Python-announce-list August 2018

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21 participants
26 discussions

[ANN] - Skyline v1.2.5-stable-luminosity
by Gary Wilson 20 Aug '18

20 Aug '18

For those interested in anomaly detection and deflection in streamed time series data. I would like to announce the new release of Skyline v1.2.5 - https://github.com/earthgecko/skyline/releases/tag/v1.2.5-stable-luminosity Project page -> https://github.com/earthgecko/skyline Documentation -> https://earthgecko-skyline.readthedocs.io/en/latest/index.html With the hope Skyline can make the universe a bit less anomalous. Regards Gary

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SatPy 0.9.1 Released
by David Hoese 19 Aug '18

19 Aug '18

On behalf of the PyTroll community I am please to announce the release of SatPy 0.9.1. This release includes many bug fixes collected over the last month since the 0.9.0 release. SatPy is a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats. SatPy comes with the ability to make various RGB composites directly from satellite instrument channel data or higher level processing output. The pyresample package (http://pyresample.readthedocs.io/en/latest/) is used to resample data to different uniform areas or grids. Various atmospheric corrections and visual enhancements are also provided, either directly in SatPy or from those in the PySpectral (https://pyspectral.readthedocs.io/en/develop/) and TrollImage (https://trollimage.readthedocs.io/en/latest/) packages. SatPy uses the xarray and dask libraries for processing data over multiple threads; allowing computations to complete in minutes on user workstations. The PyTroll community is a group of researchers, scientists, and programmers from around the world who work together to build tools for processing data from remote sensing satellites and other meteorological data sources. PyPI: https://pypi.org/project/satpy/ GitHub: https://github.com/pytroll/satpy Documentation: http://satpy.readthedocs.io/en/latest/ Examples: http://satpy.readthedocs.io/en/latest/examples.html Change log: https://github.com/pytroll/satpy/blob/master/CHANGELOG.md

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ANN: NumExpr 2.6.8
by Robert McLeod 19 Aug '18

19 Aug '18

========================== Announcing Numexpr 2.6.8 ========================== Hi everyone, Our attempt to fix the memory leak in 2.6.7 had an unforseen consequence that the `f_locals` from the top-most frame is actually `f_globals`, and clearing it to fix the extra reference count deletes all global variables. Needless to say this is undesired behavior. A check has been added to prevent clearing the globals dict, tested against both `python` and `ipython`. As such, we recommend skipping 2.6.7 and upgrading straight to 2.6.8 from 2.6.6. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.7 to 2.6.8 --------------------------- - Add check to make sure that `f_locals` is not actually `f_globals` when we do the `f_locals` clear to avoid the #310 memory leak issue. - Compare NumPy versions using `distutils.version.LooseVersion` to avoid issue #312 when working with NumPy development versions. - As part of `multibuild`, wheels for Python 3.7 for Linux and MacOSX are now available on PyPI. What's Numexpr? --------------- 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 has 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. 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 Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod, Ph.D. robbmcleod(a)gmail.com robbmcleod(a)protonmail.com robert.mcleod(a)hitachi-hhtc.ca www.entropyreduction.al

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[ANN] iPOPO 0.8.0
by Thomas Calmant 19 Aug '18

19 Aug '18

Hello, I'm happy to announce that iPOPO v0.8.0 has just been released! What is iPOPO ============= iPOPO is a Service-Oriented Component Model (SOCM) based on Pelix, a dynamic service platform. Both are inspired on two popular Java technologies for the development of long-lived applications: the iPOJO component model and the OSGi Service Platform. iPOPO enables to conceive long-running and modular IT services. It is based on the concepts specified by OSGi: - Bundle: a Python module imported using Pelix and associated to a context. A bundle has a life-cycle (install, start, updated, stop, uninstall) - Service: a Python object registered in a service registry, associated to a specification and to properties. - Component: the instance of a class described/manipulated by iPOPO decorators Components are bound together by the specification(s) of the service(s) they provide. The required services are injected into components by iPOPO. For more information about those concepts, see https://ipopo.readthedocs.io/en/latest/refcards/index.html#refcards iPOPO provides many services out-of-the-box, like an HTTP server, local and remote shell, remote services... iPOPO is released under the terms of Apache Software License 2.0 What's new in 0.8.0 =================== This version mainly adds the implementation of the Remote Service Admin specification, contributed by Scott Lewis (thanks! :D ) This feature is young and might still contain some bugs, all feedback is welcome on the mailing list and as GitHub issues. A reference card and two tutorials have been added to the documentation to introduce this feature. It should be preferred to the Pelix Remote Service as it follows the OSGi specification. Note that the Pelix Remote Service will continue to be maintained for compatibility reasons. Version has leaped to 0.8.x as the addition of the RSA feature is huge and might change the usage of iPOPO in some projects based on remote services. You can take a look at the documentation at https://ipopo.readthedocs.io/ iPOPO is available on PyPI: https://pypi.python.org/pypi/iPOPO Source is available on GitHub: https://github.com/tcalmant/ipopo Feel free to send feedback on your experience of Pelix/iPOPO, via the mailing lists: User list : http://groups.google.com/group/ipopo-users Development list : http://groups.google.com/group/ipopo-dev Have fun!

