diff --git a/docs/EHupdates.rst b/docs/EHupdates.rst index 76e40c8..415e905 100644 --- a/docs/EHupdates.rst +++ b/docs/EHupdates.rst @@ -14,11 +14,68 @@ Latest Updates .............................................................. -January 2024 +March 2024 ############ -**New announcements coming soon! :)** - +Version 1.0 is here! +==================== + +| EntropyHub is continuously growing to incorporate the lastest developments in the scientific literature. +| This new major release (v1.0) reflects that with many new functions and features to provide you with a versatile environment that makes complex entropy methods easy to implement. +| The following list summarizes some of the main updates available in v1.0. + + **+ New entropy methods** + | Two new base entropy functions (and their multiscale versions) have been added: + | + `Diversity Entropy `_ + | + `Range Entropy `_ + + **+ New fuzzy membership functions** + | Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors. + | These include *trapezoidal*, *triangular* and *gaussian*, among others. + | Further info on these membership functions can be found `here. `_ + + **+ Phase Permutation Entropy** + | A new variant - '*phase*' permutation entropy - has been added to PermEn. + | This method employs a hilbert transformation of the data sequence, based on the methods outlined `here. `_ + + **+ Cross-Entropy with different length sequences** + | EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (*except XCondEn and XPermEn*). + | As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2). + + **+ Refined-Composite Multiscale Fuzzy Entropy** + | In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined `here. `_ + | What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn. + + **+ Generalized Multiscale Entropy** + | Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the '*generalized*' as the graining procedure in MSEn or XMSEn. + + **+ Variance of sample entropy estimate** + | Based on the `method outlined by Lake et al., `_ it is now possible to obtain a measure of the variance in the sample entropy estimate. + | This is achieved by approximating the number of overlapping embedding vectors. + | To do so, just set the parameter '*Vcp*'==true in SampEn and XSampEn, but note that doing so requires *a lot* of computer memory. + +Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs. +Most of these bugs have been noted via the `GitHub issues portal `_. + + **Bug fixes** + | - The DispEn2D function in python has now fixed `this issue `_. + | - The type hint for FuzzEn in python has been updated `as requested `_. + | - `Compatbility issues with EntropyHub.jl `_ are now resolved. + | - A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected. + + **Other Changes** + | - The *'equal'* method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. + | This is unlikely to have impacted any users previously. + | - The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1). + | - The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the *pooled* standard deviation [i.e. 0.2*SDpooled(X,Y)]. + + +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 :) .............................................................. diff --git a/docs/Home.rst b/docs/Home.rst index a92beea..3c386dd 100644 --- a/docs/Home.rst +++ b/docs/Home.rst @@ -9,7 +9,7 @@ EntropyHub ********** -An open-source toolkit for entropic time series analysis +An open-source toolkit for entropic data analysis ######################################################## .. image:: ./_images/EntropyHub_Profiler.png @@ -32,30 +32,22 @@ An open-source toolkit for entropic time series analysis * `Take part in our user survey `_ -Welcome -####### - -Welcome to EntropyHub! - -This toolkit provides a wide range of functions to calculate different entropy statistics. +Welcome! +######## -There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. - -The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +| This toolkit provides a wide range of functions to calculate different entropy statistics. +| There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. +| The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +......................................................................................................... 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. +In the context of dynamical systems and information theory, **Entropy** quantifies that uncertainty. -The concept of entropy has its origins in `classical physics `_ under the second law of thermodynamics, -a law `considered to underpin our fundamental understanding `_ -of `time in physics `_. In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises @@ -68,25 +60,21 @@ To overcome this, we have developed EntropyHub - an open-source toolkit designed 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.** +.. note:: -......................................................................................................... + It is important to clarify that the entropy functions herein described estimate entropy in the context of nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics. +......................................................................................................... Documentation & Help #################### -The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively -(available for :download:`download here <./_static/EntropyHubGuide.pdf>`). - -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.* - +| The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively (available for :download:`download here <./_static/EntropyHubGuide.pdf>`). +| 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 MatLab help browser under* **Supplementary Software**. ......................................................................................................... - Citation and Licensing ###################### @@ -94,7 +82,7 @@ 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 scientific outputs realized using the software: - | **Matthew W. Flood and Bernd Grimm,** + | **Matthew W. Flood** | **EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,** | **PLoS One 16(11):e0259448 (2021),** | **DOI: 10.1371/journal.pone.0259448** @@ -104,7 +92,7 @@ realized using the software: __________________________________________________________________ - © Copyright 2021 Matthew W. Flood, EntropyHub + © 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. diff --git a/docs/_build/doctrees/EHupdates.doctree b/docs/_build/doctrees/EHupdates.doctree index e95a4db..172a03f 100644 Binary files a/docs/_build/doctrees/EHupdates.doctree and b/docs/_build/doctrees/EHupdates.doctree differ diff --git a/docs/_build/doctrees/Home.doctree b/docs/_build/doctrees/Home.doctree index aceb8d6..8b0504e 100644 Binary files a/docs/_build/doctrees/Home.doctree and b/docs/_build/doctrees/Home.doctree differ diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle index a3640f3..b1f1e5d 100644 Binary files a/docs/_build/doctrees/environment.pickle and b/docs/_build/doctrees/environment.pickle differ diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree index aefcfc6..06be7de 100644 Binary files a/docs/_build/doctrees/index.doctree and 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used when building these files. When it is not found, a full rebuild will be done. -config: fcb175b2998db6b15323ea5f97d34dc3 +config: a08dde90a3c13e8b0243414d25c4c8a4 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/EHupdates.html b/docs/_build/html/EHupdates.html index 905173a..8281226 100644 --- a/docs/_build/html/EHupdates.html +++ b/docs/_build/html/EHupdates.html @@ -4,7 +4,7 @@ - Latest Updates — EntropyHub 0.2 documentation + Latest Updates — EntropyHub 1.0 documentation @@ -161,9 +161,87 @@

Latest Updates
-
-

January 2024

-

New announcements coming soon! :)

+
+

March 2024

+
+

Version 1.0 is here!

+
+
EntropyHub is continuously growing to incorporate the lastest developments in the scientific literature.
+
This new major release (v1.0) reflects that with many new functions and features to provide you with a versatile environment that makes complex entropy methods easy to implement.
+
The following list summarizes some of the main updates available in v1.0.
+
+
+
+
+ New entropy methods
+
Two new base entropy functions (and their multiscale versions) have been added:
+
+ Diversity Entropy
+
+ Range Entropy
+
+
+
+ New fuzzy membership functions
+
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
+
These include trapezoidal, triangular and gaussian, among others.
+
Further info on these membership functions can be found here.
+
+
+
+ Phase Permutation Entropy
+
A new variant - ‘phase’ permutation entropy - has been added to PermEn.
+
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.
+
+
+
+ Cross-Entropy with different length sequences
+
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
+
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).
+
+
+
+ Refined-Composite Multiscale Fuzzy Entropy
+
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
+
What’s more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.
+
+
+
+ Generalized Multiscale Entropy
+
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the ‘generalized’ as the graining procedure in MSEn or XMSEn.
+
+
+
+ Variance of sample entropy estimate
+
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
+
This is achieved by approximating the number of overlapping embedding vectors.
+
To do so, just set the parameter ‘Vcp’==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.
+
+
+
+
+

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs. +Most of these bugs have been noted via the GitHub issues portal.

