Hi!
I'm pleased to announce the availability of wxGlade revision 1.0.1
Please download from https://sourceforge.net/projects/wxglade/files/wxglade/1.0.1/
wxGlade is a GUI builder for wxWidgets and wxPython.
The documentation includes a tutorial for people who have not used wxPython
before.
Included are also examples for integration with matplotlib.
A snapshot of the documentation is available at http://wxglade.sourceforge.net/docs/index.html
For support, there's a mailing list at https://sourceforge.net/p/wxglade/mailman/wxglade-general/
git repository and bug tracker are at https://github.com/wxGlade/wxGlade
(These pages are also linked from the help menu.)
Changes in revision 1.0.x:
==========================
Besides many improvements in usability, code generation and widget support,
this is also a major internal refactoring of the main data structure and how
widgets in the Design window are created / updated / destroyed.
*General:*
- sizers only required where wx requires them; not required e.g. for
Frame->Panel (used to be Frame->Sizer->Panel)
- better handling of display updates when properties are edited
- accessibility and usability improvements
- Dialog example
- documentation update
*Widgets:*
- all: separate class related properties into Class / Base Classes /
Instance Class
- Dialog: add StdDialogButtonSizer and standard buttons (stock items);
support SetAffirmativeId, SetEscapeId
- Button: support for image direction
- MenuBar: support lambda event handlers
- GridBagSizer: indicate overlapped slots in the Tree view
*Generated Code:*
- no separation into __set_properties/__do_layout any more
- support for instantiation classes
*Internal:*
- internal structures refactored
- add shell window and Tree Printer
wxGlade is released under the MIT license.
Happy New Year,
Dietmar Schwertberger
dietmar(a)schwertberger.de
<P><A HREF="https://sourceforge.net/projects/wxglade/files/wxglade/1.0.1/">wxGlade 1.0.1</A> - GUI builder for wxPython (31-Dec-20)
Dear Python Community,
We hope that you are all well to that end and that you have been busy
working on various awesome Python Code Bases. It's almost that time of the
year and we would like to engage the community for the Fourth ever Python
Conference which is planned to take place from* 4th - 7th December 2022** in
the beautiful and magnificent Island of ZANZIBAR.*
*PyCon Tanzania*, is seeking keynote speakers and instructors to contribute
to the Python Conference Program! *We are looking for speakers who would:*
- Offer a Keynote speaker on an appropriate technical topic;
- Offer a Technical Tutorial or Hackathon on an appropriate Python
topic;
*Topics must be relevant to the Python Language and Open Source Software:*
- Python in Education
- Python in Statistical Research
- Python in Scientific Research
- Python Machine Learning
- Python & Artificial Intelligence
- Open Source Software
- Python & Cyber Security
- Python Gaming Development
- Cloud Computing & Virtualisation
- Ideas on improving diversity and inclusiveness
- Python Functional programming etc
- Python and IoT
*SUBMIT YOUR PRESENTATION / WORKSHOP/ HACKATHON / TUTORIAL BEFORE 05th Nov
2022 **To*: *speak(a)pycon.or.tz <speak(a)pycon.or.tz> *
Regards,
PyCon Tanzania 2022
Program Committee
http://www.pycon.or.tz/
I am delighted to announce the 3.4 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
This latest release builds on top of the significant performance improvements introduced in the 3.3 release. Benchmarking figures (see https://github.com/P403n1x87/austin/pull/126#issuecomment-1279765640 for some of the numbers) show that Austin 3.4 continues to provide a high sample rate at low sampling intervals, with improvements of about 4x compared to Austin 3.2.
One major feature of this new release is support for Python 3.11. Profiles of applications run with this version of the interpreter will have fully qualified scope names, which makes for more granular profiling data.
Besides the new support for Python 3.11, the other major feature of this new release is the new MOJO binary output format that builds on top of the just-mentioned performance improvements to generate much more compact sample output files. The VS Code extension provides support for the new format starting from version 0.11.0 and so it has you covered already! More details about the MOJO file format can be found on the Wiki:
https://github.com/P403n1x87/austin/wiki/The-MOJO-File-Format
Other utilities to convert between file formats can be found in the austin-python Python package
https://github.com/P403n1x87/austin-python
More details about what's new and bugfixes can be found in the changelog
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 3.4</a> - frame stack sampler for CPython. (30-Oct-22)</p>
========================
Announcing NumExpr 2.8.4
========================
Hi everyone,
This is a maintenance and bug-fix release for NumExpr. In particular, now
we have
added Python 3.11 support.
Project documentation is available at:
http://numexpr.readthedocs.io/
Changes from 2.8.3 to 2.8.4
---------------------------
* Support for Python 3.11 has been added.
