stdeb produces Debian source packages from Python packages via a new
distutils command, sdist_dsc. Automatic defaults are provided for the
Debian package, but many aspects of the resulting package can be
customized. An additional command, bdist_deb, creates a Debian binary
package, a .deb file.
Two convenience utilities are also provided. pypi-install will query the
Python Package Index (PyPI) for a package, download it, create a .deb
from it, and then install the .deb. py2dsc will convert a
distutils-built source tarball into a Debian source package.
stdeb: http://github.com/astraw/stdeb
This email announces release 0.5.0.
download: http://pypi.python.org/pypi/stdeb/0.5.0
Highlights for this release (you may also wish to consult the full
changelog):
* A new pypi-install script will automatically download, make a .deb,
and install packages from the Python Package Index (PyPI).
* Removal of the setuptools dependency.
* New option (--guess-conflicts-provides-replaces) to query original
Debian packages for Conflicts/Provides/Replaces information.
* As a result of these changes and to fix a couple bugs/warts, some
minor backwards incompatible changes and deprecations were made. Please
check the release notes:
http://github.com/astraw/stdeb/blob/release-0.5.0/RELEASE_NOTES.txt
The full changelog is here:
http://github.com/astraw/stdeb/blob/release-0.5.0/CHANGELOG.txt
what is it
----------
A Python package to parse and build CSS Cascading Style Sheets. (Not a
renderer though!)
about this release
------------------
0.9.7a2 is an alpha release.
main changes
------------
0.9.7 is quite a bit faster than 0.9.6.
0.9.7a2 091230
- **API CHANGE**: Setting a style declarations' property to
``None`` or the empty string effectively removes this property
from the declaration. See also Issue #32.
+ **BUGFIX/FEATURE**: Fixed Issue 33: URL references (like ``url()``
values) in combined sheets are now adjusted even if sheets are
not in the same folder. Only relative paths are adjusted.
- **BUGFIX**: Fixed parsing of FUNCTIONS in CSSUnknownRule like
``@bottom { counter(page) }`` which raised a false error of a
mismatch of parenthesis
license
-------
cssutils is published under the LGPL version 3 or later, see
http://cthedot.de/cssutils/
If you have other licensing needs please let me know.
download
--------
For download options see http://cthedot.de/cssutils/
cssutils needs Python 2.4 or higher or Jython 2.5 and higher (tested
with Python 2.6.4(64), 2.5.4(64), 2.4.4 and Jython 2.5.1 on Win 7 64bit
only)
Bug reports (via Google code), comments, etc are very much appreciated!
Thanks.
Christof
Deadline: Feb 1, 2010
OSCON, the O'Reilly Open Source Convention
July 19 - 23, 2010
Oregon Convention Center
Portland, OR
http://post.oreilly.com/rd/9z1zg6ii2gsi1l6cshb1806k2apmotnacpkrk77ttgg
Faster, Freer, Smarter: Whatever your Goal, Make It Happen with Open Source
More than 2,500 experts, developers, sys admins, and hackers will meet up
at OSCON 2010 to explore the tools, services, and platforms that make up
the vibrant open source ecosystem. Join us!
The OSCON Call for Participation is now open. If you have winning
techniques, favorite lifesavers, war stories, productivity tips, or other
ideas to share, we want to hear from you. We're especially on the
look-out for ways to do more with less, design and usability best
practices, mobile device innovations, cloud computing, parallelization,
open standards and data, open source in government, business models, and
beyond.
Speak up about the freedom--and opportunity--of open source at OSCON
2010. Submit your proposal by February 1, 2010 at:
http://post.oreilly.com/rd/9z1zr5embktof4hi37tr30hm2qshjaug3mfrdjltsmg
--
Aahz (aahz(a)pythoncraft.com) <*> http://www.pythoncraft.com/
Weinberg's Second Law: If builders built buildings the way programmers wrote
programs, then the first woodpecker that came along would destroy civilization.
