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.. note:: | ||
.. raw:: html | ||
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This page has been moved to <https://matplotlib.org/mpl-third-party/>, | ||
where you will find an up-to-date list of packages. | ||
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******************** | ||
Third party packages | ||
******************** | ||
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Several external packages that extend or build on Matplotlib functionality are | ||
listed below. You can find more packages at `PyPI <https://pypi.org/search/?q=&o=&c=Framework+%3A%3A+Matplotlib>`_. | ||
They are maintained and distributed separately from Matplotlib, | ||
and thus need to be installed individually. | ||
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If you have a created a package that extends or builds on Matplotlib | ||
and would like to have your package listed on this page, please submit | ||
an issue or pull request on GitHub. The pull request should include a short | ||
description of the library and an image demonstrating the functionality. | ||
To be included in the PyPI listing, please include ``Framework :: Matplotlib`` | ||
in the classifier list in the ``setup.py`` file for your package. We are also | ||
happy to host third party packages within the `Matplotlib GitHub Organization | ||
<https://github.com/matplotlib>`_. | ||
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Mapping toolkits | ||
**************** | ||
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Basemap | ||
======= | ||
`Basemap <https://matplotlib.org/basemap/>`_ plots data on map projections, | ||
with continental and political boundaries. | ||
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.. image:: /_static/basemap_contour1.png | ||
:height: 400px | ||
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Cartopy | ||
======= | ||
`Cartopy <https://scitools.org.uk/cartopy/docs/latest/>`_ builds on top | ||
of Matplotlib to provide object oriented map projection definitions | ||
and close integration with Shapely for powerful yet easy-to-use vector | ||
data processing tools. An example plot from the `Cartopy gallery | ||
<https://scitools.org.uk/cartopy/docs/latest/gallery/index.html>`_: | ||
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.. image:: /_static/cartopy_hurricane_katrina_01_00.png | ||
:height: 400px | ||
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Geoplot | ||
======= | ||
`Geoplot <https://residentmario.github.io/geoplot/index.html>`_ builds on top | ||
of Matplotlib and Cartopy to provide a "standard library" of simple, powerful, | ||
and customizable plot types. An example plot from the `Geoplot gallery | ||
<https://residentmario.github.io/geoplot/index.html>`_: | ||
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.. image:: /_static/geoplot_nyc_traffic_tickets.png | ||
:height: 400px | ||
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Ridge Map | ||
========= | ||
`ridge_map <https://github.com/ColCarroll/ridge_map>`_ uses Matplotlib, | ||
SRTM.py, NumPy, and scikit-image to make ridge plots of your favorite | ||
ridges. | ||
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.. image:: /_static/ridge_map_white_mountains.png | ||
:height: 364px | ||
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Declarative libraries | ||
********************* | ||
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ggplot | ||
====== | ||
`ggplot <https://github.com/yhat/ggplot>`_ is a port of the R ggplot2 package | ||
to python based on Matplotlib. | ||
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.. image:: /_static/ggplot.png | ||
:height: 195px | ||
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holoviews | ||
========= | ||
`holoviews <http://holoviews.org>`_ makes it easier to visualize data | ||
interactively, especially in a `Jupyter notebook <https://jupyter.org>`_, by | ||
providing a set of declarative plotting objects that store your data and | ||
associated metadata. Your data is then immediately visualizable alongside or | ||
overlaid with other data, either statically or with automatically provided | ||
widgets for parameter exploration. | ||
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.. image:: /_static/holoviews.png | ||
:height: 354px | ||
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plotnine | ||
======== | ||
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`plotnine <https://plotnine.readthedocs.io/en/stable/>`_ implements a grammar | ||
of graphics, similar to R's `ggplot2 <https://ggplot2.tidyverse.org/>`_. | ||
The grammar allows users to compose plots by explicitly mapping data to the | ||
visual objects that make up the plot. | ||
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.. image:: /_static/plotnine.png | ||
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Specialty plots | ||
*************** | ||
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Broken Axes | ||
=========== | ||
`brokenaxes <https://github.com/bendichter/brokenaxes>`_ supplies an axes | ||
class that can have a visual break to indicate a discontinuous range. | ||
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.. image:: /_static/brokenaxes.png | ||
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DeCiDa | ||
====== | ||
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`DeCiDa <https://pypi.org/project/DeCiDa/>`_ is a library of functions | ||
and classes for electron device characterization, electronic circuit design and | ||
general data visualization and analysis. | ||
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matplotlib-scalebar | ||
=================== | ||
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`matplotlib-scalebar <https://github.com/ppinard/matplotlib-scalebar>`_ provides a new artist to display a scale bar, aka micron bar. | ||
It is particularly useful when displaying calibrated images plotted using ``plt.imshow(...)``. | ||
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.. image:: /_static/gold_on_carbon.jpg | ||
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Matplotlib-Venn | ||
