Python-announce-list October 2018

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16 participants
22 discussions

[baseline 0.2.1] Easy String Baseline
by Dan Gass 29 Mar '20

29 Mar '20

I am please to announce the availability of the "baseline" package. This tool streamlines creation and maintenance of tests which compare string output against a baseline. It offers a mechanism to compare a string against a baselined copy and update the baselined copy to match the new value when a mismatch occurs. The update process includes a manual step to facilitate a review of the change before acceptance. The tool uses multi-line string format for string baselines to improve readability for human review. Docs: https://baseline.readthedocs.io/en/latest/ PyPi: https://pypi.org/project/baseline/ Repo: https://github.com/dmgass/baseline License: MIT With Regards, Dan Gass (dan.gass at gmail) *********** Quick Start *********** Create an empty baseline with a triple quoted multi-line string. Place the ending triple quote on a separate line and indent it to the level you wish the string baseline update to be indented to. Add a compare of the string being tested to the baseline string. Then save the file as ``fox.py``: .. code-block:: python from baseline import Baseline expected = Baseline(""" """) test_string = "THE QUICK BROWN FOX\n JUMPS\nOVER THE LAZY DOG." assert test_string == expected Run ``fox.py`` and observe that the ``assert`` raises an exception since the strings are not equal. Because the comparison failed, the tool located the triple quoted baseline string in the source file and updated it with the miscompared value. When the interpretter exited, the tool saved the updated source file but changed the file name to ``fox.update.py``: .. code-block:: python from baseline import Baseline expected = Baseline(""" THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG. """) test_string = "THE QUICK BROWN FOX\n JUMPS\nOVER THE LAZY DOG." assert test_string == expected After reviewing the change with your favorite file differencing tool, accept the change by either manually overwriting the original file or use the ``baseline`` command line tool to scan the directory for updated scripts and accept them: .. code-block:: shell $ python -m baseline * Found updates for: fox.py Hit [ENTER] to update, [Ctrl-C] to cancel fox.update.py -> fox.py Run ``fox.py`` again and observe the ``assert`` does not raise an exception nor is a source file update generated. If in the future the test value changes, the ``assert`` will raise an exception and cause a new source file update to be generated. Simply repeat the review and acceptance step and you are back in business! <P><A HREF="https://baseline.readthedocs.io/en/latest/"> baseline 0.2.1</A> - Easy String Baseline (07-Jun-18)

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The Real-Time Use of Python in Data Science World!
by Anita Goyal 06 Mar '20

06 Mar '20

This course will help you to expertise the usage of Python in Data Science world. Carter your Python Knowledge so that it can be utilized to get the Insights of Data using Methodologies and Techniques of Data Science... Objective: Understand the concepts of Data science and Python You will be able to use Python in Discovering Data. You will have an idea of Statistical and Analytical methods to deal with huge data sets. You will gain an expertise on Regular Expressions, looping functions and concepts of Object Oriented Programming. You will be able to create business algorithms and data models using Python and it's techniques. Work on Real-life Projects will help you to get a practical experience of real scenarios of IT Industry. Start learning Python for Data Science from basics to advance levels here... https://goo.gl/070wXw

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[ANN] Bloscpack v0.15.0
by Valentin Haenel 31 Oct '18

31 Oct '18

============================ Announcing Bloscpack v0.15.0 ============================ What is new? ============ Two new high-level API functions have been added: * 'pack_bytes_to_bytes' * 'unpack_bytes_from_bytes' As you might expect from the naming, these allow you to perform fully in-memory based compression and decompression via the bytes datatype. Additionally there are a few bugfixes, support for python-blosc 1.6.1 and support for Python 3.7. For more info, have a look at the changelog: https://github.com/Blosc/bloscpack#changelog Documentation and examples are available at: https://github.com/Blosc/bloscpack What is it? =========== Bloscpack is a command-line interface and serialization format for Blosc. Blosc (http://www.blosc.org) is an extremely fast meta-codec designed for high compression speeds. Bloscpack allows you to use Blosc from the command-line to compress and decompress files. Additionally, Bloscpack has a Python-API that allows you to compress and serialize data to a file system. Additionally, Bloscpack supports efficient serialization and de-serialization of Numpy arrays and might in fact be one of the fastest ways to save arrays to disk. Bloscpack uses the Python bindings for Blosc (http://python-blosc.blosc.org/) under the hood. ---- **Enjoy data!**

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ANN: psutil 5.4.8 released
by Giampaolo Rodola' 30 Oct '18

30 Oct '18

Hello all, I'm glad to announce the release of psutil 5.4.8: 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 ========== 2018-10-30 **Enhancements** - #1197: [Linux] cpu_freq() is now implemented by parsing /proc/cpuinfo in case /sys/devices/system/cpu/* filesystem is not available. - #1310: [Linux] psutil.sensors_temperatures() now parses /sys/class/thermal in case /sys/class/hwmon fs is not available (e.g. Raspberry Pi). (patch by Alex Manuskin) - #1320: [Posix] better compilation support when using g++ instead of gcc. (patch by Jaime Fullaondo) **Bug fixes** - #715: do not print exception on import time in case cpu_times() fails. - #1004: [Linux] Process.io_counters() may raise ValueError. - #1277: [OSX] available and used memory (psutil.virtual_memory()) metrics are not accurate. - #1294: [Windows] psutil.Process().connections() may sometimes fail with intermittent 0xC0000001. (patch by Sylvain Duchesne) - #1307: [Linux] disk_partitions() does not honour PROCFS_PATH. - #1320: [AIX] system CPU times (psutil.cpu_times()) were being reported with ticks unit as opposed to seconds. (patch by Jaime Fullaondo) - #1332: [OSX] psutil debug messages are erroneously printed all the time. (patch by Ilya Yanok) - #1346: [SunOS] net_connections() returns an empty list. (patch by Oleksii Shevchuk) 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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pytest 3.9.3
by Bruno Oliveira 27 Oct '18

