RedNotebook 1.1.2 has been released.
You can get the tarball, the Windows installer and links to distribution
What is RedNotebook?
RedNotebook is a **graphical journal** and diary helping you keep track
of notes and thoughts. It includes a calendar navigation, customizable
templates, export functionality and word clouds. You can also format,
tag and search your entries. RedNotebook is available in the
repositories of most common Linux distributions and a Windows installer
is available. It is written in Python and uses GTK+ for its interface.
* Add fullscreen mode (F11)
* Highlight all found occurences of the searched word (LP:614353)
* Highlight mixed markups (**__Bold underline__**)
* Highlight structured headers (=Part=, ==Subpart==, ===Section===,
* Document structured headers
* Highlight ``, "", ''
* Write documentation about ``, "", ''
* Let the preview and edit button have the same size
* Fix: Correctly highlight lists (LP:622456)
* Fix: Do not set maximized to True when sending RedNotebook to the tray
* Fix: Add Ctrl-P shortcut for edit button (LP:685609)
* Fix: Add "\" to the list of ignored chars for word clouds
* Fix: Escape characters before adding results to the search list
* Fix: Local links with whitespace in latex
* Windows: Fix opening linked files
* Windows: Do not center window to prevent alignment issues
* Windows: Fix image preview (LP:663944)
* Internal: Replace tabs by whitespace in source code
* Many translations updated
faulthandler is an handler for SIGSEGV, SIGFPE, SIGBUS and SIGILL
signals: display the Python backtrace on a crash. Just import the module
to enable it.
It works on Windows, Linux and FreeBSD with Python 2.6, 2.7, 3.1 and
3.2. I should work with Python 2.5 and on other OSes.
Tell me if the module works on your OS / Python version.
Dummy example of a segmentation fault on Linux:
>>> import faulthandler
Fatal Python error: Segmentation fault
Traceback (most recent call first):
File "<stdin>", line 1 in <module>
What is cx_Freeze?
cx_Freeze is a set of scripts and modules for freezing Python scripts
into executables in much the same way that py2exe and py2app do.
Unlike these two tools, however, cx_Freeze is cross platform and
should work on any platform that Python itself works on.
Where do I get it?
Changes from 4.2.1 to 4.2.2
1) Added support for namespace packages which are loaded implicitly
upon startup by injection into sys.modules.
2) Added support for a Zope sample which makes use of namespace packages.
3) Use the Microsoft compiler on Windows for Python 2.6 and up as some
strange behaviors were identified with Python 2.7 when compiled using
4) Eliminate warning about -mwindows when using the Microsoft compiler
for building the Win32GUI base executable.
5) Added support for creating version resources on Windows.
6) Ensure that modules that are not truly required for bootstrapping
are not included in the frozen modules compiled in to the executable;
otherwise, some packages and modules (such as the logging package)
cannot be found at runtime. This problem only seems to be present in
Python 2.7.1 but it is a good improvement for earlier releases of
Python as well.
7) Added support for setting the description for Windows services.
8) Added hook for using the widget plugins which are part of the
9) Added additional hooks to remove spurious errors about missing
modules and to force inclusion of implicitly imported modules (twitter
module and additional submodules of the PyQt4 package).
10) Fixed support for installing frozen executables under Python 3.x on Windows.
11) Removed optional import of setuptools which is not a complete
drop-in replacement for distutils and if found, replaces distutils
with itself, resulting in some distutils features not being available;
for those who require or prefer the use of setuptools, import it in
What is cx_Logging?
cx_Logging is a Python extension module which operates in a fashion
similar to the logging module that ships with Python 2.3 and higher.
It also has a C interface which allows applications to perform logging
independently of or in tandem with Python.
Where do I get it?
1) Added support for Python 2.7 and Python 3.1.
2) Only perform the repr() calculation if the logging level is at the
level that a log message needs to be written in order to improve
3) Ensure that stdcall calling convention is used on Windows across
the board so that other applications which assume stdcall work as
expected without extra work.
4) Include export symbols to make the Microsoft compiler on Windows
export symbols; otherwise, it ignores the request when using the
5) Fix determination of import library for the Microsoft compiler.
6) Expose WriteMessageForPython() and IsLoggingAtLevelForPython()
which are needed by ceODBC.
7) Eliminate segmentation fault if unicode string cannot be encoded.
8) Remove situation where a failure in writing to the log file was masked.
9) Fix support for AIX as suggested by Tamas Gulacsi.
omniORB 4.1.5 and omniORBpy 3.5 are now available. omniORB is a high
performance, robust CORBA implementation for C++; omniORBpy is a
version for Python.