+
+
+
Bug fixes
+
- The DispEn2D function in python has now fixed this issue.
+
- The type hint for FuzzEn in python has been updated as requested.
+
- Compatbility issues with EntropyHub.jl are now resolved.
+
- A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.
+
+
+
Other Changes
+
- The ‘equal’ method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia.
+
+
This is unlikely to have impacted any users previously.
+
+
- The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
+
- The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].
+
+
+
+
+
+
+

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 :)
+
+
@@ -266,7 +344,7 @@

June 2021 -

© Copyright 2023, Matthew W. Flood.

+

© Copyright 2024, Matthew W. Flood.

diff --git a/docs/_build/html/Home.html b/docs/_build/html/Home.html index 9462cc4..ea47a09 100644 --- a/docs/_build/html/Home.html +++ b/docs/_build/html/Home.html @@ -4,7 +4,7 @@ - EntropyHub — EntropyHub 0.2 documentation + EntropyHub — EntropyHub 1.0 documentation @@ -159,8 +159,8 @@

EntropyHub

-
-

An open-source toolkit for entropic time series analysis

+
+

An open-source toolkit for entropic data analysis

_images/EntropyHub_Profiler.png

Available in:

@@ -172,23 +172,22 @@

An open-source toolkit for entropic time series analysis -

Welcome

-

Welcome to EntropyHub!

-

This toolkit provides a wide range of functions to calculate different entropy statistics.

-

There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax.

-

The goal of EntropyHub is to integrate the many established entropy methods in one open-source package.

+

Welcome!

+
+
This toolkit provides a wide range of functions to calculate different entropy statistics.
+
There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature.
+
Although many functions for estimating these entropies can be found in various corners of the internet,
+
there is currently no toolkit to perform entropic data analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax.
+
The goal of EntropyHub is to integrate the many established entropy methods in one open-source package.
+
+

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.

-

The concept of entropy has its origins in classical physics under the second law of thermodynamics, -a law considered to underpin our fundamental understanding -of time in physics. -In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used +In the context of dynamical systems and information theory, Entropy quantifies that uncertainty.

+

In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises our ability to measure entropy accurately. Various measures have been derived to estimate entropy (uncertainty) from discrete time series, each seeking to @@ -198,15 +197,19 @@

About To overcome this, we have developed EntropyHub - an open-source toolkit designed to integrate the many established entropy methods into one package. 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.

+
+

Note

+

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

+

Documentation & Help

-

The EntropyHub Guide is a .pdf booklet written to help you use the toolkit effectively -(available for download 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.

+
+
The EntropyHub Guide is a .pdf booklet written to help you use the toolkit effectively (available for download 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 MatLab help browser under Supplementary Software.
+
@@ -216,7 +219,7 @@

Citation and Licensing
-

© Copyright 2021 Matthew W. Flood, EntropyHub

+ diff --git a/docs/_build/html/Publications.html b/docs/_build/html/Publications.html index 90528bc..fba0020 100644 --- a/docs/_build/html/Publications.html +++ b/docs/_build/html/Publications.html @@ -4,7 +4,7 @@ - Publications — EntropyHub 0.2 documentation + Publications — EntropyHub 1.0 documentation @@ -573,7 +573,7 @@

2022<
-

© Copyright 2023, Matthew W. Flood.

+

© Copyright 2024, Matthew W. Flood.

