Stop plotting your data - annotate your data and let it visualize itself.
Project description
holoviews
Stop plotting your data - annotate your data and let it visualize itself.
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.
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
Lets you build data structures that both contain and visualize your data.
Includes a rich library of composable elements that can be overlaid, nested and positioned with ease.
Supports rapid data exploration that naturally develops into a fully reproducible workflow.
You can create complex animated or interactive visualizations with minimal code.
Rich semantics for indexing and slicing of data in arbitrarily high-dimensional spaces.
Every parameter of every object includes easy-to-access documentation.
All features available in vanilla Python 2 or 3, 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.
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.
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