Mailman 3 February 2017 - Python-announce-list

bcolz, a column store for Python, 1.1.2 released
by Francesc Alted Feb. 10, 2017

Feb. 10, 2017

====================== Announcing bcolz 1.1.2 ====================== What's new ========== This is a maintenance release that brings quite a lot of improvements. Here are the highlights: - Zstd is a supported codec now. Fixes #331. - C-Blosc updated to 1.11.2. - Added a new `defaults_ctx` context so that users can select defaults easily without changing global behaviour. For example:: with bcolz.defaults_ctx(vm="python", cparams=bcolz.cparams(clevel=0)): cout = bcolz.eval("(x + 1) < 0") - Fixed a crash occurring in `ctable.todataframe()` when both `columns` and `orient='columns'` were specified. PR #311. Thanks to Peter Quackenbush. - Use `pkg_resources.parse_version()` to test for version of packages. Fixes #322 (PY27 bcolz with dask unicode error). - New package recipe for conda-forge. Now you can install bcolz with: `conda install -c conda-forge bcolz`. Thanks to Alistair Miles. For a more detailed change log, see: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst For some comparison between bcolz and other compressed data containers, see: https://github.com/FrancescAlted/DataContainersTutorials specially chapters 3 (in-memory containers) and 4 (on-disk containers). What it is ========== *bcolz* provides **columnar and compressed** data containers that can live either on-disk or in-memory. The compression is carried out transparently by Blosc, an ultra fast meta-compressor that is optimized for binary data. Compression is active by default. Column storage allows for efficiently querying tables with a large number of columns. It also allows for cheap addition and removal of columns. Lastly, high-performance iterators (like ``iter()``, ``where()``) for querying the objects are provided. bcolz can use diffent backends internally (currently numexpr, Python/NumPy or dask) so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). Moreover, since the carray/ctable containers can be disk-based, it is possible to use them for seamlessly performing out-of-memory computations. While NumPy is used as the standard way to feed and retrieve data from bcolz internal containers, but it also comes with support for high-performance import/export facilities to/from `HDF5/PyTables tables <http://www.pytables.org>`_ and `pandas dataframes <http://pandas.pydata.org>`_. Have a look at how bcolz and the Blosc compressor, are making a better use of the memory without an important overhead, at least for some real scenarios: http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/queryi… bcolz has minimal dependencies (NumPy is the only strict requisite), comes with an exhaustive test suite, and it is meant to be used in production. Example users of bcolz are Visualfabriq (http://www.visualfabriq.com/), Quantopian (https://www.quantopian.com/) and scikit-allel: * Visualfabriq: * *bquery*, A query and aggregation framework for Bcolz: * https://github.com/visualfabriq/bquery * Quantopian: * Using compressed data containers for faster backtesting at scale: * https://quantopian.github.io/talks/NeedForSpeed/slides.html * scikit-allel: * Exploratory analysis of large scale genetic variation data. * https://github.com/cggh/scikit-allel Resources ========= Visit the main bcolz site repository at: http://github.com/Blosc/bcolz Manual: http://bcolz.blosc.org Home of Blosc compressor: http://blosc.org User's mail list: bcolz(a)googlegroups.com http://groups.google.com/group/bcolz License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt Release notes can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst ---- **Enjoy data!** -- Francesc Alted

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ANN: python-blosc 1.5.0 released
by Francesc Alted Feb. 8, 2017

Feb. 8, 2017

============================= Announcing python-blosc 1.5.0 ============================= What is new? ============ A new `blosc.set_releasegil()` function that allows to release/acquire the GIL at will. Thanks to Robert McLeod. Also, C-Blosc has been updated to 1.11.2. For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc/blob/master/RELEASE_NOTES.rst More docs and examples are available in the documentation site: http://python-blosc.blosc.org What is it? =========== Blosc (http://www.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. 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. python-blosc (http://python-blosc.blosc.org/) is the Python wrapper for the Blosc compression library, with added functions (`compress_ptr()` and `pack_array()`) for efficiently compressing NumPy arrays, minimizing the number of memory copies during the process. python-blosc can be used to compress in-memory data buffers for transmission to other machines, persistence or just as a compressed cache. There is also a handy tool built on top of python-blosc called Bloscpack (https://github.com/Blosc/bloscpack). It features a commmand line interface that allows you to compress large binary datafiles on-disk. It also comes with a Python API that has built-in support for serializing and deserializing Numpy arrays both on-disk and in-memory at speeds that are competitive with regular Pickle/cPickle machinery. Sources repository ================== The sources and documentation are managed through github services at: http://github.com/Blosc/python-blosc ---- **Enjoy data!** -- Francesc Alted

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ANN: psutil 5.1.3 released
by Giampaolo Rodola' Feb. 7, 2017

