer in \`zeta\` for negative...
* `#10630 <https://github.com/scipy/scipy/pull/10630>`__: TST: GH10271,
relax test assertion, fixes #10271
* `#10631 <https://github.com/scipy/scipy/pull/10631>`__: DOC: nelder-mean
uses xatol fixes #10036
* `#10633 <https://github.com/scipy/scipy/pull/10633>`__: BUG: interpolate:
integral(a, b) should be zero when both limits...
* `#10635 <https://github.com/scipy/scipy/pull/10635>`__: DOC: special:
complete hypergeometric functions documentation
* `#10636 <https://github.com/scipy/scipy/pull/10636>`__: BUG: special: use
series for \`hyp1f1\` when it converges rapidly
* `#10641 <https://github.com/scipy/scipy/pull/10641>`__: ENH: allow
matching of general bipartite graphs
* `#10643 <https://github.com/scipy/scipy/pull/10643>`__: ENH:
scipy.sparse.linalg.spsolve triangular unit diagonal
* `#10650 <https://github.com/scipy/scipy/pull/10650>`__: ENH: Cythonize
sosfilt
* `#10654 <https://github.com/scipy/scipy/pull/10654>`__: DOC: Vertical
alignment of table entries
* `#10655 <https://github.com/scipy/scipy/pull/10655>`__: ENH: Dockerfile
for scipy development
* `#10660 <https://github.com/scipy/scipy/pull/10660>`__: TST: clean up
tests for rvs in scipy.stats
* `#10664 <https://github.com/scipy/scipy/pull/10664>`__: Throw error on
non-finite input for binned_statistic_dd()
* `#10665 <https://github.com/scipy/scipy/pull/10665>`__: DOC: special:
improve the docstrings for \`gamma\` and \`gammasgn\`
* `#10669 <https://github.com/scipy/scipy/pull/10669>`__: TST: Update
scipy.fft real transform tests
* `#10670 <https://github.com/scipy/scipy/pull/10670>`__: DOC: Clarify docs
and error messages for scipy.signal.butter
* `#10672 <https://github.com/scipy/scipy/pull/10672>`__: ENH: return
solution attribute when using events in solve_ivp
* `#10675 <https://github.com/scipy/scipy/pull/10675>`__: MAINT: special:
add an explicit NaN check for \`iv\` arguments
* `#10679 <https://github.com/scipy/scipy/pull/10679>`__: DOC: special: Add
documentation for \`beta\` function
* `#10681 <https://github.com/scipy/scipy/pull/10681>`__: TST:
sparse.linalg: fix arnoldi test seed
* `#10682 <https://github.com/scipy/scipy/pull/10682>`__: DOC: special: Add
documentation for \`betainc\` function
* `#10684 <https://github.com/scipy/scipy/pull/10684>`__: TST: special:
require Mpmath 1.1.0 for \`test_hyperu_around_0\`
* `#10686 <https://github.com/scipy/scipy/pull/10686>`__: FIX: sphinx
isattributedescriptor is not available in sphinx...
* `#10687 <https://github.com/scipy/scipy/pull/10687>`__: DOC: added Docker
quickstart guide by @andyfaff
* `#10689 <https://github.com/scipy/scipy/pull/10689>`__: DOC: special:
clarify format of parameters/returns sections for...
* `#10690 <https://github.com/scipy/scipy/pull/10690>`__: DOC: special:
improve docstrings of incomplete gamma functions
* `#10692 <https://github.com/scipy/scipy/pull/10692>`__: ENH:
higher-dimensional input in \`spatial.SphericalVoronoi\`
* `#10694 <https://github.com/scipy/scipy/pull/10694>`__: ENH:
ScalarFunction.fun_and_grad
* `#10698 <https://github.com/scipy/scipy/pull/10698>`__: DOC: special: Add
documentation for \`betaincinv\`
* `#10699 <https://github.com/scipy/scipy/pull/10699>`__: MAINT: remove
time print lbfgsb fixes #8417
* `#10701 <https://github.com/scipy/scipy/pull/10701>`__: TST, MAINT: bump
OpenBLAS to 0.3.7 stable
* `#10702 <https://github.com/scipy/scipy/pull/10702>`__: DOC: clarify
iterations consume multiple function calls
* `#10703 <https://github.com/scipy/scipy/pull/10703>`__: DOC: iprint doc
lbfgsb closes #5482
* `#10708 <https://github.com/scipy/scipy/pull/10708>`__: TST: test
suggested in gh1758
