We are happy to announce Pint 0.2. Pint is a Python package to define,
operate and manipulate physical quantities: the product of a numerical
value and a unit of measurement. This release brings a lot of new
exciting features including extended NumPy support, temperature
conversion, implementation of the Buckingham Pi Theorem and support
for values with uncertainties.
Check out the blog post for more details:
You can get pint using pip:
$ pip install pint
or get the source code:
and check the docs:
What is Pint?
Pint is Python package to define, operate and manipulate physical
quantities: the product of a numerical value and a unit of
measurement. It allows arithmetic operations between them and
conversions from and to different units.
It is distributed with a comprehensive list of physical units,
prefixes and constants. Due to it’s modular design, you can extend (or
even rewrite!) the complete list without changing the source code.
It has a complete test coverage. It runs in Python 2.7 and 3.X with no
other dependency. It licensed under BSD.
* Unit parsing: prefixed and pluralized forms of units are recognized
without explicitly defining them. In other words: as the prefix kilo
and the unit meter are defined, Pint understands kilometers. This
results in a much shorter and maintainable unit definition list as
compared to other packages.
* Standalone unit definitions: units definitions are loaded from a
text file which is simple and easy to edit. Adding and changing units
and their definitions does not involve changing the code.
* Advanced string formatting: a quantity can be formatted into string
using PEP 3101 syntax. Extended conversion flags are given to provide
symbolic, latex and pretty formatting.
* Free to choose the numerical type: You can use any numerical type
(fraction, float, decimal, numpy.ndarray, etc). NumPy is not required
* NumPy integration: When you choose to use a NumPy ndarray, its
methods and ufuncs are supported including automatic conversion of
units. For example numpy.arccos(q) will require a dimensionless q and
the units of the output quantity will be radian.
* Handle temperature: conversion between units with different
reference points, like positions on a map or absolute temperature
* Small codebase: easy to maintain codebase with a flat hierarchy.
* Dependency free: it depends only on Python and it’s standard library.
* Python 2 and 3: a single codebase that runs unchanged in Python 2.7+
and Python 3.0+.
Thanks to the people that contributed bug reports, suggestions and
patches. In particular to: Richard Barnes, Alexander Böhn, Dave
Brooks, Giel van Schijndel, Brend Wanders
I'm pleased to announce version 0.9.9.7 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 0.9.9.7
The following features have been added:
* Support for plain text templates
* More C code, speeding up the parsing and filtering process
* Support for more python implementations
Supported Python Versions
* Python 2.4 - 2.7
* PyPy 1.9, 2.0 (Python only)
* Jython 2.5, 2.7 (Python only)
TDI is available under the terms and conditions of the "Apache License,
* 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
* inserttext, objtimeout, guitimeout, getcellsize, getcellvalue,
getobjectnameatcoords, getcombovalue, getaccesskey in Python client
* doubleClick, doubleClickRow, onWindowCreate, getCellSize, getComboValue,
appUnderTest, getAccessKey in Java client
* getcellsize, getcellvalue in Ruby client
* GetCellSize, GetComboValue, AppUnderTest, GetAccessKey, MouseRightClick,
DoubleClick, DoubleClickRow, RightClick in C# client
New control type:
* POPUP MENU for Ubuntu environment
* Fixed optional arguments to imagecapture
* Check window_name parameter, if empty then use @window_name passed in
* Fixed optional argument APIs to work on both Windows and Linux
* imagecapture x, y offset, height and width parameters are disregarded if
window parameter is provided - Bug#685548
* Return unicode string all the time on gettextvalue
* Fix partial match argument in selectrow, compatible with Windows
* Patch by ebass to support Python 2.6
* Added Errno 101 as we see in ebass Ubuntu 10.04 environment
* Include label type on gettextvalue
* Don't include separators in the list
* Added perl client
* Sawyer X for the Perl interface
* ebass (IRC nick name)
* Marek Rosa <marek.j.rosa(a)gmail.com>
* Thanks to all others who have reported bugs through forum / email /
in-person / IRC
Cross Platform GUI Automation tool Linux version is LDTP, Windows version
is Cobra and Mac version is PyATOM.
