joseph-crowley/dynamic-mode-decomposition

Uncover the hidden dynamics in complex data with Dynamic Mode Decomposition, a powerful tool for insightful time-series analysis and predictive modeling.

Dynamic Mode Decomposition (DMD)

Dynamic Mode Decomposition (DMD) is a powerful analysis tool for capturing the essential features of complex and high-dimensional datasets, particularly time-series data. This Python package provides a straightforward and flexible implementation of DMD, suitable for a wide range of applications from fluid dynamics to financial modeling.

Table of Contents

• Installation
• Quick Start
• Documentation
• Examples
• License
• References

Installation

To install the Dynamic Mode Decomposition package, you can clone the repository and install the dependencies with pip:

git clone https://github.com/joseph-crowley/dynamic-mode-decomposition.git
cd dynamic-mode-decomposition
pip install -r requirements.txt

Quick Start

To use DMD in your project, simply import the dmd function from the core module in your main.py script:

# ./main.py

from dmd.core import dmd

# Your data matrix X
# X = ...

# Applying DMD with a specified rank
Phi, Lambda, b = dmd(X, rank=2)

Alternatively, you can copy one of the examples to main.py:

cp examples/harmonic_oscillator.py main.py

Documentation

Documentation is available in the docs folder. Start with dmd_explained.md for an explanation of the Dynamic Mode Decomposition algorithm and its applications.

Examples

For practical examples, check out the examples directory. The harmonic_oscillator.py script demonstrates how to apply DMD to a simple harmonic oscillator system, including visualizations of the results and advanced analyses.

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

• "Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems" by J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, and Joshua L. Proctor. link