D3 Viewer for Matplotlib
Author: Jake Vanderplas <firstname.lastname@example.org>
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.
mpld3 requires [jinja2](http://jinja.pocoo.org/) version 2.7+ and [matplotlib](http://matplotlib.org) version 1.3+.
Optionally, mpld3 can be used with [IPython](http://ipython.org), and requires version 1.1+.
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:
[~]$ make build
To install the package via setup.py, type
[~]$ make install
Or, to install locally, after running make build use
[~]$ python setup.py install –prefix=/path/to/location/
Then make sure your Python path 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/examples/) available in the examples directory.
To explore the comparison between D3 renderings and matplotlib renderings for various plot types, run the script process_testplots.py. This will generate an html page with the D3 renderings beside corresponding matplotlib renderings.
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
additional interactivity tools, such as brushing and box-zoom.
If any of these look like something you’d like to tackle, feel free to submit a pull request!
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