* `#10710 <https://github.com/scipy/scipy/pull/10710>`__: ENH: Added
nan_policy to circ functions in \`stats\`
* `#10712 <https://github.com/scipy/scipy/pull/10712>`__: ENH: add axis
parameter to stats.entropy
* `#10714 <https://github.com/scipy/scipy/pull/10714>`__: DOC: Formatting
fix rv_continuous.expect docs
* `#10715 <https://github.com/scipy/scipy/pull/10715>`__: DOC: BLD: update
doc Makefile for python version; add scipy version...
* `#10717 <https://github.com/scipy/scipy/pull/10717>`__: MAINT: modernize
doc/Makefile
* `#10719 <https://github.com/scipy/scipy/pull/10719>`__: Enable setting
minres initial vector
* `#10720 <https://github.com/scipy/scipy/pull/10720>`__: DOC: silence
random warning in doc build for \`stats.binned_statistic_dd\`
* `#10724 <https://github.com/scipy/scipy/pull/10724>`__: DEV: Add doc
option to runtests.py
* `#10728 <https://github.com/scipy/scipy/pull/10728>`__: MAINT: get rid of
gramA, gramB text files that lobpcg tests leave...
* `#10732 <https://github.com/scipy/scipy/pull/10732>`__: DOC: add min_only
to docstring for Dijkstra's algorithm
* `#10734 <https://github.com/scipy/scipy/pull/10734>`__: DOC: spell out
difference between source and target in shortest...
* `#10735 <https://github.com/scipy/scipy/pull/10735>`__: Fix for Python 4
* `#10739 <https://github.com/scipy/scipy/pull/10739>`__: BUG:
optimize/slsqp: deal with singular BFGS update
* `#10741 <https://github.com/scipy/scipy/pull/10741>`__: ENH: LAPACK
wrappers for ?geequ, ?geequb, ?syequb, ?heequb
* `#10742 <https://github.com/scipy/scipy/pull/10742>`__: DOC: special: add
to the docstring of \`gammaln\`
* `#10743 <https://github.com/scipy/scipy/pull/10743>`__: ENH: special: add
a real dispatch for \`wrightomega\`
* `#10746 <https://github.com/scipy/scipy/pull/10746>`__: MAINT: Fix typos
in comments, docs and test name
* `#10747 <https://github.com/scipy/scipy/pull/10747>`__: Remove spurious
quotes
* `#10750 <https://github.com/scipy/scipy/pull/10750>`__: MAINT: make
cython code more precise
* `#10751 <https://github.com/scipy/scipy/pull/10751>`__: MAINT: Check that
scipy.linalg.lapack functions are documented
* `#10752 <https://github.com/scipy/scipy/pull/10752>`__: MAINT: special:
use \`sf_error\` in Cephes
* `#10755 <https://github.com/scipy/scipy/pull/10755>`__: DOC: cluster: Add
'See Also' and 'Examples' for kmeans2.
* `#10763 <https://github.com/scipy/scipy/pull/10763>`__: MAINT: list of
minimize methods
* `#10768 <https://github.com/scipy/scipy/pull/10768>`__: BUG: Fix corner
case for sos2zpk
* `#10773 <https://github.com/scipy/scipy/pull/10773>`__: Fix error type
for complex input to scipy.fftpack.rfft and irfft
* `#10776 <https://github.com/scipy/scipy/pull/10776>`__: ENH: handle
geodesic input in \`spatial.SphericalVoronoi\`
* `#10777 <https://github.com/scipy/scipy/pull/10777>`__: MAINT:
minimizer-->custom should handle the kinds of bounds/constrain?...
* `#10781 <https://github.com/scipy/scipy/pull/10781>`__: ENH:
solve_triangular C order improvement
* `#10787 <https://github.com/scipy/scipy/pull/10787>`__: Fix behavior of
\`exp1\` on branch cut and add docstring
* `#10789 <https://github.com/scipy/scipy/pull/10789>`__: DOC: special: add
parameters/returns doc sections for erfc/erfcx/erfi
* `#10790 <https://github.com/scipy/scipy/pull/10790>`__: Travis CI: sudo
is deprecated and Xenial is default distro
* `#10792 <https://github.com/scipy/scipy/pull/10792>`__: DOC: special: add
full docstring for \`expi\`
* `#10799 <https://github.com/scipy/scipy/pull/10799>`__: DOC: special: add
a complete docstring for \`expn\`
* `#10800 <https://github.com/scipy/scipy/pull/10800>`__: Docs edits (GSoD)
