.. _thirdparty-index: ******************** Third party packages ******************** Several external packages that extend or build on Matplotlib functionality are listed below. They are maintained and distributed separately from Matplotlib and thus need to be installed individually. Please submit an issue or pull request on Github if you have created a package that you would like to have included. We are also happy to host third party packages within the `Matplotlib Github Organization `_. Mapping toolkits **************** Basemap ======= `Basemap `_ plots data on map projections, with continental and political boundaries. .. image:: /_static/basemap_contour1.png :height: 400px Cartopy ======= `Cartopy `_ 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 `_: .. image:: /_static/cartopy_hurricane_katrina_01_00.png :height: 400px Geoplot ======= `Geoplot `_ 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 `_: .. image:: /_static/geoplot_nyc_traffic_tickets.png :height: 400px Ridge Map ========= `ridge_map `_ uses Matplotlib, SRTM.py, NumPy, and scikit-image to make ridge plots of your favorite ridges. .. image:: /_static/ridge_map_white_mountains.png :height: 364px Declarative libraries ********************* ggplot ====== `ggplot `_ is a port of the R ggplot2 package to python based on Matplotlib. .. image:: /_static/ggplot.png :height: 195px holoviews ========= `holoviews `_ makes it easier to visualize data interactively, especially in a `Jupyter notebook `_, 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. .. image:: /_static/holoviews.png :height: 354px plotnine ======== `plotnine `_ implements a grammar of graphics, similar to R's `ggplot2 `_. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot. .. image:: /_static/plotnine.png Specialty plots *************** Broken Axes =========== `brokenaxes `_ supplies an axes class that can have a visual break to indicate a discontinuous range. .. image:: /_static/brokenaxes.png DeCiDa ====== `DeCiDa `_ is a library of functions and classes for electron device characterization, electronic circuit design and general data visualization and analysis. Matplotlib-Venn =============== `Matplotlib-Venn `_ provides a set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn diagrams. mpl-probscale ============= `mpl-probscale `_ is a small extension that allows Matplotlib users to specify probabilty 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')``. .. image:: /_static/probscale_demo.png mpl-scatter-density =================== `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: .. image:: /_static/mpl-scatter-density.png :height: 400px When used in interactive mode, the density map is downsampled on-the-fly while panning/zooming in order to provide a smooth interactive experience. mplstereonet ============ `mplstereonet `_ provides stereonets for plotting and analyzing orientation data in Matplotlib. Natgrid ======= `mpl_toolkits.natgrid `_ is an interface to the natgrid C library for gridding irregularly spaced data. pyUpSet ======= `pyUpSet `_ is a static Python implementation of the `UpSet suite by Lex et al. `_ to explore complex intersections of sets and data frames. seaborn ======= `seaborn `_ 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. .. image:: /_static/seaborn.png :height: 157px WCSAxes ======= The `Astropy `_ core package includes a submodule called WCSAxes (available at `astropy.visualization.wcsaxes `_) 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: .. image:: /_static/wcsaxes.jpg :height: 400px Windrose ======== `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. Yellowbrick =========== `Yellowbrick `_ 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. .. image:: /_static/yellowbrick.png :height: 400px Interactivity ************* mplcursors ========== `mplcursors `_ provides interactive data cursors for Matplotlib. MplDataCursor ============= `MplDataCursor `_ is a toolkit written by Joe Kington to provide interactive "data cursors" (clickable annotation boxes) for Matplotlib. animatplot ========== `animatplot `_ is a library for producing interactive animated plots with the goal of making production of animated plots almost as easy as static ones. .. image:: /_static/animatplot.png For an animated version of the above picture and more examples, see the `animatplot gallery. `_ Rendering backends ****************** mplcairo ======== `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. gr == `gr `_ is a framework for cross-platform visualisation applications, which can be used as a high-performance Matplotlib backend. Miscellaneous ************* adjustText ========== `adjustText `_ is a small library for automatically adjusting text position in Matplotlib plots to minimize overlaps between them, specified points and other objects. .. image:: /_static/adjustText.png iTerm2 terminal backend ======================= `matplotlib_iterm2 `_ is an external Matplotlib backend using the iTerm2 nightly build inline image display feature. .. image:: /_static/matplotlib_iterm2_demo.png mpl-template ============ `mpl-template `_ provides a customizable way to add engineering figure elements such as a title block, border, and logo. .. image:: /_static/mpl_template_example.png :height: 330px numpngw ======= `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 `_. .. image:: /_static/numpngw_animated_example.png