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EntropyHub: An open-source toolkit for entropic data analysis

EntropyHub Git

See full documentation at www.EntropyHub.xyz

Available in MatLab // Python // Julia

Latest Update

v2.0

----- New multivariate methods -----
Five new multivariate entropy functions incorporating several method-specific variations
> Multivariate Sample Entropy
> Multivariate Fuzzy Entropy [++ many fuzzy functions]
> Multivariate Dispersion Entropy [++ many symbolic sequence transforms]
> Multivariate Cosine Similarity Entropy
> Multivariate Permutation Entropy [++ amplitude-aware, edge, phase, weighted and modified variants]

----- New multivariate multiscale methods -----
Two new multivariate multiscale entropy functions
> Multivariate Multiscale Entropy [++ coarse, modified and generalized graining procedures]
> Composite and Refined-composite Multivariate Multiscale Entropy

----- Extra signal processing tools -----
WindowData() is a new function that allows users to segment data (univariate or multivariate time series) into windows with/without overlapping samples! This allows users to calculate entropy on subsequences of their data to perform analyses with greater time resolution.

Other little fixes...

----- Docs edits -----
- Examples in the www.EntropyHub.xyz documentation were updated to match the latest package syntax.

More to come!

We are currently adding several new elements to EntropyHub that we hope will benefit many users. However, this is a time-consuming effort.
Keep checking in here to find out more in the future!
Thanks for all your support so far :)

About

Information and uncertainty can be regarded as two sides of the same coin: the more uncertainty there is, the more information we gain by removing that uncertainty. In the context of information and probability theory, Entropy quantifies that uncertainty.

Various measures have been derived to estimate entropy (uncertainty) from discrete data sequences, each seeking to best capture the uncertainty of the system under examination. This has resulted in many entropy statistics from approximate entropy and sample entropy, to multiscale sample entropy and refined-composite multiscale cross-sample entropy.

The goal of EntropyHub is to provide a comprehensive set of functions with a simple and consistent syntax that allows the user to augment parameters at the command line, enabling a range from basic to advanced entropy methods to be implemented with ease.

It is important to clarify that the entropy functions herein described estimate entropy in the context of probability theory and information theory as defined by Shannon, and not thermodynamic or other entropies from classical physics.

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Installation

To install EntropyHub with Matlab, Python or Julia, please follow the instructions given in the relevant folder above.

Requirements


MatLab

There are two additional MatLab toolboxes required to exploit the full functionality of the EntropyHub toolkit:

Signal Processing Toolbox and Statistics and Machine Learning Toolbox.

However, most functions will work without these toolboxes.

EntropyHub is intended for use with MatLab versions >= 2016a. In some cases the toolkit may work on versions 2015a and 2015b, but it is not recommended to install on MatLab versions older than 2016.


Python

There are several package dependencies which will be installed alongside EntropyHub: Numpy, Scipy, Matplotlib, PyEMD, Requests

EntropyHub was designed using Python 3 and thus is not intended for use with Python 2. Python versions > 3.6 are recommended for using EntropyHub.


Julia

There are several package dependencies which will be installed alongside EntropyHub (if not already installed):

DSP, FFTW, HTTP, Random, Plots, StatsBase, StatsFuns, GroupSlices, Statistics, DelimitedFiles, Combinatorics, LinearAlgebra, DataInterpolations, Clustering

EntropyHub was designed using Julia 1.5 and is intended for use with Julia versions >= 1.2.

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Documentation & Help

The EntropyHub Guide is a .pdf booklet written to help you use the toolkit effectively. (available here)

In this guide you will find descriptions of function syntax, examples of function use, and references to the source literature of each function.

The MatLab version of the toolkit has a comprehensive help section which can be accessed through the help browser.

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License and Terms of Use

EntropyHub is licensed under the Apache License (Version 2.0) and is free to use by all on condition that the following reference be included on any outputs realized using the software:

Matthew W. Flood (2021)
EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,
PLoS ONE 16(11):e0259448
DOI: 10.1371/journal.pone.0259448
www.EntropyHub.xyz


    © Copyright 2024 Matthew W. Flood, EntropyHub
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
    
             http://www.apache.org/licenses/LICENSE-2.0
    
    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
    
    For Terms of Use see https://github.com/MattWillFlood/EntropyHub

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Contact

If you find this package useful, please consider starring it on GitHub, MatLab File Exchange, PyPI or Julia Packages as this helps us to gauge user satisfaction.

If you have any questions about the package or identify any issues, please do not hesitate to contact us at: [email protected]

Thank you for using EntropyHub.

