Mailman 3 May 2022 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How … [View More]

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Documentation Team meeting - Monday June 8th
by Melissa Mendonça Dec. 3, 2022

Dec. 3, 2022

Hi all! A reminder that on Monday, June 8, we have another documentation team meeting at 3PM UTC**. If you wish to join on Zoom, you need to use this link https://zoom.us/j/420005230 Here's the permanent hackmd document with the meeting notes: https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg <https://www.google.com/url?q=https%3A%2F%2Fhackmd.io%2FoB_boakvRqKR-_2jRV-Q…> Hope to see you around (especially if you want to introduce yourself or discuss ideas for Google Season of Docs). ** You … [View More]

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An extension of the .npy file format
by Michael Siebert Aug. 25, 2022

Aug. 25, 2022

Dear all, originally, I have planned to make an extension of the .npy file format a dedicated follow-up pull request, but I have upgraded my current request instead, since it was not as difficult to implement as I initially thought and probably a more straight-forward solution: https://github.com/numpy/numpy/pull/20321/ What is this pull request about? It is about appending to Numpy .npy files. Why? I see two main use cases: 1. creating .npy files larger than the main memory. They can, … [View More]once finished, be loaded as memory maps 2. creating binary log files, which can be processed very efficiently without parsing Are there not other good file formats to do this? Theoretically yes, but practically they can be pretty complex and with very little tweaking .npy could do efficient appending too. Use case 1 is already covered by the Pip/Conda package npy-append-array I have created and getting the functionality directly into Numpy was the original goal of the pull request. This would have been possible without introducing a new file format version, just by adding some spare space in the header. During the pull request discussion it turned out that rewriting the header after each append would be desirable in case the writing program crashes to minimize data loss. Use case 2 however would highly profit from a new file format version as it would make rewriting the header unnecessary: since efficient appending can only take place along one axis, setting shape[-1] = -1 in case of Fortran order or shape[0] = -1 otherwise (default) in the .npy header on file creation could indicate that the array size is determined by the file size: when np.load (typically with memory mapping on) gets called, it constructs the ndarray with the actual shape by replacing the -1 in the constructor call. Otherwise, the header is not modified anymore, neither on append nor on file write finish. Concurrent appends to a single file would not be advisable and should be channeled through a single AppendArray instance. Concurrent reads while writes take place however should work relatively smooth: every time an np.load (ideally with mmap) is called, the ndarray would provide access to all data written until that time. Currently, my pull request provides: 1. A definition of .npy version 4.0 that supports -1 in the shape 2. implementations for fortran order and non-fortran order (default) including test cases 3. Updated np.load 4. The AppendArray class that does the actual appending Although there is a certain hassle with introducing a new .npy version, the changes themselves are very small. I could also implement a fallback mode for older Numpy installations, if someone is interested. What do you think about such a feature, would it make sense? Anyone available for some more code review? Best from Berlin, Michael PS thank you so far, I could improve my npy-append-array module as well and from what I have seen so far the Numpy code readability exceeded my already high expectations. [View Less]

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NumPy community meeting
by Inessa Pawson July 31, 2022

July 31, 2022

The next NumPy community meeting will be held this Wednesday, May 25th at 18:00 (6 pm) UTC. Join us via Zoom: https://berkeley.zoom.us/j/762261535 Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy

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copy="never" discussion and no deprecation cycle?
by Sebastian Berg July 21, 2022