diff --git a/docs/_build/html/_sources/EHupdates.rst.txt b/docs/_build/html/_sources/EHupdates.rst.txt index 76e40c8..415e905 100644 --- a/docs/_build/html/_sources/EHupdates.rst.txt +++ b/docs/_build/html/_sources/EHupdates.rst.txt @@ -14,11 +14,68 @@ Latest Updates .............................................................. -January 2024 +March 2024 ############ -**New announcements coming soon! :)** - +Version 1.0 is here! +==================== + +| EntropyHub is continuously growing to incorporate the lastest developments in the scientific literature. +| This new major release (v1.0) reflects that with many new functions and features to provide you with a versatile environment that makes complex entropy methods easy to implement. +| The following list summarizes some of the main updates available in v1.0. + + **+ New entropy methods** + | Two new base entropy functions (and their multiscale versions) have been added: + | + `Diversity Entropy `_ + | + `Range Entropy `_ + + **+ New fuzzy membership functions** + | Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors. + | These include *trapezoidal*, *triangular* and *gaussian*, among others. + | Further info on these membership functions can be found `here. `_ + + **+ Phase Permutation Entropy** + | A new variant - '*phase*' permutation entropy - has been added to PermEn. + | This method employs a hilbert transformation of the data sequence, based on the methods outlined `here. `_ + + **+ Cross-Entropy with different length sequences** + | EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (*except XCondEn and XPermEn*). + | As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2). + + **+ Refined-Composite Multiscale Fuzzy Entropy** + | In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined `here. `_ + | What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn. + + **+ Generalized Multiscale Entropy** + | Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the '*generalized*' as the graining procedure in MSEn or XMSEn. + + **+ Variance of sample entropy estimate** + | Based on the `method outlined by Lake et al., `_ it is now possible to obtain a measure of the variance in the sample entropy estimate. + | This is achieved by approximating the number of overlapping embedding vectors. + | To do so, just set the parameter '*Vcp*'==true in SampEn and XSampEn, but note that doing so requires *a lot* of computer memory. + +Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs. +Most of these bugs have been noted via the `GitHub issues portal `_. + + **Bug fixes** + | - The DispEn2D function in python has now fixed `this issue `_. + | - The type hint for FuzzEn in python has been updated `as requested `_. + | - `Compatbility issues with EntropyHub.jl `_ are now resolved. + | - A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected. + + **Other Changes** + | - The *'equal'* method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. + | This is unlikely to have impacted any users previously. + | - The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1). + | - The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the *pooled* standard deviation [i.e. 0.2*SDpooled(X,Y)]. + + +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 :) .............................................................. diff --git a/docs/_build/html/_sources/Home.rst.txt b/docs/_build/html/_sources/Home.rst.txt index a92beea..249f440 100644 --- a/docs/_build/html/_sources/Home.rst.txt +++ b/docs/_build/html/_sources/Home.rst.txt @@ -9,7 +9,7 @@ EntropyHub ********** -An open-source toolkit for entropic time series analysis +An open-source toolkit for entropic data analysis ######################################################## .. image:: ./_images/EntropyHub_Profiler.png @@ -32,30 +32,24 @@ An open-source toolkit for entropic time series analysis * `Take part in our user survey `_ -Welcome -####### - -Welcome to EntropyHub! - -This toolkit provides a wide range of functions to calculate different entropy statistics. +Welcome! +######## -There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. - -The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +| This toolkit provides a wide range of functions to calculate different entropy statistics. +| There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. +| Although many functions for estimating these entropies can be found in various corners of the internet, +| there is currently no toolkit to perform entropic data analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. +| The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +......................................................................................................... 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. +In the context of dynamical systems and information theory, **Entropy** quantifies that uncertainty. -The concept of entropy has its origins in `classical physics `_ under the second law of thermodynamics, -a law `considered to underpin our fundamental understanding `_ -of `time in physics `_. In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises @@ -68,25 +62,21 @@ To overcome this, we have developed EntropyHub - an open-source toolkit designed 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.** +.. note:: -......................................................................................................... + It is important to clarify that the entropy functions herein described estimate entropy in the context of nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics. +......................................................................................................... Documentation & Help #################### -The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively -(available for :download:`download here <./_static/EntropyHubGuide.pdf>`). - -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.* - +| The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively (available for :download:`download here <./_static/EntropyHubGuide.pdf>`). +| 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 MatLab help browser under* **Supplementary Software**. ......................................................................................................... - Citation and Licensing ###################### @@ -94,7 +84,7 @@ 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 scientific outputs realized using the software: - | **Matthew W. Flood and Bernd Grimm,** + | **Matthew W. Flood** | **EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,** | **PLoS One 16(11):e0259448 (2021),** | **DOI: 10.1371/journal.pone.0259448** @@ -104,7 +94,7 @@ realized using the software: __________________________________________________________________ - © Copyright 2021 Matthew W. Flood, EntropyHub + © 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. diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt index 1af4e5b..dcfdf26 100644 --- a/docs/_build/html/_sources/index.rst.txt +++ b/docs/_build/html/_sources/index.rst.txt @@ -9,8 +9,9 @@ EntropyHub ********** -An open-source toolkit for entropic time series analysis +An open-source toolkit for entropic data analysis ######################################################## + .. image:: ./_images/EntropyHub_Profiler.png :width: 150px :align: center @@ -33,30 +34,24 @@ An open-source toolkit for entropic time series analysis * `Take part in our user survey `_ -Welcome -####### - -Welcome to EntropyHub! - -This toolkit provides a wide range of functions to calculate different entropy statistics. - -There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. +Welcome! +######## -The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +| This toolkit provides a wide range of functions to calculate different entropy statistics. +| There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. +| Although many functions for estimating these entropies can be found in various corners of the internet, +| there is currently no toolkit to perform entropic data analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. +| The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +......................................................................................................... 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. +In the context of dynamical systems and information theory, **Entropy** quantifies that uncertainty. -The concept of entropy has its origins in `classical physics `_ under the second law of thermodynamics, -a law `considered to underpin our fundamental understanding `_ -of `time in physics `_. In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises @@ -69,25 +64,21 @@ To overcome this, we have developed EntropyHub - an open-source toolkit designed 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.** +.. note:: -......................................................................................................... + It is important to clarify that the entropy functions herein described estimate entropy in the context of nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics. +......................................................................................................... Documentation & Help #################### -The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively -(available for :download:`download here <./_static/EntropyHubGuide.pdf>`). - -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.* - +| The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively (available for :download:`download here <./_static/EntropyHubGuide.pdf>`). +| 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 MatLab help browser under* **Supplementary Software**. ......................................................................................................... - Citation and Licensing ###################### @@ -95,7 +86,7 @@ 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 scientific outputs realized using the software: - | **Matthew W. Flood and Bernd Grimm,** + | **Matthew W. Flood** | **EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,** | **PLoS One 16(11):e0259448 (2021),** | **DOI: 10.1371/journal.pone.0259448** @@ -105,7 +96,7 @@ realized using the software: __________________________________________________________________ - © Copyright 2021 Matthew W. Flood, EntropyHub + © 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. diff --git a/docs/_build/html/_sources/julia/EHjulia.rst.txt b/docs/_build/html/_sources/julia/EHjulia.rst.txt index cf0d870..6f17ba1 100644 --- a/docs/_build/html/_sources/julia/EHjulia.rst.txt +++ b/docs/_build/html/_sources/julia/EHjulia.rst.txt @@ -33,7 +33,7 @@ There are several package dependencies which will be installed alongside Entropy - *Plots* - *StatsBase*, *StatsFuns*, *Statistics* - *GroupSlices*, *Combinatorics*, *Clustering* - - *LinearAlgebra*, *Dierckx* + - *LinearAlgebra*, *DataInterpolations* EntropyHub was designed using Julia 1.5 and is intended for use with Julia versions >= 1.2. diff --git a/docs/_build/html/_sources/julia/jlexamples.rst.txt b/docs/_build/html/_sources/julia/jlexamples.rst.txt index 532c7c6..7490da7 100644 --- a/docs/_build/html/_sources/julia/jlexamples.rst.txt +++ b/docs/_build/html/_sources/julia/jlexamples.rst.txt @@ -4,7 +4,7 @@ EntropyHub: Julia **EntropyHub.jl** is the EntropyHub package for *Julia*. -Examples in the Julia language can be found `here `_ +Examples in the Julia language can be found `here `_ .. image:: ../_images/EntropyHubJuliaLogo.png :width: 250px diff --git a/docs/_build/html/_sources/matlab/Functions/matBase.rst.txt b/docs/_build/html/_sources/matlab/Functions/matBase.rst.txt index d7f247f..56a5d47 100644 --- a/docs/_build/html/_sources/matlab/Functions/matBase.rst.txt +++ b/docs/_build/html/_sources/matlab/Functions/matBase.rst.txt @@ -17,5 +17,5 @@ Functions for estimating the entropy of a single univariate time series. ................................................................................ .. mat:automodule:: EntropyHub - :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn + :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn diff --git a/docs/_build/html/_sources/matlab/Functions/matBidimensional.rst.txt b/docs/_build/html/_sources/matlab/Functions/matBidimensional.rst.txt index 4479914..a462f3c 100644 --- a/docs/_build/html/_sources/matlab/Functions/matBidimensional.rst.txt +++ b/docs/_build/html/_sources/matlab/Functions/matBidimensional.rst.txt @@ -12,7 +12,7 @@ Bidimensional Entropies Functions for estimating the entropy of a two-dimensional univariate matrix. **************************************************************************** -While EntropyHub functions primarily apply to time series data, with the following +While EntropyHub functions primarily apply to univariate data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis. diff --git a/docs/_build/html/_sources/matlab/Functions/matCross.rst.txt b/docs/_build/html/_sources/matlab/Functions/matCross.rst.txt index 0d4baf1..a1ac356 100644 --- a/docs/_build/html/_sources/matlab/Functions/matCross.rst.txt +++ b/docs/_build/html/_sources/matlab/Functions/matCross.rst.txt @@ -14,13 +14,6 @@ Functions for estimating the entropy between two univariate time series. *The following functions also form the cross-entropy method used by* **multiscale cross-entropy** *functions.* -..................................................................................................... - -.. attention:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - We are currently working to enable different signal lengths for cross-entropy estimation. ..................................................................................................... diff --git a/docs/_build/html/_sources/matlab/Functions/matMultiscale.rst.txt b/docs/_build/html/_sources/matlab/Functions/matMultiscale.rst.txt index 300ebd6..0821cec 100644 --- a/docs/_build/html/_sources/matlab/Functions/matMultiscale.rst.txt +++ b/docs/_build/html/_sources/matlab/Functions/matMultiscale.rst.txt @@ -13,14 +13,14 @@ Functions for estimating the multiscale entropy of a univariate time series. Multiscale entropy can be calculated using any of the :ref:`matBase`: ``ApEn``, ``AttnEn``, ``BubbEn``, ``CondEn``, ``CoSiEn``, ``DistEn``, -``DispEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, -``PermEn``, ``PhasEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. +``DispEn``, ``DivEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, +``PermEn``, ``PhasEn``, ``RangEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. .. important:: Multiscale cross-entropy functions have two positional arguments: - 1. the time series signal, ``Sig`` (a vector > 10 elements), + 1. the data sequence, ``Sig`` (a vector > 10 elements), 2. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/_build/html/_sources/matlab/Functions/matMultiscaleCross.rst.txt b/docs/_build/html/_sources/matlab/Functions/matMultiscaleCross.rst.txt index 697bf06..f9b3402 100644 --- a/docs/_build/html/_sources/matlab/Functions/matMultiscaleCross.rst.txt +++ b/docs/_build/html/_sources/matlab/Functions/matMultiscaleCross.rst.txt @@ -18,15 +18,11 @@ To do so, we again use the ``MSobject`` function to pass a multiscale object (`` .. important:: - Multiscale cross-entropy functions have two positional arguments: + Multiscale cross-entropy functions have three positional arguments: - 1. the time series signals, ``Sig`` (an Nx2 matrix), - 2. the multiscale entropy object, ``Mobj``. - -.. important:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. + 1. the first data sequence, ``Sig1`` (a vector > 10 elements), + 2. the second data sequence, ``Sig2`` (a vector > 10 elements), + 3. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/_build/html/_sources/matlab/matAPI.rst.txt b/docs/_build/html/_sources/matlab/matAPI.rst.txt index 9f0b163..2c3580d 100644 --- a/docs/_build/html/_sources/matlab/matAPI.rst.txt +++ b/docs/_build/html/_sources/matlab/matAPI.rst.txt @@ -77,6 +77,10 @@ Base Entropies: +------------------------------+----------------+ |Attention Entropy | AttnEn | +------------------------------+----------------+ +|Diversity Entropy | DivEn | ++------------------------------+----------------+ +|Range Entropy | RangEn | ++------------------------------+----------------+ Cross Entropies: diff --git a/docs/_build/html/_sources/matlab/matexamples.rst.txt b/docs/_build/html/_sources/matlab/matexamples.rst.txt index 7e15b33..6850142 100644 --- a/docs/_build/html/_sources/matlab/matexamples.rst.txt +++ b/docs/_build/html/_sources/matlab/matexamples.rst.txt @@ -46,7 +46,7 @@ These examples are merely a snippet of the full range of EntropyHub functionalit :``'uniform'``: vector of uniformly distributed random numbers in range [0 1] :``'gaussian'``: vector of normally distributed random numbers with mean = 0; SD = 1 :``'randintegers'``: vector of uniformly distributed pseudorandom integers in range [1 8] -:``'chirp'``: vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025 +:``'chirp'``: vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025 :``'lorenz'``: 3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10] :``'henon'``: 2-column matrix: X, Y components of the Henon attractor (alpha = 1.4; beta = 0.3); [Xo = 0; Yo = 0] :``'uniform2'``: 2-column matrix: uniformly distributed random numbers in range [0 1] @@ -61,14 +61,11 @@ These examples are merely a snippet of the full range of EntropyHub functionalit .. admonition:: THINGS TO REMEMBER - For *cross-entropy* and *multiscale cross-entropy* functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - Parameters of the *base* or *cross-* entropy methods are passed to *multiscale* and *multiscale cross-* entropy functions using the multiscale entropy object given by ``MSobject()``. *Base* and *cross-* entropy methods are declared with ``MSobject()`` using a string of the function name. - Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*) has + Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*, *EspEn2D*) has an important keyword argument - ``Lock``. *Bidimensional* entropy functions are "locked" by default (``Lock == true``) to only permit matrices with a maximum size of 128 x 128. diff --git a/docs/_build/html/_sources/python/Functions/Base.rst.txt b/docs/_build/html/_sources/python/Functions/Base.rst.txt index 6bd3f80..ae1c0b9 100644 --- a/docs/_build/html/_sources/python/Functions/Base.rst.txt +++ b/docs/_build/html/_sources/python/Functions/Base.rst.txt @@ -27,4 +27,4 @@ These functions are directly available when EntropyHub is imported: .. automodule:: EntropyHub - :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn + :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn diff --git a/docs/_build/html/_sources/python/Functions/Bidimensional.rst.txt b/docs/_build/html/_sources/python/Functions/Bidimensional.rst.txt index 31e7cfd..d6bc753 100644 --- a/docs/_build/html/_sources/python/Functions/Bidimensional.rst.txt +++ b/docs/_build/html/_sources/python/Functions/Bidimensional.rst.txt @@ -11,7 +11,7 @@ Bidimensional Entropies Functions for estimating the entropy of a two-dimensional univariate matrix. **************************************************************************** -While EntropyHub functions primarily apply to time series data, with the following +While EntropyHub functions primarily apply to univarite data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis. diff --git a/docs/_build/html/_sources/python/Functions/Cross.rst.txt b/docs/_build/html/_sources/python/Functions/Cross.rst.txt index 615204f..2fe9606 100644 --- a/docs/_build/html/_sources/python/Functions/Cross.rst.txt +++ b/docs/_build/html/_sources/python/Functions/Cross.rst.txt @@ -15,13 +15,6 @@ Functions for estimating the entropy between two univariate time series. ..................................................................................................... -.. attention:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - We are currently working to enable different signal lengths for cross-entropy estimation. - - These functions are directly available when EntropyHub is imported: .. code-block:: python diff --git a/docs/_build/html/_sources/python/Functions/Multiscale.rst.txt b/docs/_build/html/_sources/python/Functions/Multiscale.rst.txt index 747e3fd..75037b0 100644 --- a/docs/_build/html/_sources/python/Functions/Multiscale.rst.txt +++ b/docs/_build/html/_sources/python/Functions/Multiscale.rst.txt @@ -11,14 +11,14 @@ Functions for estimating the multiscale entropy of a univariate time series. Multiscale entropy can be calculated using any of the :ref:`pyBase`: ``ApEn``, ``AttnEn``, ``BubbEn``, ``CondEn``, ``CoSiEn``, ``DistEn``, -``DispEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, -``PermEn``, ``PhasEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. +``DispEn``, ``DivEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, +``PermEn``, ``PhasEn``, ``RangEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. .. important:: Multiscale cross-entropy functions have two positional arguments: - 1. the time series signal, ``Sig`` (a vector > 10 elements), + 1. the data sequence, ``Sig`` (a vector > 10 elements), 2. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/_build/html/_sources/python/Functions/MultiscaleCross.rst.txt b/docs/_build/html/_sources/python/Functions/MultiscaleCross.rst.txt index 6d60ef1..2e7aa33 100644 --- a/docs/_build/html/_sources/python/Functions/MultiscaleCross.rst.txt +++ b/docs/_build/html/_sources/python/Functions/MultiscaleCross.rst.txt @@ -16,15 +16,11 @@ To do so, we again use the ``MSobject`` function to pass a multiscale object (`` .. important:: - Multiscale cross-entropy functions have two positional arguments: + Multiscale cross-entropy functions have three positional arguments: - 1. the time series signals, ``Sig`` (an Nx2 matrix), - 2. the multiscale entropy object, ``Mobj``. - -.. important:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. + 1. the first data sequence, ``Sig1`` (a vector > 10 elements), + 2. the second data sequence, ``Sig2`` (a vector > 10 elements), + 3. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/_build/html/_sources/python/pyAPI.rst.txt b/docs/_build/html/_sources/python/pyAPI.rst.txt index 8f5b206..aec16ee 100644 --- a/docs/_build/html/_sources/python/pyAPI.rst.txt +++ b/docs/_build/html/_sources/python/pyAPI.rst.txt @@ -90,7 +90,10 @@ Base Entropies: +------------------------------+----------------+ |Attention Entropy | AttnEn | +------------------------------+----------------+ - +|Diversity Entropy | DivEn | ++------------------------------+----------------+ +|Range Entropy | RangEn | ++------------------------------+----------------+ Cross Entropies: **************** diff --git a/docs/_build/html/_sources/python/pyexamples.rst.txt b/docs/_build/html/_sources/python/pyexamples.rst.txt index 41e388b..8bb43df 100644 --- a/docs/_build/html/_sources/python/pyexamples.rst.txt +++ b/docs/_build/html/_sources/python/pyexamples.rst.txt @@ -47,7 +47,7 @@ These examples are merely a snippet of the full range of EntropyHub functionalit :``'uniform'``: vector of uniformly distributed random numbers in range [0 1] :``'gaussian'``: vector of normally distributed random numbers with mean = 0; SD = 1 :``'randintegers'``: vector of uniformly distributed pseudorandom integers in range [1 8] -:``'chirp'``: vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025 +:``'chirp'``: vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025 :``'lorenz'``: 3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10] :``'henon'``: 2-column matrix: X, Y components of the Henon attractor (alpha = 1.4; beta = 0.3); [Xo = 0; Yo = 0] :``'uniform2'``: 2-column matrix: uniformly distributed random numbers in range [0 1] @@ -62,14 +62,11 @@ These examples are merely a snippet of the full range of EntropyHub functionalit .. admonition:: THINGS TO REMEMBER - For *cross-entropy* and *multiscale cross-entropy* functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - Parameters of the *base* or *cross-* entropy methods are passed to *multiscale* and *multiscale cross-* entropy functions using the multiscale entropy object given by ``MSobject()``. *Base* and *cross-* entropy methods are declared with ``MSobject()`` using a string of the function name. - Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*) has + Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*, *EspEn2D*) has an important keyword argument - ``Lock``. *Bidimensional* entropy functions are "locked" by default (``Lock == True``) to only permit matrices with a maximum size of 128 x 128. diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js index df08236..c238f42 100644 --- a/docs/_build/html/_static/documentation_options.js +++ b/docs/_build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.2', + VERSION: '1.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html index 0da0fd7..e077433 100644 --- a/docs/_build/html/genindex.html +++ b/docs/_build/html/genindex.html @@ -3,7 +3,7 @@ - Index — EntropyHub 0.2 documentation + Index — EntropyHub 1.0 documentation @@ -220,6 +220,8 @@

