[Numpy-discussion] ANN: Stop plotting your data -- HoloViews 1.4 released!

[James A. Bednar]

We are pleased to announce the fifth public release of HoloViews,
a Python package for exploring and visualizing numerical data:

   http://holoviews.org

HoloViews provides composable, sliceable, declarative data structures
for building even complex visualizations easily.  Instead of you
having to explicitly and laboriously plot your data, HoloViews lets
you simply annotate your data so that any part of it visualizes itself
automatically.  You can now work with large datasets as easily as
you work with simple datatypes at the Python prompt.

The new version can be installed using conda:

   conda install -c ioam holoviews

Release 1.4 introduces major new features, incorporating over 1700 new
commits and closing 142 issues:

- Now supports both Bokeh (bokeh.pydata.org) and matplotlib backends,
  with Bokeh providing extensive interactive features such as panning
  and zooming linked axes, and customizable callbacks

- DynamicMap: Allows exploring live streams from ongoing data 
  collection or simulation, or parameter spaces too large to fit into
  your
  computer's or your browser's memory, from within a Jupyter notebook

- Columnar data support: Underlying data storage can now be in Pandas
  dataframes, NumPy arrays, or Python dictionaries, allowing you to
  define HoloViews objects without copying or reformatting your data

- New Element types: Area (area under or between curves),
  Spikes (sequence of lines, e.g. spectra, neural spikes, or rug
  plots),
  BoxWhisker (summary of a distribution), QuadMesh (nonuniform
  rasters),
  Trisurface (Delaunay-triangulated surface plots)

- New Container type: GridMatrix (grid of heterogenous Elements)

- Improved layout handling, with better support for varying aspect
  ratios and plot sizes

- Improved help system, including recursively listing and searching
  the help for all the components of a composite object

- Improved Jupyter/IPython notebook support, including improved
  export using nbconvert, and standalone HTML output that supports
  dynamic widgets even without a Python server

- Significant performance improvements for large or highly nested data

And of course we have fixed a number of bugs found by our very
dedicated users; please keep filing Github issues if you find any!

For the full list of changes, see:

   https://github.com/ioam/holoviews/releases

HoloViews is now supported by Continuum Analytics, and is being used
in a wide range of scientific and industrial projects.  HoloViews
remains freely available under a BSD license, is Python 2 and 3
compatible, and has minimal external dependencies, making it easy to
integrate into your workflow. Try out the extensive tutorials at
holoviews.org today!

Jean-Luc R. Stevens
Philipp Rudiger
James A. Bednar

Continuum Analytics, Inc., Austin, TX, USA
School of Informatics, The University of Edinburgh, UK

-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.

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Re: [Numpy-discussion] Numpy 1.11.0rc1 released.

[Charles R Harris]

On Tue, Feb 23, 2016 at 8:44 AM, Pauli Virtanen  wrote:

> 23.02.2016, 03:47, Charles R Harris kirjoitti:
> > I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the
> > issues discovered in 1.11.0b3 have been dealt with and this release can
> go
> > on to become the official release. Source files and documentation can be
> > found on Sourceforge
> > , while
> > source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
> > installed from Pypi. Please test thoroughly.
>
> FWIW https://travis-ci.org/pv/testrig/builds/108384173


Thanks for that. Most of the new errors look to be the result of the change
in divmod, where before divmod(float64(1.0), 0.0) was (inf, nan) and it is
now (nan, nan).  There are also two errors in matplotlib that look to be
the result of the slight change in the numerical values of remainder due to
improved precision. I would class those errors as more of a test problem
resulting from the inherent imprecision of floating point than a numpy
regression.

Chuck
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[Numpy-discussion] ANN: SfePy 2016.1

[Robert Cimrman]

I am pleased to announce release 2016.1 of SfePy.

Description
---

SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by the finite element method or by the
isogeometric analysis (preliminary support). It is distributed under the new
BSD license.

Home page: http://sfepy.org
Mailing list: http://groups.google.com/group/sfepy-devel
Git (source) repository, issue tracker, wiki: http://github.com/sfepy

Highlights of this release
--

- major simplification of finite element field code
- automatic checking of shapes of term arguments
- improved mesh parametrization code and documentation
- support for fieldsplit preconditioners of PETSc

For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1
(rather long and technical).

Best regards,
Robert Cimrman on behalf of the SfePy development team

---

Contributors to this release in alphabetical order:

Robert Cimrman
Vladimir Lukes
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Re: [Numpy-discussion] fromnumeric.py internal calls

[G Young]

Hello all,

I have PR #7325  up that
changes the internal calls for functions in *fromnumeric.py* from
positional arguments to keyword arguments.  I made this change for two
reasons:

1) It is consistent with the external function signature
2)

The inconsistency caused a breakage in *pandas* in its own implementation
of *searchsorted* in which the *sorter* argument is not really used but is
accepted so as to make it easier for *numpy* users who may be used to the
*searchsorted* signature in *numpy*.

The standard in *pandas* is to "swallow" those unused arguments into a
*kwargs* argument so that we don't have to document an argument that we
don't really use.  However, that turned out not to be possible when
*searchsorted* is called from the *numpy* library.

Does anyone have any objections to the changes I made?

Thanks!

Greg
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