July 21, 2022

Hi all, (sorry for the length, details/discussion below) On the triage call, there seemed a preference to just try to skip the deprecation and introduce `copy="never"`, `copy="if_needed"`, and `copy="always"` (i.e. string options for the `copy` keyword argument). Strictly speaking, this is against the typical policy (one year of warning/errors). But nobody could think of a reasonable chance that anyone actually uses it. (For me just "policy" will be enough of an argument to just take it … [View More]slow.) BUT: If nobody has *any* concerns at all, I think we may just end up introducing the change right away. The PR is: https://github.com/numpy/numpy/pull/19173 ## The Feature There is the idea to add `copy=never` (or similar). This would modify the existing `copy` argument to make it a 3-way decision: * `copy=always` or `copy=True` to force a copy * `copy=if_needed` or `copy=False` to prefer no-copy behavior * `copy=never` to error when no-copy behavior is not possible (this ensures that a view is returned) this would affect the functions: * np.array(object, copy=...) * arr.astype(new_dtype, copy=...) * np.reshape(arr, new_shape, copy=...), and the method arr.reshape() * np.meshgrid and possibly Where `reshape` currently does not have the option and would benefit by allowing for `arr.reshape(-1, copy=never)`, which would guarantee a view. ## The Options We have three options that are currently being discussed: 1. We introduce a new `np.CopyMode` or `np.<something>.Copy` Enum with values `np.CopyMode.NEVER`, `np.CopyMode.IF_NEEDED`, and `np.CopyMode.ALWAYS` * Plus: No compatibility concerns * Downside(?): This would be a first in NumPy, and is untypical API due to that. 2. We introduce `copy="never"`, `copy="if_needed"` and `copy="always"` as strings (all other strings will be a `TypeError`): * Problem: `copy="never"` currently means `copy=True` (the opposite) Which means new code has to take care when it may run on older NumPy versions. And in theory could make old code return the wrong thing. * Plus: Strings are the typical for options in NumPy currently. 3. Same as 2. But we take it very slow: Make strings an error right now and only introduce the new options after two releases as per typical deprecation policy. ## Discussion We discussed it briefly today in the triage call and we were leaning towards strings. I was honestly expecting to converge to option 3 to avoid compatibility issues (mainly surprises with `copy="never"` on older versions). But considering how weird it is to currently pass `copy="never"`, the question was whether we should not change it with a release note. The probability of someone currently passing exactly one of those three (and no other) strings seems exceedingly small. Personally, I don't have a much of an opinion. But if *nobody* voices any concern about just changing the meaning of the string inputs, I think the current default may be to just do it. Cheers, Sebastian [View Less]

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NumPy Newcomers' Hour – Thursday, March 24th
by Inessa Pawson July 10, 2022

July 10, 2022

Our next Newcomers' Hour will be held tomorrow, March 24th, at 4 pm UTC. We have no agenda this time. Stop by to ask questions or just to say hi. Join the meeting via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson NumPy Contributor Experience Lead

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Changes to `np.loadtxt`
by Sebastian Berg June 29, 2022

June 29, 2022

Hi all, just a brief heads up that: https://github.com/numpy/numpy/pull/20580 is now merged. This moves `np.loadtxt` to C. Mainly making it much faster. There are also some other improvements and changes though: * It now supports `quotechar='"'` to support Excel dialect CSV. * Parsing some numbers is stricter (e.g. removed support for `_` or hex float parsing by default). * `max_rows` now actually counts rows and not lines. A warning is given if this makes a difference (blank … [View More]

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Numpy binary wheels and CI for win/arm64 platform
by Niyas Sait June 22, 2022

June 22, 2022

Hello, Numpy v1.21.2 <https://github.com/numpy/numpy/releases/tag/v1.21.2> added support for windows/arm64 platforms but we still don't have any systems in place to produce binary wheels or test win/arm64 packages. I think it will be good to start looking into this. CPython has an official buildbot worker running for win/arm64 and official python support for the platform will be available from the 3.11 release. It is not yet clear to me how the build and CI system for numpy is deployed … [View More]

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next NumPy Newcomers Hour
by Inessa Pawson June 5, 2022

June 5, 2022

The next NumPy Newcomers Hour will be held this Thursday, June 2nd at 4 pm UTC. Sebastian Berg, one of the NumPy core developers, will talk about deprecations in NumPy. Join us via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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overhead of numpy scalars
by Pieter Eendebak June 1, 2022