D

  • DistEn() (in module EntropyHub), [1]
  • DistEn2D() (in module EntropyHub), [1] +
    • +
  • DivEn() (in module EntropyHub), [1]
    • @@ -329,10 +331,12 @@

    P

    R

    @@ -391,7 +395,7 @@

    X

    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index f959ec1..be4bb0a 100644 --- a/docs/_build/html/index.html +++ b/docs/_build/html/index.html @@ -4,7 +4,7 @@ - EntropyHub — EntropyHub 0.2 documentation + EntropyHub — EntropyHub 1.0 documentation @@ -158,8 +158,8 @@

    EntropyHub

    -
    -

    An open-source toolkit for entropic time series analysis

    +
    +

    An open-source toolkit for entropic data analysis

    _images/EntropyHub_Profiler.png

    Available in:

    @@ -171,23 +171,22 @@

    An open-source toolkit for entropic time series analysis -

    Welcome

    -

    Welcome to EntropyHub!

    -

    This toolkit provides a wide range of functions to calculate different entropy statistics.

    -

    There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax.

    -

    The goal of EntropyHub is to integrate the many established entropy methods in one open-source package.

    +

    Welcome!

    +
    +
    This toolkit provides a wide range of functions to calculate different entropy statistics.
    +
    There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature.
    +
    Although many functions for estimating these entropies can be found in various corners of the internet,
    +
    there is currently no toolkit to perform entropic data analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax.
    +
    The goal of EntropyHub is to integrate the many established entropy methods in one open-source package.
    +
    +

    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.

    -

    The concept of entropy has its origins in classical physics under the second law of thermodynamics, -a law considered to underpin our fundamental understanding -of time in physics. -In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used +In the context of dynamical systems and information theory, Entropy quantifies that uncertainty.

    +

    In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises our ability to measure entropy accurately. Various measures have been derived to estimate entropy (uncertainty) from discrete time series, each seeking to @@ -197,15 +196,19 @@

    About To overcome this, we have developed EntropyHub - an open-source toolkit designed to integrate the many established entropy methods into one package. 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.

    +
    +

    Note

    +

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

    +

    Documentation & Help

    -

    The EntropyHub Guide is a .pdf booklet written to help you use the toolkit effectively -(available for download 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.

    +
    +
    The EntropyHub Guide is a .pdf booklet written to help you use the toolkit effectively (available for download 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 MatLab help browser under Supplementary Software.
    +
    @@ -215,7 +218,7 @@

    Citation and Licensing
    -

    © Copyright 2021 Matthew W. Flood, EntropyHub

    +     diff --git a/docs/_build/html/julia/EHjulia.html b/docs/_build/html/julia/EHjulia.html index 78caadd..985f3bc 100644 --- a/docs/_build/html/julia/EHjulia.html +++ b/docs/_build/html/julia/EHjulia.html @@ -4,7 +4,7 @@ - EntropyHub: Julia — EntropyHub 0.2 documentation + EntropyHub: Julia — EntropyHub 1.0 documentation @@ -171,7 +171,7 @@

    Requirements & Installation:
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/julia/jlexamples.html b/docs/_build/html/julia/jlexamples.html index 9a320d3..09ac1fb 100644 --- a/docs/_build/html/julia/jlexamples.html +++ b/docs/_build/html/julia/jlexamples.html @@ -4,7 +4,7 @@ - EntropyHub: Julia — EntropyHub 0.2 documentation + EntropyHub: Julia — EntropyHub 1.0 documentation @@ -155,7 +155,7 @@

    EntropyHub: Julia

    EntropyHub.jl is the EntropyHub package for Julia.

