/ antropy Public

AntroPy: entropy and complexity of (EEG) time-series in Python

# raphaelvallat/antropy

Switch branches/tags
Nothing to show

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

## Files

Failed to load latest commit information.
Type
Name
Commit time

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.

## Installation

AntroPy can be installed with pip

pip install antropy

or conda

conda config --set channel_priority strict
conda 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:

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

AntroPy: entropy and complexity of (EEG) time-series in Python

v0.1.6 Latest
Jul 29, 2023

+ 89