Groups | Search | Server Info | Keyboard shortcuts | Login | Register [http] [https] [nntp] [nntps]


Groups > comp.lang.python.announce > #3032 > unrolled thread

ANN: NumExpr 2.6.9

Started byRobert McLeod <robbmcleod@gmail.com>
First post2018-12-21 10:44 -0800
Last post2018-12-21 10:44 -0800
Articles 1 — 1 participant

Back to article view | Back to comp.lang.python.announce

This discussion starts older than the indexed window; earlier articles aren't shown. The article labeled Started by below is the oldest one visible, not the original post.


Contents

  ANN: NumExpr 2.6.9 Robert McLeod <robbmcleod@gmail.com> - 2018-12-21 10:44 -0800

#3032 — ANN: NumExpr 2.6.9

FromRobert McLeod <robbmcleod@gmail.com>
Date2018-12-21 10:44 -0800
SubjectANN: NumExpr 2.6.9
Message-ID<mailman.2.1545608686.2779.python-announce-list@python.org>
Hi everyone,

This is a version-bump release to provide wheels for Python 3.7.1 on
Windows
platforms. Also Mike Toews made the handling of our environment variables
more robust.

Project documentation is available at:

http://numexpr.readthedocs.io/

Changes from 2.6.8 to 2.6.9
---------------------------

- Thanks to Mike Toews for more robust handling of the thread-setting
  environment variables.
- With Appveyor updating to Python 3.7.1, wheels for Python 3.7 are now
  available in addition to those for other OSes.

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@gmail.com
robbmcleod@protonmail.com
robert.mcleod@hitachi-hhtc.ca
www.entropyreduction.al

[toc] | [standalone]


Back to top | Article view | comp.lang.python.announce


csiph-web