Hi there folks,
I'm pleased to announce the 0.7.0 release of psutil:
http://code.google.com/p/psutil/
This is mainly a bugfix release addressing a couple of high priority
issues on Linux and FreeBSD.
Complete list of bugfixes and enhancements is here:
https://psutil.googlecode.com/hg/HISTORY
=== New features ===
- psutil.get_boot_time()
- get/set process IO priority on Windows
- psutil.cpu_times() on Linux now includes new 'steal', 'guest' and
guest_nice' fields available on recent kernels
- psutil.cpu_times_percent() which provides utilization percentages
for each specific CPU time as is returned by cpu_times()
- source code has been migrated from SVN to Mercurial
=== Compatitility notes ===
- Process cmdline and ppid properties are no longer cached after first access
- psutil.error module is deprecated and scheduled for removal
=== Links ===
- Home: http://code.google.com/p/psutil
- Sources: http://psutil.googlecode.com/files/psutil-0.7.0.tar.gz
- Docs: http://code.google.com/p/psutil/wiki/Documentation
Please try out this new release and let me know if you experience any
problem by filing issues on the bug tracker.
Thanks in advance.
--- Giampaolo Rodola'
http://code.google.com/p/pyftpdlib/http://code.google.com/p/psutil/http://code.google.com/p/pysendfile/
PiCloud's (http://www.picloud.com) latest feature gives Python users a
distributed, fault-tolerant message queue with a scalable message
processing system. We ensure that you never lose any messages, and that
they're processed at the speed you need--even thousands of messages per
second.
More information:
Blog Post:
http://blog.picloud.com/2013/04/03/introducing-queues-creating-a-pipeline-i…
Documentation: http://docs.picloud.com/queue.html
What's it for?
Queues let you decouple discrete components of your application, and
simplify communication between them as the pushing and popping of messages.
Queues are perfect for integrating PiCloud into a web application backend,
or any realtime, high-throughput system. If your workloads have been too
short (< 1s) to use our job system effectively, or you've found difficulty
creating a multi-stage pipeline with jobs, then you'll want to give queues
a try!
You'll need the latest cloud library v2.7.7 to use the feature.
Best Regards,
John
phileas = "Python/HTML integration: Larry's Elegant Alternative Solution"
Source and documentation can be accessed at:
http://larry.myerscough.nl/phileas_project/
N.B. This really is an alternative solution; the other available
approaches to this subject don't work for me... ... but may work for
you, so be sure also to check pypi.python.org to see the bigger picture!
Larry Myerscough, aka paphippo
________________________________________________________________________
ANNOUNCING
eGenix.com mxODBC Connect
Python ODBC Database Interface
Version 2.0.3
mxODBC Connect is our commercially supported client-server product for
connecting Python applications to relational databases
in a truly platform independent way.
This announcement is also available on our web-site for online reading:
http://www.egenix.com/company/news/eGenix-mxODBC-Connect-2.0.3-GA.html
________________________________________________________________________
INTRODUCTION
The mxODBC Connect Database Interface for Python allows users to
easily connect Python applications to all major databases on the
market today in a highly portable, convenient and secure way.
Python Database Connectivity the Easy Way
-----------------------------------------
Unlike our mxODBC Python extension, mxODBC Connect is designed as
client-server application, so you no longer need to find production
quality ODBC drivers for all the platforms you target with your Python
application.
Instead you use an easy to install royalty-free Python client library
which connects directly to the mxODBC Connect database server over the
network.
This makes mxODBC Connect a great basis for writing cross-platform
multi-tier database applications and utilities in Python, especially
if you run applications that need to communicate with databases such
as MS SQL Server and MS Access, Oracle Database, IBM DB2 and Informix,
Sybase ASE and Sybase Anywhere, MySQL, PostgreSQL, SAP MaxDB and many
more, that run on Windows or Linux machines.
Ideal for Database Driven Client Applications
---------------------------------------------
By removing the need to install and configure ODBC drivers on the
client side and dealing with complicated network setups for each set
of drivers, mxODBC Connect greatly simplifies deployment of database
driven client applications, while at the same time making the network
communication between client and database server more efficient and
more secure.
For more information, please have a look at the mxODBC Connect product
page, in particular, the full list of available features.