For more details, see
These are primarily bug fix releases, with a number of minor new
- Incoming SSL connections can time out waiting for SSL_accept to
- Ability to disable longdouble support during compilation.
- Support for building with the newest versions of Cygwin.
- Python interceptors can receive peer address and identity.
- Python exceptions can be pickled.
Files are available for download from SourceForge:
Source packages are available now. Windows binaries for various VC++ and
Python versions will be available shortly.
-- Duncan Grisby --
-- duncan(a)grisby.org --
-- http://www.grisby.org --
Announcing carray 0.3
A lot of stuff. The most outstanding feature in this version is the
introduction of a `ctable` object. A `ctable` is similar to a
structured array in NumPy, but instead of storing the data row-wise, it
uses a column-wise arrangement. This allows for much better performance
for very wide tables, which is one of the scenarios where a `ctable`
makes more sense. Of course, as `ctable` is based on `carray` objects,
it inherits all its niceties (like on-the-flight compression and fast
Also, the `carray` object itself has received many improvements, like
new constructors (arange(), fromiter(), zeros(), ones(), fill()),
iterators (where(), wheretrue()) or resize mehtods (resize(), trim()).
Most of these also work with the new `ctable`.
Besides, Numexpr is supported now (but it is optional) in order to carry
out stunningly fast queries on `ctable` objects. For example, doing a
query on a table with one million rows and one thousand columns can be
up to 2x faster than using a plain structured array, and up to 20x
faster than using SQLite (using the ":memory:" backend and indexing).
See 'bench/ctable-query.py' for details.
Finally, binaries for Windows (both 32-bit and 64-bit) are provided.
For more detailed info, see the release notes in:
What it is
carray is a container for numerical data that can be compressed
in-memory. The compression process is carried out internally by Blosc,
a high-performance compressor that is optimized for binary data.
Having data compressed in-memory can reduce the stress of the memory
subsystem. The net result is that carray operations may be faster than
using a traditional ndarray object from NumPy.
carray also supports fully 64-bit addressing (both in UNIX and Windows).
Below, a carray with 1 trillion of rows has been created (7.3 TB total),
filled with zeros, modified some positions, and finally, summed-up::
>>> %time b = ca.zeros(1e12)
CPU times: user 54.76 s, sys: 0.03 s, total: 54.79 s
Wall time: 55.23 s
>>> %time b[[1, 1e9, 1e10, 1e11, 1e12-1]] = (1,2,3,4,5)
CPU times: user 2.08 s, sys: 0.00 s, total: 2.08 s
Wall time: 2.09 s
nbytes: 7450.58 GB; cbytes: 2.27 GB; ratio: 3275.35
cparams := cparams(clevel=5, shuffle=True)
[0.0, 1.0, 0.0, ..., 0.0, 0.0, 5.0]
>>> %time b.sum()
CPU times: user 10.08 s, sys: 0.00 s, total: 10.08 s
Wall time: 10.15 s
['%time' is a magic function provided by the IPyhton shell]
Please note that the example above is provided for demonstration
purposes only. Do not try to run this at home unless you have more than
3 GB of RAM available, or you will get into trouble.
Visit the main carray site repository at:
You can download a source package from:
Home of Blosc compressor:
User's mail list:
Share your experience
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
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On behalf of the Python development team, I'm happy to announce the
second beta preview release of Python 3.2.
Python 3.2 is a continuation of the efforts to improve and stabilize the
Python 3.x line. Since the final release of Python 2.7, the 2.x line
will only receive bugfixes, and new features are developed for 3.x only.
Since PEP 3003, the Moratorium on Language Changes, is in effect, there
are no changes in Python's syntax and built-in types in Python 3.2.
Development efforts concentrated on the standard library and support for
porting code to Python 3. Highlights are:
* numerous improvements to the unittest module
* PEP 3147, support for .pyc repository directories
* PEP 3149, support for version tagged dynamic libraries
* PEP 3148, a new futures library for concurrent programming
* PEP 384, a stable ABI for extension modules
* PEP 391, dictionary-based logging configuration
* an overhauled GIL implementation that reduces contention
* an extended email package that handles bytes messages
* countless fixes regarding bytes/string issues; among them full
support for a bytes environment (filenames, environment variables)
* many consistency and behavior fixes for numeric operations
* a sysconfig module to access configuration information
* a pure-Python implementation of the datetime module
* additions to the shutil module, among them archive file support
* improvements to pdb, the Python debugger
For a more extensive list of changes in 3.2, see
To download Python 3.2 visit:
Please consider trying Python 3.2 with your code and reporting any bugs
you may notice to:
Georg Brandl, Release Manager
georg at python.org
(on behalf of the entire python-dev team and 3.2's contributors)
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