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Visualize Network Data with Hive Plots

Project description

Hiveplotlib Logo

A plotting package for generating and visualizing static Hive Plots in Python.

Supported Python Versions Matches PSF support

Matplotlib Support Bokeh Support Holoviews Support Plotly Support Datashader Support NetworkX Support

Installation

hiveplotlib can be installed via pypi:

pip install hiveplotlib

To uninstall, run:

pip uninstall hiveplotlib

By default, hiveplotlib supports visualization only with the matplotlib backend, but hiveplotlib also supports bokeh, holoviews, and plotly visualizations, which can be installed via pip install hiveplotlib[bokeh], pip install hiveplotlib[holoviews], and pip install hiveplotlib[plotly], respectively.

hiveplotlib also supports large network visualization via the datashader backend, which can be installed as pip install hiveplotlib[datashader].

Beyond the visualization backends, hiveplotlib integrates with networkx: build a hive plot directly from a networkx graph and compute graph metrics on it. Install this integration with pip install hiveplotlib[networkx].

How to Use and Examples

For more on how to use the software and examples, see the tutorials and gallery examples.

We recommend starting with our Introduction to Hive Plots and Quick Start Hive Plots pages.

All the example notebooks are available for download as jupyter notebooks in the repository under the examples directory.

To install this environment and associated jupyter kernel used to run the notebooks, clone the repository and run:

cd <path/to/repository>
bash install.sh

The resulting hiveplotlib kernel can run any of those notebooks.

More on Hive Plots

For more on Hive Plots, see our Introduction to Hive Plots.

For additional resources, see:

  • https://hiveplot.com/

  • Krzywinski M, Birol I, Jones S, Marra M (2011). Hive Plots — Rational Approach to Visualizing Networks. Briefings in Bioinformatics (early access 9 December 2011, doi: 10.1093/bib/bbr069).

Contributing

For more on contributing to the project, see CONTRIBUTING.md

Acknowledgements

We'd like to thank Rodrigo Garcia-Herrera for his work on pyveplot, which we referenced as a starting point for our structural design. We also translated some of his utility methods for use in this repository.

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