This package is a collection of easy-to-use commands for plotting using SymPy. It is designed to fit the requirements of the Mathematics 1 course at the Technical University of Denmark. This includes, but is not limited to:
- Plotting of vector fields and vectors
- 2D plotting of SymPy functions
- 3D plotting of SymPy functions
- Scatterplots in 2D and 3D
The plotting functions are an extension of sympy-plot-backends, and so documentation and examples of plots (for all functions but dtuplot.scatter() and dtuplot.quiver()) can be found at sympy-plot-backends.readthedocs.io/. Additional functionality and usage is given through course material.
To install dtumathtools using PyPi, run the following command
pip install dtumathtoolsThen one can import all the utility by writing
from dtumathtools import *All plotting functionality can be found using dtuplot.xxx (including all spb plotting functions).
Use is designed for the Mathematics 1 course at the Technical University of Denmark. Any use-case outside this scope is thus not considered, but very welcome!
Multiple different backends (plotting libraries) can be used (especially helpful for some 3D plots). Currently, Matplotlib (MB), Plotly (PB), Bokeh (BB, 2D only), and K3D-Jupyter (KB, 3D only) are supported (Mayavi (MAB, 3D only) might be supported in the future). The user can change the backend for a plot using the backend keyword argument (eg. backend=dtuplot.PB), or change the default (here for 3D, but similar for 2D) using:
dtuplot.cfg["backend_3D"] = "plotly"
dtuplot.set_defaults(dtuplot.cfg)For further descriptions of the strengths and benefits of each, see this page.
You are very welcome to contribute in the way that makes sense to you! The development team will consider all pull requests at the repo here. For changes to plotting functionality outside scatter and quiver, direct queries to spb.
The project would have never gotten off the ground without Jakob Lemvig and his engagement with SymPy in Mathematics 1. Thank you to Ulrik Engelund Pedersen for trusting us with this task. Finally, a huge thanks to the professors of the course for making great and engaging education: Michael Pedersen and Karsten Schmidt.
Open-source under the BSD license. This means that you are free to use it whatever you like, be it academic, commercial, creating forks or derivatives, as long as you copy the BSD statement if you redistribute it (see the LICENSE file for details).
