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
==========================
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
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!
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
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
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.
web site : http://www.skipole.ski
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
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)
==========================
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