* `#10802 <https://github.com/scipy/scipy/pull/10802>`__: BUG: fix
UnboundLocalError in Radau (scipy#10775)
* `#10804 <https://github.com/scipy/scipy/pull/10804>`__: ENH: Speed up
next_fast_len with LRU cache
* `#10805 <https://github.com/scipy/scipy/pull/10805>`__: DOC: Fix
unbalanced quotes in signal.place_poles
* `#10809 <https://github.com/scipy/scipy/pull/10809>`__: ENH: Speed up
next_fast_len
* `#10810 <https://github.com/scipy/scipy/pull/10810>`__: ENH: Raise
catchable exceptions for bad Fortran files
* `#10811 <https://github.com/scipy/scipy/pull/10811>`__: MAINT: optimize:
Remove extra variable from _remove_redundancy_dense
* `#10813 <https://github.com/scipy/scipy/pull/10813>`__: MAINT: special:
Remove unused variables from _kolmogi and _smirnovi
* `#10815 <https://github.com/scipy/scipy/pull/10815>`__: DOC, API:
scipy.stats.reciprocal is "log-uniform"
* `#10816 <https://github.com/scipy/scipy/pull/10816>`__: MAINT: special:
remove deprecated \`bessel_diff_formula\`
* `#10817 <https://github.com/scipy/scipy/pull/10817>`__: DOC: special:
complete the docstring for \`fresnel\`
* `#10820 <https://github.com/scipy/scipy/pull/10820>`__: Fixed
compiler_helper.py to allow compilation with ICC on Linux
* `#10823 <https://github.com/scipy/scipy/pull/10823>`__: DOC: updated
reference guide text for consistency in writing...
* `#10825 <https://github.com/scipy/scipy/pull/10825>`__: MAINT: special:
change some features of the Voigt function
* `#10828 <https://github.com/scipy/scipy/pull/10828>`__: MAINT: integrate:
Remove unused variable from init_callback
* `#10830 <https://github.com/scipy/scipy/pull/10830>`__: Adding LOBPCG
solver in svds in addition to ARPACK
* `#10837 <https://github.com/scipy/scipy/pull/10837>`__: WIP: ENH:
reduction function for \`spatial.tranform.Rotation\`...
* `#10843 <https://github.com/scipy/scipy/pull/10843>`__: ENH: Adding
optional parameter to stats.zscores to allow for...
* `#10845 <https://github.com/scipy/scipy/pull/10845>`__: Rebase kruskal fix
* `#10847 <https://github.com/scipy/scipy/pull/10847>`__: remove redundant
__getitem__ from scipy.sparse.lil
* `#10848 <https://github.com/scipy/scipy/pull/10848>`__: Better handling
of empty (not missing) docstrings
* `#10849 <https://github.com/scipy/scipy/pull/10849>`__: ENH: implement
rmatmat for LinearOperator
* `#10850 <https://github.com/scipy/scipy/pull/10850>`__: MAINT :
Refactoring lil List of Lists
* `#10851 <https://github.com/scipy/scipy/pull/10851>`__: DOC: add a
generative art example to the scipy.spatial tutorial.
* `#10852 <https://github.com/scipy/scipy/pull/10852>`__: DOC: linalg:
fixed gh-10838 unused imports in example deleted
* `#10854 <https://github.com/scipy/scipy/pull/10854>`__: DOC: special: add
a full docstring for \`pdtr\`
* `#10861 <https://github.com/scipy/scipy/pull/10861>`__: ENH: option to
reuse binnumbers in stats.binned_statistic_dd
* `#10863 <https://github.com/scipy/scipy/pull/10863>`__: DOC: partial
standardization and validation of scipy.stats reference...
* `#10865 <https://github.com/scipy/scipy/pull/10865>`__: BUG: special: fix
incomplete gamma functions for infinite \`a\`
* `#10866 <https://github.com/scipy/scipy/pull/10866>`__: ENH: calculation
of mean in spatial.transform.Rotation
* `#10867 <https://github.com/scipy/scipy/pull/10867>`__: MAINT: Also store
latex directory
* `#10869 <https://github.com/scipy/scipy/pull/10869>`__: ENH: Implement
overlap-add convolution
* `#10870 <https://github.com/scipy/scipy/pull/10870>`__: ENH: Do not raise
EOF error if wavfile data read
* `#10876 <https://github.com/scipy/scipy/pull/10876>`__: ENH: Add
beta-binomial distribution to scipy.stats
