🌐 Mailman 3 January 2014 - Python-announce-list - python.org

ANN: EmPy 3.3.2 released (Python 3.x compatibility)
by Erik Max Francis Jan. 28, 2014

Jan. 28, 2014

Summary A powerful and robust templating system for Python. Overview EmPy is a system for embedding Python expressions and statements in template text; it takes an EmPy source file, processes it, and produces output. This is accomplished via expansions, which are special signals to the EmPy system and are set off by a special prefix (by default the at sign, '@'). EmPy can expand arbitrary Python expressions and statements in this way, as well as a variety of special forms. Textual data not explicitly delimited in this way is sent unaffected to the output, allowing Python to be used in effect as a markup language. Also supported are callbacks via hooks, recording and playback via diversions, and dynamic, chainable filters. The system is highly configurable via command line options and embedded commands. Expressions are embedded in text with the '@(...)' notation; variations include conditional expressions with '@(...?...!...)' and the ability to handle thrown exceptions with '@(...$...)'. As a shortcut, simple variables and expressions can be abbreviated as '@variable', '@object.attribute', '@function(arguments)', '@sequence' [index], and combinations. Full-fledged statements are embedded with '@{...}'. Control flow in terms of conditional or repeated expansion is available with '@[...]'. A '@' followed by a whitespace character (including a newline) expands to nothing, allowing string concatenations and line continuations. Comments are indicated with '@#' and consume the rest of the line, up to and including the trailing newline. '@%' indicate "significators," which are special forms of variable assignment intended to specify per-file identification information in a format which is easy to parse externally. Context name and line number changes can be done with '@?' and '@!' respectively. '@<...>' markups are customizeable by the user and can be used for any desired purpose. Escape sequences analogous to those in C can be specified with '@\...', and finally a '@@' sequence expands to a single literal at sign. Getting the software The current version of empy is 3.3.2. The latest version of the software is available in a tarball here: http://www.alcyone.com/software/empy/empy-latest.tar.gz. The official URL for this Web site is http://www.alcyone.com/software/empy/. Requirements EmPy should work with any version of Python from 2.4 onward, including 3.x. License This code is released under the LGPL. .... Release history [since 3.3] - 3.3.2; 2014 Jan 24. Additional fix for source compatibility between 2.x and 3.0. - 3.3.1; 2014 Jan 22. Source compatibility for 2.x and 3.0; 1.x and Jython compatibility dropped. -- Erik Max Francis && max(a)alcyone.com && http://www.alcyone.com/max/ San Jose, CA, USA && 37 18 N 121 57 W && AIM/Y!M/Jabber erikmaxfrancis Sitting in the den and / Looking at the phone as if it owed / Owed me a favor -- Blu Cantrell

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[RELEASED] Python 3.3.4 release candidate 1
by Georg Brandl Jan. 27, 2014

Jan. 27, 2014

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On behalf of the Python development team, I'm reasonably happy to announce the Python 3.3.4 release candidate 1. Python 3.3.4 includes several security fixes and over 120 bug fixes compared to the Python 3.3.3 release. This release fully supports OS X 10.9 Mavericks. In particular, this release fixes an issue that could cause previous versions of Python to crash when typing in interactive mode on OS X 10.9. Python 3.3 includes a range of improvements of the 3.x series, as well as easier porting between 2.x and 3.x. In total, almost 500 API items are new or improved in Python 3.3. For a more extensive list of changes in the 3.3 series, see http://docs.python.org/3.3/whatsnew/3.3.html and for the detailed changelog of 3.3.4, see http://docs.python.org/3.3/whatsnew/changelog.html To download Python 3.3.4 rc1 visit: http://www.python.org/download/releases/3.3.4/ This is a preview release, please report any bugs to http://bugs.python.org/ The final version is scheduled to be released in two weeks' time, on or about the 10th of February. Enjoy! - -- Georg Brandl, Release Manager georg at python.org (on behalf of the entire python-dev team and 3.3's contributors) -----BEGIN PGP SIGNATURE----- Version: GnuPG v2.0.22 (GNU/Linux) iEYEARECAAYFAlLmDI4ACgkQN9GcIYhpnLAr6wCePRbHF80k5goV4RUDBA5FfkwF rLUAnRg0RpL/b6apv+Dt2/sgnUd3hTPA =Z4Ss -----END PGP SIGNATURE-----

