pykoopman 0.1.0

Python package for computing data-driven approximations to the Koopman operator.

PyKoopman is a Python package for computing data-driven approximations to the Koopman operator. TODO: improve description

Installation

Installing with pip

If you are using Linux or macOS you can install PyKoopman with pip:

pip install pykoopman

Installing from source

First clone this repository:

git clone https://github.com/dynamicslab/pykoopman

Then, to install the package, run

pip install .

If you do not have pip you can instead use

python setup.py install

If you do not have root access, you should add the --user option to the above lines.

Documentation

The documentation for PyKoopman is hosted on Read the Docs.

Community guidelines

Contributing code

We welcome contributions to PyKoopman. To contribute a new feature please submit a pull request. To get started we recommend installing the packages in requirements-dev.txt via

pip install -r requirements-dev.txt

This will allow you to run unit tests and automatically format your code. To be accepted your code should conform to PEP8 and pass all unit tests. Code can be tested by invoking

pytest

We recommed using pre-commit to format your code. Once you have staged changes to commit

git add path/to/changed/file.py

you can run the following to automatically reformat your staged code

pre-commit -a -v

Note that you will then need to re-stage any changes pre-commit made to your code.

Reporting issues or bugs

If you find a bug in the code or want to request a new feature, please open an issue.

References

TODO: Add appropriate references

• Williams, Matthew O., Ioannis G. Kevrekidis, and Clarence W. Rowley. “A data–driven approximation of the koopman operator: Extending dynamic mode decomposition.” Journal of Nonlinear Science 25, no. 6 (2015): 1307-1346. [DOI]