{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"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.","skillfed_tags":["signal-processing","feature-extraction","time-series"],"use_cases":["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"],"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.\n\nTypical 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.","worth_installing":"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."},"id":"antropy","links":{"html":"https://skillfed.io/packages/antropy","md":"https://skillfed.io/packages/antropy.md","pypi":"https://pypi.org/project/antropy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-01","license_spdx":null,"license_treatment":"permissive","name":"antropy","python_support":"supports_current","summary":"AntroPy: entropy and complexity of time-series in Python"},"popularity":{"monthly_downloads":195866,"position":9803,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.2"}