Mailman 3 February 2023 - Python-announce-list

REMINDER - Call for Proposals for SciPy 2023!
by Arliss Collins 27 Feb '23

27 Feb '23

[image: image.png] Hello Folks, [image: :mega:] *Call for Proposals for SciPy 2023 closes March 1st* [image: :mega:] *Call for Proposals* <https://www.scipy2023.scipy.org/present> for the 22nd annual Scientific Computing with Python Conference — also known as SciPy 2023 <https://www.scipy2023.scipy.org/> — *CLOSES ON MARCH 1st*. We would love to receive proposals from folks in your organization. We seek submissions for Talks, Posters <https://www.scipy2023.scipy.org/present#talks-posters>, and Tutorials <https://www.scipy2023.scipy.org/present#tutorials>. This year we are happy to announce two highlighted tracks that run parallel to the general conference track: (1) Machine Learning, Data Science and Ethics in AI, and (2) Tending Your Open Source Garden: Maintenance and Community. You can learn more about this year's conference at SciPy 2023 | Home <https://www.scipy2023.scipy.org/>. The full schedule will be published later in May. In the meantime, please check out last year's program <https://www.scipy2022.scipy.org/schedule> and videos of previous year's talks and events <https://www.youtube.com/playlist?list=PLYx7XA2nY5Gc-kJnbqeUudXp90Uo62HdC> to better understand what SciPy conferences are like. Please share this information with anyone you think would be interested. SciPy 2023 is shaping up to be a fantastic conference. We hope you'll join us in July! Arliss (On behalf of the SciPy 2023 Communications Committee) Follow Us <https://twitter.com/scipyconf> <https://www.youtube.com/c/enthought/playlists>

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ANN: Austin -- CPython frame stack sampler v3.5 is now available
by Gabriele Tornetta 26 Feb '23

26 Feb '23

I am delighted to announce the 3.5 release 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 Austin VS Code extension provides a smooth interactive profiling experience, with 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 Like the most recent releases, this new one also come with some performance improvements, this time in the shape of higher sampling rates in multiprocess mode. The interpreter detection has also been improved across all supported platforms, and the alternative format has been dropped. But the main new feature is the support for the new column-level location information that is built into Python 3.11 code objects. This additional information allows extracting finer-grained profiling data, down to the expression level. The VS Code extension has been improved to support this extra location data, which will be visualised in the form of source heat maps. More details about what's new and bug-fixes can be found in the change-log https://github.com/P403n1x87/austin/blob/master/ChangeLog 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 the latest Ubuntu image, 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). Austin is a free and open-source project. A lot of effort goes into its development to ensure the best performance and that it stays up-to-date with the latest Python releases. If you find it useful, consider sponsoring this project on GitHub at https://github.com/sponsors/P403n1x87. All the best, Gabriele <phoenix1987 (at) gmail.com> <p><a href="https://github.com/P403n1x87/austin">Austin 3.5</a> - frame stack sampler for CPython. (26-Feb-23)</p>

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Cython 3.0 beta 1 is released
by Stefan Behnel 26 Feb '23

26 Feb '23

Hi all, Cython 3.0 has left the alpha status – the first beta release is available from PyPI. https://cython.org/ https://pypi.org/project/Cython/ The changes in this release are huge – and the full list of improvements compared to the 0.29.x release series is entirely incredible. Cython 3.0 is better than any other Cython release before, in all aspects. It's much more Python, integrates better with C++, supports more Python implementations and configurations, provides many great new language features – it's faster, safer and easier to use. It's simply better. https://cython.readthedocs.io/en/latest/src/changes.html#beta-1-2023-02-25 What is Cython? In case you didn't hear about Cython before, it's the most widely used statically optimising Python compiler out there. It translates Python (2/3) code to C, and makes it as easy as Python itself to tune the code all the way down into fast native code. If you have any non-trivial Python application running, chances are you'll find some piece of Cython generated package in it. The development of the Cython 3.0 release series started all the way back in 2018, with the first branch commit happening on October 27, 2018. https://github.com/cython/cython/commit/c2de8efb67f80bff59975641aac387d6523… A list of Milestones along the way, and a long list of contributors: https://github.com/cython/cython/issues/4022#issuecomment-1404305257 Thank you to everyone who contributed. A couple of people have also joined in an effort to make the documentation reflect what this great new Cython has to offer. https://cython.readthedocs.io/en/latest/ Now, go and give it a try. We've taken great care to make the transition from Cython 0.29.x as smooth as possible, which was not easy given the large amount of changes, including some well-motivated breaking changes. We wanted to let all users benefit from this new release. Let us know how it works for you, and tell others about it. :) Have fun, Stefan