For more information, please see the product page:
http://www.egenix.com/products/python/mxODBCConnect/
________________________________________________________________________
NEWS
The 2.0.3 release of mxODBC Connect includes the following
enhancements and fixes:
Security Enhancements
---------------------
* Upgraded client and server to the most recent eGenix
pyOpenSSL 0.13.1.1.0.1.5.
Server Enhancements
-------------------
* Updated the server to use mxODBC 3.2.2 for database connectivity.
Client Enhancements
-------------------
* Added support for the new .cursortype attribute in mxODBC 3.2.2
which is available on both connections and cursors to adjust the
used ODBC cursor type.
Performance Enhancements
------------------------
* MS SQL Server performance can now be much enhanced, and increased
to levels beyond that of mxODBC Connect 2.0.2 and previous
releases, by adjusting the default cursor type to forward-only
cursors instead of static cursors:
# Connect to the remote database
from mx.ODBCConnect.Client import ServerSession
session = ServerSession(...)
ODBC = session.open()
connection = ODBC.DriverConnect(...)
# Use the faster forward-only cursors
connection.cursortype = ODBC.SQL.CURSOR_FORWARD_ONLY
# Cursors created on this connection will then default
# to forward only cursors
cursor = connection.cursor()
* The performance increase compared to mxODBC Connect 2.0.2 is
enormous: from 2-3x faster executes/fetches for average queries, up
to 300x faster for simple cases.
In mxODBC Connect 2.1, we will switch to using forward-only cursors
per default for all database backends.
* IBM DB2 can benefit from the same performance enhancements using
forward-only cursors.
The effect is a lot smaller compared to MS SQL Server, but still
noticeable: up to 2x faster executes/fetches with forward-only
cursors, compared to mxODBC Connect 2.0.2.
* Added documentation to explain the different cursor types,
compatibility with different database backends and effects on
performance to the mxODBC documentation.
New License Structure
---------------------
Due to popular demand, we have added an unlimited connections server
license, so now you can choose between:
* the entry-level 20-connections server license,
* an average use 50-connections server license.
* and the unlimited connections server license for our
high-end users.
For the full set of changes, please check the mxODBC Connect change
log.
http://www.egenix.com/products/python/mxODBCConnect/changelog.html
mxODBC Connect 2.0 Highlights
-----------------------------
mxODBC Connect 2.0 was released on 2012-08-20. These are the most
important highlights:
* mxODBC Connect Server now uses mxODBC 3.2 internally and makes its
API available in the mxODBC Connect Client. This is a major step
forward from the mxODBC 3.0 version used in mxODBC Connect Server
1.0.
* We've added native Windows x64 builds.
* mxODBC Connect Client now integrates directly with gevent, allowing
client applications to run asynchronous tasks while performing
remote database queries.
Please see the release announcement for full details:
http://www.egenix.com/company/news/eGenix-mxODBC-Connect-2.0.0-GA.html
________________________________________________________________________
UPGRADING
You are encouraged to upgrade to this latest mxODBC Connect release.
When upgrading, please always upgrade both the server and the client
installations to the same version - even for patch level releases.
Customers who have purchased mxODBC Connect 2.0 licenses can continue
to use their licenses with this patch level release.
Customers who have purchased mxODBC Connect 1.x licenses can request
20% discount coupons for upgrade purchases. Please contact the
eGenix.com Sales Team (sales(a)egenix.com) with your existing license
serials for details.
Users of our stand-alone mxODBC product will have to purchase new
licenses from our online shop in order to use mxODBC Connect.
You can request 30-day evaluation licenses by visiting our web-site
or writing to sales(a)egenix.com, stating your name (or the name of the
company) and the number of eval licenses that you need.
http://www.egenix.com/products/python/mxODBCConnect/#Evaluation
________________________________________________________________________
DOWNLOADS
The download archives as well as instructions for installation and
configuration of the product can be found on the product page:
http://www.egenix.com/products/python/mxODBCConnect/
If you want to try the package, jump straight to the download
instructions:
https://cms.egenix.com/products/python/mxODBCConnect/#Download
Fully functional evaluation licenses for the mxODBC Connect Server are
available free of charge:
http://www.egenix.com/products/python/mxODBCConnect/#Evaluation
mxODBC Connect Client is always free of charge.