* `#10878 <https://github.com/scipy/scipy/pull/10878>`__: MAINT: Update R
project URL
* `#10883 <https://github.com/scipy/scipy/pull/10883>`__: MAINT: (ndimage)
More robust check for output being a numpy dtype
* `#10884 <https://github.com/scipy/scipy/pull/10884>`__: DOC: Added
instructions on adding a new distribution to scipy.stats.
* `#10885 <https://github.com/scipy/scipy/pull/10885>`__: [BUG] fix lobpcg
with maxiter=None results in Exception
* `#10899 <https://github.com/scipy/scipy/pull/10899>`__: ENH: Match R
functionality for hmean
* `#10900 <https://github.com/scipy/scipy/pull/10900>`__: MAINT: stats: Use
keepdims to simplify a few lines in power_divergence.
* `#10901 <https://github.com/scipy/scipy/pull/10901>`__: ENH:
sparse/linalg: support pydata/sparse matrices
* `#10907 <https://github.com/scipy/scipy/pull/10907>`__: Check whether
\`maxiter\` is integer
* `#10912 <https://github.com/scipy/scipy/pull/10912>`__: ENH: warn user
that quad() ignores \`points=...\` when \`weight=...\`...
* `#10918 <https://github.com/scipy/scipy/pull/10918>`__: CI: fix Travis CI
py3.8 build
* `#10920 <https://github.com/scipy/scipy/pull/10920>`__: MAINT: Update
constants to codata 2018 values (second try)
* `#10921 <https://github.com/scipy/scipy/pull/10921>`__: ENH:
scipy.sparse.lil: tocsr accelerated
* `#10924 <https://github.com/scipy/scipy/pull/10924>`__: BUG: Forbid
passing 'args' as kwarg in scipy.optimize.curve_fit
* `#10928 <https://github.com/scipy/scipy/pull/10928>`__: DOC: Add examples
to io.wavfile docstrings
* `#10934 <https://github.com/scipy/scipy/pull/10934>`__: typo fix
* `#10935 <https://github.com/scipy/scipy/pull/10935>`__: BUG: Avoid
undefined behaviour on float to unsigned conversion
* `#10936 <https://github.com/scipy/scipy/pull/10936>`__: DOC: Added
missing example to stats.mstats.variation
* `#10939 <https://github.com/scipy/scipy/pull/10939>`__: ENH:
scipy.sparse.lil: tocsr accelerated depending on density
* `#10946 <https://github.com/scipy/scipy/pull/10946>`__: BUG: setting
verbose > 2 in minimize with trust-constr method...
* `#10947 <https://github.com/scipy/scipy/pull/10947>`__: DOC: special:
small improvements to the \`poch\` docstring
* `#10949 <https://github.com/scipy/scipy/pull/10949>`__: BUG: fix return
type of erlang_gen._argcheck
* `#10951 <https://github.com/scipy/scipy/pull/10951>`__: DOC: fixed Ricker
wavelet formula
* `#10954 <https://github.com/scipy/scipy/pull/10954>`__: BUG: special: fix
\`factorial\` return type for 0-d inputs
* `#10955 <https://github.com/scipy/scipy/pull/10955>`__: MAINT: Relax the
assert_unitary atol value
* `#10956 <https://github.com/scipy/scipy/pull/10956>`__: WIP: make
pdtr(int, double) be pdtr(double, double)
* `#10957 <https://github.com/scipy/scipy/pull/10957>`__: BUG: Ensure full
binary compatibility of long double test data
* `#10964 <https://github.com/scipy/scipy/pull/10964>`__: ENH: Make Slerp
callable with a scalar argument
* `#10972 <https://github.com/scipy/scipy/pull/10972>`__: BUG: Handle
complex gains in zpk2sos
* `#10975 <https://github.com/scipy/scipy/pull/10975>`__: TST: skip
test_kendalltau ppc64le
* `#10978 <https://github.com/scipy/scipy/pull/10978>`__: BUG: boxcox data
dimension and constancy check #5112
* `#10979 <https://github.com/scipy/scipy/pull/10979>`__: API: Rename dcm
to (rotation) matrix in Rotation class
* `#10981 <https://github.com/scipy/scipy/pull/10981>`__: MAINT: add
support for a==0 and x>0 edge case to igam and igamc
* `#10986 <https://github.com/scipy/scipy/pull/10986>`__: MAINT: Remove
direct imports from numpy in signaltools.py
* `#10988 <https://github.com/scipy/scipy/pull/10988>`__: BUG: signal:
