| Name | antropy JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | None |
| Summary | AntroPy: entropy and complexity of time-series in Python |
| upload_time | 2024-12-21 12:58:14 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD (3-clause) |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
.. -*- mode: rst -*-
|
.. image:: https://img.shields.io/github/license/raphaelvallat/antropy.svg
:target: https://github.com/raphaelvallat/antropy/blob/master/LICENSE
.. image:: https://github.com/raphaelvallat/antropy/actions/workflows/python_tests.yml/badge.svg
:target: https://github.com/raphaelvallat/antropy/actions/workflows/python_tests.yml
.. image:: https://codecov.io/gh/raphaelvallat/antropy/branch/master/graph/badge.svg
:target: https://codecov.io/gh/raphaelvallat/antropy
----------------
.. figure:: https://github.com/raphaelvallat/antropy/blob/master/docs/pictures/logo.png?raw=true
:align: center
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/>`_
- `stochastic <https://github.com/crflynn/stochastic>`_
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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"description": ".. -*- mode: rst -*-\n\n|\n\n.. image:: https://img.shields.io/github/license/raphaelvallat/antropy.svg\n :target: https://github.com/raphaelvallat/antropy/blob/master/LICENSE\n\n.. image:: https://github.com/raphaelvallat/antropy/actions/workflows/python_tests.yml/badge.svg\n :target: https://github.com/raphaelvallat/antropy/actions/workflows/python_tests.yml\n\n.. image:: https://codecov.io/gh/raphaelvallat/antropy/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/raphaelvallat/antropy\n\n----------------\n\n.. figure:: https://github.com/raphaelvallat/antropy/blob/master/docs/pictures/logo.png?raw=true\n :align: center\n\nAntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series.\nIt can be used for example to extract features from EEG signals.\n\nDocumentation\n=============\n\n- `Link to documentation <https://raphaelvallat.com/antropy/build/html/index.html>`_\n\nInstallation\n============\n\nAntroPy can be installed with pip\n\n.. code-block:: shell\n\n pip install antropy\n\nor conda\n\n.. code-block:: shell\n\n conda config --add channels conda-forge\n conda config --set channel_priority strict\n conda install antropy\n\nTo build and install from source, clone this repository or download the source archive and decompress the files\n\n.. code-block:: shell\n\n cd antropy\n pip install \".[test]\" # install the package\n pip install -e \".[test]\" # or editable install\n pytest\n\n**Dependencies**\n\n- `numpy <https://numpy.org/>`_\n- `scipy <https://www.scipy.org/>`_\n- `scikit-learn <https://scikit-learn.org/>`_\n- `numba <http://numba.pydata.org/>`_\n- `stochastic <https://github.com/crflynn/stochastic>`_\n\nFunctions\n=========\n\n**Entropy**\n\n.. code-block:: python\n\n import numpy as np\n import antropy as ant\n np.random.seed(1234567)\n x = np.random.normal(size=3000)\n # Permutation entropy\n print(ant.perm_entropy(x, normalize=True))\n # Spectral entropy\n print(ant.spectral_entropy(x, sf=100, method='welch', normalize=True))\n # Singular value decomposition entropy\n print(ant.svd_entropy(x, normalize=True))\n # Approximate entropy\n print(ant.app_entropy(x))\n # Sample entropy\n print(ant.sample_entropy(x))\n # Hjorth mobility and complexity\n print(ant.hjorth_params(x))\n # Number of zero-crossings\n print(ant.num_zerocross(x))\n # Lempel-Ziv complexity\n print(ant.lziv_complexity('01111000011001', normalize=True))\n\n.. parsed-literal::\n\n 0.9995371694290871\n 0.9940882825422431\n 0.9999110978316078\n 2.015221318528564\n 2.198595813245399\n (1.4313385010057378, 1.215335712274099)\n 1531\n 1.3597696150205727\n\n**Fractal dimension**\n\n.. code-block:: python\n\n # Petrosian fractal dimension\n print(ant.petrosian_fd(x))\n # Katz fractal dimension\n print(ant.katz_fd(x))\n # Higuchi fractal dimension\n print(ant.higuchi_fd(x))\n # Detrended fluctuation analysis\n print(ant.detrended_fluctuation(x))\n\n.. parsed-literal::\n\n 1.0310643385753608\n 5.954272156665926\n 2.005040632258251\n 0.47903505674073327\n\nExecution time\n~~~~~~~~~~~~~~\n\nHere are some benchmarks computed on a MacBook Pro (2020).\n\n.. code-block:: python\n\n import numpy as np\n import antropy as ant\n np.random.seed(1234567)\n x = np.random.rand(1000)\n # Entropy\n %timeit ant.perm_entropy(x)\n %timeit ant.spectral_entropy(x, sf=100)\n %timeit ant.svd_entropy(x)\n %timeit ant.app_entropy(x) # Slow\n %timeit ant.sample_entropy(x) # Numba\n # Fractal dimension\n %timeit ant.petrosian_fd(x)\n %timeit ant.katz_fd(x)\n %timeit ant.higuchi_fd(x) # Numba\n %timeit ant.detrended_fluctuation(x) # Numba\n\n.. parsed-literal::\n\n 106 \u00b5s \u00b1 5.49 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n 138 \u00b5s \u00b1 3.53 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n 40.7 \u00b5s \u00b1 303 ns per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n 2.44 ms \u00b1 134 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n 2.21 ms \u00b1 35.4 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n 23.5 \u00b5s \u00b1 695 ns per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n 40.1 \u00b5s \u00b1 2.09 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n 13.7 \u00b5s \u00b1 251 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\n 315 \u00b5s \u00b1 10.7 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n\nDevelopment\n===========\n\nAntroPy was created and is maintained by `Raphael Vallat <https://raphaelvallat.com>`_. 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