pyentrp 2.1.0

A Python library for computing entropy measures for time series analysis.

pyEntropy (pyEntrp)

1. Quick start
2. Usage
3. Contributors and participation

pyEntropy is a lightweight library built on top of NumPy that provides functions for computing various types of entropy for time series analysis.

The library currently supports the following types of entropy computation:

• Shannon Entropy shannon_entropy
• Sample Entropy sample_entropy
• Multiscale Entropy multiscale_entropy
• Composite Multiscale Entropy composite_multiscale_entropy
• Permutation Entropy permutation_entropy
• Multiscale Permutation Entropy multiscale_permutation_entropy
• Weighted Permutation Entropy weighted_permutation_entropy

Quick start

Install pyEntropy using pip:

pip install pyentrp

Install pyEntropy using poetry:

poetry add pyentrp

Usage

from pyentrp import entropy as ent
import numpy as np

ts = [1, 4, 5, 1, 7, 3, 1, 2, 5, 8, 9, 7, 3, 7, 9, 5, 4, 3]
std_ts = np.std(ts)
sample_entropy = ent.sample_entropy(ts, 4, 0.2 * std_ts)

Contributors and participation

pyEntropy is an open-source project, and contributions are highly encouraged. If you would like to contribute, you can:

• Fork the repository and submit pull requests with your improvements, bug fixes, or new features.
• Report any issues or bugs you encounter on the issue tracker.
• Help improve the documentation by submitting documentation improvements or corrections.
• Participate in discussions and share your ideas.

The following contributors have made significant contributions to pyEntropy:

• Nikolay Donets
• Jakob Dreyer
• Raphael Vallat
• Christopher Schölzel
• Sam Dotson

Contributions are very welcome, documentation improvements/corrections, bug reports, even feature requests :)

If you find pyEntropy useful, please consider giving it a star.

Your support is greatly appreciated!