fixed issue #10360
* `#10989 <https://github.com/scipy/scipy/pull/10989>`__: FIX
binned_statistic_dd Mac wheel test fails
* `#10990 <https://github.com/scipy/scipy/pull/10990>`__: BUG: Fix memory
leak in shgo triangulation
* `#10992 <https://github.com/scipy/scipy/pull/10992>`__: TST: Relax
tolerance in upfirdn test_modes
* `#10993 <https://github.com/scipy/scipy/pull/10993>`__: TST: bump
tolerance in optimize tests
* `#10997 <https://github.com/scipy/scipy/pull/10997>`__: MAINT: Rework
residue and residuez
* `#11001 <https://github.com/scipy/scipy/pull/11001>`__: DOC: Updated
Windows build tutorial
* `#11004 <https://github.com/scipy/scipy/pull/11004>`__: BUG:
integrate/quad_vec: fix several bugs in quad_vec
* `#11005 <https://github.com/scipy/scipy/pull/11005>`__: TST: add Python
3.8 Win CI
* `#11006 <https://github.com/scipy/scipy/pull/11006>`__: DOC: special: add
a reference for \`kl_div\`
* `#11012 <https://github.com/scipy/scipy/pull/11012>`__: MAINT: Rework
invres and invresz
* `#11015 <https://github.com/scipy/scipy/pull/11015>`__: DOC: special: add
references for \`rel_entr\`
* `#11017 <https://github.com/scipy/scipy/pull/11017>`__: DOC: numpydoc
validation of morestats.py
* `#11018 <https://github.com/scipy/scipy/pull/11018>`__: MAINT: Filter
unrelated warning
* `#11031 <https://github.com/scipy/scipy/pull/11031>`__: MAINT: update
choose_conv_method for pocketfft implementation
* `#11034 <https://github.com/scipy/scipy/pull/11034>`__: MAINT: TST: Skip
tests with multiprocessing that use "spawn"...
* `#11036 <https://github.com/scipy/scipy/pull/11036>`__: DOC: update
doc/README with some more useful content.
* `#11037 <https://github.com/scipy/scipy/pull/11037>`__: DOC: special: add
a complete docstring for \`rgamma\`
* `#11038 <https://github.com/scipy/scipy/pull/11038>`__: DOC: special: add
a reference for the polygamma function
* `#11042 <https://github.com/scipy/scipy/pull/11042>`__: TST: fix tf2zpk
test failure due to incorrect complex sorting.
* `#11044 <https://github.com/scipy/scipy/pull/11044>`__: MAINT:
choose_conv_method can choose fftconvolution for longcomplex
* `#11046 <https://github.com/scipy/scipy/pull/11046>`__: TST: Reduce
tolerance for ppc64le with reference lapack
* `#11048 <https://github.com/scipy/scipy/pull/11048>`__: DOC: special: add
reference for orthogonal polynomial functions
* `#11049 <https://github.com/scipy/scipy/pull/11049>`__: MAINT: proper
random number initialization and readability fix
* `#11051 <https://github.com/scipy/scipy/pull/11051>`__: MAINT: pep8
cleanup
* `#11054 <https://github.com/scipy/scipy/pull/11054>`__: TST: bump test
precision for dual_annealing SLSQP test
* `#11055 <https://github.com/scipy/scipy/pull/11055>`__: DOC: special: add
a reference for \`zeta\`
* `#11056 <https://github.com/scipy/scipy/pull/11056>`__: API: Deprecated
normalized keyword in Rotation
* `#11065 <https://github.com/scipy/scipy/pull/11065>`__: DOC: Ubuntu
Development Environment Quickstart should not modify...
* `#11066 <https://github.com/scipy/scipy/pull/11066>`__: BUG: skip
deprecation for numpy top-level types
* `#11067 <https://github.com/scipy/scipy/pull/11067>`__: DOC: updated
documentation for consistency in writing style
* `#11070 <https://github.com/scipy/scipy/pull/11070>`__: DOC: Amendment to
Ubuntu Development Environment Quickstart should...
* `#11073 <https://github.com/scipy/scipy/pull/11073>`__: DOC: fix 1.4.0
release notes
* `#11083 <https://github.com/scipy/scipy/pull/11083>`__: DOC: more 1.4.0
release note fixes
* `#11092 <https://github.com/scipy/scipy/pull/11092>`__: BUG: stats: fix
freezing of some distributions