27 Oct '18

Hi everyone, pytest 3.9.3 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 https://docs.pytest.org/en/latest/changelog.html. Thanks to all who contributed to this release, among them: * Andreas Profous * Ankit Goel * Anthony Sottile * Bruno Oliveira * Daniel Hahler * Jon Dufresne * Ronny Pfannschmidt Happy testing, The pytest Development Team

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ANN: Bokeh 1.0 Released
by Bryan Van de Ven 24 Oct '18

24 Oct '18

On behalf of the Bokeh team I am pleased to announce the release of version 1.0 of Bokeh! Please read all about it in the announcement post at: https://bokeh.github.io/blog/2018/10/24/release-1-0-0/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/latest/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org There are over 321 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. If you are interested in contributing, please come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven

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Vulture 1.0
by Jendrik Seipp 23 Oct '18

23 Oct '18

Vulture - Find dead code ======================== Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused. Nonetheless, Vulture can be a very helpful tool for higher code quality. Download ======== https://github.com/jendrikseipp/vulture http://pypi.python.org/pypi/vulture Features ======== * fast: uses static code analysis * tested: tests itself and has complete test coverage * complements pyflakes and has the same output syntax * sorts unused classes and functions by size with ``--sort-by-size`` * supports Python 2.7 and Python >= 3.4 News ==== * Add ``--ignore-decorators`` flag (thanks @RJ722). * Add whitelist for ``threading`` module (thanks @andrewhalle). Cheers Jendrik

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pytest 3.9.2
by Bruno Oliveira 22 Oct '18

22 Oct '18

Hi folks, pytest 3.9.2 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 https://docs.pytest.org/en/latest/changelog.html. Thanks to all who contributed to this release, among them: * Ankit Goel * Anthony Sottile * Bruno Oliveira * Ronny Pfannschmidt * Vincent Barbaresi * ykantor Happy testing, The pytest Development Team

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NumPy 1.15.3 release
by Charles R Harris 22 Oct '18

22 Oct '18

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.15.3. This is a bugfix release for bugs and regressions reported following the 1.15.2 release. The most noticeable fix is probably for the memory leak <https://github.com/numpy/numpy/issues/12037> encountered when slicing classes derived from Numpy. The Python versions supported by this release are 2.7, 3.4-3.7. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.15.3/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.15.3>. Compatibility Note ================== The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit binaries. That will also be the case in future releases. See `#11625 <https://github.com/numpy/numpy/issues/11625>`__ for the related discussion. Those needing 32-bit support should look elsewhere or build from source. Contributors ============ A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Allan Haldane * Charles Harris * Jeroen Demeyer * Kevin Sheppard * Matthew Bowden + * Matti Picus * Tyler Reddy Pull requests merged ==================== A total of 12 pull requests were merged for this release. * `#12080 <https://github.com/numpy/numpy/pull/12080>`__: MAINT: Blacklist some MSVC complex functions. * `#12083 <https://github.com/numpy/numpy/pull/12083>`__: TST: Add azure CI testing to 1.15.x branch. * `#12084 <https://github.com/numpy/numpy/pull/12084>`__: BUG: test_path() now uses Path.resolve() * `#12085 <https://github.com/numpy/numpy/pull/12085>`__: TST, MAINT: Fix some failing tests on azure-pipelines mac and... * `#12187 <https://github.com/numpy/numpy/pull/12187>`__: BUG: Fix memory leak in mapping.c * `#12188 <https://github.com/numpy/numpy/pull/12188>`__: BUG: Allow boolean subtract in histogram * `#12189 <https://github.com/numpy/numpy/pull/12189>`__: BUG: Fix in-place permutation * `#12190 <https://github.com/numpy/numpy/pull/12190>`__: BUG: limit default for get_num_build_jobs() to 8 * `#12191 <https://github.com/numpy/numpy/pull/12191>`__: BUG: OBJECT_to_* should check for errors * `#12192 <https://github.com/numpy/numpy/pull/12192>`__: DOC: Prepare for NumPy 1.15.3 release. * `#12237 <https://github.com/numpy/numpy/pull/12237>`__: BUG: Fix MaskedArray fill_value type conversion. * `#12238 <https://github.com/numpy/numpy/pull/12238>`__: TST: Backport azure-pipeline testing fixes for Mac Cheers, Charles Harris

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[ANN] python-blosc 1.6.1
by Valentin Haenel 21 Oct '18

21 Oct '18

============================= Announcing python-blosc 1.6.1 ============================= What is new? ============ C-Blosc has been updated to 1.14.3. Additionally there have been a number of improvments such as not compiling snappy by default and the implementation of the `get_blocksize()` function. 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!**

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Python-announce-list October 2018