June 1, 2022

Hi everyone, Operations such as np.sqrt or np.cos on numpy scalars are much slower than operations on an array of size 1 (benchmark below). The reason seems to be that a numpy scalar (e.g. np.float64(1.1)) is converted internally to an array, an array is created for the result of the operation and the result is converted back to a scalar (so three object constructions in total). Operating directly on an array creates an array for the result, which is returned (so one object construction in … [View More]

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg May 30, 2022

May 30, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 1st at 16:00 UTC (9:00am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. A calendar to can be found at: https://scientific-python.org/calendars/ please make sure to not import the static `ics` but the url. Best regards Sebastian

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NumPy 1.23.0rc2 released
by Charles R Harris May 30, 2022

May 30, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.23.0rc2. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of ``loadtxt`` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of … [View More]structured dtypes. - Improvements to f2py. The Python versions supported in this release are 3.8-3.10, 3.11 will be supported when it comes out. Note that 32 bit wheels are only provided for Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.0rc2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.0rc2>. *Contributors* A total of 150 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @GalaxySnail + - @code-review-doctor + - @h-vetinari - Aaron Meurer - Alexander Shadchin - Alexandre de Siqueira - Allan Haldane - Amrit Krishnan - Andrei Batomunkuev - Andrew J. Hesford + - Andrew Murray + - Andrey Andreyevich Bienkowski + - André Elimelek de Weber + - Andy Wharton + - Arryan Singh - Arushi Sharma - Bas van Beek - Bharat Raghunathan - Bhavuk Kalra + - Brigitta Sipőcz - Brénainn Woodsend + - Burlen Loring + - Caio Agiani + - Charles Harris - Chiara Marmo - Cornelius Roemer + - Dahyun Kim + - Damien Caliste - David Prosin + - Denis Laxalde - Developer-Ecosystem-Engineering - Devin Shanahan + - Diego Wang + - Dimitri Papadopoulos Orfanos - Ding Liu + - Diwakar Gupta + - Don Kirkby + - Emma Simon + - Eric Wieser - Evan Miller + - Evgeni Burovski - Evgeny Posenitskiy + - Ewout ter Hoeven + - Felix Divo - Francesco Andreuzzi + - Ganesh Kathiresan - Gaëtan de Menten - Geoffrey Gunter + - Hans Meine - Harsh Mishra + - Henry Schreiner - Hood Chatham + - Ilhan Polat - Inessa Pawson - Isuru Fernando - Ivan Gonzalez + - Ivan Meleshko + - Ivan Yashchuk + - Janus Heide + - Jarrod Millman - Jason Thai + - Jeremy Volkman + - Jesús Carrete Montaña + - Jhong-Ken Chen (陳仲肯) + - John Kirkham - John-Mark Gurney + - Jonathan Deng + - Joseph Fox-Rabinovitz - Jouke Witteveen + - Junyan Ou + - Jérôme Richard + - Kassian Sun + - Kazuki Sakamoto + - Kenichi Maehashi - Kevin Sheppard - Kilian Lieret + - Kushal Beniwal + - Leo Singer - Logan Thomas + - Lorenzo Mammana + - Margret Pax - Mariusz Felisiak + - Markus Mohrhard + - Mars Lee - Marten van Kerkwijk - Masamichi Hosoda + - Matthew Barber - Matthew Brett - Matthias Bussonnier - Matthieu Darbois - Matti Picus - Melissa Weber Mendonça - Michael Burkhart + - Morteza Mirzai + - Motahhar Mokf + - Muataz Attaia + - Muhammad Motawe + - Mukulika Pahari - Márton Gunyhó + - Namami Shanker + - Nihaal Sangha + - Niyas Sait - Omid Rajaei + - Oscar Gustafsson + - Ovee Jawdekar + - P. L. Lim + - Pamphile Roy + - Pantelis Antonoudiou + - Pearu Peterson - Peter Andreas Entschev - Peter Hawkins - Pierre de Buyl - Pieter Eendebak + - Pradipta Ghosh + - Rafael Cardoso Fernandes Sousa + - Raghuveer Devulapalli - Ralf Gommers - Raphael Kruse - Raúl Montón Pinillos - Robert Kern - Rohit Goswami - Ross Barnowski - Ruben Garcia + - Sadie Louise Bartholomew + - Saswat Das + - Sayed Adel - Sebastian Berg - Serge Guelton - Simon Surland Andersen + - Siyabend Ürün + - Somasree Majumder + - Soumya + - Stefan van der Walt - Stefano Miccoli + - Stephan Hoyer - Stephen Worsley + - Tania Allard - Thomas Duvernay + - Thomas Green + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Ting Sun + - Tirth Patel - Toshiki Kataoka - Tyler Reddy - Warren Weckesser - Yang Hau - Yoon, Jee Seok + Cheers, Charles Harris Reply allReplyForward <https://drive.google.com/u/0/settings/storage?hl=en&utm_medium=web&utm_sour…> <https://www.google.com/intl/en/policies/terms/> <https://www.google.com/intl/en/policies/privacy/> <https://www.google.com/gmail/about/policy/> [View Less]

