I'm pleased to announce version 0.9.9.9 of TDI.
TDI is a markup templating system written in python with (optional but
recommended) speedup code written in C. It features strict markup / logic
separation, is very fast and provides powerful tools for template
About Release 0.9.9.9
*) Removed support for Python < 2.7, PyPy < 2.0, Jython < 2.7 and started a
formal unit test suite as a basis to port to python 3 (ongoing).
*) Deprecated utility code not specific to tdi and removed references to
previously deprecated code.
*) Updated the builtin minifiers to their newest versions (rJSmin and
*) Made lots of stylistic adjustments to satisfy pylint and flake8.
Supported Python Versions
* Python 2.7
* PyPy 2.0 (Python only)
* Jython 2.7 (Python only)
TDI is available under the terms and conditions of the "Apache License,
* asset dependency handling
* better framework integration
* python 3 support
* Homepage + Documentation: http://opensource.perlig.de/tdi/
* PyPI: https://pypi.python.org/pypi/tdi
* License: http://www.apache.org/licenses/LICENSE-2.0
André "nd" Malo
On behalf of the PyWavelets development team I am pleased to announce the
release of PyWavelets 0.4.0.
As always, new developers interested in wavelets are welcome to join us at:
PyWavelets is a free Open Source library for wavelet transforms in Python.
The main features of PyWavelets are:
- 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and
- 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform)
- 1D and 2D Wavelet Packet decomposition and reconstruction
- Approximating wavelet and scaling functions
- Over seventy built-in wavelet filters and custom wavelets supported
- Single and double precision calculations
- Results compatible with Matlab Wavelet Toolbox (TM)
Git (source) repository: https://github.com/PyWavelets/pywt
Mailing list: https://groups.google.com/forum/#!forum/pywavelets
Highlights of this release
- 1D and 2D inverse stationary wavelet transforms
- Substantially faster 2D and nD discrete wavelet transforms
- Complex number support
- nD versions of the multilevel DWT and IDWT
- modernization/streamlining of the API
Full release notes are available here:
For those of you interested in probabilities and probabilistic programming, I’m
happy to announce that Lea 2.2.0 is now under beta-test.
What is Lea?
Lea is a Python package aiming at working with discrete probability
distributions in an intuitive way. It allows you to model a broad range of
random phenomenons, like dice throwing, coin tossing, gambling, weather, etc. It
offers several high-level modelling features for probabilistic programming,
including bayesian inference and Markov chains. Lea is open-source (LGPL) and
runs on Python 2 or 3. See project page below for more information
(installation, tutorials, examples, etc).
Compared to latest version (2.1.2), many things have been made in 2.2.0 to
improve ease-of-use and overall performance, without breaking backward
compatibility. Maybe one of the most notable feature is that you can now get
individual probability very easily, as a fraction or float, thanks to the new
'P' and 'Pf' functions, e.g.
>>> P(dice <= 5)
>>> Pf(dice <= 5)
New methods allow you to read a CSV file or Pandas dataframe, then build the
corresponding joint probability distribution. Also, Monte-Carlo sampling
estimation is now available, should Lea’s default exact evaluation is
intractable. Most of the new features are documented in a new tutorial on Lea's
The latest version, Lea 2.2.0-beta.4, is fairly stable (no known bug) so you can
start to use it and report any problem or dislike, if any.
Lea project page
Download Lea (PyPI)
With the hope that Lea can make the Force less uncertain,
On behalf of the Python development community and the Python 3.4 release
team, I'm pleased to announce the availability of Python 3.4.4. Python
3.4.4 is the last version of Python 3.4.4 with binary installers, and
the end of "bugfix" support. After this release, Python 3.4.4 moves
into "security fixes only" mode, and future releases will be
You can see what's changed in Python 3.4.4 (as compared to previous
versions of 3.4) here:
And you can download Python 3.4.4 here:
Windows and Mac users: please read the important platform-specific
"Notes on this release" section near the end of that page.
One final note. 3.4.4 final marks the end of an era: it contains the
last Windows installers that will be built by Martin von Loewis. Martin
has been the Windows release "Platform Expert" since the Python 2.4
release cycle started more than twelve years ago--in other words, for
more than half of Python's entire existence! On behalf of the Python
community, and particularly on behalf of the Python release managers,
I'd like to thank Martin for his years of service to the community, and
for the care and professionalism he brought to his role. It was a
pleasure working with him, and we wish him the very best in his future
We hope you enjoy Python 3.4.4!
I am pleased to announce the release of xlwings v0.6.2:
Finally you can use the VBA module with the "RunPython" command from Excel 2016 on Mac.
Check the Release Notes for full details:
xlwings is a BSD-licensed python library that makes it easy to call python from
Excel and vice versa:
Interact with Excel from python using a syntax that is close to VBA yet pythonic.
Replace your VBA macros/UDFs with python code and still pass around your workbooks as easily as before.
xlwings fully supports NumPy arrays and Pandas DataFrames.
It works with Microsoft Excel on Windows and Mac.
We are very happy to announce the v1.1 release of the Astropy package,
a core Python package for Astronomy:
Astropy is a community-driven Python package intended to contain much
of the core functionality and common tools needed for astronomy and
New and improved major functionality in this release includes:
* New functions to automatically determine histogram bins, including
the Bayesian blocks algorithm
* A new interface to transform between Table objects and pandas
* Support for table indexing
* Support for supergalactic and ecliptic coordinates
* A new .info attribute to get summary information about tables and
* A new show_in_notebook() method to show a table in Jupyter/IPython
notebooks with additional interactivity features
* Support for new units, including logarithmic units such as
magnitudes, dex, and decibels
* Support for the Planck 2015 cosmology and significant performance
improvements in the cosmology sub-package
In addition, hundreds of smaller improvements and fixes have been made.
An overview of the changes is provided at:
Instructions for installing Astropy are provided on our website, and
extensive documentation can be found at:
If you make use of the Anaconda Python Distribution, you can update to
Astropy v1.1 with:
conda update astropy
If you normally use pip, you can upgrade with:
pip install astropy --upgrade
Please report any issues, or request new features via our GitHub
Over 160 developers have contributed code to Astropy so far, and you
can find out more about the team behind Astropy here:
As a reminder, Astropy v1.0 (our long term support release) will
continue to be supported with bug fixes until Feb 19th 2017, so if you
need to use Astropy in a very stable environment, you may want to
consider staying on the v1.0.x set of releases rather than upgrading to
If you use Astropy directly for your work, or as a dependency to
another package, please remember to include the following
acknowledgment at the end of papers:
This research made use of Astropy, a community-developed core Python
package for Astronomy (Astropy Collaboration, 2013).
where (Astropy Collaboration, 2013) is a reference to the Astropy paper:
Please feel free to forward this announcement to anyone you think might
be interested in this release!
Thomas Robitaille, Erik Tollerud, and Perry Greenfield
on behalf of The Astropy Collaboration
just did a quick bugfix release, tox-2.3.1, which re-allows cross-section
substitution for setenv. Thanks all for the patience and the reporting.
For docs, as always see http://testrun.org/tox