entropyhub
An open-source toolkit for entropic data analysis.
Science Score: 49.0%
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Repository
An open-source toolkit for entropic data analysis.
Basic Info
- Host: GitHub
- Owner: MattWillFlood
- License: apache-2.0
- Language: TeX
- Default Branch: main
- Homepage: https://www.entropyhub.xyz
- Size: 56.2 MB
Statistics
- Stars: 167
- Watchers: 6
- Forks: 23
- Open Issues: 0
- Releases: 2
Topics
Metadata Files
README.md
EntropyHub: An open-source toolkit for entropic data analysis
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 2025 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: info@entropyhub.xyz
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 | \_______/ |
|_| |_|\_____/|_____/ \___________/
Owner
- Name: Matt
- Login: MattWillFlood
- Kind: user
- Website: https://mattwillflood.github.io/Bio/
- Repositories: 3
- Profile: https://github.com/MattWillFlood
GitHub Events
Total
- Issues event: 8
- Watch event: 20
- Issue comment event: 5
- Push event: 37
- Fork event: 3
Last Year
- Issues event: 8
- Watch event: 20
- Issue comment event: 5
- Push event: 37
- Fork event: 3
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Matt | m****d@g****m | 204 |
| github-actions[bot] | 4****] | 121 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 18
- Total pull requests: 0
- Average time to close issues: about 2 months
- Average time to close pull requests: N/A
- Total issue authors: 16
- Total pull request authors: 0
- Average comments per issue: 2.06
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 7
- Pull requests: 0
- Average time to close issues: 13 days
- Average time to close pull requests: N/A
- Issue authors: 7
- Pull request authors: 0
- Average comments per issue: 0.57
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
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Issue Authors
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- AashishKhilnani (2)
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- Ildaron (1)
- AceKijani (1)
- JulianKlug (1)
- YingFan-Chen (1)
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- yang-xiaosheng (1)
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Total downloads:
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- Total versions: 8
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pypi.org: entropyhub
An open-source toolkit for entropic data analysis.
- Homepage: https://www.EntropyHub.xyz/
- Documentation: https://www.entropyhub.xyz/
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