=============== | ||
`Matplotlib-Venn <https://github.com/konstantint/matplotlib-venn>`_ provides a | ||
set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn | ||
diagrams. | ||
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mpl-probscale | ||
============= | ||
`mpl-probscale <https://matplotlib.org/mpl-probscale/>`_ is a small extension | ||
that allows Matplotlib users to specify probability scales. Simply importing the | ||
``probscale`` module registers the scale with Matplotlib, making it accessible | ||
via e.g., ``ax.set_xscale('prob')`` or ``plt.yscale('prob')``. | ||
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.. image:: /_static/probscale_demo.png | ||
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mpl-scatter-density | ||
=================== | ||
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`mpl-scatter-density <https://github.com/astrofrog/mpl-scatter-density>`_ is a | ||
small package that makes it easy to make scatter plots of large numbers | ||
of points using a density map. The following example contains around 13 million | ||
points and the plotting (excluding reading in the data) took less than a | ||
second on an average laptop: | ||
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.. image:: /_static/mpl-scatter-density.png | ||
:height: 400px | ||
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When used in interactive mode, the density map is downsampled on-the-fly while | ||
panning/zooming in order to provide a smooth interactive experience. | ||
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mplstereonet | ||
============ | ||
`mplstereonet <https://github.com/joferkington/mplstereonet>`_ provides | ||
stereonets for plotting and analyzing orientation data in Matplotlib. | ||
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Natgrid | ||
======= | ||
`mpl_toolkits.natgrid <https://github.com/matplotlib/natgrid>`_ is an interface | ||
to the natgrid C library for gridding irregularly spaced data. | ||
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pyUpSet | ||
======= | ||
`pyUpSet <https://github.com/ImSoErgodic/py-upset>`_ is a | ||
static Python implementation of the `UpSet suite by Lex et al. | ||
<http://caleydo.org/tools/upset/>`_ to explore complex intersections of | ||
sets and data frames. | ||
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seaborn | ||
======= | ||
`seaborn <http://seaborn.pydata.org/>`_ is a high level interface for drawing | ||
statistical graphics with Matplotlib. It aims to make visualization a central | ||
part of exploring and understanding complex datasets. | ||
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.. image:: /_static/seaborn.png | ||
:height: 157px | ||
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WCSAxes | ||
======= | ||
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The `Astropy <https://www.astropy.org/>`_ core package includes a submodule | ||
called WCSAxes (available at `astropy.visualization.wcsaxes | ||
<http://docs.astropy.org/en/stable/visualization/wcsaxes/index.html>`_) which | ||
adds Matplotlib projections for Astronomical image data. The following is an | ||
example of a plot made with WCSAxes which includes the original coordinate | ||
system of the image and an overlay of a different coordinate system: | ||
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.. image:: /_static/wcsaxes.jpg | ||
:height: 400px | ||
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Windrose | ||
======== | ||
`Windrose <https://github.com/scls19fr/windrose>`_ is a Python Matplotlib, | ||
Numpy library to manage wind data, draw windroses (also known as polar rose | ||
plots), draw probability density functions and fit Weibull distributions. | ||
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Yellowbrick | ||
=========== | ||
`Yellowbrick <https://www.scikit-yb.org/>`_ is a suite of visual diagnostic tools for machine learning that enables human steering of the model selection process. Yellowbrick combines scikit-learn with matplotlib using an estimator-based API called the ``Visualizer``, which wraps both sklearn models and matplotlib Axes. ``Visualizer`` objects fit neatly into the machine learning workflow allowing data scientists to integrate visual diagnostic and model interpretation tools into experimentation without extra steps. | ||
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.. image:: /_static/yellowbrick.png | ||
:height: 400px | ||
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Animations | ||
********** | ||
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animatplot | ||
========== | ||
`animatplot <https://animatplot.readthedocs.io/>`_ is a library for | ||
producing interactive animated plots with the goal of making production of | ||
animated plots almost as easy as static ones. | ||
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.. image:: /_static/animatplot.png | ||
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For an animated version of the above picture and more examples, see the | ||
`animatplot gallery. <https://animatplot.readthedocs.io/en/stable/gallery.html>`_ | ||
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gif | ||
=== | ||
`gif <https://github.com/maxhumber/gif/>`_ is an ultra lightweight animated gif API. | ||
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.. image:: /_static/gif_attachment_example.png | ||
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numpngw | ||
======= | ||
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`numpngw <https://pypi.org/project/numpngw/>`_ provides functions for writing | ||
NumPy arrays to PNG and animated PNG files. It also includes the class | ||
``AnimatedPNGWriter`` that can be used to save a Matplotlib animation as an | ||
animated PNG file. See the example on the PyPI page or at the ``numpngw`` | ||
`github repository <https://github.com/WarrenWeckesser/numpngw>`_. | ||
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.. image:: /_static/numpngw_animated_example.png | ||
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Interactivity | ||
************* | ||
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mplcursors | ||
