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 <https://raphaelvallat.com/antropy/build/html/index.html>_

Installation

AntroPy can be installed with pip

.. code-block:: shell

pip install antropy

or conda

.. code-block:: shell

conda config --add channels conda-forge conda config --set channel_priority strict conda install antropy

To build and install from source, clone this repository or download the source archive and decompress the files

.. code-block:: shell

cd antropy pip install ".[test]" # install the package pip install -e ".[test]" # or editable install pytest

Dependencies

numpy <https://numpy.org/>_
scipy <https://www.scipy.org/>_
scikit-learn <https://scikit-learn.org/>_
numba <http://numba.pydata.org/>_

Functions

Entropy

.. code-block:: python

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))

.. parsed-literal::

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

Fractal dimension

.. code-block:: python

# 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))

.. parsed-literal::

1.0310643385753608
5.954272156665926
2.005040632258251
0.47903505674073327

Execution time


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

.. code-block:: python

    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

.. parsed-literal::

    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 <https://raphaelvallat.com>`_. 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 <https://github.com/raphaelvallat/antropy>`_.

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.

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