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
- Sample Entropy
- Multiscale Entropy
- Composite Multiscale Entropy
- Permutation Entropy
- Multiscale Permutation Entropy
- Weighted Permutation Entropy
Install pyEntropy using pip:
pip install pyentrp
Install pyEntropy using poetry:
poetry add pyentrp
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:
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!