tcchriszhao/antropy

https://travis-ci.org/raphaelvallat/antropy.svg?branch=master

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals.

Documentation

Link to documentation

Installation

pip install antropy

Dependencies

Functions

Entropy

import numpy as np
import antropy as ant
np.random.seed(1234567)
x = np.random.normal(size=3000)
# Permutation entropy
print(ant.perm_entropy(x, normalize=True))
# Spectral entropy
print(ant.spectral_entropy(x, sf=100, method='welch', normalize=True))
# Singular value decomposition entropy
print(ant.svd_entropy(x, normalize=True))
# Approximate entropy
print(ant.app_entropy(x))
# Sample entropy
print(ant.sample_entropy(x))
# Hjorth mobility and complexity
print(ant.hjorth_params(x))
# Number of zero-crossings
print(ant.num_zerocross(x))
# Lempel-Ziv complexity
print(ant.lziv_complexity('01111000011001', normalize=True))

0.9995371694290871
0.9940882825422431
0.9999110978316078
2.015221318528564
2.198595813245399
(1.4313385010057378, 1.215335712274099)
1531
1.3597696150205727

Fractal dimension

# Petrosian fractal dimension
print(ant.petrosian_fd(x))
# Katz fractal dimension
print(ant.katz_fd(x))
# Higuchi fractal dimension
print(ant.higuchi_fd(x))
# Detrended fluctuation analysis
print(ant.detrended_fluctuation(x))

1.0310643385753608
5.954272156665926
2.005040632258251
0.47903505674073327

Execution time

Here are some benchmarks computed on a MacBook Pro (2020).

import numpy as np
import antropy as ant
np.random.seed(1234567)
x = np.random.rand(1000)
# Entropy
%timeit ant.perm_entropy(x)
%timeit ant.spectral_entropy(x, sf=100)
%timeit ant.svd_entropy(x)
%timeit ant.app_entropy(x)  # Slow
%timeit ant.sample_entropy(x)  # Numba
# Fractal dimension
%timeit ant.petrosian_fd(x)
%timeit ant.katz_fd(x)
%timeit ant.higuchi_fd(x) # Numba
%timeit ant.detrended_fluctuation(x) # Numba

106 µs ± 5.49 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
138 µs ± 3.53 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
40.7 µs ± 303 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
2.44 ms ± 134 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.21 ms ± 35.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
23.5 µs ± 695 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
40.1 µs ± 2.09 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
13.7 µs ± 251 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
315 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Development

AntroPy was created and is maintained by Raphael Vallat. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!

To see the code or report a bug, please visit the GitHub repository.

Note that this program is provided with NO WARRANTY OF ANY KIND. Always double check the results.

Acknowledgement

Several functions of AntroPy were adapted from:

MNE-features: https://github.com/mne-tools/mne-features
pyEntropy: https://github.com/nikdon/pyEntropy
pyrem: https://github.com/gilestrolab/pyrem
nolds: https://github.com/CSchoel/nolds

All the credit goes to the author of these excellent packages.

tcchriszhao / antropy