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pytest 3.7.2 released
by Bruno Oliveira 18 Aug '18

18 Aug '18

pytest 3.7.2 has just been released to PyPI. This is a bug-fix release, being a drop-in replacement. To upgrade:: pip install --upgrade pytest The full changelog is available at http://doc.pytest.org/en/latest/changelog.html. Thanks to all who contributed to this release, among them: * Anthony Sottile * Bruno Oliveira * Daniel Hahler * Josh Holland * Ronny Pfannschmidt * Sankt Petersbug * Wes Thomas * turturica Happy testing, The pytest Development Team

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skipole 1.0.0 released
by Bernard Czenkusz 16 Aug '18

16 Aug '18

SKIPOLE is an application which creates a web service that you can tailor with your own Python functions. It can be used to create a web service for any application but was particularly designed with the Raspberry Pi in mind. It gives you the capability to create a web front end for your wierdest applications, be they robots, sensors or whatever you are using your Pi for. More specifically; skipole.py is a script with associated files, which, when run, can create a project resulting in a tar file containing a WSGI application. This WSGI application can then be served by any WSGI compatible web server. Project web site: http://www.skipole.ski

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skipole 1.0.0 released
by Bernard Czenkusz 15 Aug '18

15 Aug '18

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ANN: psutil 5.4.7 released
by Giampaolo Rodola' 14 Aug '18

14 Aug '18

Hello all, I'm glad to announce the release of psutil 5.4.7: 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, macOS, Sun Solaris, FreeBSD, OpenBSD, NetBSD and AIX, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.6. PyPy is also known to work. What's new ========== 2018-08-14 **Enhancements** - #1286: [macOS] psutil.OSX constant is now deprecated in favor of new psutil.MACOS. - #1309: [Linux] added psutil.STATUS_PARKED constant for Process.status(). - #1321: [Linux] add disk_io_counters() dual implementation relying on /sys/block filesystem in case /proc/diskstats is not available. (patch by Lawrence Ye) **Bug fixes** - #1209: [macOS] Process.memory_maps() may fail with EINVAL due to poor task_for_pid() syscall. AccessDenied is now raised instead. - #1278: [macOS] Process.threads() incorrectly return microseconds instead of seconds. (patch by Nikhil Marathe) - #1279: [Linux, macOS, BSD] net_if_stats() may return ENODEV. - #1294: [Windows] psutil.Process().connections() may sometime fail with MemoryError. (patch by sylvainduchesne) - #1305: [Linux] disk_io_stats() may report inflated r/w bytes values. - #1309: [Linux] Process.status() is unable to recognize "idle" and "parked" statuses (returns '?'). - #1313: [Linux] disk_io_counters() can report inflated IO counters due to erroneously counting base disk device and its partition(s) twice. - #1323: [Linux] sensors_temperatures() may fail with ValueError. Links ===== - Home page: https://github.com/giampaolo/psutil - Download: https://pypi.org/project/psutil/#files - Documentation: http://psutil.readthedocs.io - What's new: https://github.com/giampaolo/psutil/blob/master/HISTORY.rst -- Giampaolo - http://grodola.blogspot.com

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PyCA cryptography 2.3.1 released
by Paul Kehrer 14 Aug '18

14 Aug '18

PyCA cryptography 2.3.1 has been released to PyPI. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions. We support Python 2.7, Python 3.4+, and PyPy. Changelog (https://cryptography.io/en/latest/changelog/#v2-3-1): * Updated Windows, macOS, and manylinux1 wheels to be compiled with OpenSSL 1.1.0i. -Paul Kehrer (reaperhulk)

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ANN: Numexpr 2.6.7
by Robert McLeod 12 Aug '18

12 Aug '18

========================== Announcing Numexpr 2.6.7 ========================== Hi everyone, This is a bug-fix release. Thanks to Lehman Garrison for a fix that could result in memory leak-like behavior. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.6 to 2.6.7 --------------------------- - Thanks to Lehman Garrison for finding and fixing a bug that exhibited memory leak-like behavior. The use in `numexpr.evaluate` of `sys._getframe` combined with `.f_locals` from that frame object results an extra refcount on objects in the frame that calls `numexpr.evaluate`, and not `evaluate`'s frame. So if the calling frame remains in scope for a long time (such as a procedural script where `numexpr` is called from the base frame) garbage collection would never occur. - Imports for the `numexpr.test` submodule were made lazy in the `numexpr` module. What's Numexpr? --------------- 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 has 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. 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 Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod, Ph.D. robbmcleod(a)gmail.com robbmcleod(a)protonmail.com robert.mcleod(a)hitachi-hhtc.ca www.entropyreduction.al

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Python-announce-list August 2018