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[RELEASED] Python 3.4.0b3
by Larry Hastings Jan. 27, 2014

Jan. 27, 2014

On behalf of the Python development team, I'm quite pleased to announce the third beta release of Python 3.4. This is a preview release, and its use is not recommended for production settings. Python 3.4 includes a range of improvements of the 3.x series, including hundreds of small improvements and bug fixes. Major new features and changes in the 3.4 release series include: * PEP 428, a "pathlib" module providing object-oriented filesystem paths * PEP 435, a standardized "enum" module * PEP 436, a build enhancement that will help generate introspection information for builtins * PEP 442, improved semantics for object finalization * PEP 443, adding single-dispatch generic functions to the standard library * PEP 445, a new C API for implementing custom memory allocators * PEP 446, changing file descriptors to not be inherited by default in subprocesses * PEP 450, a new "statistics" module * PEP 451, standardizing module metadata for Python's module import system * PEP 453, a bundled installer for the *pip* package manager * PEP 454, a new "tracemalloc" module for tracing Python memory allocations * PEP 456, a new hash algorithm for Python strings and binary data * PEP 3154, a new and improved protocol for pickled objects * PEP 3156, a new "asyncio" module, a new framework for asynchronous I/O Python 3.4 is now in "feature freeze", meaning that no new features will be added. The final release is projected for mid-March 2014. To download Python 3.4.0b3 visit: http://www.python.org/download/releases/3.4.0/ Please consider trying Python 3.4.0b3 with your code and reporting any new issues you notice to: http://bugs.python.org/ Enjoy! -- Larry Hastings, Release Manager larry at hastings.org (on behalf of the entire python-dev team and 3.4's contributors)

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ANN: numexpr 2.3 released
by Francesc Alted Jan. 26, 2014

Jan. 26, 2014

========================== Announcing Numexpr 2.3 ========================== Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. What's new ========== The repository has been migrated to https://github.com/pydata/numexpr. All new tickets and PR should be directed there. Also, a `conj()` function for computing the conjugate of complex arrays has been added. Thanks to David Menéndez. See PR #125. Finallly, we fixed a DeprecationWarning derived of using ``oa_ndim == 0`` and ``op_axes == NULL`` when using `NpyIter_AdvancedNew()` and NumPy 1.8. Thanks to Mark Wiebe for advise on how to fix this properly. Many thanks to Christoph Gohlke and Ilan Schnell for his help during the testing of this release in all kinds of possible combinations of platforms and MKL. In case you want to know more in detail what has changed in this version, see: https://github.com/pydata/numexpr/wiki/Release-Notes or have a look at RELEASE_NOTES.txt in the tarball. Where I can find Numexpr? ========================= The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted

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ANN: python-blosc 1.2.0 released
by Francesc Alted Jan. 26, 2014