    -

    Examples in the Julia language can be found here

    +

    Examples in the Julia language can be found here

    ../_images/EntropyHubJuliaLogo.png
    @@ -167,7 +167,7 @@

    EntropyHub: Julia -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/mat-modindex.html b/docs/_build/html/mat-modindex.html index faafdd5..e05a94e 100644 --- a/docs/_build/html/mat-modindex.html +++ b/docs/_build/html/mat-modindex.html @@ -3,7 +3,7 @@ - MATLAB Module Index — EntropyHub 0.2 documentation + MATLAB Module Index — EntropyHub 1.0 documentation @@ -183,7 +183,7 @@

    MATLAB Module Index

    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/EHmatlab.html b/docs/_build/html/matlab/EHmatlab.html index 8a85949..735731a 100644 --- a/docs/_build/html/matlab/EHmatlab.html +++ b/docs/_build/html/matlab/EHmatlab.html @@ -4,7 +4,7 @@ - EntropyHub: MatLab — EntropyHub 0.2 documentation + EntropyHub: MatLab — EntropyHub 1.0 documentation @@ -261,7 +261,7 @@

    Documentation & Help:
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex1.html b/docs/_build/html/matlab/Examples/Ex1.html index d7a578a..ecddafb 100644 --- a/docs/_build/html/matlab/Examples/Ex1.html +++ b/docs/_build/html/matlab/Examples/Ex1.html @@ -4,7 +4,7 @@ - Example 1: Sample Entropy — EntropyHub 0.2 documentation + Example 1: Sample Entropy — EntropyHub 1.0 documentation @@ -194,7 +194,7 @@

    Example 1: Sample Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex10.html b/docs/_build/html/matlab/Examples/Ex10.html index 5190517..020579b 100644 --- a/docs/_build/html/matlab/Examples/Ex10.html +++ b/docs/_build/html/matlab/Examples/Ex10.html @@ -4,7 +4,7 @@ - Example 10: Bidimensional Fuzzy Entropy — EntropyHub 0.2 documentation + Example 10: Bidimensional Fuzzy Entropy — EntropyHub 1.0 documentation @@ -192,7 +192,7 @@

    Example 10: Bidimensional Fuzzy Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex2.html b/docs/_build/html/matlab/Examples/Ex2.html index c1b398e..951f9cd 100644 --- a/docs/_build/html/matlab/Examples/Ex2.html +++ b/docs/_build/html/matlab/Examples/Ex2.html @@ -4,7 +4,7 @@ - Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 0.2 documentation + Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 1.0 documentation @@ -201,7 +201,7 @@

    Example 2: (Fine-Grained) Permutation Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex3.html b/docs/_build/html/matlab/Examples/Ex3.html index 2bbe356..5b6f359 100644 --- a/docs/_build/html/matlab/Examples/Ex3.html +++ b/docs/_build/html/matlab/Examples/Ex3.html @@ -4,7 +4,7 @@ - Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 0.2 documentation + Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 1.0 documentation @@ -200,7 +200,7 @@

    Example 3: Phase Entropy w/ Pioncare Plot -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex4.html b/docs/_build/html/matlab/Examples/Ex4.html index a83de9d..494f308 100644 --- a/docs/_build/html/matlab/Examples/Ex4.html +++ b/docs/_build/html/matlab/Examples/Ex4.html @@ -4,7 +4,7 @@ - Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 0.2 documentation + Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 1.0 documentation @@ -192,7 +192,7 @@

    Example 4: Cross-Distribution Entropy w/ Different Binning Methods -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex5.html b/docs/_build/html/matlab/Examples/Ex5.html index 08c65f9..f0f30a5 100644 --- a/docs/_build/html/matlab/Examples/Ex5.html +++ b/docs/_build/html/matlab/Examples/Ex5.html @@ -4,7 +4,7 @@ - Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 0.2 documentation + Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 1.0 documentation @@ -193,7 +193,7 @@

    Example 5: Multiscale Entropy Object [MSobject()]
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex6.html b/docs/_build/html/matlab/Examples/Ex6.html index 417aae1..e7733fa 100644 --- a/docs/_build/html/matlab/Examples/Ex6.html +++ b/docs/_build/html/matlab/Examples/Ex6.html @@ -4,7 +4,7 @@ - Example 6: Multiscale [Increment] Entropy — EntropyHub 0.2 documentation + Example 6: Multiscale [Increment] Entropy — EntropyHub 1.0 documentation @@ -197,7 +197,7 @@

    Example 6: Multiscale [Increment] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex7.html b/docs/_build/html/matlab/Examples/Ex7.html index ed4d3c8..831bc1e 100644 --- a/docs/_build/html/matlab/Examples/Ex7.html +++ b/docs/_build/html/matlab/Examples/Ex7.html @@ -4,7 +4,7 @@ - Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 0.2 documentation + Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 1.0 documentation @@ -197,7 +197,7 @@

    Example 7: Refined Multiscale [Sample] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex8.html b/docs/_build/html/matlab/Examples/Ex8.html index df605ed..b9d6afe 100644 --- a/docs/_build/html/matlab/Examples/Ex8.html +++ b/docs/_build/html/matlab/Examples/Ex8.html @@ -4,7 +4,7 @@ - Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 0.2 documentation + Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 1.0 documentation @@ -195,7 +195,7 @@

    Example 8: Composite Multiscale Cross-[Approximate] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Examples/Ex9.html b/docs/_build/html/matlab/Examples/Ex9.html index 90ad1ea..02c17ce 100644 --- a/docs/_build/html/matlab/Examples/Ex9.html +++ b/docs/_build/html/matlab/Examples/Ex9.html @@ -4,7 +4,7 @@ - Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 0.2 documentation + Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 1.0 documentation @@ -207,7 +207,7 @@

    Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Functions/matBase.html b/docs/_build/html/matlab/Functions/matBase.html index 6912148..577fbf8 100644 --- a/docs/_build/html/matlab/Functions/matBase.html +++ b/docs/_build/html/matlab/Functions/matBase.html @@ -4,7 +4,7 @@ - Base Entropies — EntropyHub 0.2 documentation + Base Entropies — EntropyHub 1.0 documentation @@ -467,6 +467,56 @@

    Functions for estimating the entropy of a single univariate time series. +
    +
    +DivEn(Sig, varargin)
    +

    DivEn estimates the diversity entropy of a univariate data sequence.

    +

    [Div, CDEn, Bm] = DivEn(Sig)

    +

    Returns the diversity entropy (Div), the cumulative diversity entropy (CDEn), +and the corresponding probabilities (Bm) estimated from the data sequence (Sig) +using the default parameters: embedding dimension = 2, time delay = 1, +# bins = 5, logarithm = natural,

    +

    [Div, CDEn, Bm] = DivEn(Sig, name, value, …)

    +

    Returns the diversity entropy (Div) estimated from the data +sequence (Sig) using the specified name/value pair arguments:

    +
    +
      +

    • m - Embedding Dimension, an integer > 1

      • +

    • tau - Time Delay, a positive integer

      • +
    • +
      r - Histogram bins #: either
        +

      • an integer [r > 1] representing the number of bins

        • +

      • a vector array of 3 or more increasing values in range [-1 1] representing the bin edges including the rightmost edge.

        • +
      +
      +
      +
      • +

    • Logx - Logarithm base, a positive scalar (enter 0 for natural log)

      • +
    +
    +
    +
    See also:
    +

    CoSiEn, PhasEn, SlopEn, GridEn, MSEn

    +
    +
    +
    References:
    +
    [1] X. Wang, S. Si and Y. Li,

    “Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault +Diagnosis of Rotating Machinery,” +IEEE Transactions on Industrial Informatics, +vol. 17, no. 8, pp. 5419-5429, Aug. 2021

    +
    +
    [2] Y. Wang, M. Liu, Y. Guo, F. Shu, C. Chen and W. Chen,

    “Cumulative Diversity Pattern Entropy (CDEn): A High-Performance, +Almost-Parameter-Free Complexity Estimator for Nonstationary Time Series,” +IEEE Transactions on Industrial Informatics +vol. 19, no. 9, pp. 9642-9653, Sept. 2023

    +
    +
    +
    +
    +
    +
    +
    +
    EnofEn(Sig, varargin)
    @@ -943,6 +993,45 @@

    Functions for estimating the entropy of a single univariate time series.