Checksums
=========

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From matti.picus at gmail.com  Fri Nov 22 11:53:48 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Fri, 22 Nov 2019 08:53:48 -0800
Subject: [Numpy-discussion] Updated numpy.random C-API
Message-ID: <e981d197-dca8-ea8d-ae55-560f5db4401b@gmail.com>

Hi everyone,


We recently officially exposed and documented[0] the C-API side of 
numpy.random. There are now working examples[3] of using numpy.random 
from numba[1], cython[2], and cffi[5].


Please try this out before the 1.18 release by installing the latest 
HEAD version and making sure the interfaces work for your use case.

There is an open issue[4] for exploring such use cases, but we need 
feedback about where documentation is missing, where functionality falls 
short, and help with sample code demonstrating any of the use cases not 
yet explicitly tested.


Matti


[0] https://numpy.org/devdocs/reference/random/extending.html

[1] https://numpy.org/devdocs/reference/random/extending.html#numba

[2] https://numpy.org/devdocs/reference/random/extending.html#cython

[3] https://numpy.org/devdocs/reference/random/extending.html#examples

[4] https://github.com/numpy/numpy/issues/14778

[5] https://github.com/numpy/numpy/pull/14954




From jfoxrabinovitz at gmail.com  Sat Nov 23 00:42:08 2019
From: jfoxrabinovitz at gmail.com (Joseph Fox-Rabinovitz)
Date: Sat, 23 Nov 2019 00:42:08 -0500
Subject: [Numpy-discussion] PR 14966: Adding a new argument to np.asfarray
Message-ID: <CAAa1KPZOK687dDcjqCr+iOq=zu0Num9AVuFe4A3cZ7ntbLNm6Q@mail.gmail.com>

Hi,

I've submitted PR #14966, which makes a couple of small, backward
compatible, changes to the API of `asfarray`:

1. Added `copy` parameter that defaults to `False`
2. Added `None` option to the `dtype` parameter

Item #1 is inspired by situations like the one in Stack Overflow question
https://stackoverflow.com/q/58998475/2988730. Sometimes, you just need to
ensure a copy, and it's nice not to have to check things like if
`asfarray(x) is x: x = x.copy()`.

Item #2 solves the problem of trying to do `asfarray(x, dtype=x.dtype)` for
`x` that don't have a `dtype` attribute, like lists or tuples. I've made
every effort to make `dtype` and `copy` play together nicely.

On an unrelated note, I've also submitted #14967 to clean up the internals
of `mintypecode` a little in the same file.

