Sympy Plotting Backends

This module contains a few plotting backends that can be used with SymPy as an alternative to the default Matplotlib backend. A backend represents the plotting library: it provides the necessary functionalities to quickly and easily plot the most common types of symbolic expressions (line plots, surface plots, parametric plots).

The following plotting libraries are supported: Matplolib, Plotly, Bokeh, K3D-Jupyter.

What's new in comparison to SymPy

On top of the usual plotting functions exposed by SymPy (plot, plot_parametric, plot3d, etc.), this module offers the capabily to:

• use a different backend.
• visualize discontinuities on 2D line plots.
• plot_piecewise to visualize 2D line plots of piecewise functions with their discontinuities.
• plot_vector to quickly visualize 2D/3D vector fields with quivers or streamlines.
• plot_real_imag, plot_complex, plot_complex_list, plot_complex_vector to visualize complex functions.
• plot_polar function.
• plot_geometry to quickly visualize entities from the sympy.geometry module.
• iplot function to create parametric-interactive plots using widgets (sliders, buttons, etc.).
• plotgrid function, which replaces the PlotGrid class: it allows to combine multiple plots into a grid-like layout. It works with Matplotlib, Bokeh and Plotly.

Please, read the following documentation page to understand the differences between this module and sympy.plotting.

If you feel like some feature could be implemented, open an issue or create a PR.

Explore the Capabilities

To explore the capabilities before the installation, you can:

1. Read the documentation, download this repository and follow the tutorials inside the tutorials folder.
2. Click the following button to run the tutorials with Binder (note that Binder is slow to load the module and execute the commands).

Installation

The repository is avaliable on PyPi:

pip install sympy_plot_backends

And also on Conda:

 conda install -c davide_sd sympy_plot_backends 

Warnings

Some backend comes with a memory cost. Since they require external libraries and/or open a server-process in order to visualize the data, memory usage can quickly rise if we are showing many plots. Keep an eye on you system monitor and act accordingly (close the kernels, restart the browser, etc.).