Connectionists: ANN: HoloViews 1.0 and ImaGen 2.0 released

[James A. Bednar]

We are pleased to announce the first public release of HoloViews,
a free Python package for scientific and engineering data visualization:

     http://ioam.github.io/holoviews

and version 2.0 of ImaGen, a free Python package for generating
two-dimensional patterns useful for vision research and computational
modeling:

     http://ioam.github.io/imagen

HoloViews provides composable, sliceable, declarative data structures
for building even complex visualizations of any scientific data very
easily.  With HoloViews, you can see your data as publication-quality
figures almost instantly, so that you can focus on the data itself,
rather than on laboriously putting together your figures.  Even
complex multi-subfigure layouts and animations are very easily built
using HoloViews.

ImaGen provides highly configurable, resolution-independent input
patterns, directly visualizable using HoloViews but also available
without any plotting package so that they can easily be incorporated
directly into your computational modeling or visual stimulus
generation code.  With ImaGen, any software with a Python interface
can immediately support configurable streams of 0D, 1D, or 2D
patterns, without any extra coding.

HoloViews and ImaGen are very general tools, but they were designed to
solve common problems faced by vision scientists and computational
modelers.  HoloViews makes it very easy to visualize data from vision
research, whether it is visual patterns, neural activity patterns, or
more abstract measurements or analyses.  Essentially, HoloViews
provides a set of general, compositional, multidimensional data
structures suitable for both discrete and continuous real-world data,
and pairs them with separate customizable plotting classes to
visualize them without extensive coding.

ImaGen 2.0 uses the continuous coordinate systems provided by
HoloViews to implement flexible resolution-independent generation of
streams of patterns, with parameters controlled by the user and
allowing randomness or other arbitrary changes over time.  These
patterns can be used for visual stimulus generation, testing or
training computational models, initializing connectivity in models, or
any other application where random or dynamic but precisely controlled
streams of patterns are needed.

Features:

- Freely available under a BSD license
- Python 2 and 3 compatible
- Minimal external dependencies -- easy to integrate into your workflow
- Declarative approach provides powerful compositionality with minimal coding
- Include extensive, continuously tested IPython Notebook tutorials
- Easily reconfigurable using documented and validated parameters
- Animations are supported natively, with no extra work
- Supports reproducible research -- simple specification, archived in
    an IPython Notebook, providing a recipe for regenerating your results
- HoloViews is one of three winners of the 2015 UK Open Source Awards

To get started, check out ioam.github.io/holoviews and ioam.github.io/imagen!

Jean-Luc Stevens
Philipp Rudiger
Christopher Ball
James A. Bednar

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