Regards,

- Joe
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From matti.picus at gmail.com  Sat Nov 23 11:43:52 2019
From: matti.picus at gmail.com (Matti Picus)
Date: Sat, 23 Nov 2019 08:43:52 -0800
Subject: [Numpy-discussion] Proposal to accept NEP 34: Disallow
 inferring dtype=object from sequence
In-Reply-To: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>
References: <8c0ee037-60ab-0139-0ef7-7a7f4bded678@gmail.com>
Message-ID: <7b4c35ed-165b-fb89-0ea7-2ce7f59f0f8e@gmail.com>

On 16/11/19 1:42 pm, Matti Picus wrote:
> I propose to move the NEP https://numpy.org/neps/nep-0034.html from 
> "draft" to "accepted" status. There were no objections (actually there 
> were no responses at all) to the mail proposing the NEP 
> https://mail.python.org/pipermail/numpy-discussion/2019-October/080200.html.
>
>
> PR 14794 https://github.com/numpy/numpy/pull/14794 is an 
> implementation of the first step, deprecating current automatic 
> detection,
>
>
> f there are no substantive objections within 7 days from this email, 
> then the NEP will be accepted; see NEP 0 for more details.
>
>
> Matti
>
There were no objections, we can move forward.

Thanks,

Matti


From tyler.je.reddy at gmail.com  Sat Nov 23 16:59:51 2019
From: tyler.je.reddy at gmail.com (Tyler Reddy)
Date: Sat, 23 Nov 2019 14:59:51 -0700
Subject: [Numpy-discussion] ANN: SciPy 1.3.3
Message-ID: <CAHPuU_Z9iOvR1zrxutLOyOdhWG2sA2zKEbKMZqd31H_ki0HfrA@mail.gmail.com>

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

Hi all,

On behalf of the SciPy development team I'm pleased to announce
the release of SciPy 1.3.3, a bug fix release that addresses
a DLL loading issue for wheels and a multiprocessing test issue.

Sources and binary wheels can be found at:
https://pypi.org/project/scipy/
and at: https://github.com/scipy/scipy/releases/tag/v1.3.3

One of a few ways to install this release with pip:

pip install scipy==1.3.3

=====================
SciPy 1.3.3 Release Notes
=====================

SciPy 1.3.3 is a bug-fix release with no new features
compared to 1.3.2. In particular, a test suite issue
involving multiprocessing was fixed for Windows and
Python 3.8 on macOS.

Wheels were also updated to place msvcp140.dll at the
appropriate location, which was previously causing issues.

Authors
=======

Ilhan Polat
Tyler Reddy
Ralf Gommers

Issues closed for 1.3.3
--------------------------------

* `#11033 <https://github.com/scipy/scipy/issues/11033>`__: deadlock on osx
for python 3.8


Pull requests for 1.3.3
---------------------------------

* `#11034 <https://github.com/scipy/scipy/pull/11034>`__: MAINT: TST: Skip
tests with multiprocessing that use "spawn" start method

Checksums
=========

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~~~~~~

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From me at diehlpk.de  Mon Nov 25 22:25:28 2019
From: me at diehlpk.de (Patrick Diehl)
Date: Mon, 25 Nov 2019 21:25:28 -0600
Subject: [Numpy-discussion] FLOSS for science podcast
Message-ID: <1442d49f-62fc-d1e4-ce50-658af23c5b9f@diehlpk.de>

Dear Sir or Madam,

I started with a colleague the podcast FLOSS for Science [0] with the
goal of showcasing free, libre and open source software uses in science.
We want to highlight how FLOSS empowers researchers and enables them to
produce high quality research. Through each of our episodes, we want to
showcase a scientist using FLOSS to produce his/her research or the
developers of software used for scientific research.

Would some one of you be interested to be interviewed about the
numpy/scipy package.