_______________________________________________________________________
SUPPORT
Commercial support for this product is available from eGenix.com.
Please see
http://www.egenix.com/services/support/
for details about our support offerings.
_______________________________________________________________________
INFORMATION
About Python (http://www.python.org/):
Python is an object-oriented Open Source programming language
which runs on all modern platforms. By integrating ease-of-use,
clarity in coding, enterprise application connectivity and rapid
application design, Python establishes an ideal programming
platform for today's IT challenges.
About eGenix (http://www.egenix.com/):
eGenix is a software project, consulting and product company
focusing on expert project services and professional quality
products for companies, Python users and developers.
Enjoy,
--
Marc-Andre Lemburg
eGenix.com
Professional Python Services directly from the Source (#1, Apr 09 2013)
>>> Python Projects, Consulting and Support ... http://www.egenix.com/
>>> mxODBC.Zope/Plone.Database.Adapter ... http://zope.egenix.com/
>>> mxODBC, mxDateTime, mxTextTools ... http://python.egenix.com/
________________________________________________________________________
2013-04-02: Released mxODBC Zope DA 2.1.1 ... http://egenix.com/go41
2013-04-10: Python Meeting Duesseldorf ... tomorrow
::::: Try our mxODBC.Connect Python Database Interface for free ! ::::::
eGenix.com Software, Skills and Services GmbH Pastor-Loeh-Str.48
D-40764 Langenfeld, Germany. CEO Dipl.-Math. Marc-Andre Lemburg
Registered at Amtsgericht Duesseldorf: HRB 46611
http://www.egenix.com/company/contact/
We are pleased to announce the availability of SciPy 0.12.0. This release
has some cool new features (see highlights below) and a large amount of bug
fixes and maintenance work under the hood. The number of contributors also
keeps rising steadily - 75 people contributed patches to this release. We
hope to see this trend continue.
Some of the highlights of this release are:
- Completed QHull wrappers in scipy.spatial.
- cKDTree now a drop-in replacement for KDTree.
- A new global optimizer, basinhopping.
- Support for Python 2 and Python 3 from the same code base (no more
2to3).
This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or
greater. Support for Python 2.4 and 2.5 has been dropped as of this release.
Sources and binaries can be found at
http://sourceforge.net/projects/scipy/files/scipy/0.12.0/, release notes
are copied below.
Enjoy,
The SciPy developers
==========================
SciPy 0.12.0 Release Notes
==========================
SciPy 0.12.0 is the culmination of 7 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and
better documentation. There have been a number of deprecations and
API changes in this release, which are documented below. All users
are encouraged to upgrade to this release, as there are a large number
of bug-fixes and optimizations. Moreover, our development attention
will now shift to bug-fix releases on the 0.12.x branch, and on adding
new features on the master branch.
Some of the highlights of this release are:
- Completed QHull wrappers in scipy.spatial.
- cKDTree now a drop-in replacement for KDTree.
- A new global optimizer, basinhopping.
- Support for Python 2 and Python 3 from the same code base (no more
2to3).
This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater.
Support for Python 2.4 and 2.5 has been dropped as of this release.
New features
============
``scipy.spatial`` improvements
------------------------------
cKDTree feature-complete
^^^^^^^^^^^^^^^^^^^^^^^^
Cython version of KDTree, cKDTree, is now feature-complete. Most operations
(construction, query, query_ball_point, query_pairs, count_neighbors and
sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree
than
in KDTree. With very minor caveats, cKDTree has exactly the same interface
as
KDTree, and can be used as a drop-in replacement.
Voronoi diagrams and convex hulls
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
`scipy.spatial` now contains functionality for computing Voronoi
diagrams and convex hulls using the Qhull library. (Delaunay
triangulation was available since Scipy 0.9.0.)
Delaunay improvements
^^^^^^^^^^^^^^^^^^^^^
It's now possible to pass in custom Qhull options in Delaunay
triangulation. Coplanar points are now also recorded, if present.
Incremental construction of Delaunay triangulations is now also
possible.
Spectral estimators (``scipy.signal``)
--------------------------------------
The functions ``scipy.signal.periodogram`` and ``scipy.signal.welch`` were
added, providing DFT-based spectral estimators.