========== | ||
`mplcursors <https://mplcursors.readthedocs.io>`_ provides interactive data | ||
cursors for Matplotlib. | ||
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MplDataCursor | ||
============= | ||
`MplDataCursor <https://github.com/joferkington/mpldatacursor>`_ is a toolkit | ||
written by Joe Kington to provide interactive "data cursors" (clickable | ||
annotation boxes) for Matplotlib. | ||
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mpl_interactions | ||
================ | ||
`mpl_interactions <https://mpl-interactions.readthedocs.io/en/latest/>`_ | ||
makes it easy to create interactive plots controlled by sliders and other | ||
widgets. It also provides several handy capabilities such as manual | ||
image segmentation, comparing cross-sections of arrays, and using the | ||
scroll wheel to zoom. | ||
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.. image:: /_static/mpl-interactions-slider-animated.png | ||
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Rendering backends | ||
****************** | ||
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mplcairo | ||
======== | ||
`mplcairo <https://github.com/anntzer/mplcairo>`_ is a cairo backend for | ||
Matplotlib, with faster and more accurate marker drawing, support for a wider | ||
selection of font formats and complex text layout, and various other features. | ||
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gr | ||
== | ||
`gr <https://gr-framework.org/>`_ is a framework for cross-platform | ||
visualisation applications, which can be used as a high-performance Matplotlib | ||
backend. | ||
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GUI integration | ||
*************** | ||
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wxmplot | ||
======= | ||
`WXMPlot <https://pypi.org/project/wxmplot/>`_ provides advanced wxPython | ||
widgets for plotting and image display of numerical data based on Matplotlib. | ||
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Miscellaneous | ||
************* | ||
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adjustText | ||
========== | ||
`adjustText <https://github.com/Phlya/adjustText>`_ is a small library for | ||
automatically adjusting text position in Matplotlib plots to minimize overlaps | ||
between them, specified points and other objects. | ||
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.. image:: /_static/adjustText.png | ||
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iTerm2 terminal backend | ||
======================= | ||
`matplotlib_iterm2 <https://github.com/oselivanov/matplotlib_iterm2>`_ is an | ||
external Matplotlib backend using the iTerm2 nightly build inline image display | ||
feature. | ||
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.. image:: /_static/matplotlib_iterm2_demo.png | ||
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mpl-template | ||
============ | ||
`mpl-template <https://austinorr.github.io/mpl-template/index.html>`_ provides | ||
a customizable way to add engineering figure elements such as a title block, | ||
border, and logo. | ||
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.. image:: /_static/mpl_template_example.png | ||
:height: 330px | ||
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figpager | ||
======== | ||
`figpager <https://pypi.org/project/figpager/>`_ provides customizable figure | ||
elements such as text, lines and images and subplot layout control for single | ||
or multi page output. | ||
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.. image:: /_static/figpager.png | ||
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blume | ||
===== | ||
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`blume <https://pypi.org/project/blume/>`_ provides a replacement for | ||
the Matplotlib ``table`` module. It fixes a number of issues with the | ||
existing table. See the `blume github repository | ||
<https://github.com/swfiua/blume>`_ for more details. | ||
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.. image:: /_static/blume_table_example.png | ||
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highlight-text | ||
============== | ||
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`highlight-text <https://pypi.org/project/highlight-text/>`_ is a small library | ||
that provides an easy way to effectively annotate plots by highlighting | ||
substrings with the font properties of your choice. | ||
See the `highlight-text github repository | ||
<https://github.com/znstrider/highlight_text>`_ for more details and examples. | ||
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.. image:: /_static/highlight_text_examples.png | ||
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DNA Features Viewer | ||
=================== | ||
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`DNA Features Viewer <https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer>`_ | ||
provides methods to plot annotated DNA sequence maps (possibly along other Matplotlib | ||
plots) for Bioinformatics and Synthetic Biology applications. | ||
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.. image:: /_static/dna_features_viewer_screenshot.png | ||
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GUI applications | ||
**************** | ||
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sviewgui | ||
======== | ||
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`sviewgui <https://pypi.org/project/sviewgui/>`_ is a PyQt-based GUI for | ||
visualisation of data from csv files or `pandas.DataFrame`\s. Main features: | ||
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- Scatter, line, density, histogram, and box plot types | ||
- Settings for the marker size, line width, number of bins of histogram, | ||
colormap (from cmocean) | ||
- Save figure as editable PDF | ||
- Code of the plotted graph is available so that it can be reused and modified | ||
outside of sviewgui | ||
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.. image:: /_static/sviewgui_sample.png | ||
<meta http-equiv="refresh" content="0; url=https://matplotlib.org/mpl-third-party/"> |