James A. Bednar
2016-02-25 00:42:44 UTC
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
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.
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.