---
id: antropy
version: "0.2.2"
license: BSD (3-clause)
license_treatment: permissive
maintenance: active
---

# antropy — AntroPy: entropy and complexity of time-series in Python

License: permissive · Maintenance: active · Downloads: 195.9K/mo

## What it is and what it does

AntroPy is a Python library for extracting entropy and fractal dimension features from time-series data. It provides functions for computing permutation entropy, spectral entropy, sample entropy, approximate entropy, Lempel-Ziv complexity, and fractal dimensions (Petrosian, Katz, Higuchi, and detrended fluctuation analysis). The library is designed for speed through Numba JIT compilation and handles both single 1-D arrays and multi-channel N-D arrays in a single call.

Typical use cases include feature extraction from physiological signals like EEG, ECG, and EMG for signal processing research and analysis. The package depends on numpy, scipy, scikit-learn, and numba, and is actively maintained with support for Python 3.10 through 3.13.

Use it for:

- Extract entropy features from EEG recordings for brain activity classification or sleep stage detection
- Compute fractal dimension measures from ECG signals to assess heart rate complexity and detect arrhythmias
- Calculate signal regularity metrics from EMG data for motor control or fatigue analysis
- Process multi-channel physiological data in batch by passing N-D arrays with axis parameter
- Benchmark signal complexity changes over time or across experimental conditions using multiple entropy measures

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

AntroPy computes entropy and fractal dimension measures from time-series data, optimized for speed via Numba JIT compilation and designed to work on both 1-D and N-D arrays.

Yes. AntroPy is actively maintained, has no known vulnerabilities, uses a permissive BSD license, and installs with low friction. It provides a focused, well-documented toolkit for entropy and fractal dimension analysis with Numba-accelerated performance. Install if you need to extract complexity features from time-series data, especially physiological signals.

## Install

pip install antropy
uv add antropy
poetry add antropy

## Installing antropy

Before you install: Low friction install with a pure-Python wheel. Requires Python 3.10+ and four standard scientific dependencies (numpy, scipy, scikit-learn, numba). Package is actively maintained with recent releases and no known vulnerabilities.

License in practice: BSD 3-clause is permissive; you can use, modify, and distribute AntroPy with minimal restrictions, provided you retain the license notice.

Quickstart:

pip install antropy

import numpy as np
import antropy as ant

x = np.random.normal(size=3000)
print(ant.perm_entropy(x, normalize=True))
print(ant.spectral_entropy(x, sf=100, method='welch', normalize=True))
print(ant.hjorth_params(x, sf=100))

Requires Python 3.10 or later. Numba functions (sample_entropy, higuchi_fd, detrended_fluctuation) incur a one-time compilation cost on first call.

Verify before relying:

- Whether all entropy and fractal dimension functions support N-D arrays with axis parameter, or only a subset
- Specific performance characteristics on hardware other than MacBook Pro M1 Max

## Package facts

- License: BSD (3-clause) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 195.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

entropy measures time series, fractal dimension analysis, signal feature extraction, physiological signal processing, complexity analysis EEG ECG, time series regularity, spectral entropy permutation entropy, signal-processing, feature-extraction, time-series

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