A new release of the dynamical systems modeling toolbox PyDSTool is
available from Sourceforge:
http://www.sourceforge.net/projects/pydstool/
Highlights from the release notes:
* Cleanup of global imports, especially: entire numpy.random and
linalg namespaces no longer imported by default
* Added support for 'min' and 'max' keywords in functional
specifications (for ODE right-hand sides, for instance)
* Optimization tools from third-party genericOpt (included with
permission) and improved parameter estimation examples making use of
this code
* Numerical phase-response calculations now possible in PRC toolbox
* Fully-fledged DSSRT toolbox for neural modeling (see wiki page)
* New tests/demonstrations in PyDSTool/tests
* Major improvements to intelligent expr2func (symbolic -> python
function conversion)
* Improved compatibility with cross-platform use and with recent
python versions and associated libraries
* Added many minor features (see timeline on Trac
http://jay.cam.cornell.edu/pydstool/timeline)
* Fixed many bugs and quirks (see timeline on Trac
http://jay.cam.cornell.edu/pydstool/timeline)
This is mainly a bugfix release in preparation for a substantial
upgrade at version 0.90, which will have a proper installer, unit
testing, symbolic expression support via SymPy, and greatly improved
interfacing to legacy ODE integrators. These features are being
actively developed in 2009/2010.
For installation and setting up, please carefully read the
GettingStarted page at our wiki for platform-specific details:
http://pydstool.sourceforge.net
Please use the bug tracker and user discussion list at Sourceforge to
report bugs or provide feedback. Code and documentation contributions
are always welcome.
Regards,
Rob Clewley
FREE TurboGears (un)tutorial, 30 January 2010 in Richardson, TX (DFW)
taught by Chris Perkins. This will be a 'build as we go' tutorial where
you are highly encouraged to bring a real database that we will map
against and write an app for.
Consider this both a tutorial and sprint. Seats are limited to 15
(unless I get overwhelmed then I will change venues) so please register
at http://dfwtgtutorial.eventbrite.com/.
For detailed information on the event including what to bring and how to
prepare, please see http://percious.com/blog/archives/152. Here you can
also see a tentative schedule for other locations.
Special thanks goes to Chris for volunteering his time and Company
Dallas (http://www.companydallas.com/) for providing the free space and
resources for this.
Hope to see you there,
Jason Galyon
Hi,
I am pleased to announce the release of numpy 1.4.0. The highlights of
this release are:
- Faster import time
- Extended array wrapping mechanism for ufuncs
- New Neighborhood iterator (C-level only)
- C99-like complex functions in npymath, and a lot of portability
fixes for basic floating point math functions
The full release notes are at the end of the email. The sources are
uploaded on Pypi, and the binary installers will soon come on the
sourceforge page:
https://sourceforge.net/projects/numpy/
Thank you to everyone involved in this release, developers, users who
reported bugs, fix documentation, etc...
enjoy,
the numpy developers.
=========================
NumPy 1.4.0 Release Notes
=========================
This minor includes numerous bug fixes, as well as a few new features. It
is backward compatible with 1.3.0 release.
Highlights
==========
* Faster import time
* Extended array wrapping mechanism for ufuncs
* New Neighborhood iterator (C-level only)
* C99-like complex functions in npymath
New features
============
Extended array wrapping mechanism for ufuncs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
An __array_prepare__ method has been added to ndarray to provide subclasses
greater flexibility to interact with ufuncs and ufunc-like functions. ndarray
already provided __array_wrap__, which allowed subclasses to set the array type
for the result and populate metadata on the way out of the ufunc (as seen in
the implementation of MaskedArray). For some applications it is necessary to
provide checks and populate metadata *on the way in*. __array_prepare__ is
therefore called just after the ufunc has initialized the output array but
before computing the results and populating it. This way, checks can be made
and errors raised before operations which may modify data in place.
Automatic detection of forward incompatibilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Previously, if an extension was built against a version N of NumPy, and used on
a system with NumPy M < N, the import_array was successfull, which could cause
crashes because the version M does not have a function in N. Starting from
NumPy 1.4.0, this will cause a failure in import_array, so the error will be
catched early on.
New iterators
~~~~~~~~~~~~~
A new neighborhood iterator has been added to the C API. It can be used to
iterate over the items in a neighborhood of an array, and can handle boundaries
conditions automatically. Zero and one padding are available, as well as
arbitrary constant value, mirror and circular padding.