Jan. 26, 2014

============================= Announcing python-blosc 1.2.0 ============================= What is new? ============ This release adds support for the multiple compressors added in Blosc 1.3 series. The new compressors are: * lz4 (http://code.google.com/p/lz4/): A very fast compressor/decompressor. Could be thought as a replacement of the original BloscLZ, but it can behave better is some scenarios. * lz4hc (http://code.google.com/p/lz4/): This is a variation of LZ4 that achieves much better compression ratio at the cost of being much slower for compressing. Decompression speed is unaffected (and sometimes better than when using LZ4 itself!), so this is very good for read-only datasets. * snappy (http://code.google.com/p/snappy/): A very fast compressor/decompressor. Could be thought as a replacement of the original BloscLZ, but it can behave better is some scenarios. * zlib (http://www.zlib.net/): This is a classic. It achieves very good compression ratios, at the cost of speed. However, decompression speed is still pretty good, so it is a good candidate for read-only datasets. Selecting the compressor is just a matter of specifying the new `cname` parameter in compression functions. For example:: in = numpy.arange(N, dtype=numpy.int64) out = blosc.pack_array(in, cname="lz4") Just to have an overview of the differences between the different compressors in new Blosc, here it is the output of the included compress_ptr.py benchmark: https://github.com/ContinuumIO/python-blosc/blob/master/bench/compress_ptr.… that compresses/decompresses NumPy arrays with different data distributions:: Creating different NumPy arrays with 10**7 int64/float64 elements: *** np.copy() **** Time for memcpy(): 0.030 s *** the arange linear distribution *** *** blosclz *** Time for comp/decomp: 0.013/0.022 s. Compr ratio: 136.83 *** lz4 *** Time for comp/decomp: 0.009/0.031 s. Compr ratio: 137.19 *** lz4hc *** Time for comp/decomp: 0.103/0.021 s. Compr ratio: 165.12 *** snappy *** Time for comp/decomp: 0.012/0.045 s. Compr ratio: 20.38 *** zlib *** Time for comp/decomp: 0.243/0.056 s. Compr ratio: 407.60 *** the linspace linear distribution *** *** blosclz *** Time for comp/decomp: 0.031/0.036 s. Compr ratio: 14.27 *** lz4 *** Time for comp/decomp: 0.016/0.033 s. Compr ratio: 19.68 *** lz4hc *** Time for comp/decomp: 0.188/0.020 s. Compr ratio: 78.21 *** snappy *** Time for comp/decomp: 0.020/0.032 s. Compr ratio: 11.72 *** zlib *** Time for comp/decomp: 0.290/0.048 s. Compr ratio: 90.90 *** the random distribution *** *** blosclz *** Time for comp/decomp: 0.083/0.025 s. Compr ratio: 4.35 *** lz4 *** Time for comp/decomp: 0.022/0.034 s. Compr ratio: 4.65 *** lz4hc *** Time for comp/decomp: 1.803/0.039 s. Compr ratio: 5.61 *** snappy *** Time for comp/decomp: 0.028/0.023 s. Compr ratio: 4.48 *** zlib *** Time for comp/decomp: 3.146/0.073 s. Compr ratio: 6.17 That means that Blosc in combination with LZ4 can compress at speeds that can be up to 3x faster than a pure memcpy operation. Decompression is a bit slower (but still in the same order than memcpy()) probably because writing to memory is slower than reading. This was using an Intel Core i5-3380M CPU @ 2.90GHz, runnng Python 3.3 and Linux 3.7.10, but YMMV (and will vary!). For more info, you can have a look at the release notes in: https://github.com/ContinuumIO/python-blosc/wiki/Release-notes More docs and examples are available in the documentation site: http://blosc.pydata.org What is it? =========== python-blosc (http://blosc.pydata.org/) is a Python wrapper for the Blosc compression library. Blosc (http://blosc.org) is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Whether this is achieved or not depends of the data compressibility, the number of cores in the system, and other factors. See a series of benchmarks conducted for many different systems: http://blosc.org/trac/wiki/SyntheticBenchmarks. Blosc works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc. There is also a handy command line for Blosc called Bloscpack (https://github.com/esc/bloscpack) that allows you to compress large binary datafiles on-disk. Although the format for Bloscpack has not stabilized yet, it allows you to effectively use Blosc from your favorite shell. Installing ========== python-blosc is in PyPI repository, so installing it is easy: $ pip install -U blosc # yes, you should omit the python- prefix Download sources ================ The sources are managed through github services at: http://github.com/ContinuumIO/python-blosc Documentation ============= There is Sphinx-based documentation site at: http://blosc.pydata.org/ Mailing list ============ There is an official mailing list for Blosc at: blosc(a)googlegroups.com http://groups.google.es/group/blosc Licenses ======== Both Blosc and its Python wrapper are distributed using the MIT license. See: https://github.com/ContinuumIO/python-blosc/blob/master/LICENSES for more details. ---- **Enjoy data!** -- Francesc Alted Continuum Analytics, Inc.

