🌐 Mailman 3 December 2021 - Python-announce-list - python.org

[ANN] Benchmark Smil vs Scikit-image (morphological features)
by Jose-Marcio Martins da Cruz Dec. 21, 2021

Dec. 21, 2021

Hello, We've done a comparative benchmark (speed and memory usage) with Smil and Scikit-Image. Smil is a mathematical morphology dedicated library of functions. So comparisons are done only on this area. We've been working on Mathematical Morphology for more than 50 years now the discipline was created here at our research department in the sixties. Smil inherits the experience of previous libraries and software we've been writing since the 70's. In just some few words, Smil can be orders of magnitude faster than Scikit-image (hundreds or even thousands) on some operations thanks to parallelization and vectorization (SIMD), depending on the computer architecture. Smil doesn't replace scikit-image but may be a good complement to scikit-image when speed is important. Benchmark results are available at : https://smil.cmm.minesparis.psl.eu/smil-vs-skimage Smil web site is here : https://smil.cmm.minesparis.psl.eu Comments are welcome, Best regards José-Marcio -- --------------------------------------------------------------- Jose Marcio MARTINS DA CRUZ, Ph.D. Ecole des Mines de Paris Centre de Morphologie Mathématique https://orcid.org/0000-0002-2981-7028 --------------------------------------------------------------- Mon livre sur le spam : http://amzn.to/LEscRu ---------------------------------------------------------------

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NumPy 1.21.5 released
by Charles R Harris Dec. 19, 2021

Dec. 19, 2021

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.5. NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered after the 1.21.4 release and does some maintenance to extend the 1.21.x lifetime. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11 you will need to use gcc-11.2+ to avoid problems. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.5/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.5>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Matti Picus - Rohit Goswami - Ross Barnowski - Sayed Adel - Sebastian Berg *Pull requests merged* A total of 11 pull requests were merged for this release. - #20357: MAINT: Do not forward `__(deep)copy__` calls of `_GenericAlias`\... - #20462: BUG: Fix float16 einsum fastpaths using wrong tempvar - #20463: BUG, DIST: Print os error message when the executable not exist - #20464: BLD: Verify the ability to compile C++ sources before initiating\... - #20465: BUG: Force `npymath` to respect `npy_longdouble` - #20466: BUG: Fix failure to create aligned, empty structured dtype - #20467: ENH: provide a convenience function to replace `npy_load_module` - #20495: MAINT: update wheel to version that supports python3.10 - #20497: BUG: Clear errors correctly in F2PY conversions - #20613: DEV: add a warningfilter to fix pytest workflow. - #20618: MAINT: Help boost::python libraries at least not crash Cheers, Charles Harris

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NumPy 1.22.0rc3 released
by Charles R Harris Dec. 18, 2021

