Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/mpld3/mpld3

An interactive data visualization tool which brings matplotlib graphics to the browser using D3.
https://github.com/mpld3/mpld3

Last synced: about 2 months ago
JSON representation

An interactive data visualization tool which brings matplotlib graphics to the browser using D3.

Lists

README 

        
mpld3: A D3 Viewer for Matplotlib

=================================



- Maintainers: [@vladh](https://github.com/vladh), [@cliffckerr](https://github.com/cliffckerr)

- Status: I ([@vladh](https://github.com/vladh)) am not currently able to dedicate much time to resolving mpld3 issues.

  However, I _will_ gladly look at your pull request and review it. Please try your hand at fixing any issues you

  may have and sending a PR rather than just filing an issue — most problems don't take as long as you might expect to

  figure out!

- Original author: Jake Vanderplas 

- License: BSD 3-clause



This is an interactive D3js-based viewer which brings matplotlib graphics to the browser.

Please visit [http://mpld3.github.io](http://mpld3.github.io) for documentation and examples.



You may also see the [blog post](http://jakevdp.github.io/blog/2013/12/19/a-d3-viewer-for-matplotlib/), or the

[IPython notebook examples](http://nbviewer.ipython.org/github/jakevdp/mpld3/tree/master/notebooks/)

available in the ``notebooks`` directory of this repository.



[![version status](https://img.shields.io/pypi/v/mpld3.svg)](https://pypi.python.org/pypi/mpld3)

[![downloads](https://img.shields.io/pypi/dm/mpld3.svg)](https://pypi.python.org/pypi/mpld3)

[![build status](https://travis-ci.org/jakevdp/mpld3.svg?branch=master)](https://travis-ci.org/jakevdp/mpld3)



About

-----

mpld3 provides a custom stand-alone javascript library built on D3, which

parses JSON representations of plots.  The mpld3 python module provides a

set of routines which parses matplotlib plots (using the 

[mplexporter](http://github.com/mpld3/mplexporter) framework) and outputs

the JSON description readable by mpld3.js.



Installation

------------

mpld3 is compatible with python 2.6-2.7 and 3.3-3.4. It requires

[matplotlib](http://matplotlib.org) version 2.2.2 and

[jinja2](http://jinja.pocoo.org/) version 2.7+.



Optionally, mpld3 can be used with [IPython](http://ipython.org) notebook,

and requires IPython version 1.x or (preferably) version 2.0+.



This package is based on the [mplexporter](http://github.com/mpld3/mplexporter)

framework for crawling and exporting matplotlib images. mplexporter is bundled

with the source distribution via git submodule.



Within the git source directory, you can download the mplexporter dependency

and copy it into the mpld3 source directory using the following command:



    $ python setup.py submodule



The submodule command is not necessary if you are installing from a distribution

rather than from the git source.



Once the submodule command has been run, you can build the package locally using



    $ python -m build



or install the package to the standard Python path using:



    $ pip install .



Or, to install to another location, use



    $ pip install . --prefix=/path/to/location/



Then make sure your PYTHONPATH environment variable points to this location.



Trying it out

-------------

The package is pure python, and very light-weight.  You can take a look at

the notebooks in the examples directory, or run ``create_example.py``, which

will create a set of plots and launch a browser window showing interactive

views of these plots.



For a more comprehensive set of examples, see the

[IPython notebook examples](http://nbviewer.ipython.org/github/jakevdp/mpld3/tree/master/notebooks/) available in the ``notebooks`` directory.



Test Plots

----------

To explore the comparison between D3 renderings and matplotlib renderings for

various plot types, run the script ``visualize_tests.py``.  This will generate

an HTML page with the D3 renderings beside corresponding matplotlib renderings.



Features

--------

Many of the core features of matplotlib are already supported.  And additionally

there is some extra interactivity provided via the plugin framework.  The

following is a non-exhausive list of features that are yet to be supported:



- tick specification & formatting

- some legend features

- blended transforms, such as those required by ``axvlines`` and ``axhlines``

- twin axes (i.e. multiple scales on one plot) tied together



If any of these look like something you'd like to tackle, feel free to submit

a pull request!