ANN: NumExpr 2.6.3 release

2017-09-14 Thread Robert McLeod 
Hi everyone,

This is primarily a maintenance release that fixes a number of newly
discovered
bugs. The NumPy requirement has increased from 1.6 to 1.7 due to changes
with
`numpy.nditer` flags. Thanks to Caleb P. Burns `ceil` and `floor` functions
are
now supported.

Project documentation is now available at:

http://numexpr.readthedocs.io/

=
 Announcing Numexpr 2.6.3
=

Changes from 2.6.2 to 2.6.3
-

- Documentation now available at numexpr.readthedocs.io
- Support for floor() and ceil() functions added by Caleb P. Burns.
- NumPy requirement increased from 1.6 to 1.7 due to changes in iterator
  flags (#245).
- Sphinx autodocs support added for documentation on readthedocs.org.
- Fixed a bug where complex constants would return an error, fixing
  problems with `sympy` when using NumExpr as a backend.
- Fix for #277 whereby arrays of shape (1,...) would be reduced as
  if they were full reduction. Behavoir now matches that of NumPy.
- String literals are automatically encoded into 'ascii' bytes for
  convience (see #281).

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.
robbmcl...@gmail.com
robbmcl...@protonmail.com
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ANN: Bokeh 0.12.9 Released

2017-09-14 Thread Bryan Van de ven 
On behalf of the Bokeh team, I am pleased to announce the release of version 
0.12.9 of Bokeh!

OF SPECIAL NOTE: *** JupyterLab and Fast Array Transport now supported ***

Please see the announcement post at:

https://bokeh.github.io/blog/2017/9/12/release-0-12-9/

which has more information and demonstrations. 

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:

http://bokeh.pydata.org/en/0.12.9/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 http://bokeh.pydata.org/en/0.12.9

There are over 250 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 (as always), for questions, technical assistance or if you're 
interested in contributing, questions can be directed to the Bokeh mailing 
list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh

Thanks,

Bryan Van de Ven
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