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numpy.sort: Ability to reverse-sort a subset of columns?
by James Anderson May 27, 2022

May 27, 2022

Near as I can tell the numpy.sort function doesn’t support requesting a descending sort on specific columns. (Docs: https://numpy.org/doc/stable/reference/generated/numpy.sort.html) I wanted to check I’m not missing how this is properly done or if should I file this as a feature request. There are many reasons one would need to choose sorting in ascending/descending order on a column by column basis. My first thought is another parameter to sort which is an array of bools to indicate sort … [View More]

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NEP 29 and the faster Python release cadence
by Aaron Meurer May 27, 2022

May 27, 2022

Was the faster CPython release cadence (PEP 602 https://peps.python.org/pep-0602/) ever discussed in relation to NEP 29 (https://numpy.org/neps/nep-0029-deprecation_policy.html)? NEP 29 currently says: "The current Python release cadence is 18 months so a 42 month window ensures that there will always be at least two minor versions of Python in the window." However, as of PEP 602, the release cadence of CPython is now 12 months, not 18 months. This schedule now means supporting 3-4 versions … [View More]

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GSoC Student Announcement 2022
by Rohit Goswami May 27, 2022

May 27, 2022

NumPy as a sub-organization under the Python Software Foundation organization will be mentoring a summer student this year funded by the Google Summer of Code program. Ralf facilitated the application process and I will be the primary mentor this year (with Gagandeep for support). Please look forward to work done by our mentee, Namami Shankar who will be working on F2PY and in particular our front-end. His proposal entitled "Restructuring the F2PY frontend and replacing the build system" can … [View More]

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NumPy 1.23.0rc1 released
by Charles R Harris May 26, 2022