New polynomial support
~~~~~~~~~~~~~~~~~~~~~~
New modules chebyshev and polynomial have been added. The new polynomial module
is not compatible with the current polynomial support in numpy, but is much
like the new chebyshev module. The most noticeable difference to most will
be that coefficients are specified from low to high power, that the low
level functions do *not* work with the Chebyshev and Polynomial classes as
arguements, and that the Chebyshev and Polynomial classes include a domain.
Mapping between domains is a linear substitution and the two classes can be
converted one to the other, allowing, for instance, a Chebyshev series in
one domain to be expanded as a polynomial in another domain. The new classes
should generally be used instead of the low level functions, the latter are
provided for those who wish to build their own classes.
The new modules are not automatically imported into the numpy namespace,
they must be explicitly brought in with an "import numpy.polynomial"
statement.
New C API
~~~~~~~~~
The following C functions have been added to the C API:
#. PyArray_GetNDArrayCFeatureVersion: return the *API* version of the
loaded numpy.
#. PyArray_Correlate2 - like PyArray_Correlate, but implements the usual
definition of correlation. Inputs are not swapped, and conjugate is
taken for complex arrays.
#. PyArray_NeighborhoodIterNew - a new iterator to iterate over a
neighborhood of a point, with automatic boundaries handling. It is
documented in the iterators section of the C-API reference, and you can
find some examples in the multiarray_test.c.src file in numpy.core.
New ufuncs
~~~~~~~~~~
The following ufuncs have been added to the C API:
#. copysign - return the value of the first argument with the sign copied
from the second argument.
#. nextafter - return the next representable floating point value of the
first argument toward the second argument.
New defines
~~~~~~~~~~~
The alpha processor is now defined and available in numpy/npy_cpu.h. The
failed detection of the PARISC processor has been fixed. The defines are:
#. NPY_CPU_HPPA: PARISC
#. NPY_CPU_ALPHA: Alpha
Testing
~~~~~~~
#. deprecated decorator: this decorator may be used to avoid cluttering
testing output while testing DeprecationWarning is effectively raised by
the decorated test.
#. assert_array_almost_equal_nulps: new method to compare two arrays of
floating point values. With this function, two values are considered
close if there are not many representable floating point values in
between, thus being more robust than assert_array_almost_equal when the
values fluctuate a lot.
#. assert_array_max_ulp: raise an assertion if there are more than N
representable numbers between two floating point values.
#. assert_warns: raise an AssertionError if a callable does not generate a
warning of the appropriate class, without altering the warning state.
Reusing npymath
~~~~~~~~~~~~~~~
In 1.3.0, we started putting portable C math routines in npymath library, so
that people can use those to write portable extensions. Unfortunately, it was
not possible to easily link against this library: in 1.4.0, support has been
added to numpy.distutils so that 3rd party can reuse this library. See coremath
documentation for more information.
Improved set operations
~~~~~~~~~~~~~~~~~~~~~~~
In previous versions of NumPy some set functions (intersect1d,
setxor1d, setdiff1d and setmember1d) could return incorrect results if
the input arrays contained duplicate items. These now work correctly
for input arrays with duplicates. setmember1d has been renamed to
in1d, as with the change to accept arrays with duplicates it is
no longer a set operation, and is conceptually similar to an
elementwise version of the Python operator 'in'. All of these
functions now accept the boolean keyword assume_unique. This is False
by default, but can be set True if the input arrays are known not
to contain duplicates, which can increase the functions' execution
speed.
Improvements
============
#. numpy import is noticeably faster (from 20 to 30 % depending on the
platform and computer)
#. The sort functions now sort nans to the end.
* Real sort order is [R, nan]
* Complex sort order is [R + Rj, R + nanj, nan + Rj, nan + nanj]
Complex numbers with the same nan placements are sorted according to
the non-nan part if it exists.
#. The type comparison functions have been made consistent with the new
sort order of nans. Searchsorted now works with sorted arrays
containing nan values.
#. Complex division has been made more resistent to overflow.
#. Complex floor division has been made more resistent to overflow.
