  The dynamic mode decomposition (DMD) is a simple and powerful data-driven
modeling technique that is capable of revealing coherent spatiotemporal
patterns from data. The method's linear algebra-based formulation additionally
allows for a variety of optimizations and extensions that make the algorithm
practical and viable for real-world data analysis. As a result, DMD has grown
to become a leading method for dynamical system analysis across multiple
scientific disciplines. PyDMD is a Python package that implements DMD and
several of its major variants. In this work, we expand the PyDMD package to
include a number of cutting-edge DMD methods and tools specifically designed to
handle dynamics that are noisy, multiscale, parameterized, prohibitively
high-dimensional, or even strongly nonlinear. We provide a complete overview of
the features available in PyDMD as of version 1.0, along with a brief overview
of the theory behind the DMD algorithm, information for developers, tips
regarding practical DMD usage, and introductory coding examples. All code is
available at https://github.com/PyDMD/PyDMD .