* Thanks to Tobias Hangleiter for an improved accuracy complex `expm1`
function.
While it is 25 % slower, it is significantly more accurate for the real
component
over a range of values and matches NumPy outputs much more closely.
* Thanks to Kirill Kouzoubov for a range of fixes to constants parsing that
was
resulting in duplicated constants of the same value.
* Thanks to Mark Harfouche for noticing that we no longer need `numpy`
version
checks. `packaging` is no longer a requirement as a result.
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
Hi,
We've just released Wing 9, which adds support for Python 3.11, reduces
debugger overhead in Python 3.7+, streamlines configuration of light and
dark theming, adds two light display themes, and makes improvements to
auto-invocation, multi-threaded debugging, code analysis, & more.
Details: https://wingware.com/news/2022-10-24
Downloads: https://wingware.com/downloads
== 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
features).
Learn more at https://wingware.com/
pytest-7.2.0
==============
The pytest team is proud to announce the 7.2.0 release!
This release contains new features, improvements, and bug fixes,
the full list of changes is available in the changelog:
https://docs.pytest.org/en/stable/changelog.html
For complete documentation, please visit:
https://docs.pytest.org/en/stable/
As usual, you can upgrade from PyPI via:
pip install -U pytest
Thanks to all of the contributors to this release:
* Aaron Berdy
* Adam Turner
* Albert Villanova del Moral
* Alice Purcell
* Anthony Sottile
* Anton Yakutovich
* Babak Keyvani
* Brandon Chinn
* Bruno Oliveira
* Chanvin Xiao
* Cheuk Ting Ho
* Chris Wheeler
* EmptyRabbit
* Ezio Melotti
* Florian Best
* Florian Bruhin
* Fredrik Berndtsson
* Gabriel Landau
* Gergely Kalmár
* Hugo van Kemenade
* James Gerity
* John Litborn
* Jon Parise
* Kevin C
* Kian Eliasi
* MatthewFlamm
* Miro Hrončok
* Nate Meyvis
* Neil Girdhar
* Nhieuvu1802
* Nipunn Koorapati
* Ofek Lev
* Paul Müller
* Paul Reece
* Pax
* Pete Baughman
* Peyman Salehi
* Philipp A
* Ran Benita
* Robert O'Shea
* Ronny Pfannschmidt
* Rowin
* Ruth Comer
* Samuel Colvin
* Samuel Gaist
* Sandro Tosi
* Shantanu
* Simon K
* Stephen Rosen
* Sviatoslav Sydorenko
* Tatiana Ovary
* Thierry Moisan
* Thomas Grainger
* Tim Hoffmann
* Tobias Diez
* Tony Narlock
* Vivaan Verma
* Wolfremium
* Zac Hatfield-Dodds
* Zach OBrien
* aizpurua23a
* gresm
* holesch
* itxasos23
* johnkangw
* skhomuti
* sommersoft
* wodny
* zx.qiu
Happy testing,
The pytest Development Team
As Pablo released Python 3.11.0 final earlier today, now it's my turn to
release Python 3.12.0 alpha 1.
*This is an early developer preview of Python 3.12*
Major new features of the 3.12 series, compared to 3.11
Python 3.12 is still in development. This release, 3.12.0a1 is the first 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 (2023-05-08) and, if necessary, may be modified or deleted up
until the release candidate phase (2023-07-31). Please keep in mind that
this is a preview release and its use is *not *recommended for production
environments.
Many new features for Python 3.12 are still being planned and written.
Among the new major new features and changes so far:
- The deprecated `wstr` and `wstr_length` members of the C
implementation of unicode objects were removed, per PEP 623
<https://peps.python.org/pep-0623/>.
- In the `unittest` module, a number of long deprecated methods and
classes were removed. (They had been deprecated since Python 3.1 or 3.2).
- The deprecated `smtpd` module has been removed.
- A number of other old, broken and deprecated functions, classes and
methods have been removed.
- (Hey, **fellow core developer,** if a feature you find important
is missing from this list, let Thomas know <thomas(a)python.org>.)
The next pre-release of Python 3.12 will be 3.12.0a2, currently scheduled
for 2022-11-14.
More resources
- Online Documentation <https://docs.python.org/3.12>
- PEP 693 <https://www.python.org/dev/peps/pep-0693/>, the 3.12 Release
Schedule
- Report bugs at https://github.com/python/cpython/issues.
- Help fund Python and its community at
https://www.python.org/psf/donations/.