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ANN: A new version (0.14.0) of sparse has been released.
by Hameer Abbasi 24 Feb '23

24 Feb '23

I’d like to announce the release of PyData/Sparse 0.14.0. PyData/Sparse is available on conda-forge and PyPI. PyData/Sparse is a package that provides sparse arrays conforming to the NumPy API, along with a subset of NumPy operations on sparse arrays. The highlights of this release include faster indexing, sampling and Sparse einsum support. The changelog from the last release is available at: https://sparse.pydata.org/en/0.14,0/changelog.html The code is available at: https://github.com/pydata/sparse/ And the latest documentation is available at https://sparse.pydata.org/

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ANN: SciPy 1.10.1
by Tyler Reddy 19 Feb '23

19 Feb '23

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.10.1. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.10.1 One of a few ways to install this release with pip: pip install scipy==1.10.1 ====================== SciPy 1.10.1 Release Notes ====================== SciPy 1.10.1 is a bug-fix release with no new features compared to 1.10.0. Authors ======= * Name (commits) * alice (1) + * Matt Borland (2) + * Evgeni Burovski (2) * CJ Carey (1) * Ralf Gommers (9) * Brett Graham (1) + * Matt Haberland (5) * Alex Herbert (1) + * Ganesh Kathiresan (2) + * Rishi Kulkarni (1) + * Loïc Estève (1) * Michał Górny (1) + * Jarrod Millman (1) * Andrew Nelson (4) * Tyler Reddy (50) * Pamphile Roy (2) * Eli Schwartz (2) * Tomer Sery (1) + * Kai Striega (1) * Jacopo Tissino (1) + * windows-server-2003 (1) A total of 21 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 1.10.1 -------------------------------- * `#14980 <https://github.com/scipy/scipy/issues/14980>`__: BUG: Johnson's algorithm fails without negative cycles * `#17670 <https://github.com/scipy/scipy/issues/17670>`__: Failed to install on Raspberry Pi (ARM) 32bit in 3.11.1 * `#17715 <https://github.com/scipy/scipy/issues/17715>`__: scipy.stats.bootstrap broke with statistic returning multiple... * `#17716 <https://github.com/scipy/scipy/issues/17716>`__: BUG: interpolate.interpn fails with read only input * `#17718 <https://github.com/scipy/scipy/issues/17718>`__: BUG: RegularGridInterpolator 2D mixed precision crashes * `#17727 <https://github.com/scipy/scipy/issues/17727>`__: BUG: RegularGridInterpolator does not work on non-native byteorder... * `#17736 <https://github.com/scipy/scipy/issues/17736>`__: BUG: SciPy requires OpenBLAS even when building against a different... * `#17775 <https://github.com/scipy/scipy/issues/17775>`__: BUG: Asymptotic computation of ksone.sf has intermediate overflow * `#17782 <https://github.com/scipy/scipy/issues/17782>`__: BUG: Segfault in scipy.sparse.csgraph.shortest_path() with v1.10.0 * `#17795 <https://github.com/scipy/scipy/issues/17795>`__: BUG: stats.pearsonr one-sided hypothesis yields incorrect p-value... * `#17801 <https://github.com/scipy/scipy/issues/17801>`__: BUG: stats.powerlaw.fit: raises OverflowError * `#17808 <https://github.com/scipy/scipy/issues/17808>`__: BUG: name of cython executable is hardcoded in _build_utils/cythoner.py * `#17811 <https://github.com/scipy/scipy/issues/17811>`__: CI job with numpy nightly build failing on missing \`_ArrayFunctionDispatcher.__code__\` * `#17839 <https://github.com/scipy/scipy/issues/17839>`__: BUG: 1.10.0 tests fail on i386 and other less common arches * `#17896 <https://github.com/scipy/scipy/issues/17896>`__: DOC: publicly expose \`multivariate_normal\` attributes \`mean\`... * `#17934 <https://github.com/scipy/scipy/issues/17934>`__: BUG: meson \`__config__\` generation - truncated unicode characters * `#17938 <https://github.com/scipy/scipy/issues/17938>`__: BUG: \`scipy.stats.qmc.LatinHypercube\` with \`optimization="random-cd"\`... Pull requests for 1.10.1 ------------------------------- * `#17712 <https://github.com/scipy/scipy/pull/17712>`__: REL, MAINT: prepare for 1.10.1 * `#17717 <https://github.com/scipy/scipy/pull/17717>`__: BUG: allow readonly input to interpolate.interpn * `#17721 <https://github.com/scipy/scipy/pull/17721>`__: MAINT: update \`meson-python\` upper bound to <0.13.0 * `#17726 <https://github.com/scipy/scipy/pull/17726>`__: BUG: interpolate/RGI: upcast float32 to float64 * `#17735 <https://github.com/scipy/scipy/pull/17735>`__: MAINT: stats.bootstrap: fix BCa with vector-valued statistics * `#17743 <https://github.com/scipy/scipy/pull/17743>`__: DOC: improve the docs on using BLAS/LAPACK libraries with Meson * `#17777 <https://github.com/scipy/scipy/pull/17777>`__: BLD: link to libatomic if necessary * `#17783 <https://github.com/scipy/scipy/pull/17783>`__: BUG: Correct intermediate overflow in KS one asymptotic in SciPy.stats * `#17790 <https://github.com/scipy/scipy/pull/17790>`__: BUG: signal: fix check_malloc extern declaration type * `#17797 <https://github.com/scipy/scipy/pull/17797>`__: MAINT: stats.pearsonr: correct p-value with negative correlation... * `#17800 <https://github.com/scipy/scipy/pull/17800>`__: [sparse.csgraph] Fix a bug in dijkstra and johnson algorithm * `#17803 <https://github.com/scipy/scipy/pull/17803>`__: MAINT: add missing \`__init__.py\` in test folder * `#17806 <https://github.com/scipy/scipy/pull/17806>`__: MAINT: stats.powerlaw.fit: fix overflow when np.min(data)==0 * `#17810 <https://github.com/scipy/scipy/pull/17810>`__: BLD: use Meson's found cython instead of a wrapper script * `#17831 <https://github.com/scipy/scipy/pull/17831>`__: MAINT, CI: GHA MacOS setup.py update * `#17850 <https://github.com/scipy/scipy/pull/17850>`__: MAINT: remove use of \`__code__\` in \`scipy.integrate\` * `#17854 <https://github.com/scipy/scipy/pull/17854>`__: TST: mark test for \`stats.kde.marginal\` as xslow * `#17855 <https://github.com/scipy/scipy/pull/17855>`__: BUG: Fix handling of \`powm1\` overflow errors * `#17859 <https://github.com/scipy/scipy/pull/17859>`__: TST: fix test failures on i386, s390x, ppc64, riscv64 (Debian) * `#17862 <https://github.com/scipy/scipy/pull/17862>`__: BLD: Meson \`__config__\` generation * `#17863 <https://github.com/scipy/scipy/pull/17863>`__: BUG: fix Johnson's algorithm * `#17872 <https://github.com/scipy/scipy/pull/17872>`__: BUG: fix powm1 overflow handling * `#17904 <https://github.com/scipy/scipy/pull/17904>`__: ENH: \`multivariate_normal_frozen\`: restore \`cov\` attribute * `#17910 <https://github.com/scipy/scipy/pull/17910>`__: CI: use nightly numpy musllinux_x86_64 wheel * `#17931 <https://github.com/scipy/scipy/pull/17931>`__: TST: test_location_scale proper 32bit Linux skip * `#17932 <https://github.com/scipy/scipy/pull/17932>`__: TST: 32-bit tol for test_pdist_jensenshannon_iris * `#17936 <https://github.com/scipy/scipy/pull/17936>`__: BUG: Use raw strings for paths in \`__config__.py.in\` * `#17940 <https://github.com/scipy/scipy/pull/17940>`__: BUG: \`rng_integers\` in \`_random_cd\` now samples on a closed... * `#17942 <https://github.com/scipy/scipy/pull/17942>`__: BLD: update classifiers for Python 3.11 * `#17963 <https://github.com/scipy/scipy/pull/17963>`__: MAINT: backports/prep for SciPy 1.10.1 * `#17981 <https://github.com/scipy/scipy/pull/17981>`__: BLD: make sure macosx_x86_64 10.9 tags are being made on maintenance/1.10.x * `#17984 <https://github.com/scipy/scipy/pull/17984>`__: DOC: update link of the logo in the readme * `#17997 <https://github.com/scipy/scipy/pull/17997>`__: BUG: at least one entry from trial should be used in exponential... 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[RELEASE] Python 3.11.2, Python 3.10.10 and 3.12.0 alpha 5 are available
by Pablo Galindo Salgado 08 Feb '23

08 Feb '23

Hi everyone, I am happy to report that after solving some last-time problems we have a bunch of fresh releases for you: ### Python 3.12.0 alpha 5 Check the new alpha of 3.12 with some Star Trek vibes: https://www.python.org/downloads/release/python-3120a5/ 210 new commits since 3.12.0a4 last month ### Python 3.11.2 A shipment of bugfixes and security releases for the newest Python! https://www.python.org/downloads/release/python-3112/ 194 new commits since 3.11.1 ### Python 3.10.10 Your trusty Python3.10 just got more stable and secure! https://www.python.org/downloads/release/python-31010/ 131 new commits since 3.10.9 ## We hope you enjoy the 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. https://www.python.org/psf/ Your friendly release team, Ned Deily @nad Steve Dower @steve.dower Pablo Galindo Salgado @pablogsal Łukasz Langa @ambv Thomas Wouters @thomas

