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

Project description

pyEntropy (pyEntrp)

py39 status py310 status py311 status py312 status

  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


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:

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!

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