PyKoopman: A Python Package for Data-Driven Approximation of the Koopman
  Operator
Shaowu Pan Eurika Kaiser Brian M. de Silva J. Nathan Kutz Steven L. Brunton
PyKoopman is a Python package for the data-driven approximation of the
Koopman operator associated with a dynamical system. The Koopman operator is a
principled linear embedding of nonlinear dynamics and facilitates the
prediction, estimation, and control of strongly nonlinear dynamics using linear
systems theory. In particular, PyKoopman provides tools for data-driven system
identification for unforced and actuated systems that build on the
equation-free dynamic mode decomposition (DMD) and its variants. In this work,
we provide a brief description of the mathematical underpinnings of the Koopman
operator, an overview and demonstration of the features implemented in
PyKoopman (with code examples), practical advice for users, and a list of
potential extensions to PyKoopman. Software is available at
http://github.com/dynamicslab/pykoopman