May 26, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.23.0rc1. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of ``loadtxt`` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of … [View More]structured dtypes. - Improvements to f2py. The Python versions supported in this release are 3.8-3.10, 3.11 will be supported when it comes out. Note that 32 bit wheels are only provided for Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.0rc1/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.0rc1>. *Contributors* A total of 150 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @GalaxySnail + - @code-review-doctor + - @h-vetinari - Aaron Meurer - Alexander Shadchin - Alexandre de Siqueira - Allan Haldane - Amrit Krishnan - Andrei Batomunkuev - Andrew J. Hesford + - Andrew Murray + - Andrey Andreyevich Bienkowski + - André Elimelek de Weber + - Andy Wharton + - Arryan Singh - Arushi Sharma - Bas van Beek - Bharat Raghunathan - Bhavuk Kalra + - Brigitta Sipőcz - Brénainn Woodsend + - Burlen Loring + - Caio Agiani + - Charles Harris - Chiara Marmo - Cornelius Roemer + - Dahyun Kim + - Damien Caliste - David Prosin + - Denis Laxalde - Developer-Ecosystem-Engineering - Devin Shanahan + - Diego Wang + - Dimitri Papadopoulos Orfanos - Ding Liu + - Diwakar Gupta + - Don Kirkby + - Emma Simon + - Eric Wieser - Evan Miller + - Evgeni Burovski - Evgeny Posenitskiy + - Ewout ter Hoeven + - Felix Divo - Francesco Andreuzzi + - Ganesh Kathiresan - Gaëtan de Menten - Geoffrey Gunter + - Hans Meine - Harsh Mishra + - Henry Schreiner - Hood Chatham + - Ilhan Polat - Inessa Pawson - Isuru Fernando - Ivan Gonzalez + - Ivan Meleshko + - Ivan Yashchuk + - Janus Heide + - Jarrod Millman - Jason Thai + - Jeremy Volkman + - Jesús Carrete Montaña + - Jhong-Ken Chen (陳仲肯) + - John Kirkham - John-Mark Gurney + - Jonathan Deng + - Joseph Fox-Rabinovitz - Jouke Witteveen + - Junyan Ou + - Jérôme Richard + - Kassian Sun + - Kazuki Sakamoto + - Kenichi Maehashi - Kevin Sheppard - Kilian Lieret + - Kushal Beniwal + - Leo Singer - Logan Thomas + - Lorenzo Mammana + - Margret Pax - Mariusz Felisiak + - Markus Mohrhard + - Mars Lee - Marten van Kerkwijk - Masamichi Hosoda + - Matthew Barber - Matthew Brett - Matthias Bussonnier - Matthieu Darbois - Matti Picus - Melissa Weber Mendonça - Michael Burkhart + - Morteza Mirzai + - Motahhar Mokf + - Muataz Attaia + - Muhammad Motawe + - Mukulika Pahari - Márton Gunyhó + - Namami Shanker + - Nihaal Sangha + - Niyas Sait - Omid Rajaei + - Oscar Gustafsson + - Ovee Jawdekar + - P. L. Lim + - Pamphile Roy + - Pantelis Antonoudiou + - Pearu Peterson - Peter Andreas Entschev - Peter Hawkins - Pierre de Buyl - Pieter Eendebak + - Pradipta Ghosh + - Rafael Cardoso Fernandes Sousa + - Raghuveer Devulapalli - Ralf Gommers - Raphael Kruse - Raúl Montón Pinillos - Robert Kern - Rohit Goswami - Ross Barnowski - Ruben Garcia + - Sadie Louise Bartholomew + - Saswat Das + - Sayed Adel - Sebastian Berg - Serge Guelton - Simon Surland Andersen + - Siyabend Ürün + - Somasree Majumder + - Soumya + - Stefan van der Walt - Stefano Miccoli + - Stephan Hoyer - Stephen Worsley + - Tania Allard - Thomas Duvernay + - Thomas Green + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Ting Sun + - Tirth Patel - Toshiki Kataoka - Tyler Reddy - Warren Weckesser - Yang Hau - Yoon, Jee Seok + Cheers, Charles Harris [View Less]

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Enhancement for AArch64 SVE instruction set
by kawakami.k＠fujitsu.com May 26, 2022

May 26, 2022

Hello, I am working on speeding up NumPy with the AArch64 SVE instruction set. I could not find a numpy implementation for SVE. Is there already a test implementation or discussion about SVE support?

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Dropping the pdf documentation.
by Charles R Harris May 25, 2022

May 25, 2022

Hi All, This is a proposal to drop the generation of pdf documentation and only generate the html version. This is a one way change due to the difficulty maintaining/fixing the pdf versions. See minimal discussion here <https://github.com/numpy/numpy/issues/21557#issuecomment-1133920412>. Chuck

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normalization of Polynomial
by Pieter Eendebak May 24, 2022