Feb. 7, 2017

Hello all, I'm glad to announce the release of psutil 5.1.3: https://github.com/giampaolo/psutil About ===== psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap. It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version). PyPy is also known to work. What's new ========== **Bug fixes** - #971: [Linux] sensors_temperatures() didn't work on CentOS 7. - #973: cpu_percent() may raise ZeroDivisionError. Links ===== - Home page: https://github.com/giampaolo/psutil - Download: https://pypi.python.org/pypi/psutil - Documentation: http://pythonhosted.org/psutil - What's new: https://github.com/giampaolo/psutil/blob/master/HISTORY.rst -- Giampaolo - http://grodola.blogspot.com

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pyasn1 0.2.1 has been released
by Ilya Etingof Feb. 5, 2017

Feb. 5, 2017

What’s new ========= * ASN.1 classes improved to better duck-type their Python friends * A new codec for transforming Python built-in types into ASN.1 objects and back implemented * BitString type usability improved in many ways * Sphinx documentation added [1] More changes noted in the CHANGELOG [2]. What is pyasn1 ============ The pyasn1 library implements ASN.1 data types and serialization protocols in pure Python. It aims at standards compliancy and universal applicability. More information on library features, use and further development directions can be found on project’s GitHub page. [3] 1. http://pyasn1.sf.net 2. http://pyasn1.sf.net/changelog.html 3. https://github.com/etingof/pyasn1

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Wing Python ID version 6.0.2 released
by Wingware Feb. 3, 2017

Feb. 3, 2017

Hi, We've just released Wing version 6.0.2 which expands and improves support for remote development, adds a drop down of found Python installations, introduces symbol name style refactoring operations, improves multi-selection with the mouse, fixes debugging Jupyter notebooks, and makes many other minor improvements. For details, see the change log at http://wingware.com/pub/wingide/6.0.2/CHANGELOG.txt Wing 6 is the latest major release in Wingware's family of Python IDEs, including Wing Pro, Wing Personal, and Wing 101. Wing 6 adds many new features, introduces a new annual license option, and makes some changes to the product line. New Features * Improved Multiple Selections: Quickly add selections and edit them all at once * Easy Remote Development: Work seamlessly on remote Linux, OS X, and Raspberry Pi systems * Debugging in the Python Shell: Reach breakpoints and exceptions in (and from) the Python Shell * Recursive Debugging: Debug code invoked in the context of stack frames that are already being debugged * PEP 484 and PEP 526 Type Hinting: Inform Wing's static analysis engine of types it cannot infer * Support for Python 3.6 and Stackless 3.4: Use async and other new language features * Optimized debugger: Run faster, particularly in multi-process and multi-threaded code * Support for OS X full screen mode: Zoom to a virtual screen, with auto-hiding menu bar * Added a new One Dark color palette: Enjoy the best dark display style yet * Updated French and German localizations: Thanks to Jean Sanchez, Laurent Fasnacht, and Christoph Heitkamp For a much more detailed overview of new features see the release notice at http://wingware.com/news/2017-02-02 Annual Use License Option Wing 6 adds the option of purchasing a lower-cost expiring annual license for Wing Pro. An annual license includes access to all available Wing Pro versions while it is valid, and then ceases to function if it is now renewed. Pricing for annual licenses is US$ 179/user for Commercial Use and US$ 69/user for Non-Commercial Use. Perpetual licenses for Wing Pro will continue to be available at the same pricing. The cost of extending Support+Upgrades subscriptions on Non-Commercial Use perpetual licenses for Wing Pro has also been dropped from US$ 89 to US$ 39 per user. For details, see https://wingware.com/store/purchase Wing Personal is Free Wing Personal is now free and no longer requires a license to run. It now also includes the Source Browser, PyLint, and OS Commands tools, and supports the scripting API and Perspectives. However, Wing Personal does not include Wing Pro's advanced editing, debugging, testing and code management features, such as remote host access, refactoring, find uses, version control, unit testing, interactive debug probe, multi-process and child process debugging, move program counter, conditional breakpoints, debug watch, framework-specific support (for matplotlib, Django, and others), find symbol in project, and other features. Links Release notice: http://wingware.com/news/2017-02-02 Free trial: http://wingware.com/wingide/trial Downloads: http://wingware.com/downloads Feature list: http://wingware.com/wingide/features Buy: http://wingware.com/store/purchase Upgrade: https://wingware.com/store/upgrade Questions? Don't hesitate to email us at support(a)wingware.com. Thanks, -- Stephan Deibel Wingware | Python IDE The Intelligent Development Environment for Python Programmers wingware.com

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ANN: psutil 5.1.2 released
by Giampaolo Rodola' Feb. 3, 2017

Feb. 3, 2017

Hello all, I'm glad to announce the release of psutil 5.1.2: https://github.com/giampaolo/psutil About ===== psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap. It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version). PyPy is also known to work. What's new ========== *2017-02-03* **Bug fixes** - #966: [Linux] sensors_battery().power_plugged may erroneously return None on Python 3. - #968: [Linux] disk_io_counters() raises TypeError on python 3. - #970: [Linux] sensors_battery()'s name and label fields on Python 3 are bytes instead of str. Links ===== - Home page: https://github.com/giampaolo/psutil - Download: https://pypi.python.org/pypi/psutil - Documentation: http://pythonhosted.org/psutil - What's new: https://github.com/giampaolo/psutil/blob/master/HISTORY.rst -- Giampaolo - http://grodola.blogspot.com