* Linux version is known to work on GNOME / KDE (QT >= 4.8) / Java Swing /
LibreOffice / Mozilla application on all major Linux distribution.
* Windows version is known to work on application written in .NET / C++ /
Java / QT on Windows XP SP3 / Windows 7 / Windows 8 development version.
* Mac GUI testing is known to work on OS X Snow Leopard/Lion/Mountain Lion.
Where ever PyATOM runs, LDTP should work on it.
Download source: https://github.com/ldtp/ldtp2
Download binary (RPM / DEB):
For detailed information on LDTP framework and latest updates visit
For information on various APIs in LDTP including those added for this
release can be got from http://ldtp.freedesktop.org/user-doc/index.html
Java doc - http://ldtp.freedesktop.org/javadoc/
Report bugs - http://ldtp.freedesktop.org/wiki/Bugs
To subscribe to LDTP mailing lists, visit
IRC Channel - #ldtp on irc.freenode.net
How can you help: Spread the news and send back your feedback to us
Cross platform GUI testing
Linux Desktop (GUI Application) Testing Project -
Cobra - Windows GUI Automation tool - https://github.com/ldtp/cobra
ATOMac - Mac GUI Automation tool - https://github.com/pyatom/pyatomhttp://nagappanal.blogspot.com
Dear fellow Pythonistas,
We're pleased to announce that Kiwi PyCon 2013's Call for Proposals is now open!
This year the conference will be held Saturday 06 and Sunday 07
September in Auckland, New Zealand. Friday 05 September will see
tutorials and workshops run during the day - a Kiwi PyCon first!
The deadline for proposal submission is Saturday 01 June 2013.
For more information please visit
Looking forward to seeing you in Auckland in September!
Danny W. Adair
Kiwi PyCon 2013
We're pleased to announce PyPy 2.0. This is a stable release that brings
a swath of bugfixes, small performance improvements and compatibility fixes.
PyPy 2.0 is a big step for us and we hope in the future we'll be able to
provide stable releases more often.
You can download the PyPy 2.0 release here:
The two biggest changes since PyPy 1.9 are:
* stackless is now supported including greenlets, which means eventlet
and gevent should work (but read below about gevent)
* PyPy now contains release 0.6 of `cffi`_ as a builtin module, which
is preferred way of calling C from Python that works well on PyPy
.. _`cffi`: http://cffi.readthedocs.org
If you're using PyPy for anything, it would help us immensely if you fill out
the following survey: http://bit.ly/pypysurvey This is for the developers
eyes and we will not make any information public without your agreement.
What is PyPy?
PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7. It's fast (`pypy 2.0 and cpython 2.7.3`_ performance comparison)
due to its integrated tracing JIT compiler.
This release supports x86 machines running Linux 32/64, Mac OS X 64 or
Windows 32. Windows 64 work is still stalling, we would welcome a volunteer
to handle that. ARM support is on the way, as you can see from the recently
released alpha for ARM.
.. _`pypy 2.0 and cpython 2.7.3`: http://speed.pypy.org
* Stackless including greenlets should work. For gevent, you need to check
out `pypycore`_ and use the `pypy-hacks`_ branch of gevent.
* cffi is now a module included with PyPy. (`cffi`_ also exists for
CPython; the two versions should be fully compatible.) It is the
preferred way of calling C from Python that works on PyPy.
* Callbacks from C are now JITted, which means XML parsing is much faster.
* A lot of speed improvements in various language corners, most of them small,
but speeding up some particular corners a lot.
* The JIT was refactored to emit machine code which manipulates a "frame"
that lives on the heap rather than on the stack. This is what makes
Stackless work, and it could bring another future speed-up (not done yet).
* A lot of stability issues fixed.
* Refactoring much of the numpypy array classes, which resulted in removal of
lazy expression evaluation. On the other hand, we now have more complete
dtype support and support more array attributes.