    +
    +
    +RangEn(Sig, varargin)
    +

    RangEn estimates the range entropy of a univariate data sequence.

    +

    [Rangx, A, B] = RangEn(Sig)

    +

    Returns the range entropy estimate (Rangx) and the number of matched state +vectors (m: B, m+1: A) estimated from the data sequence (Sig) +using the sample entropy algorithm and the following default parameters: +embedding dimension = 2, time delay = 1, radius threshold = 0.2, logarithm = natural.

    +

    Rangx, A, B = RangEn(Sig, name, value, …)

    +

    Returns the range entropy estimates (Rangx) for dimensions = m +estimated for the data sequence (Sig) using the specified name-value arguments:

    +
    +
      +

    • m - Embedding Dimension, a positive integer

      • +

    • tau - Time Delay, a positive integer

      • +

    • r - Radius Distance Threshold, a positive scalar between 0 and 1

      • +

    • Methodx - Base entropy method, either ‘SampEn’ [default] or ‘ApEn’

      • +

    • Logx - Logarithm base, a positive scalar

      • +
    +
    +
    +
    See also:

    ApEn, SampEn, FuzzEn, MSEn

    +
    +
    References:
    +
    [1] Omidvarnia, Amir, et al.

    “Range entropy: A bridge between signal complexity and self-similarity” +Entropy +20.12 (2018): 962.

    +
    +
    [2] Joshua S Richman and J. Randall Moorman.

    “Physiological time-series analysis using approximate entropy +and sample entropy.” +American Journal of Physiology-Heart and Circulatory Physiology +2000

    +
    +
    +
    +
    +
    +
    SampEn(Sig, varargin)
    @@ -1158,7 +1247,7 @@

    Functions for estimating the entropy of a single univariate time series.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Functions/matBidimensional.html b/docs/_build/html/matlab/Functions/matBidimensional.html index 1fae992..57bb667 100644 --- a/docs/_build/html/matlab/Functions/matBidimensional.html +++ b/docs/_build/html/matlab/Functions/matBidimensional.html @@ -4,7 +4,7 @@ - Bidimensional Entropies — EntropyHub 0.2 documentation + Bidimensional Entropies — EntropyHub 1.0 documentation @@ -162,7 +162,7 @@

    Bidimensional Entropies

    Functions for estimating the entropy of a two-dimensional univariate matrix.

    -

    While EntropyHub functions primarily apply to time series data, with the following +

    While EntropyHub functions primarily apply to univariate data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis.

    @@ -635,7 +635,7 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Functions/matCross.html b/docs/_build/html/matlab/Functions/matCross.html index e68907a..5c07eeb 100644 --- a/docs/_build/html/matlab/Functions/matCross.html +++ b/docs/_build/html/matlab/Functions/matCross.html @@ -4,7 +4,7 @@ - Cross Entropies — EntropyHub 0.2 documentation + Cross Entropies — EntropyHub 1.0 documentation @@ -164,13 +164,6 @@

    Functions for estimating the entropy between two univariate time series.

    The following functions also form the cross-entropy method used by multiscale cross-entropy functions.

    -
    -

    Attention

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately. -We are currently working to enable different signal lengths for cross-entropy estimation.

    -
    -
    XApEn(Sig1, Sig2, varargin)
    @@ -607,7 +600,7 @@

    Functions for estimating the entropy between two univariate time series.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Functions/matMultiscale.html b/docs/_build/html/matlab/Functions/matMultiscale.html index 3ee83d1..ec90c6f 100644 --- a/docs/_build/html/matlab/Functions/matMultiscale.html +++ b/docs/_build/html/matlab/Functions/matMultiscale.html @@ -4,7 +4,7 @@ - Multiscale Entropies — EntropyHub 0.2 documentation + Multiscale Entropies — EntropyHub 1.0 documentation @@ -164,13 +164,13 @@

    Multiscale EntropiesFunctions for estimating the multiscale entropy of a univariate time series.

    Multiscale entropy can be calculated using any of the Base Entropies: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, -DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, -PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn.

    +DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, +PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn.

    Important

    Multiscale cross-entropy functions have two positional arguments:

      -

    1. the time series signal, Sig (a vector > 10 elements),

      2. +

    2. the data sequence, Sig (a vector > 10 elements),

    3. the multiscale entropy object, Mobj.

    @@ -579,7 +579,7 @@

    Functions for estimating the multiscale entropy of a univariate time series.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/Functions/matMultiscaleCross.html b/docs/_build/html/matlab/Functions/matMultiscaleCross.html index 0345da1..f9b094a 100644 --- a/docs/_build/html/matlab/Functions/matMultiscaleCross.html +++ b/docs/_build/html/matlab/Functions/matMultiscaleCross.html @@ -4,7 +4,7 @@ - Multiscale Cross-Entropies — EntropyHub 0.2 documentation + Multiscale Cross-Entropies — EntropyHub 1.0 documentation @@ -167,17 +167,13 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    To do so, we again use the MSobject function to pass a multiscale object (Mobj) to the multiscale cross-entropy functions.

    Important

    -

    Multiscale cross-entropy functions have two positional arguments:

    +

    Multiscale cross-entropy functions have three positional arguments:

      -

    1. the time series signals, Sig (an Nx2 matrix),

      2. +

    2. the first data sequence, Sig1 (a vector > 10 elements),

      3. +

    3. the second data sequence, Sig2 (a vector > 10 elements),

    4. the multiscale entropy object, Mobj.

    -
    -

    Important

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately.

    -
    @@ -583,7 +579,7 @@

    Functions for estimating the multiscale cross-entropy between two univariate
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/matAPI.html b/docs/_build/html/matlab/matAPI.html index d3d52df..dd2750f 100644 --- a/docs/_build/html/matlab/matAPI.html +++ b/docs/_build/html/matlab/matAPI.html @@ -4,7 +4,7 @@ - MatLab Functions: — EntropyHub 0.2 documentation + MatLab Functions: — EntropyHub 1.0 documentation @@ -241,6 +241,12 @@

    Base Entropies:

    + + + + + +

    Attention Entropy

    AttnEn

    Diversity Entropy

    DivEn

    Range Entropy

    RangEn

    @@ -374,7 +380,7 @@

    Multiscale Cross-Entropies: -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/matlab/matexamples.html b/docs/_build/html/matlab/matexamples.html index 055b428..bd14e94 100644 --- a/docs/_build/html/matlab/matexamples.html +++ b/docs/_build/html/matlab/matexamples.html @@ -4,7 +4,7 @@ - MatLab Examples: — EntropyHub 0.2 documentation + MatLab Examples: — EntropyHub 1.0 documentation @@ -200,7 +200,7 @@

    MatLab Examples:

    vector of uniformly distributed pseudorandom integers in range [1 8]

    'chirp':
    -

    vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025

    +

    vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025

    'lorenz':

    3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10]

    @@ -235,12 +235,10 @@

    MatLab Examples:

    THINGS TO REMEMBER

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately.

    Parameters of the base or cross- entropy methods are passed to multiscale and multiscale cross- entropy functions using the multiscale entropy object given by MSobject(). Base and cross- entropy methods are declared with MSobject() using a string of the function name.

