Stop plotting your data - annotate your data and let it visualize itself
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Latest commit aeb487b Jan 19, 2017 @jlstevens jlstevens committed on GitHub Set setup.py version to latest dev release (1.7dev4)

README.rst

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holoviews

Stop plotting your data - annotate your data and let it visualize itself.

http://assets.holoviews.org/demo.gif

HoloViews requires Param and Numpy and is designed to work together with Matplotlib or Bokeh, making use of the Jupyter/IPython Notebook.

Clone holoviews directly from GitHub with:

git clone git://github.com/ioam/holoviews.git

Please visit our website for official releases, installation instructions, documentation, and many detailed example notebooks and tutorials. Additional user contributed notebooks may be found in the holoviews-contrib repository including examples that may be run live on mybinder.org.

For general discussion, we have a gitter channel. In addition we have a wiki page 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.

Features

Overview

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.
  • Provides export and archival facilities 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.
  • Easily slice and access 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.

Visualization features

  • Useful default settings make it easy to inspect data, with minimal code.
  • Powerful normalization system to make understanding your data across plots easy.
  • Build complex animations or interactive visualizations in seconds 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, 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
  • Immediately visualize pandas data as any HoloViews object.
  • Seamlessly combine and animate your Seaborn plots in HoloViews rich, compositional data-structures.