Matt

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Functions

EntropyHub functions fall into 8 categories:

* Base                       functions for estimating the entropy of a single univariate time series.
* Cross                      functions for estimating the entropy between two univariate time series.
* Multivariate               functions for estimating the entropy of a multivariate dataset.
* Bidimensional              functions for estimating the entropy of a two-dimensional univariate matrix.
* Multiscale                 functions for estimating the multiscale entropy of a single univariate time series using any of the Base entropy functions.
* Multiscale Cross           functions for estimating the multiscale entropy between two univariate time series using any of the Cross-entropy functions.
* Multivariate Multiscale    functions for estimating the multivariate multiscale entropy of multivariate dataset using any of the Multivariate-entropy functions.
* Other                      Supplementary functions for various tasks related to EntropyHub and signal processing.

The following tables outline the functions available in the EntropyHub package.

When new entropies are published in the scientific literature, efforts will be made to incorporate them in future releases.

Base Entropies:

Entropy Type Function Name
Approximate Entropy ApEn
Sample Entropy SampEn
Fuzzy Entropy FuzzEn
Kolmogorov Entropy K2En
Permutation Entropy PermEn
Conditional Entropy CondEn
Distribution Entropy DistEn
Spectral Entropy SpecEn
Dispersion Entropy DispEn
Symbolic Dynamic Entropy SyDyEn
Increment Entropy IncrEn
Cosine Similarity Entropy CoSiEn
Phase Entropy PhasEn
Slope Entropy SlopEn
Bubble Entropy BubbEn
Gridded Distribution Entropy GridEn
Entropy of Entropy EnofEn
Attention Entropy AttnEn
Range Entropy RangEn
Diversity Entropy DivEn

Cross Entropies:

Entropy Type Function Name
Cross Sample Entropy XSampEn
Cross Approximate Entropy XApEn
Cross Fuzzy Entropy XFuzzEn
Cross Permutation Entropy XPermEn
Cross Conditional Entropy XCondEn
Cross Distribution Entropy XDistEn
Cross Spectral Entropy XSpecEn
Cross Kolmogorov Entropy XK2En

Multivariate Entropies:

Entropy Type Function Name
Multivariate Sample Entropy MvSampEn
Multivariate Fuzzy Entropy MvFuzzEn
Multivariate Permutation Entropy MvPermEn
Multivariate Dispersion Entropy MvDispEn
Multivariate Cosine Similarity Entropy MvCoSiEn

Bidimensional Entropies

Entropy Type Function Name
Bidimensional Sample Entropy SampEn2D
Bidimensional Fuzzy Entropy FuzzEn2D
Bidimensional Distribution Entropy DistEn2D
Bidimensional Dispersion Entropy DispEn2D
Bidimensional Permutation Entropy PermEn2D
Bidimensional Espinosa Entropy EspEn2D

Multiscale Entropy Functions

Entropy Type Function Name
Multiscale Entropy MSEn
Composite/Refined-Composite Multiscale Entropy cMSEn
Refined Multiscale Entropy rMSEn
Hierarchical Multiscale Entropy hMSEn

Multiscale Cross-Entropy Functions

Entropy Type Function Name
Multiscale Cross-Entropy XMSEn
Composite/Refined-Composite Multiscale Cross-Entropy cXMSEn
Refined Multiscale Cross-Entropy rXMSEn
Hierarchical Multiscale Cross-Entropy hXMSEn

Multivariate Multiscale Entropy Functions

Entropy Type Function Name
Multivariate Multiscale Entropy MvMSEn
Composite/Refined-Composite Multivariate Multiscale Entropy cMvMSEn

Other Functions

Entropy Type Function Name
Example Data Import Tool ExampleData
Window Data Tool WindowData
Multiscale Entropy Object MSobject
     ___  _   _  _____  _____  ____  ____  _     _          
    |  _|| \ | ||_   _||     \|    ||    || \   / |   ___________ 
    | \_ |  \| |  | |  |   __/|    ||  __| \ \_/ /   /  _______  \
    |  _|| \ \ |  | |  |   \  |    || |     \   /   |  /  ___  \  |
    | \_ | |\  |  | |  | |\ \ |    || |      | |    | |  /   \  | | 
    |___||_| \_|  |_|  |_| \_||____||_|      |_|   _|_|__\___/  | | 
     _   _  _   _  ____                           / |__\______\/  | 
    | | | || | | ||    \     An open-source      |  /\______\__|_/ 
    | |_| || | | ||    |     toolkit for         | |  /   \  | | 
    |  _  || | | ||    \     entropic time-      | |  \___/  | |          
    | | | || |_| ||     \    series analysis     |  \_______/  |
    |_| |_|\_____/|_____/                         \___________/