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PyCA cryptography 39.0.1 released
by Paul Kehrer 07 Feb '23

07 Feb '23

PyCA cryptography 39.0.1 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/#v39-0-1) * SECURITY ISSUE - Fixed a bug where Cipher.update_into accepted Python buffer protocol objects, but allowed immutable buffers. CVE-2023-23931 * Updated Windows, macOS, and Linux wheels to be compiled with OpenSSL 3.0.8. -Paul Kehrer (reaperhulk)

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ANN: pvlib-0.9.4 released
by Dr. Mark Alexander Mikofski PhD 06 Feb '23

06 Feb '23

Dear Pythonistas and solar power enthusiasts, The maintainers are happy to announce a new release of pvlib python: software for simulating performance of photovoltaic solar energy systems. *See what's new for v0.9.4:* ** *https://pvlib-python.readthedocs.io/en/stable/whatsnew.html *Releases are available from PyPI and the conda-forge channel:* * https://pypi.org/project/pvlib/ * https://anaconda.org/conda-forge/pvlib and https://anaconda.org/conda-forge/pvlib-python *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *v0.9.4 Highlights:* * Fitting <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> and prediction <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> functions for the ADR PV efficiency model, along with several gallery examples <https://pvlib-python.readthedocs.io/en/stable/gallery/index.html#adr-model-…> . * An extension of the Faiman temperature model <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tem…> that includes a radiative loss term. * Efficient direct <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> and diffuse <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> IAM models based on the Schlick approximation to the Fresnel equations. * A convenience function <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> to calculate one of DNI, DHI, GHI from the other two. * The Hay-Davies <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> transposition model can now return individual components in addition to global tilted irradiance. * An implementation of ASTM E1036 <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.ivt…> for extracting the basic characteristics of an I-V curve. * There were several new contributors. *Thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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NumPy 1.24. 2 Release
by Charles R Harris 05 Feb '23

05 Feb '23

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.24.2. NumPy 1.24.2 is a maintenance release that fixes bugs and regressions discovered after the 1.24.1 release. The Python versions supported by this release are 3.8-3.11 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. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.24.2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.24.2>. *Contributors* A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Khem Raj + - Mark Harfouche - Matti Picus - Panagiotis Zestanakis + - Peter Hawkins - Pradipta Ghosh - Ross Barnowski - Sayed Adel - Sebastian Berg - Syam Gadde + - dmbelov + - pkubaj + *Pull requests merged* A total of 17 pull requests were merged for this release. - #22965: MAINT: Update python 3.11-dev to 3.11. - #22966: DOC: Remove dangling deprecation warning - #22967: ENH: Detect CPU features on FreeBSD/powerpc64* - #22968: BUG: np.loadtxt cannot load text file with quoted fields separated... - #22969: TST: Add fixture to avoid issue with randomizing test order. - #22970: BUG: Fix fill violating read-only flag. (#22959) - #22971: MAINT: Add additional information to missing scalar AttributeError - #22972: MAINT: Move export for scipy arm64 helper into main module - #22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang - #22989: BUG: Ensure correct loop order in sin, cos, and arctan2 - #23030: DOC: Add version added information for the strict parameter in... - #23031: BUG: use ``_Alignof`` rather than ``offsetof()`` on most compilers - #23147: BUG: Fix for npyv__trunc_s32_f32 (VXE) - #23148: BUG: Fix integer / float scalar promotion - #23149: BUG: Add missing <type_traits> header. - #23150: TYP, MAINT: Add a missing explicit ``Any`` parameter to the ``npt.ArrayLike``... - #23161: BLD: remove redundant definition of npy_nextafter [wheel build] Cheers, Charles Harris

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[Python announce] unexpected-isaves 1.5.0 released!
by Eric Mendes 01 Feb '23

01 Feb '23

A quick scroll through social media and you'll find very talented people making portraits out of dice, rubik's cubes, in minecraft as pixel art, or even using MIDI notes on a DAW. This package gives you the ability to do the same, with or without talent! Currently it can only generate arts using minecraft or the cells of a spreadsheet, but if you have any ideas that fit into the project's goal please let me know via github issues, or - even better - submit a merge request with your work! Source code: https://github.com/Eric-Mendes/unexpected-isaves PyPI: https://pypi.org/project/unexpected-isaves/

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Python-announce-list February 2023