May 24, 2022

Hi, The new Polynomial objects includes a domain and window. How can I normalize such an object to a standard domain and window? There is the .convert method, but it returns a strange format. A minimal example: import numpy as np p =np.polynomial.Polynomial([1, 1]) x=np.linspace(0, 3, 15); y=p(x) y[1]+=.1 q=np.polynomial.Polynomial.fit(x, y, 1) print(f'p: {repr(p)}') print(f'fitted q: {repr(q)}') print(f'fitted q: {str(q)}') qc=q.convert() print(f'q.convert(): str {qc}') print(f'q.convert(): {… [View More]

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Maintenance/1.23.x is branched
by Charles R Harris May 23, 2022

May 23, 2022

Hi All, The 1.23.x maintenance branch has been made and main is now open for 1.24.0 development. Chuck

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NumPy 1.22.4 released
by Charles R Harris May 21, 2022

May 21, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.4. NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with debugging <https://github.com/numpy/numpy/issues/21008>. The Python versions supported in this release are 3.8-3.10. Wheels can be downloaded from PyPI <https://pypi.… [View More]org/project/numpy/1.22.4>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.4>. Note that the Mac wheels are based on OS X 10.15 rather than 10.9 that was used in previous NumPy release cycles. *Contributors* A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexander Shadchin - Bas van Beek - Charles Harris - Hood Chatham - Jarrod Millman - John-Mark Gurney + - Junyan Ou + - Mariusz Felisiak + - Ross Barnowski - Sebastian Berg - Serge Guelton - Stefan van der Walt *Pull requests merged* A total of 22 pull requests were merged for this release. - #21191: TYP, BUG: Fix ``np.lib.stride_tricks`` re-exported under the... - #21192: TST: Bump mypy from 0.931 to 0.940 - #21243: MAINT: Explicitly re-export the types in ``numpy._typing`` - #21245: MAINT: Specify sphinx, numpydoc versions for CI doc builds - #21275: BUG: Fix typos - #21277: ENH, BLD: Fix math feature detection for wasm - #21350: MAINT: Fix failing simd and cygwin tests. - #21438: MAINT: Fix failing Python 3.8 32-bit Windows test. - #21444: BUG: add linux guard per #21386 - #21445: BUG: Allow legacy dtypes to cast to datetime again - #21446: BUG: Make mmap handling safer in frombuffer - #21447: BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11. - #21448: ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS - #21472: BUG: Ensure compile errors are raised correctly - #21473: BUG: Fix segmentation fault - #21474: MAINT: Update doc requirements - #21475: MAINT: Mark ``npy_memchr`` with ``no_sanitize("alignment")`` on clang - #21512: DOC: Proposal - make the doc landing page cards more similar... - #21525: MAINT: Update Cython version to 0.29.30. - #21536: BUG: Fix GCC error during build configuration - #21541: REL: Prepare for the NumPy 1.22.4 release. - #21547: MAINT: Skip tests that fail on PyPy. Cheers, Charles Harris [View Less]

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New stable documentation
by Charles R Harris May 21, 2022

May 21, 2022

Hi All, I've put up new stable documentation for NumPy 1.22.4. I'd appreciate it if those familiar with how they want the documentation to look could take a look at it so that fixes can be made while I'm still in the documentation state. Chuck

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ANN: SciPy 1.8.1
by Tyler Reddy May 18, 2022

May 18, 2022

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.8.1, which is a bug fix release that restores Pythran usage on Windows. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.8.1 One of a few ways to install this release with pip: pip install scipy==1.8.1 ===================== SciPy 1.8.1 Release Notes ===================== SciPy 1.8.1 is a bug-fix release with no new … [View More]

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ndarray shape permutation
by Paul Korir May 18, 2022

May 18, 2022

Hellos, I would like to propose `numpy.ndarray.permute_shape(<permuted_shape>)` method to predictably permute the shape of an ndarray. In my opinion, the current alternatives (`swapaxes`, `transform`, `moveaxes` and friends) are counterintuitive and rely on referring to the axis indices. It would be abundantly helpful to have something like reshape but which moves the data around (unlike reshape, which doesn't). Scenario: structural biology uses MRC files, which define a number of fields … [View More]

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