I am pleased to announce the release of SfePy 2022.2.
SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by finite element methods. It is
distributed under the new BSD license.
Home page: https://sfepy.org
Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/
Git (source) repository, issue tracker: https://github.com/sfepy/sfepy
Highlights of this release
- custom testing code replaced by pytest
- improved pyvista-based visualization script
For full release notes see .
Contributors to this release in alphabetical order:
PyConZA 2022 will take place on the 13th & 14th of October, 2022. This
year, PyConZA will be a hybrid conference (with in-person and online
access) hosted at the Premier Splendid Inn in Umhlanga, Durban.
We are looking for the following presentations:
- Keynotes (45 minute long talks on a subject of general interest)
- Talks (30 minute long talks on more specific topics)
We are accepting submissions for tutorials, which will run on the 12th
of October. Tutorials can either be half-day (4 hours) or full-day (8
Currently, we are only accepting talks to be delivered in person at the venue.
If you would like to give a presentation, please register at
https://za.pycon.org/ and submit your proposal, following the
instructions at https://za.pycon.org/talks/submit-talk/ . We have a
number of tracks available, including: Data Science, Teaching and
Learning with Python, Web, Scientific Computing, Testing and Other
(which includes all talks that don't fall under the mentioned tracks).
We hope to notify accepted presenters by no later than the 31st of
Speakers will be expected to be available after the presentation for a
short Q&A session. Shared sessions are also possible. The
presentations will be in English.
PyConZA offers a mentorship program for inexperienced speakers. If you
would like assistance preparing your submission, email
team(a)za.pycon.org with a rough draft of your talk proposal and we'll
find a suitable experienced speaker to act as a mentor.
If you want to present something that doesn't fit into the standard
talk categories at PyConZA, please contact the organising committee at
team(a)za.pycon.org so we can discuss whether that will be feasible.
On behalf of the PyConZA organising committee
I'm pleased to announce version 3.0 of RSFile I/O Library, which adds
support for python 3.8, 3.9, and 3.10, drops support for Python<=3.5,
and strengthens testing on OSX.
RSFile provides cross-platform drop-in replacements for the classes of
the io module, and for the open() builtin.
Its goal is to provide a cross-platform, reliable, and comprehensive
synchronous file I/O API, with advanced features like fine-grained
opening modes, shared/exclusive file record locking, thread-safety, disk
cache synchronization, file descriptor inheritability, and handy stat
getters (size, inode, times…).
Locking is performed using actual file record locking capabilities of
the OS, not by using separate files/directories as locking markers, or
other fragile gimmicks. Unix users might particularly be interested by
the workaround that this library provides, concerning the weird semantic
of fcntl() locks (when any descriptor to a disk file is closed, the
process loses ALL locks acquired on this file through any descriptor).
Possible use cases for this library: concurrently writing to logs
without ending up with garbled data, manipulating sensitive data like
disk-based databases, synchronizing heterogeneous producer/consumer
processes when multiprocessing semaphores aren’t an option…
Please find here another maintenance release of NumExpr. Support for Python
has been dropped to enable support for NumPy 1.23 (and by extension Python
when it is released). Wheels for ARM64 multilinux should be available again
troubles with GitHub Actions and Apple Silicon wheels are also now
PyPi for download.
Project documentation is available at:
Changes from 2.8.1 to 2.8.2
* Support for Python 3.6 has been dropped due to the need to substitute the
`NPY_ARRAY_WRITEBACKIFCOPY` for `NPY_ARRAY_UPDATEIFCOPY`. This flag
initiated in NumPy 1.14 and finalized in 1.23. The only changes were made
cases where an unaligned constant was passed in with a pre-allocated
x = np.empty(5, dtype=np.uint8)[1:].view(np.int32)
We think the risk of issues is very low, but if you are using NumExpr as
expression evaluation tool you may want to write a test for this edge
* Thanks to Matt Einhorn (@matham) for improvements to the GitHub Actions
build process to add support for Apple Silicon and aarch64.
* Thanks to Biswapriyo Nath (@biswa96) for a fix to allow `mingw` builds on
* There have been some changes made to not import `platform.machine()` on
but it is highly advised to upgrade to Python 3.9+ to avoid this issue
the Python core package `platform`.