Dec. 18, 2021

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.0rc3. NumPy 1.22.0rc3 is a big release featuring the work of 152 contributors spread over 602 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across applications such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. Thenew methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on 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 integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.0rc3/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.0rc3>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 151 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @Illviljan - @h-vetinari - @yan-wyb + - Aaron Meurer - Abel Aoun + - Adrian Gao + - Ahmet Can Solak + - Ajay DS + - Alban Colley + - Alberto Rubiales + - Alessia Marcolini + - Amit Kumar + - Andrei Batomunkuev + - Andrew Watson + - Anirudh Dagar + - Ankit Dwivedi + - Antony Lee - Arfy Slowy + - Arryan Singh + - Arun Palaniappen + - Arushi Sharma + - Bas van Beek - Brent Brewington + - Carl Johnsen + - Carl Michal + - Charles Harris - Chiara Marmo - Chris Fu (傅立业) + - Christoph Buchner + - Christoph Reiter + - Chunlin Fang - Clément Robert + - Constanza Fierro - Damien Caliste - Daniel Ching - David Badnar + - David Cortes + - David Okpare + - Derek Huang + - Developer-Ecosystem-Engineering + - Dima Pasechnik - Dimitri Papadopoulos + - Dmitriy Fishman + - Eero Vaher + - Elias Koromilas + - Eliaz Bobadilla + - Elisha Hollander + - Eric Wieser - Eskild Eriksen + - Evan Miller + - Fayas Noushad + - Gagandeep Singh + - Ganesh Kathiresan - Ghiles Meddour + - Greg Lucas - Gregory R. Lee - Guo Shuai + - Gwyn Ciesla + - Hameer Abbasi - Hector Martin + - Henry Schreiner + - Himanshu + - Hood Chatham + - Hugo Defois + - Hugo van Kemenade - I-Shen Leong + - Imen Rajhi + - Irina Maria Mocan + - Irit Katriel + - Isuru Fernando - Jakob Jakobson - Jerry Morrison + - Jessi J Zhao + - Joe Marshall + - Johan von Forstner + - Jonas I. Liechti + - Jonathan Reichelt Gjertsen + - Joshua Himmens + - Jérome Eertmans - Jérôme Kieffer + - KIU Shueng Chuan + - Kazuki Sakamoto + - Kenichi Maehashi - Kenny Huynh + - Kent R. Spillner + - Kevin Granados + - Kevin Modzelewski + - Kevin Sheppard - Lalit Musmade + - Malik Idrees Hasan Khan + - Marco Aurelio da Costa + - Margret Pax + - Mars Lee + - Marten van Kerkwijk - Matthew Barber + - Matthew Brett - Matthias Bussonnier - Matthieu Dartiailh - Matti Picus - Melissa Weber Mendonça - Michael McCann + - Mike Jarvis + - Mike McCann + - Mike Toews - Mukulika Pahari - Nick Pope + - Nick Wogan + - Niels Dunnewind + - Niko Savola + - Nikola Forró - Niyas Sait + - Pamphile ROY - Paul Ganssle + - Pauli Virtanen - Pearu Peterson - Peter Hawkins + - Peter Tillema + - Prathmesh Shirsat + - Raghuveer Devulapalli - Ralf Gommers - Robert Kern - Rohit Goswami + - Ronan Lamy - Ross Barnowski - Roy Jacobson + - Samyak S Sarnayak + - Sayantika Banik + - Sayed Adel - Sebastian Berg - Sebastian Schleehauf + - Serge Guelton - Shriraj Hegde + - Shubham Gupta + - Sista Seetaram + - Stefan van der Walt - Stephannie Jimenez Gacha + - Tania Allard - Theodoros Nikolaou + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Tom Tan + - Tyler Reddy - Vijay Arora + - Vinith Kishore + - Warren Weckesser - Yang Hau - Yashasvi Misra - Yuval Ofek + - Zac Hatfield-Dodds - Zhang Na + Cheers, Charles Harris

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ANN: Austin -- CPython frame stack sampler v3.2.0 is now available
by Gabriele Tornetta Dec. 17, 2021

