[Numpy-discussion] NumPy Community Meeting Wednesday (Today)

[Sebastian Berg]

Hi all,

There will be a NumPy Community meeting Wednesday March 3rd at 12pm
Pacific Time (20:00 UTC). Everyone is invited and encouraged to
join in and edit the work-in-progress meeting topics and notes at:

https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both

Best wishes

Sebastian


PS: Sorry for the late reminder.


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[Numpy-discussion] ANN: NumExpr 2.7.3

[Robert McLeod]

Announcing NumExpr 2.7.3


Hi everyone,

This is a maintenance release to make use of the oldest supported NumPy
version
when building wheels, in an effort to alleviate issues seen on Windows
machines
that do not have the latest Windows MSVC runtime installed. It also adds
wheels built via GitHub Actions for ARMv8 platforms.

Project documentation is available at:

http://numexpr.readthedocs.io/

Changes from 2.7.2 to 2.7.3
---

- Pinned Numpy versions to minimum supported version in an effort to
alleviate
  issues seen in Windows machines not having the same Windows SDK installed
as
  was used to build the wheels.
- ARMv8 wheels are now available, thanks to `odidev` for the pull request.

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
robbmcl...@gmail.com
robert.mcl...@hitachi-hhtc.ca
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[Numpy-discussion] Development branches renamed

[Stefan van der Walt]

Hi everyone,

The development branches of most of the repositories on github.com/numpy have 
been renamed to `main` (this is the GitHub default for newly created 
repositories).  The move has not yet been made for sub-projects such as 
`numpydoc` or `numpy.org`, but those should follow soon.

We were able to preserve all PRs, other than those for which the original 
branches have been deleted.

You can update your locally cloned repository to have a `main` branch as 
follows:

git branch -m master main
git fetch 
git branch -u /main main

(where YOUR_UPSTREAM_REMOTE is typically called `upstream` or `origin`)

If you have any trouble, let us know.

Best regards,
Stéfan
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