Deprecations
============
The following functions are deprecated:
#. correlate: it takes a new keyword argument old_behavior. When True (the
default), it returns the same result as before. When False, compute the
conventional correlation, and take the conjugate for complex arrays. The
old behavior will be removed in NumPy 1.5, and raises a
DeprecationWarning in 1.4.
#. unique1d: use unique instead. unique1d raises a deprecation
warning in 1.4, and will be removed in 1.5.
#. intersect1d_nu: use intersect1d instead. intersect1d_nu raises
a deprecation warning in 1.4, and will be removed in 1.5.
#. setmember1d: use in1d instead. setmember1d raises a deprecation
warning in 1.4, and will be removed in 1.5.
The following raise errors:
#. When operating on 0-d arrays, ``numpy.max`` and other functions accept
only ``axis=0``, ``axis=-1`` and ``axis=None``. Using an out-of-bounds
axes is an indication of a bug, so Numpy raises an error for these cases
now.
#. Specifying ``axis > MAX_DIMS`` is no longer allowed; Numpy raises now an
error instead of behaving similarly as for ``axis=None``.
Internal changes
================
Use C99 complex functions when available
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The numpy complex types are now guaranteed to be ABI compatible with C99
complex type, if availble on the platform. Moreoever, the complex ufunc now use
the platform C99 functions intead of our own.
split multiarray and umath source code
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The source code of multiarray and umath has been split into separate logic
compilation units. This should make the source code more amenable for
newcomers.
Separate compilation
~~~~~~~~~~~~~~~~~~~~
By default, every file of multiarray (and umath) is merged into one for
compilation as was the case before, but if NPY_SEPARATE_COMPILATION env
variable is set to a non-negative value, experimental individual compilation of
each file is enabled. This makes the compile/debug cycle much faster when
working on core numpy.
Separate core math library
~~~~~~~~~~~~~~~~~~~~~~~~~~
New functions which have been added:
* npy_copysign
* npy_nextafter
* npy_cpack
* npy_creal
* npy_cimag
* npy_cabs
* npy_cexp
* npy_clog
* npy_cpow
* npy_csqr
* npy_ccos
* npy_csin
Pyspread 0.0.13 released
========================
I am pleased to announce the new release 0.0.13 of pyspread.
About:
------
Pyspread is a cross-platform Python spreadsheet application.
It is based on and written in the programming language Python.
Instead of spreadsheet formulas, Python expressions are entered into
the spreadsheet cells. Each expression returns a Python object that can
be accessed from other cells. These objects can represent anything
including lists or matrices.
Pyspread runs on Linux and *nix platforms with GTK support as well as
on Windows (XP and Vista tested). On Mac OS X, some icons are too small
but the application basically works.
Homepage
--------
http://pyspread.sourceforge.net
New features in 0.0.13
----------------------
* Print framework now supports colors and drawn elements
* Splash screen removed
* Some drawing speed improvements
Bug fixes
---------
+ Small white rectangle in upper left corner removed (BUG 2918360)
+ setup.py does not copy directories fixed (BUG 2913911)
+ Folder renaming to fix installation errors (BUG 2909017)
Enjoy
Martin
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Hello Python Community.
I'm pleased to announce pyxser-1.4, a python extension which
contains functions to serialize and deserialize Python Objects
into XML. It is a model based serializer. Here is the ChangeLog
entry for this release:
- ---8<---
1.4r (2009.12.26):
Daniel Molina Wegener <dmw(a)coder.cl>
* pyxser_typem.c: Added type map serialization and deserialization
routines and arguments. Now pyxser is able to serialize and
deserialize objects using custom serialization functions, but
preserving the pyxser XML schema and the serialization model.
- ---8<---
The project is hosted at:
http://sourceforge.net/projects/pyxser/
The web page for the project is located at:
http://coder.cl/products/pyxser/
PyPi entry is:
http://pypi.python.org/pypi?name=pyxser&version=1.4r&:action=display
For a sample article on how to integrate
pyxser with ZSI WebServices:
http://coder.cl/2009/10/18/pyxser-and-zsi-webservices/
Thanks and best regards,
- --
| Daniel Molina <dmw [at] coder [dot] cl> |
| IT Consulting & Software Development |
| http://coder.cl/ |
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