Dec. 17, 2021

I am delighted to announce the release 3.2.0 of Austin. If you haven't heard of Austin before, it is an open-source frame stack sampler for CPython, distributed under the GPLv3 license. It can be used to obtain statistical profiling data out of a running Python application without a single line of instrumentation. This means that you can start profiling a Python application straight away, even while it's running in a production environment, with minimal impact on performance. https://github.com/P403n1x87/austin The best way to leverage Austin is to use the new extension for VS Code, which brings interactive flame graphs straight into the text editor to allow you to quickly jump to the source code with a simple click. You can find the extension on the Visual Studio Marketplace and install it directly from VS Code: https://marketplace.visualstudio.com/items?itemName=p403n1x87.austin-vscode To see how to make the best of Austin with VS Code to find and fix performance issues, check out this blog post, which shows you the editor extension in action on a real Python project: https://p403n1x87.github.io/how-to-bust-python-performance-issues.html The latest release comes with improved support on all supported platforms, but more importantly with increased accuracy, thanks to a double-heap mechanism. To find out more about the details, check out this blog post: https://p403n1x87.github.io/increasing-austin-accuracy-with-a-dobule-heap-t… The 3.2 release also marks the official introduction of the `austinp` variant into the main branch. The `austinp` variant is a special build of Austin for Linux that adds `ptrace` support and leverages `libunwind` to unwind native call stacks. The additional option `-k` allows you to sample even at the Linux kernel level. The special `austinp` builds are available from the GitHub Releases area of the Austin repository: https://github.com/P403n1x87/austin/releases/latest Austin is a pure C application that has no dependencies other than the C standard library. Its source code is hosted on GitHub at https://github.com/P403n1x87/austin The README contains installation and usage details, as well as some examples of Austin in action. Details on how to contribute to Austin's development can be found at the bottom of the page. Austin can be installed easily on the following platforms and from the following sources: Linux: - Snap Store - AUR - Conda Forge macOS: - Homebrew - Conda Forge Windows: - Chocolatey - Scoop An Austin docker image, based on Ubuntu 20.04, is also available from Docker Hub: https://hub.docker.com/r/p403n1x87/austin Austin is also simple to compile from sources as it only depends on the standard C library, if you don't have access to the above-listed sources. You can stay up-to-date with the project's development by following Austin on Twitter (https://twitter.com/AustinSampler). All the best, Gabriele <phoenix1987 (at) gmail.com> <p><a href="https://github.com/P403n1x87/austin">Austin 3.2.0</a> - frame stack sampler for CPython. (17-Dec-21)</p>

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PyCA cryptography 36.0.1 released
by Paul Kehrer Dec. 14, 2021

Dec. 14, 2021

PyCA cryptography 36.0.1 has been released to PyPI. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, asymmetric algorithms, message digests, X509, key derivation functions, and much more. We support Python 3.6+, and PyPy3. Changelog (https://cryptography.io/en/latest/changelog/#v36-0-1): * Updated Windows, macOS, and Linux wheels to be compiled with OpenSSL 1.1.1m. -Paul Kehrer (reaperhulk)

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Wing Python IDE 8.1.3 has been released
by Wingware Dec. 14, 2021

Dec. 14, 2021

Wing 8.1.2 improves debug stepping & exception handling in async coroutines & generators, correctly detects package manager type on remote hosts, fixes several issues with debugging, analysis & project creation for Django, improves analysis of union type hints, and makes a number of other improvements. Details: https://wingware.com/news/2021-12-13 Downloads: https://wingware.com/downloads == About Wing == Wing is a light-weight but full-featured Python IDE designed specifically for Python, with powerful editing, code inspection, testing, and debugging capabilities. Wing's deep code analysis provides auto-completion, auto-editing, and refactoring that speed up development. Its top notch debugger works with any Python code, locally or on a remote host, container, or cluster. Wing also supports test-driven development, version control, UI color and layout customization, and includes extensive documentation and support. Wing is available in three product levels: Wing Pro is the full-featured Python IDE for professional developers, Wing Personal is a free Python IDE for students and hobbyists (omits some features), and Wing 101 is a very simplified free Python IDE for beginners (omits many features). Learn more at https://wingware.com/

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NumPy 1.22. 0rc2 Release
by Charles R Harris Dec. 10, 2021

