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Stop plotting your data - annotate your data and let it visualize itself.
Param <http://ioam.github.com/param/> and
Numpy <http://www.numpy.org/> and is designed to work
Matplotlib <http://matplotlib.org/> or
Bokeh <http://bokeh.pydata.org>, making use of the
Jupyter/IPython Notebook <http://jupyter.org>_.
Clone holoviews directly from GitHub with::
git clone git://github.com/ioam/holoviews.git
our website <http://ioam.github.com/holoviews/>
for official releases, installation instructions, documentation,
and many detailed
example notebooks and tutorials <http://holoviews.org/Tutorials>. Additional user contributed
notebooks may be found in the
holoviews-contrib <https://github.com/ioam/holoviews-contrib> repository
including examples that may be run live on
For general discussion, we have a
gitter channel <https://gitter.im/ioam/holoviews>. In addition we have a
wiki page <https://github.com/ioam/holoviews/wiki/Experimental-Features>
describing current work-in-progress and experimental features. If
you find any bugs or have any feature suggestions please file a
GitHub Issue or submit a pull request.
- Lets you build data structures that both contain and visualize your data.
- Includes a rich
library of composable elements <https://ioam.github.io/holoviews/Tutorials/Elements>_ that can be overlaid, nested and positioned with ease.
rapid data exploration <https://ioam.github.io/holoviews/Tutorials/Exploring_Data>that naturally develops into a
fully reproducible workflow <Tutorials/Exporting>.
- You can create complex animated or interactive visualizations with minimal code.
- Rich semantics for
indexing and slicing of data in arbitrarily high-dimensional spaces <https://ioam.github.io/holoviews/Tutorials/Transforming_Data>_.
- Every parameter of every object includes easy-to-access documentation.
- All features
available in vanilla Python 2 or 3 <https://ioam.github.io/holoviews/Tutorials/Options>_, with minimal dependencies.
Support for maintainable, reproducible research
- Supports a truly reproducible workflow by minimizing the code needed for analysis and visualization.
- Already used in a variety of research projects, from conception to final publication.
- All HoloViews objects can be pickled and unpickled.
- Provides comparison utilities for testing, so you know when your results have changed and why.
- Core data structures only depend on the numpy and param libraries.
export and archival facilities <https://ioam.github.io/holoviews/Tutorials/Exporting>_ for keeping track of your work throughout the lifetime of a project.
Analysis and data access features
- Allows you to annotate your data with dimensions, units, labels and data ranges.
slice and access <https://ioam.github.io/holoviews/Tutorials/Transforming_Data>_ regions of your data, no matter how high the dimensionality.
- Apply any suitable function to collapse your data or reduce dimensionality.
- Helpful textual representation to inform you how every level of your data may be accessed.
- Includes small library of common operations for any scientific or engineering data.
- Highly extensible: add new operations to easily apply the data transformations you need.
- Useful default settings make it easy to inspect data, with minimal code.
- Powerful normalization system to make understanding your data across plots easy.
complex animations or interactive visualizations in seconds <https://ioam.github.io/holoviews/Tutorials/Exploring_Data>_ instead of hours or days.
- Refine the visualization of your data interactively and incrementally.
- Separation of concerns: all visualization settings are kept separate from your data objects.
- Support for interactive tooltips/panning/zooming, via the optional mpld3 backend.
IPython Notebook support
- Support for both IPython 2 and 3.
- Automatic tab-completion everywhere.
- Exportable sliders and scrubber widgets.
- Automatic display of animated formats in the notebook or for export, including gif, webm, and mp4.
- Useful IPython magics for configuring global display options and for customizing objects.
Automatic archival and export of notebooks <https://ioam.github.io/holoviews/Tutorials/Exporting>_, including extracting figures as SVG, generating a static HTML copy of your results for reference, and storing your optional metadata like version control information.
Integration with third-party libraries
- Flexible interface to both the
pandas and Seaborn libraries <https://ioam.github.io/holoviews/Tutorials/Pandas_Seaborn>_
- Immediately visualize pandas data as any HoloViews object.
- Seamlessly combine and animate your Seaborn plots in HoloViews rich, compositional data-structures.
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.. |holoviewsDocs| image:: http://buildbot.holoviews.org:8010/png?builder=website .. _holoviewsDocs: http://buildbot.holoviews.org:8010/waterfall
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