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)
Hi everyone,
NumExpr 2.8.7 is a release to deal with issues related to downstream
`pandas`
and other projects where the sanitization blacklist was triggering issues
in their
evaluate. Hopefully, the new sanitization code would be much more robust
now.
For those who do not wish to have sanitization on by default, it can be
changed
by setting an environment variable, `NUMEXPR_SANITIZE=0`.
If you use `pandas` in your packages it is advisable you pin
`numexpr >= 2.8.7`
in your requirements.
Project documentation is available at:
http://numexpr.readthedocs.io/
Changes from 2.8.6 to 2.8.7
---------------------------
* More permissive rules in sanitizing regular expression: allow to access
digits
after the . with scientific notation. Thanks to Thomas Vincent.
* Don't reject double underscores that are not at the start or end of a
variable
name (pandas uses those), or scientific-notation numbers with digits
after the
decimal point. Thanks to Rebecca Palmer.
* Do not use `numpy.alltrue` in the test suite, as it has been deprecated
(replaced by `numpy.all`). Thanks to Rebecca Chen.
* Wheels for Python 3.12. Wheels for 3.7 and 3.8 are not generated anymore.
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!
--
Francesc Alted
Trac 1.6 Released
==============
Trac 1.6, the first version to support Python 3, has been released.
You will find this release at the usual places:
https://trac.edgewall.org/wiki/TracDownload#Trac16StableRelease
You can find the detailed release notes for 1.6 on the following
pages:
https://trac.edgewall.org/wiki/TracChangeLoghttps://trac.edgewall.org/wiki/TracDev/ReleaseNotes/1.6
Now to the packages themselves:
URLs:
https://download.edgewall.org/trac/Trac-1.6.tar.gzhttps://download.edgewall.org/trac/Trac-1.6-py3-none-any.whl
MD5 sums:
fdf48328c1e744d1ce6c5212cd7f3af6 Trac-1.6.tar.gz
fbe2a2dec779b8b04d64c0a5b58b2f00 Trac-1.6-py3-none-any.whl
SHA256 sums:
61d73c61f670d68ffc346829d24b2f1d2050aa561aa71cb98e2fb43992c27304
Trac-1.6.tar.gz
5ff2f3394ebee7cc5b8ee465871cfdecb78ee492a74215b47b6d9ddbcc93b5f7
Trac-1.6-py3-none-any.whl
Acknowledgements
===============
Many thanks to the growing number of people who have, and continue to,
support the project. Also our thanks to all people providing feedback
and bug reports that help us make Trac better, easier to use and
more effective. Without your invaluable help, Trac would not evolve.
Thank you all.
Finally, we hope that Trac will be useful to like-minded programmers
around the world, and that this release will be an improvement over
the last version.
Please let us know.
/The Trac Team https://trac.edgewall.org/
PyCA cryptography 41.0.4 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 3.7+, and PyPy3 7.3.10+.
Changelog (https://cryptography.io/en/latest/changelog/#v41-0-4):
* Updated Windows, macOS, and Linux wheels to be compiled with OpenSSL 3.1.3.
Alex
--
All that is necessary for evil to succeed is for good people to do nothing.
Hi All,
On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.0. The NumPy 1.26.0 release is a continuation of the 1.25.x release
cycle with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have chosen
to use the Meson build system instead, and this is the first NumPy release
supporting it. This is also the first release that supports Cython 3.0 in
addition to retaining 0.29.X compatibility. Supporting those two upgrades
was a large project, over 100 files have been touched in this release. The
changelog doesn't capture the full extent of the work, special thanks to
Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much
of the work in the main development branch.
The highlights of this release are:
- Python 3.12.0 support.
- Cython 3.0.0 compatibility.
- Use of the Meson build system
- Updated SIMD support
- f2py fixes, meson and bind(x) support
- Support for the updated Accelerate BLAS/LAPACK library
The Python versions supported by this release are 3.9-3.12. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.26.0/>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.0>
*Contributors*
A total of 20 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- @DWesl
- Albert Steppi +
- Bas van Beek
- Charles Harris
- Developer-Ecosystem-Engineering
- Filipe Laíns +
- Jake Vanderplas
- Liang Yan +
- Marten van Kerkwijk
- Matti Picus
- Melissa Weber Mendonça
- Namami Shanker
- Nathan Goldbaum
- Ralf Gommers
- Rohit Goswami
- Sayed Adel
- Sebastian Berg
- Stefan van der Walt
- Tyler Reddy
- Warren Weckesser
*Build system changes*
In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel can
be
done with standard tools like ``pip`` and ``pypa/build``. The following are
supported:
- Regular installs: ``pip install numpy`` or (in a cloned repo) ``pip
install .``
- Building a wheel: ``python -m build`` (preferred), or ``pip wheel .``
- Editable installs: ``pip install -e . --no-build-isolation``
- Development builds through the custom CLI implemented with spin
<https://github.com/scientific-python/spin>: ``spin build``.
All the regular ``pip`` and ``pypa/build`` flags (e.g.,
``--no-build-isolation``) should work as expected.
*NumPy-specific build customization*
Many of the NumPy-specific ways of customizing builds have changed.
The ``NPY_*`` environment variables which control BLAS/LAPACK, SIMD,
threading,
and other such options are no longer supported, nor is a ``site.cfg`` file
to
select BLAS and LAPACK. Instead, there are command-line flags that can be
passed to the build via ``pip``/``build``'s config-settings interface. These
flags are all listed in the ``meson_options.txt`` file in the root of the
repo.
Detailed documented will be available before the final 1.26.0 release; for
now,
see the SciPy building from source
<http://scipy.github.io/devdocs/building/index.html> docs since most build
customization
works in an almost identical way in SciPy as it does in NumPy.
*Build dependencies*
While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the ``[build-system]``
section
of ``pyproject.toml`` for details.
*Troubleshooting*
This build system change is quite large. In case of unexpected issues, it is
still possible to use a ``setup.py``-based build as a temporary workaround
(on
Python 3.9-3.11, not 3.12), by copying ``pyproject.toml.setuppy`` to
``pyproject.toml``. However, please open an issue with details on the NumPy
issue tracker. We aim to phase out ``setup.py`` builds as soon as possible,
and
therefore would like to see all potential blockers surfaced early on in the
1.26.0 release cycle.
Cheers,
Charles Harris
Hi everyone,
NumExpr 2.8.6 is a release to deal with issues related to downstream
`pandas`
where the sanitization blacklist was hitting private variables used in their
evaluate. In addition the sanitization was hitting on scientific notation.
For those who do not wish to have sanitization on by default, it can be
changed
by setting an environment variable, `NUMEXPR_SANITIZE=0`.
If you use `pandas` in your packages it is advisable you pin
`numexpr >= 2.8.6`
in your requirements.
Project documentation is available at:
http://numexpr.readthedocs.io/
Changes from 2.8.5 to 2.8.6
---------------------------
* The sanitization can be turned off by default by setting an environment
variable,
`set NUMEXPR_SANITIZE=0`
* Improved behavior of the blacklist to avoid triggering on private
variables
and scientific notation numbers.
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
Greetings! Triv is a syntax for the web and more...
Now available as a public release at version 0.50.0 for either
Python (recommended) or Node.js.
pip install triv
3v file.3v > file.xml
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Please address correspondence to: maintainer(a)triv.co
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