Best,

Patrick

[0] https://flossforscience.com/

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From sebastian at sipsolutions.net  Wed Nov 27 09:37:07 2019
From: sebastian at sipsolutions.net (Sebastian Berg)
Date: Wed, 27 Nov 2019 08:37:07 -0600
Subject: [Numpy-discussion] NumPy community meeting Today: Wed Nov 27
Message-ID: <80a4f26d3db3b3cb6a843074e45b1fa8f775b5ee.camel@sipsolutions.net>

Hi all,

There will be a NumPy Community meeting today (Wednesday Nov 27) at 11
am Pacific Time. Everyone is invited to join in and edit the work-in-
progress meeting topics and notes:
https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both

Note that from now on, we may try to focus on more technical discussion
(such as PR and issue triage) every second week.

Best wishes

Sebastian
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From magdalena.proszewska at gmail.com  Wed Nov 27 12:00:54 2019
From: magdalena.proszewska at gmail.com (mpro)
Date: Wed, 27 Nov 2019 10:00:54 -0700 (MST)
Subject: [Numpy-discussion] Reverse parameter in ordering functions
Message-ID: <1574874054151-0.post@n7.nabble.com>

Hi all,

I created pull request #14989, which adds reverse parameter in np.sort
function.

As suggested in the comments, I want to ask for some feedback on adding
parameter 'reverse' . Not only in sort, but in ordering functions in
general.

Magdalena



--
Sent from: http://numpy-discussion.10968.n7.nabble.com/

From sebastian at sipsolutions.net  Wed Nov 27 11:57:56 2019
From: sebastian at sipsolutions.net (Sebastian Berg)
Date: Wed, 27 Nov 2019 10:57:56 -0600
Subject: [Numpy-discussion] Reverse parameter in ordering functions
In-Reply-To: <1574874054151-0.post@n7.nabble.com>
References: <1574874054151-0.post@n7.nabble.com>
Message-ID: <34822fe03f6a637eb3155caec0a5198f0a3403c0.camel@sipsolutions.net>

On Wed, 2019-11-27 at 10:00 -0700, mpro wrote:
> Hi all,
> 
> I created pull request #14989, which adds reverse parameter in
> np.sort
> function.
> 
> As suggested in the comments, I want to ask for some feedback on
> adding
> parameter 'reverse' . Not only in sort, but in ordering functions in
> general.
> 

Since the standard python functions all have the `reverse` parameter,
it seems like a good idea to me.

I agree that when we add it, it probably would be good to aim for
adding it for all sorting related functions at the same time.

Best,

Sebastian



> Magdalena
> 
> 
> 
> --
> Sent from: http://numpy-discussion.10968.n7.nabble.com/
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
> 
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From ralf.gommers at gmail.com  Thu Nov 28 17:00:08 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Thu, 28 Nov 2019 14:00:08 -0800
Subject: [Numpy-discussion] Updated numpy.random C-API
In-Reply-To: <e981d197-dca8-ea8d-ae55-560f5db4401b@gmail.com>
References: <e981d197-dca8-ea8d-ae55-560f5db4401b@gmail.com>
Message-ID: <CABL7CQj5vSqq6tLSeij828g-2hecf=gPch19c6ykOB_Y8eS=Cg@mail.gmail.com>

On Fri, Nov 22, 2019 at 8:54 AM Matti Picus <matti.picus at gmail.com> wrote:

> Hi everyone,
>
>
> We recently officially exposed and documented[0] the C-API side of
> numpy.random. There are now working examples[3] of using numpy.random
> from numba[1], cython[2], and cffi[5].
>
>
> Please try this out before the 1.18 release by installing the latest
> HEAD version and making sure the interfaces work for your use case.
>
> There is an open issue[4] for exploring such use cases, but we need
> feedback about where documentation is missing, where functionality falls
> short, and help with sample code demonstrating any of the use cases not
> yet explicitly tested.
>

Thanks Matti! I added a bunch of comments in
https://github.com/numpy/numpy/issues/14778#issuecomment-559609624

Cheers,
Ralf


>
> Matti
>
>
> [0] https://numpy.org/devdocs/reference/random/extending.html
>
> [1] https://numpy.org/devdocs/reference/random/extending.html#numba
>
> [2] https://numpy.org/devdocs/reference/random/extending.html#cython
>
> [3] https://numpy.org/devdocs/reference/random/extending.html#examples
>
> [4] https://github.com/numpy/numpy/issues/14778
>
> [5] https://github.com/numpy/numpy/pull/14954
>
>
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
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