.. _`pypycore`: https://github.com/gevent-on-pypy/pypycore/
.. _`pypy-hacks`: https://github.com/schmir/gevent/tree/pypy-hacks
fijal, arigo and the PyPy team
MacroPy is an implementation of Macros in the Python Programming, providing a mechanism for user-defined functions (macros) to perform transformations on the abstract syntax tree(AST) of Python code at module import time. This is an easy way to modify the semantics of a python program in ways which are otherwise impossible, for example providing an extremely concise way of declaring classes:
class Point(x, y)
p = Point(1, 2)
print p.x # 1
print p # Point(1, 2)
Apart from this, we've used MacroPy's macros to implement a pretty impressive list of features:
- String Interpolation
- Pyxl, integrating XML markup into a Python program
- Tracing and Smart Asserts
- Case Classes, easy Algebraic Data Types from Scala
- Pattern Matching from the Functional Programming world
- LINQ to SQL from C#
- Quick Lambdas from Scala and Groovy,
- Parser Combinators, inspired by Scala's.
The full documentation is over on github (https://github.com/lihaoyi/macropy) if anyone wants to check it out. It runs fine on both CPython 2.7 and PyPy 1.9, and I've just pushed the last up-to-date version of MacroPy to PyPI:
Hope someone finds this useful!
I would like to introduce Scopt - Python ERP like project.
Project URL: http://code.google.com/p/scopt/
Project version: 0.0.005 - POC
Project license: Apache License 2.0 (License matrix is not finished yet.)
Project was started out as search for Java alternative in area of business
Project is divided into:
- Scopt - business Core(engine),
- Magua - referential GUI implementation
- TinyESB - mediation like engine and framework.
Architecture of solution is built on asynchronous message passing (mediation).
We try to make Scopt easy testable, client and transport layer agnostic with
flexible deployment model.
Language - Python 2.7.3
DB abstraction - SQLAlchemy - core
DB - SQLite, (Oracle and PostgreSql will be supported)
HTTP connector - Tornado async web server.
GUI referential implementation is developed in Dojo. Android client is planned.
For now, main design is outlined and most of implementation details and API
We learn Python as we code the project. Any comments on programming style,
or following python standards and idioms are welcomed.
<a href="http://code.google.com/p/scopt/">Scopt 0.0.005</a>
Python ERP like project (09-May-2013)
On the behalf of Spyder's development team (
http://code.google.com/p/spyderlib/people/list), I'm pleased to announce
that Spyder v2.2 has been released and is available for Windows
XP/Vista/7/8, GNU/Linux and MacOS X: http://code.google.com/p/spyderlib/.
This release represents 18 months of development since v2.1 and introduces
major enhancements and new features:
* Full support for IPython v0.13, including the ability to attach to
* New MacOS X application
* Much improved debugging experience
* Various editor improvements for code completion, zooming, auto
insertion, and syntax highlighting
* Better looking and faster Object Inspector
* Single instance mode
* Spanish tranlation of the interface
* And many other changes: http://code.google.com/p/spyderlib/wiki/ChangeLog
This is the last release to support Python 2.5:
* Spyder 2.2 supports Python 2.5 to 2.7
* Spyder 2.3 will support Python 2.7 and Python 3
* (Spyder 2.1.14dev4 is a development release which already supports
See also https://code.google.com/p/spyderlib/downloads/list.
Spyder is a free, open-source (MIT license) interactive development
environment for the Python language with advanced editing, interactive
testing, debugging and introspection features. Originally designed to
provide MATLAB-like features (integrated help, interactive console,
variable explorer with GUI-based editors for dictionaries, NumPy arrays,
...), it is strongly oriented towards scientific computing and software
development. Thanks to the `spyderlib` library, Spyder also provides
powerful ready-to-use widgets: embedded Python console (example:
http://packages.python.org/guiqwt/_images/sift3.png), NumPy array editor
(example: http://packages.python.org/guiqwt/_images/sift2.png), dictionary
editor, source code editor, etc.
Description of key features with tasty screenshots can be found at:
Don't forget to follow Spyder updates/news:
* on the project website: http://code.google.com/p/spyderlib/
* and on our official blog: http://spyder-ide.blogspot.com/
Last, but not least, we welcome any contribution that helps making Spyder
an efficient scientific development/computing environment. Join us to help
creating your favourite environment!