Dec. 10, 2021

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.0rc1. NumPy 1.22.0rc1 is a big release featuring the work of 151 contributers spread over 589 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across applications such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. Thenew methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on 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 integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.0rc2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.0rc2>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 151 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @Illviljan - @h-vetinari - @yan-wyb + - Aaron Meurer - Abel Aoun + - Adrian Gao + - Ahmet Can Solak + - Ajay DS + - Alban Colley + - Alberto Rubiales + - Alessia Marcolini + - Amit Kumar + - Andrei Batomunkuev + - Andrew Watson + - Anirudh Dagar + - Ankit Dwivedi + - Antony Lee - Arfy Slowy + - Arryan Singh + - Arun Palaniappen + - Arushi Sharma + - Bas van Beek - Brent Brewington + - Carl Johnsen + - Carl Michal + - Charles Harris - Chiara Marmo - Chris Fu (傅立业) + - Christoph Buchner + - Christoph Reiter + - Chunlin Fang - Clément Robert + - Constanza Fierro - Damien Caliste - Daniel Ching - David Badnar + - David Cortes + - David Okpare + - Derek Huang + - Developer-Ecosystem-Engineering + - Dima Pasechnik - Dimitri Papadopoulos + - Dmitriy Fishman + - Eero Vaher + - Elias Koromilas + - Eliaz Bobadilla + - Elisha Hollander + - Eric Wieser - Eskild Eriksen + - Evan Miller + - Fayas Noushad + - Gagandeep Singh + - Ganesh Kathiresan - Ghiles Meddour + - Greg Lucas - Gregory R. Lee - Guo Shuai + - Gwyn Ciesla + - Hameer Abbasi - Hector Martin + - Henry Schreiner + - Himanshu + - Hood Chatham + - Hugo Defois + - Hugo van Kemenade - I-Shen Leong + - Imen Rajhi + - Irina Maria Mocan + - Irit Katriel + - Isuru Fernando - Jakob Jakobson - Jerry Morrison + - Jessi J Zhao + - Joe Marshall + - Johan von Forstner + - Jonas I. Liechti + - Jonathan Reichelt Gjertsen + - Joshua Himmens + - Jérome Eertmans - Jérôme Kieffer + - KIU Shueng Chuan + - Kenichi Maehashi - Kenny Huynh + - Kent R. Spillner + - Kevin Granados + - Kevin Modzelewski + - Kevin Sheppard - Lalit Musmade + - Malik Idrees Hasan Khan + - Marco Aurelio da Costa + - Margret Pax + - Mars Lee + - Marten van Kerkwijk - Matthew Barber + - Matthew Brett - Matthias Bussonnier - Matthieu Dartiailh - Matti Picus - Melissa Weber Mendonça - Michael McCann + - Mike Jarvis + - Mike McCann + - Mike Toews - Mukulika Pahari - Nick Pope + - Nick Wogan + - Niels Dunnewind + - Niko Savola + - Nikola Forró - Niyas Sait + - Pamphile ROY - Paul Ganssle + - Pauli Virtanen - Pearu Peterson - Peter Hawkins + - Peter Tillema + - Prathmesh Shirsat + - Raghuveer Devulapalli - Ralf Gommers - Robert Kern - Rohit Goswami + - Ronan Lamy - Ross Barnowski - Roy Jacobson + - Samyak S Sarnayak + - Sayantika Banik + - Sayed Adel - Sebastian Berg - Sebastian Schleehauf + - Serge Guelton - Shriraj Hegde + - Shubham Gupta + - Sista Seetaram + - Stefan van der Walt - Stephannie Jimenez Gacha + - Tania Allard - Theodoros Nikolaou + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Tom Tan + - Tyler Reddy - Vijay Arora + - Vinith Kishore + - Warren Weckesser - Yang Hau - Yashasvi Misra - Yuval Ofek + - Zac Hatfield-Dodds - Zhang Na + Cheers, Charles Harris

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Announcing NumExpr 2.8.1
by Robert McLeod Dec. 10, 2021

Dec. 10, 2021

Hi everyone, This is another maintenance release to further modernize our install script for distributions that do not include `setuptools` by default. Thanks to Antonio Valentino for the changes. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.8.0 to 2.8.1 --------------------------- * Fixed dependency list. * Added ``pyproject.toml`` and modernize the ``setup.py`` script. Thanks to Antonio Valentino for the PR. What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod robbmcleod(a)gmail.com robert.mcleod(a)hitachi-hightech.com

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[RELEASE] Python 3.11.0a3 is available
by Pablo Galindo Salgado Dec. 8, 2021