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:
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:
You can get the packages from PyPI as well (but not for RC releases):
Documentation is hosted at:
Share your experience
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.0. 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 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/1.23.0/>; source archives, release notes,
and wheel hashes are available on Github
A total of 151 people contributed to this release. People with a "+" by
names contributed a patch for the first time.
- @GalaxySnail +
- @code-review-doctor +
- 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
- 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 +
- I-Shen Leong
- 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 +
Yesterday, PyCA cryptography 37.0.3 was released to PyPI. Today, we
yanked the release from PyPI due to a regression in OpenSSL that was
producing heap corruption for users.
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.
* Updated Windows, macOS, and Linux wheels to be compiled with OpenSSL 3.0.4.
All that is necessary for evil to succeed is for good people to do nothing.
Wing 8.3.2 fixes several code intelligence issues for f-string
expressions, avoids problems when using ~ or a non-default Base
Directory with remote hosts, allows running a pytest parameterized test
with a float value, adds the option Use Fixed Width Font for Output to
the Testing tool's right-click context menu, scrolls correctly to the
current line in version control blame/praise output, fixes intermittent
PyQt & PySide autocompletion problems, and makes a number of other
== About Wing ==
Wing is a full-featured but light-weight 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, code navigation, early error detection,
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,
Python package management, 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
Learn more at https://wingware.com/
it's with great pleasure that I announce the release of
This release extends compatibility fixers so that you can painlessly
upgrade your project to *Django 4.0*, without breaking your existing
DCP is a companion package which adds backwards/forwards compatibility
patches to Django, so that your dependencies don't get broken by trivial
changes made to the core of the framework.
It injects compatibility shims like function/attribute aliases, restores
data structures which were replaced by stdlib ones, extends the
behaviour of callables (eg. referring to a view by object, by name, or
by dotted path), and can even preserve deprecated modules as “import
This allows to you upgrade your dependencies one at a time, to
fork/patch them when you have a proper opportunity, and most importantly
to not get stuck, when deadlines are tight.
Technically, DCP manages a set of “fixers”, small utilities which
advertise the change that they make, the versions of Django that they
support, and which monkey-patch the Django framework on demand.
Beware, DCP is aimed at project maintainers. If you are developing a
reusable Django application, you can’t expect all your users to
integrate DCP as well. In this case, to support a wide range of Django
versions, you should rather use a toolkit like Django-compat
<https://github.com/arteria/django-compat>. You may think of DCP as a
“runtime 2to3 for Django’, wherease Django-Compat is rather a “six
module for Django”.
Feel free to contribute new fixers, for backwards or forwards
compatibility, depending on the compatibility troubles you encounter on
friendly_idle is a modified version of IDLE that incorporates friendly/friendly-traceback to give more helpful tracebacks. You can see some screenshot in a recent blog post: https://aroberge.blogspot.com/2022/06/friendly-idle.html
Since that blog post was written, the only remaining issue that was mentioned has been solved.
You can install it using "python -m pip install friendly_idle". Then launch it from a terminal using "friendly_idle". If you are not familiar with friendly/friendly-traceback, you might want to consult https://friendly-traceback.github.io/docs/index.html.
friendly_idle's version is currently listed as "0.3.2" - however, it already does everything I had planned for and could have called it "version 1.0".
friendly_idle assumes that IDLE is already installed and "monkeypatches" it as it launches.
It requires a version of IDLE that support excepthook():
Python 3.8.10 or greater
Python 3.9.5 or greater
It has not been tested with Python 3.11 as friendly-traceback has not yet been made 100% compatible with the latest 3.11 release. It might still work, but would fail to offer useful help in some cases.
CodraFT is a new open-source generic signal and image processing software, based on Python, Qt, NumPy, SciPy and others:
I am pleased to announce that CodraFT v2.0.2 has been released.
This is the first public release of version 2, compatible with Python 3.7+.
CodraFT provides signal/image processing and visualisation features, and is available either as a stand-alone application or may be embedded in your own Python-Qt application thanks to advanced automation capabilities. CodraFT also has many I/O features, supporting a lot of images format as well as HDF5 (for saving/loading CodraFT workspace or importing any other data file).
Always a pleasure to share with Python community, since 2008.