antropy
AntroPy: entropy and complexity of time-series in Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Numba functions (sample_entropy, higuchi_fd, detrended_fluctuation) incur a one-time compilation cost on first call.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
BSD (3-clause) (permissive) — BSD 3-clause is permissive; you can use, modify, and distribute AntroPy with minimal restrictions, provided you retain the license notice.
last release 2026-04-01 (135 days) · last repo commit 2026-04-01 · 377 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 195,866 downloads/mo, #9,803 on PyPI
Alternatives
Verify before relying
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))- 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
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 on this page. Verify before relying on it.
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
antropy on PyPI
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.
Requires Python 3.10 or later. Numba functions (sample_entropy, higuchi_fd, detrended_fluctuation) incur a one-time compilation cost on first call.
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))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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyscipyscikit-learnnumba |
| Maintenance | Actively maintained 135 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 195,866 / month, #9,803 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchOperating System :: MacOSOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Mathematics |
Evidence: antropy-0.2.2-py3-none-any.whl
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