nodepy 1.1.1

A package for the analysis of numerical ODE solvers

NodePy: A package for the analysis of numerical ODE solvers

Installation

NodePy requires Python 3.5 or later. To install with pip, do:

pip install nodepy

This will automatically fetch dependencies also. It will not fetch optional dependencies, which include networkx, cvxpy and scipy (that are used only in a few specialized routines and/or examples). The optional dependencies can be installed with pip.

Overview

NodePy (Numerical ODEs in Python) is a Python package for designing, analyzing, and testing numerical methods for initial value ODEs. Its development was motivated by my own research in time integration methods for PDEs. I found that I was frequently repeating tasks that could be automated and integrated. Initially I developed a collection of MATLAB scripts, but this became unwieldy due to the large number of files that were necessary and the more limited capability for code reuse.

NodePy represents an object-oriented approach, in which the basic object is a numerical ODE solver. The idea is to design a laboratory for such methods in the same sense that MATLAB is a laboratory for matrices.

Documentation can be found online at

http://nodepy.readthedocs.org/en/latest/

To get started, you can also have a look at the examples folder, beginning with an introduction as Jupyter notebook.

The development version can be obtained from

http://github.com/ketch/nodepy

Citation

If you use NodePy in a published work, please cite it as follows:

Ketcheson, D. I.  NodePy software version <version number>,
http://github.com/ketch/nodepy/.

Please insert the version number that you used.

Support

If you encounter an error or need help, please raise an issue.

Contributing

Contributions of new features or other improvements are very welcome! Please submit a pull request or contact the authors.

License

NodePy is distributed under the terms of the modified Berkeley Software Distribution (BSD) license.

Funding

NodePy development has been supported by:

• A U.S. Dept. of Energy Computational Science Graduate Fellowship
• Grants from King Abdullah University of Science & Technology