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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============================
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!**
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
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
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
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/vulturehttp://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
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
=============================
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!**