``scipy.optimize`` improvements
-------------------------------
Callback functions in L-BFGS-B and TNC
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A callback mechanism was added to L-BFGS-B and TNC minimization solvers.
Basin hopping global optimization (``scipy.optimize.basinhopping``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A new global optimization algorithm. Basinhopping is designed to
efficiently
find the global minimum of a smooth function.
``scipy.special`` improvements
------------------------------
Revised complex error functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The computation of special functions related to the error function now uses
a
new `Faddeeva library from MIT <http://ab-initio.mit.edu/Faddeeva>`__ which
increases their numerical precision. The scaled and imaginary error
functions
``erfcx`` and ``erfi`` were also added, and the Dawson integral ``dawsn``
can
now be evaluated for a complex argument.
Faster orthogonal polynomials
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Evaluation of orthogonal polynomials (the ``eval_*`` routines) in now
faster in ``scipy.special``, and their ``out=`` argument functions
properly.
``scipy.sparse.linalg`` features
--------------------------------
- In ``scipy.sparse.linalg.spsolve``, the ``b`` argument can now be either
a vector or a matrix.
- ``scipy.sparse.linalg.inv`` was added. This uses ``spsolve`` to compute
a sparse matrix inverse.
- ``scipy.sparse.linalg.expm`` was added. This computes the exponential of
a sparse matrix using a similar algorithm to the existing dense array
implementation in ``scipy.linalg.expm``.
Listing Matlab(R) file contents in ``scipy.io``
-----------------------------------------------
A new function ``whosmat`` is available in ``scipy.io`` for inspecting
contents
of MAT files without reading them to memory.
Documented BLAS and LAPACK low-level interfaces (``scipy.linalg``)
------------------------------------------------------------------
The modules `scipy.linalg.blas` and `scipy.linalg.lapack` can be used
to access low-level BLAS and LAPACK functions.
Polynomial interpolation improvements (``scipy.interpolate``)
-------------------------------------------------------------
The barycentric, Krogh, piecewise and pchip polynomial interpolators in
``scipy.interpolate`` accept now an ``axis`` argument.
Deprecated features
===================
`scipy.lib.lapack`
------------------
The module `scipy.lib.lapack` is deprecated. You can use
`scipy.linalg.lapack`
instead. The module `scipy.lib.blas` was deprecated earlier in Scipy 0.10.0.
`fblas` and `cblas`
-------------------
Accessing the modules `scipy.linalg.fblas`, `cblas`, `flapack`, `clapack` is
deprecated. Instead, use the modules `scipy.linalg.lapack` and
`scipy.linalg.blas`.
Backwards incompatible changes
==============================
Removal of ``scipy.io.save_as_module``
--------------------------------------
The function ``scipy.io.save_as_module`` was deprecated in Scipy 0.11.0,
and is
now removed.
Its private support modules ``scipy.io.dumbdbm_patched`` and
``scipy.io.dumb_shelve`` are also removed.
Other changes
=============
Authors
=======
* Anton Akhmerov +
* Alexander Eberspächer +
* Anne Archibald
* Jisk Attema +
* K.-Michael Aye +
* bemasc +
* Sebastian Berg +
* François Boulogne +
* Matthew Brett
* Lars Buitinck
* Steven Byrnes +
* Tim Cera +
* Christian +
* Keith Clawson +
* David Cournapeau
* Nathan Crock +
* endolith
* Bradley M. Froehle +
* Matthew R Goodman
* Christoph Gohlke
* Ralf Gommers
* Robert David Grant +
* Yaroslav Halchenko
* Charles Harris
* Jonathan Helmus
* Andreas Hilboll
* Hugo +
* Oleksandr Huziy
* Jeroen Demeyer +
* Johannes Schönberger +
* Steven G. Johnson +
* Chris Jordan-Squire
* Jonathan Taylor +
* Niklas Kroeger +
* Jerome Kieffer +
* kingson +
* Josh Lawrence
* Denis Laxalde
* Alex Leach +
* Tim Leslie
* Richard Lindsley +
* Lorenzo Luengo +
* Stephen McQuay +
* MinRK
* Sturla Molden +
* Eric Moore +
* mszep +
* Matt Newville +
* Vlad Niculae
* Travis Oliphant
* David Parker +
* Fabian Pedregosa
* Josef Perktold
* Zach Ploskey +
* Alex Reinhart +
* Gilles Rochefort +
* Ciro Duran Santillli +
* Jan Schlueter +
* Jonathan Scholz +
* Anthony Scopatz
* Skipper Seabold
* Fabrice Silva +
* Scott Sinclair
* Jacob Stevenson +
* Sturla Molden +
* Julian Taylor +
* thorstenkranz +
* John Travers +
* True Price +
* Nicky van Foreest
* Jacob Vanderplas
* Patrick Varilly
* Daniel Velkov +
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser
A total of 75 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1
On behalf of the Python development team, I am pleased to announce the
final releases of Python 3.2.4 and 3.3.1.