Dec. 8, 2021

You can tell that we are slowly getting closer to the first beta as the number of release blockers that we need to fix on every release starts to increase [image: :sweat_smile:] But we did it! Thanks to Steve Dower, Ned Deily, Christian Heimes, Łukasz Langa and Mark Shannon that helped get things ready for this release :) Go get the new version here: https://www.python.org/downloads/release/python-3110a3/ **This is an early developer preview of Python 3.11** # Major new features of the 3.11 series, compared to 3.10 Python 3.11 is still in development. This release, 3.11.0a3 is the third of seven planned alpha releases. Alpha releases are intended to make it easier to test the current state of new features and bug fixes and to test the release process. During the alpha phase, features may be added up until the start of the beta phase (2022-05-06) and, if necessary, may be modified or deleted up until the release candidate phase (2022-08-01). Please keep in mind that this is a preview release and its use is **not** recommended for production environments. Many new features for Python 3.11 are still being planned and written. Among the new major new features and changes so far: * [PEP 657](https://www.python.org/dev/peps/pep-0657/) -- Include Fine-Grained Error Locations in Tracebacks * [PEP 654](https://www.python.org/dev/peps/pep-0654/) -- Exception Groups and except* * The [Faster Cpython Project](https://github.com/faster-cpython) is already yielding some exciting results: this version of CPython 3.11 is ~12% faster on the geometric mean of the [PyPerformance benchmarks]( speed.python.org), compared to 3.10.0. * Hey, **fellow core developer,** if a feature you find important is missing from this list, let me know. The next pre-release of Python 3.11 will be 3.11.0a4, currently scheduled for Monday, 2022-01-03. # More resources * [Online Documentation](https://docs.python.org/3.11/) * [PEP 664](https://www.python.org/dev/peps/pep-0664/), 3.11 Release Schedule * Report bugs at [https://bugs.python.org](https://bugs.python.org). * [Help fund Python and its community](/psf/donations/). # And now for something completely different Rayleigh scattering, named after the nineteenth-century British physicist Lord Rayleigh is the predominantly elastic scattering of light or other electromagnetic radiation by particles much smaller than the wavelength of the radiation. For light frequencies well below the resonance frequency of the scattering particle, the amount of scattering is inversely proportional to the fourth power of the wavelength. Rayleigh scattering results from the electric polarizability of the particles. The oscillating electric field of a light wave acts on the charges within a particle, causing them to move at the same frequency. The particle, therefore, becomes a small radiating dipole whose radiation we see as scattered light. The particles may be individual atoms or molecules; it can occur when light travels through transparent solids and liquids but is most prominently seen in gases. The strong wavelength dependence of the scattering means that shorter (blue) wavelengths are scattered more strongly than longer (red) wavelengths. This results in the indirect blue light coming from all regions of the sky. # We hope you enjoy those new releases! Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation. Your friendly release team, Pablo Galindo @pablogsal Ned Deily @nad Steve Dower @steve.dower

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ANN: distlib 0.3.4 released on PyPI
by Vinay Sajip Dec. 8, 2021

Dec. 8, 2021

I've recently released version 0.3.4 of distlib on PyPI [1]. For newcomers, distlib is a library of packaging functionality which is intended to be usable as the basis for third-party packaging tools. The main changes in this release are as follows: * Fixed #153: Raise warnings in get_distributions() if bad metadata seen, but keep going. * Fixed #154: Determine Python versions correctly for Python >= 3.10. * Updated launcher executables with changes to handle duplication logic. Code relating to support for Python 2.6 was also removed (support for Python 2.6 was dropped in an earlier release, but supporting code wasn't removed until now). A more detailed change log is available at [2]. Please try it out, and if you find any problems or have any suggestions for improvements, please give some feedback using the issue tracker! [3] Regards, Vinay Sajip [1] https://pypi.org/project/distlib/0.3.4/ [2] https://distlib.readthedocs.io/en/0.3.4/ [3] https://bitbucket.org/pypa/distlib/issues/new

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