And now for something completely different
This is Not the Poem that I Had Hoped to Write
<https://twitter.com/brian_bilston/status/1579378460610662401>
This is not the poem that I had hoped to write
when I sat at my desk and the page was white.
You see, there were other words that I’d had in mind,
yet this is what I leave behind.
I thought it was a poem to eradicate war;
one of such power, it would heal all the sores
of a world torn apart by conflict and schism.
But it isn’t.
Lovers, I’d imagined, would quote from it daily,
Mothers would sing it to soothe crying babies.
And whole generations would be given new hope.
Nope.
I had grand aspirations. Believe me, I tried.
Humanity examined with lessons applied.
But the right words escaped me; so often they do.
Have these in lieu.
Brian Bilston <https://twitter.com/brian_bilston>
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.
Regards from dusky California,
Your release team,
Thomas Wouters @Yhg1s
Ned Deily @nad
Steve Dower @steve.dower
--
Thomas Wouters <thomas(a)python.org>
Python 3.11 is finally released. In the CPython release team, we have put a
lot of effort into making 3.11 the best version of Python possible. Better
tracebacks, faster Python, exception groups and except*, typing
improvements and much more. Get it here:
https://www.python.org/downloads/release/python-3110/
## This is the stable release of Python 3.11.0
Python 3.11.0 is the newest major release of the Python programming
language, and it contains many new features and optimizations.
# Major new features of the 3.11 series, compared to 3.10
Some of the new major new features and changes in Python 3.11 are:
## General changes
* [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*`
* [PEP 680](https://www.python.org/dev/peps/pep-0680/) -- tomllib: Support
for Parsing TOML in the Standard Library
* [gh-90908](https://github.com/python/cpython/issues/90908) -- Introduce
task groups to asyncio
* [gh-34627](https://github.com/python/cpython/issues/34627/) -- Atomic
grouping (`(?>...)`) and possessive quantifiers (`*+, ++, ?+, {m,n}+`) are
now supported in regular expressions.
* The [Faster CPython Project](https://github.com/faster-cpython/) is
already yielding some exciting results. Python 3.11 is up to 10-60% faster
than Python 3.10. On average, we measured a 1.22x speedup on the standard
benchmark suite. See [Faster CPython](
https://docs.python.org/3.11/whatsnew/3.11.html#faster-cpython) for details.
## Typing and typing language changes
* [PEP 673](https://www.python.org/dev/peps/pep-0673/) -- Self Type
* [PEP 646](https://www.python.org/dev/peps/pep-0646/) -- Variadic Generics
* [PEP 675](https://www.python.org/dev/peps/pep-0675/) -- Arbitrary Literal
String Type
* [PEP 655](https://www.python.org/dev/peps/pep-0655/) -- Marking
individual TypedDict items as required or potentially-missing
* [PEP 681](https://www.python.org/dev/peps/pep-0681/) -- Data Class
Transforms
# 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://github.com/python/cpython/issues](https://github.com/python/cpython…
.
* [Help fund Python and its community](/psf/donations/).
# And now for something completely different
When a spherical non-rotating body of a critical radius collapses under its
own gravitation under general relativity, theory suggests it will collapse
to a single point. This is not the case with a rotating black hole (a Kerr
black hole). With a fluid rotating body, its distribution of mass is not
spherical (it shows an equatorial bulge), and it has angular momentum.
Since a point cannot support rotation or angular momentum in classical
physics (general relativity being a classical theory), the minimal shape of
the singularity that can support these properties is instead a ring with
zero thickness but non-zero radius, and this is referred to as a
ringularity or Kerr singularity.
This kind of singularity has the following peculiar property. The spacetime
allows a geodesic curve (describing the movement of observers and photons
in spacetime) to pass through the center of this ring singularity. The
region beyond permits closed time-like curves. Since the trajectory of
observers and particles in general relativity are described by time-like
curves, it is possible for observers in this region to return to their
past. This interior solution is not likely to be physical and is considered
a purely mathematical artefact.
There are some other interesting free-fall trajectories. For example, there
is a point in the axis of symmetry that has the property that if an
observer is below this point, the pull from the singularity will force the
observer to pass through the middle of the ring singularity to the region
with closed time-like curves and it will experience repulsive gravity that
will push it back to the original region, but then it will experience the
pull from the singularity again and will repeat this process forever. This
is, of course, only if the extreme gravity doesn’t destroy the observer
first.
# 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/
If you have any questions, please reach out to me or another member of the
release team :)
Your friendly release team,
Ned Deily @nad https://discuss.python.org/u/nad
Steve Dower @steve.dower https://discuss.python.org/u/steve.dower
Pablo Galindo Salgado @pablogsal https://discuss.python.org/u/pablogsal