Python 3.2.4 is the final regular maintenance release for the Python 3.2
series, while Python 3.3.1 is the first maintenance release for the 3.3
series. Both releases include hundreds of bugfixes.
There has recently been a lot of discussion about XML-based denial of service
attacks. Specifically, certain XML files can cause XML parsers, including ones
in the Python stdlib, to consume gigabytes of RAM and swamp the CPU. These
releases do not include any changes in Python XML code to address these issues.
Interested parties should examine the defusedxml package on PyPI:
https://pypi.python.org/pypi/defusedxml
To download Python 3.2.4 or Python 3.3.1, visit:
http://www.python.org/download/releases/3.2.4/ or
http://www.python.org/download/releases/3.3.1/
respectively. As always, please report bugs to
http://bugs.python.org/
Enjoy!
- --
Georg Brandl, Release Manager
georg at python.org
(on behalf of the entire python-dev team and all contributors)
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I'm thrilled to announce the release of Python 2.7.4.
2.7.4 is the latest maintenance release in the Python 2.7 series. It includes
hundreds of bugfixes to the core language and standard library.
Downloads are at
http://python.org/download/releases/2.7.4/
As always, please report bugs to
http://bugs.python.org/
Several regressions found in the release candidate have been fixed. Many thanks
to those who tested the preview release.
There has recently been a lot of discussion about XML-based denial of service
attacks. Specifically, certain XML files can cause XML parsers, including ones
in the Python stdlib, to consume gigabytes of RAM and swamp the CPU. 2.7.4 does
not include any changes in Python XML code to address these issues. Interested
parties should examine the defusedxml package on PyPI:
https://pypi.python.org/pypi/defusedxml
This is a production release.
Best wishes,
Benjamin Peterson
2.7 Release Manager
(on behalf of all of Python 2.7's contributors)
Hi,
I'm pleased to announce sqlparse 0.1.7, a bug fix release.
sqlparse is a non-validating SQL parser module.
Download: https://pypi.python.org/packages/source/s/sqlparse/sqlparse-0.1.7.tar.gz
Bug Fixes
* Fix Python 3 compatibility of sqlformat script (by Piet Delport).
* Fix parsing of SQL statements that contain binary data (by Alexey
Malyshev).
* Fix a bug where keywords were identified as aliased identifiers in
invalid SQL statements.
* Fix parsing of identifier lists where identifiers are keywords too
(issue10).
Enhancements
* Top-level API functions now accept encoding keyword to parse
statements in certain encodings more reliable (issue20).
* Improve parsing speed when SQL contains CLOBs or BLOBs (issue86).
* Improve formatting of ORDER BY clauses (issue89).
* Formatter now tries to detect runaway indentations caused by
parsing errors or invalid SQL statements. When re-indenting such
statements the formatter flips back to column 0 before going crazy.
Other
* Documentation updates.
What is sqlparse?
=================
sqlparse is a non-validating SQL parser module for Python.
The module provides functions for splitting, formatting and parsing
SQL statements.
Please file bug reports and feature request on the issue tracker.
Project Page: https://github.com/andialbrecht/sqlparse
Documentation: http://readthedocs.org/docs/sqlparse/en/latest/
Discussions: http://groups.google.com/group/sqlparse
Issues/Bugs: https://github.com/andialbrecht/sqlparse/issues
Online Demo: http://sqlformat.org
Regards,
Andi