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ANN: BLZ 0.6.1 has been released
by Francesc Alted Jan. 26, 2014

Jan. 26, 2014

Announcing BLZ 0.6 series ========================= What it is ---------- BLZ is a chunked container for numerical data. Chunking allows for efficient enlarging/shrinking of data container. In addition, it can also be compressed for reducing memory/disk needs. The compression process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. The main objects in BLZ are `barray` and `btable`. `barray` is meant for storing multidimensional homogeneous datasets efficiently. `barray` objects provide the foundations for building `btable` objects, where each column is made of a single `barray`. Facilities are provided for iterating, filtering and querying `btables` in an efficient way. You can find more info about `barray` and `btable` in the tutorial: http://blz.pydata.org/blz-manual/tutorial.html BLZ can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too) either from memory or from disk. In the future, it is planned to use Numba as the computational kernel and to provide better Blaze (http://blaze.pydata.org) integration. What's new ---------- BLZ has been branched off from the Blaze project (http://blaze.pydata.org). BLZ was meant as a persistent format and library for I/O in Blaze. BLZ in Blaze is based on previous carray 0.5 and this is why this new version is labeled 0.6. BLZ supports completely transparent storage on-disk in addition to memory. That means that *everything* that can be done with the in-memory container can be done using the disk as well. The advantages of a disk-based container is that the addressable space is much larger than just your available memory. Also, as BLZ is based on a chunked and compressed data layout based on the super-fast Blosc compression library, the data access speed is very good. The format chosen for the persistence layer is based on the 'bloscpack' library and described in the "Persistent format for BLZ" chapter of the user manual ('docs/source/persistence-format.rst'). More about Bloscpack here: https://github.com/esc/bloscpack You may want to know more about BLZ in this blog entry: http://continuum.io/blog/blz-format In this version, support for Blosc 1.3 has been added, that meaning that a new `cname` parameter has been added to the `bparams` class, so that you can select you preferred compressor from 'blosclz', 'lz4', 'lz4hc', 'snappy' and 'zlib'. Also, many bugs have been fixed, providing a much smoother experience. CAVEAT: The BLZ/bloscpack format is still evolving, so don't trust on forward compatibility of the format, at least until 1.0, where the internal format will be declared frozen. Resources --------- Visit the main BLZ site repository at: http://github.com/ContinuumIO/blz Read the online docs at: http://blz.pydata.org/blz-manual/index.html Home of Blosc compressor: http://www.blosc.org User's mail list: blaze-dev(a)continuum.io ---- Enjoy! Francesc Alted Continuum Analytics, Inc.

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[ANN] Python Dinner at FOSDEM 2014
by Stéphane Wirtel Jan. 22, 2014

Jan. 22, 2014

Dear Pythonista friends, write down in your agenda the date of the next AFPyro! The next Aperos Python Belgium will take place on saturday, February 1st in Brussels, during the FOSDEM conference that takes place at ULB from 1st to 2nd February. In addition to the regulars of AFPyro, we are also pleased to meet the attendees of FOSDEM using Python. As usual, you can come just for drinking and sharing one (or many) drinks or if you wish, you can also register for the meal that follows, in a near place, at “Chez Léon” for 20:30, rue des Bouchers 18, B-1000 Brussels. Meeting from 18:45, on first floor of Delirium cafe, Impasse De La Fidélité 4, 1000 Brussels. If you are a FOSDEM attendee, you can meet us at the Python stand, 1st floor of K Building, from 18h to go to Delirium cafe together. Please sign up in order to book the place: http://doodle.com/mn3yck6n3xxidsim Further information about the next AFPyros in Belgium: Aperos Python Belgium Here is the official Announce: http://afpyro.afpy.org/dates/2014/2014_02_01.html Regards, Stéphane Wirtel

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ANN: rom 0.25.0 - Redis object mapper for Python
by Josiah Carlson Jan. 21, 2014