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ANN: psutil 5.1.1 released
by Giampaolo Rodola' Feb. 3, 2017

Feb. 3, 2017

~/svn/psutil {master}$ make print-announce Hello all, I'm glad to announce the release of psutil 5.1.1: https://github.com/giampaolo/psutil About ===== psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap. It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version). PyPy is also known to work. What's new ========== *2017-02-03* **Enhancements** - #966: [Linux] sensors_battery().percent is a float and is more precise. **Bug fixes** - #964: [Windows] Process.username() and psutil.users() may return badly decoding character on Python 3. - #965: [Linux] disk_io_counters() may miscalculate sector size and report the wrong number of bytes read and written. - #966: [Linux] sensors_battery() may fail with "no such file error". - #966: [Linux] sensors_battery().power_plugged may lie. Links ===== - Home page: https://github.com/giampaolo/psutil - Download: https://pypi.python.org/pypi/psutil - Documentation: http://pythonhosted.org/psutil - What's new: https://github.com/giampaolo/psutil/blob/master/HISTORY.rst -- Giampaolo - http://grodola.blogspot.com

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ANN: psutil 5.1.0 with hardware sensors released
by Giampaolo Rodola' Feb. 1, 2017

Feb. 1, 2017

Hello all, I'm glad to announce the release of psutil 5.1.1: https://github.com/giampaolo/psutil A detailed blog post about this release is available here: http://grodola.blogspot.com/2017/02/psutil-510-temperatures-batteries-and.h… About ===== psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap. It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version). PyPy is also known to work. What's new ========== *2017-02-01* **Enhancements** - #357: added psutil.Process.cpu_num() (what CPU a process is on). - #371: added psutil.sensors_temperatures() (Linux only). - #941: added psutil.cpu_freq() (CPU frequency). - #955: added psutil.sensors_battery() (Linux, Windows, only). - #956: cpu_affinity([]) can now be used as an alias to set affinity against all eligible CPUs. **Bug fixes** - #687: [Linux] pid_exists() no longer returns True if passed a process thread ID. - #948: cannot install psutil with PYTHONOPTIMIZE=2. - #950: [Windows] Process.cpu_percent() was calculated incorrectly and showed higher number than real usage. - #951: [Windows] the uploaded wheels for Python 3.6 64 bit didn't work. - #959: psutil exception objects could not be pickled. - #960: Popen.wait() did not return the correct negative exit status if process is ``kill()``ed by a signal. - #961: [Windows] WindowsService.description() may fail with ERROR_MUI_FILE_NOT_FOUND. Links ===== - Home page: https://github.com/giampaolo/psutil - Download: https://pypi.python.org/pypi/psutil - Documentation: http://pythonhosted.org/psutil - What's new: https://github.com/giampaolo/psutil/blob/master/HISTORY.rst -- Giampaolo - http://grodola.blogspot.com

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Announcing django-compat-patcher 0.2
by Pascal Chambon Feb. 1, 2017

Feb. 1, 2017

*** django-compat-patcher 0.2 has been released. *** DCP is a “magic” package which adds backwards/forwards compatibility patches to Django, so that your app ecosystem doesn’t get broken by trivial changes made to the core of the framework. It injects compatibility shims like function/attribute aliases, restores data structures which were replaced by stdlib ones, extends the behaviour of callables (eg. referring to a view by object, by name, or by dotted path), and can even preserve deprecated module as “import aliases” (ex. keep importing from “django.contrib.comments” instead of the now external “django_comments”). This allows to you upgrade your dependencies one at a time, to fork/patch them when you have a proper opportunity, and most importantly to not get stuck, when deadlines are tight and your dependencies suddenly have conflicting requirements. DCP somehow relaxes the deprecation policy of Django, so that it comes closer to semantic versioning. Technically, DCP manages a set of “fixers”, small utilities which advertise the change that they make, the versions of Django that they support, and which on monkey-patch the Django framework on demand. By applying these fixers in a proper order (sometimes before, sometimes after django.setup()), DCP can workaround multiple breaking changes which target the same part of the code (eg. a tag library being added and then removed). Beware, DCP is aimed at project maintainers. If you are developing a reusable Django application, you can’t expect all your users to integrate DCP as well. In this case, to support a wide range of Django versions, you should rather use a toolkit like Django-compat <https://github.com/arteria/django-compat>. You may think of DCP as a “runtime 2to3 for Django’, wherease Django-Compat is rather a “six module for Django”. Feel free to contribute new fixers, for backwards or forwards compatibility, depending on the compatibility troubles you encounter on your projects https://pypi.python.org/pypi/django-compat-patcher https://github.com/pakal/django-compat-patcher

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