Jan. 21, 2014

Hey everyone, Big change today: rom now supports fast prefix, suffix, and pattern match queries over your data. The method is based on the autocomplete process described in my book, Redis in Action The "rom" package is a Redis object mapper for Python. It sports an interface similar to Django's ORM, SQLAlchemy + Elixir, or Appengine's datastore. The changelog for recent releases can be seen below my signature. You can find the package at: https://www.github.com/josiahcarlson/rom https://pypi.python.org/pypi/rom And docs can be found at: http://pythonhosted.org/rom/ Please CC me on any replies if you have any questions or comments. Thank you, - Josiah #---------------------------------- 0.25.0 ----------------------------------- [changed] version numbers to account for bugfixes vs. feature updates. [added] columns can now be defined to allow for prefix and/or suffix queries. Enabling prefix queries also enables arbitrary pattern matching over your data. [fixed] in some cases, rom would allow the definition of multiple primary keys, of which only one would ever be used (inconsistently). This will now result in an error. [changed] defaulted to assume Lua is available on Redis, which has been released for over 15 months at this point. You can disable support via a call to rom._disable_lua_writes(). [added] the ability to cache and get the key that holds the result of a query, which can be used for pagination, etc. See: Query.cached_result() [warning] using rom versions of 0.23 with 0.25.0 when prefix and suffix indexes are enabled can result in improper results from prefix, suffix, and/or pattern queries, and can result in orphan data living in prefix or suffix indexes. Upgrade all of your clients! [changed] temporary keys for queries are now prefixed with the name of the model over which queries are being executed on. This should effect basically zero people, but can allow for query cleanup in the off chance of a failure during execution. #----------------------------- 0.23 (unreleased) ----------------------------- [changed] reduced number of round trips for single-filter queries by 1, thanks to https://github.com/MickeyKim for the report. #----------------------------------- 0.22 ------------------------------------ [fixed] size estimation for intersection ordering when filtering has now been fixed, thank you to https://github.com/MickeyKim for the report and the change (should improve performance). [fixed] an issue with some types when trying to update attributes has now been fixed, thank you to https://github.com/denisvolokh for the report. [changed] improved performance for simple numeric range queries of the form Model.get_by(attr=value) or Model.get_by(attr=(min, max)) by roughly a factor of 60x or better in some cases. Thank you to https://github.com/MickeyKim for the report on poor performance. #----------------------------------- 0.21 ------------------------------------ [fixed] upload for rom 0.20 was missing new columns.py, now fixed

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ANN: Yappi 0.82
by Sümer Cip Jan. 21, 2014

Jan. 21, 2014

Hi all, Yet Another Python Profiler v0.82 is out: Lots of bugfixes/improvements over the existing API. Features: - Profiler results can be saved in callgrind<http://valgrind.org/docs/manual/cl-format.html> and pstat <http://docs.python.org/3.4/library/profile.html#pstats.Stats> formats. (*new in 0.82*) - Profiler results can be merged from different sessions on-the-fly. (*new in 0.82*) - Profiler results can be easily converted to pstats. (*new in 0.82*) - Profiling of multithreaded Python applications transparently. - Supports profiling per-thread CPU time<http://en.wikipedia.org/wiki/CPU_time> (*new in 0.62*) - Profiler can be started from any thread at any time. - Ability to get statistics at any time without even stopping the profiler. - Various flags to arrange/sort profiler results. - Supports Python 2.6.x <= x <= Python 3.4 See: https://code.google.com/p/yappi/ -- Sümer Cip

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[ANN] pypiserver 1.1.5 - minimal private pypi server
by Ralf Schmitt Jan. 20, 2014

Jan. 20, 2014

Hi, I've just uploaded pypiserver 1.1.5 to the python package index. pypiserver is a minimal PyPI compatible server. It can be used to serve a set of packages and eggs to easy_install or pip. pypiserver is easy to install (i.e. just 'pip install pypiserver'). It doesn't have any external dependencies. https://pypi.python.org/pypi/pypiserver/ should contain enough information to easily get you started running your own PyPI server in a few minutes. The code is available on github: https://github.com/schmir/pypiserver I didn't announce the previous version, so here's the changelog for the last two versions: 1.1.5 (2014-01-20) ------------------ - only list devpi-server and proxypypi as alternatives - fix wheel file handling for certain wheels - serve wheel files as application/octet-stream - make pypiserver executable from wheel file - build universal wheel - remove scripts subdirectory - add --index-url cli parameter 1.1.4 (2014-01-03) ------------------ - make pypiserver compatible with pip 1.5 (https://github.com/schmir/pypiserver/pull/42) -- Cheers Ralf

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