    -

    Each bidimensional entropy function (SampEn2D, FuzzEn2D, DistEn2D, DispEn2D) has +

    Each bidimensional entropy function (SampEn2D, FuzzEn2D, DistEn2D, DispEn2D, EspEn2D) has an important keyword argument - Lock. Bidimensional entropy functions are “locked” by default (Lock == true) to only permit matrices with a maximum size of 128 x 128.

    In hierarchical multiscale entropy (hMSEn()) and hierarchical multiscale cross- @@ -261,7 +259,7 @@

    MatLab Examples: -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv index 2075155..91305ce 100644 Binary files a/docs/_build/html/objects.inv and b/docs/_build/html/objects.inv differ diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html index 47189c7..5070d47 100644 --- a/docs/_build/html/py-modindex.html +++ b/docs/_build/html/py-modindex.html @@ -3,7 +3,7 @@ - Python Module Index — EntropyHub 0.2 documentation + Python Module Index — EntropyHub 1.0 documentation @@ -183,7 +183,7 @@

    Python Module Index

    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/EHpython.html b/docs/_build/html/python/EHpython.html index 00d0f8a..834e3d5 100644 --- a/docs/_build/html/python/EHpython.html +++ b/docs/_build/html/python/EHpython.html @@ -4,7 +4,7 @@ - EntropyHub: Python — EntropyHub 0.2 documentation + EntropyHub: Python — EntropyHub 1.0 documentation @@ -274,7 +274,7 @@

    Documentation & Help:
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex1.html b/docs/_build/html/python/Examples/Ex1.html index 3c3b5d3..f2c224b 100644 --- a/docs/_build/html/python/Examples/Ex1.html +++ b/docs/_build/html/python/Examples/Ex1.html @@ -4,7 +4,7 @@ - Example 1: Sample Entropy — EntropyHub 0.2 documentation + Example 1: Sample Entropy — EntropyHub 1.0 documentation @@ -193,7 +193,7 @@

    Example 1: Sample Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex10.html b/docs/_build/html/python/Examples/Ex10.html index ba1e536..e19d280 100644 --- a/docs/_build/html/python/Examples/Ex10.html +++ b/docs/_build/html/python/Examples/Ex10.html @@ -4,7 +4,7 @@ - Example 10: Bidimensional Fuzzy Entropy — EntropyHub 0.2 documentation + Example 10: Bidimensional Fuzzy Entropy — EntropyHub 1.0 documentation @@ -192,7 +192,7 @@

    Example 10: Bidimensional Fuzzy Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex2.html b/docs/_build/html/python/Examples/Ex2.html index 32708ad..2914ae0 100644 --- a/docs/_build/html/python/Examples/Ex2.html +++ b/docs/_build/html/python/Examples/Ex2.html @@ -4,7 +4,7 @@ - Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 0.2 documentation + Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 1.0 documentation @@ -201,7 +201,7 @@

    Example 2: (Fine-Grained) Permutation Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex3.html b/docs/_build/html/python/Examples/Ex3.html index 71eda31..62ac22d 100644 --- a/docs/_build/html/python/Examples/Ex3.html +++ b/docs/_build/html/python/Examples/Ex3.html @@ -4,7 +4,7 @@ - Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 0.2 documentation + Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 1.0 documentation @@ -202,7 +202,7 @@

    Example 3: Phase Entropy w/ Pioncare Plot -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex4.html b/docs/_build/html/python/Examples/Ex4.html index 28c35f7..d8d3111 100644 --- a/docs/_build/html/python/Examples/Ex4.html +++ b/docs/_build/html/python/Examples/Ex4.html @@ -4,7 +4,7 @@ - Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 0.2 documentation + Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 1.0 documentation @@ -193,7 +193,7 @@

    Example 4: Cross-Distribution Entropy w/ Different Binning Methods -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex5.html b/docs/_build/html/python/Examples/Ex5.html index 968ded3..31b8d1a 100644 --- a/docs/_build/html/python/Examples/Ex5.html +++ b/docs/_build/html/python/Examples/Ex5.html @@ -4,7 +4,7 @@ - Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 0.2 documentation + Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 1.0 documentation @@ -195,7 +195,7 @@

    Example 5: Multiscale Entropy Object [MSobject()]
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex6.html b/docs/_build/html/python/Examples/Ex6.html index 9410dbd..2577fe3 100644 --- a/docs/_build/html/python/Examples/Ex6.html +++ b/docs/_build/html/python/Examples/Ex6.html @@ -4,7 +4,7 @@ - Example 6: Multiscale [Increment] Entropy — EntropyHub 0.2 documentation + Example 6: Multiscale [Increment] Entropy — EntropyHub 1.0 documentation @@ -197,7 +197,7 @@

    Example 6: Multiscale [Increment] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex7.html b/docs/_build/html/python/Examples/Ex7.html index 4266c12..258f2b7 100644 --- a/docs/_build/html/python/Examples/Ex7.html +++ b/docs/_build/html/python/Examples/Ex7.html @@ -4,7 +4,7 @@ - Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 0.2 documentation + Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 1.0 documentation @@ -198,7 +198,7 @@

    Example 7: Refined Multiscale [Sample] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex8.html b/docs/_build/html/python/Examples/Ex8.html index 6320d19..0636282 100644 --- a/docs/_build/html/python/Examples/Ex8.html +++ b/docs/_build/html/python/Examples/Ex8.html @@ -4,7 +4,7 @@ - Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 0.2 documentation + Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 1.0 documentation @@ -195,7 +195,7 @@

    Example 8: Composite Multiscale Cross-[Approximate] Entropy -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Examples/Ex9.html b/docs/_build/html/python/Examples/Ex9.html index 92477d2..aa69c27 100644 --- a/docs/_build/html/python/Examples/Ex9.html +++ b/docs/_build/html/python/Examples/Ex9.html @@ -4,7 +4,7 @@ - Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 0.2 documentation + Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 1.0 documentation @@ -212,7 +212,7 @@

    Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Functions/Base.html b/docs/_build/html/python/Functions/Base.html index 0c821a0..8bb9fad 100644 --- a/docs/_build/html/python/Functions/Base.html +++ b/docs/_build/html/python/Functions/Base.html @@ -4,7 +4,7 @@ - Base Entropies — EntropyHub 0.2 documentation + Base Entropies — EntropyHub 1.0 documentation @@ -343,7 +343,7 @@

    Functions for estimating the entropy of a single univariate time series.

    Returns the cosine similarity entropy (CoSi) and the corresponding -global probabilities estimated from the data sequence (Sig) using the +global probabilities (Bm) estimated from the data sequence (Sig) using the default parameters: embedding dimension = 2, time delay = 1, angular threshold = .1, logarithm = base 2,

    CoSi, Bm = CoSiEn(Sig, keyword = value, ...)
@@ -634,6 +634,71 @@ 
    Functions for estimating the entropy of a single univariate time series.

    +
    +
    +DivEn(Sig, m=2, tau=1, r=5, Logx=numpy.exp)
    +

    DivEn estimates the diversity entropy of a univariate data sequence.

    +
    Div, CDEn, Bm = DivEn(Sig) 
+
    +
    +

    Returns the diversity entropy (Div), the cumulative diversity entropy (CDEn), +and the corresponding probabilities (Bm) estimated from the data sequence (Sig) +using the default parameters: embedding dimension = 2, time delay = 1, +# bins = 5, logarithm = natural,

    +
    Div, CDEn, Bm = DivEn(Sig, keyword = value, ...)
+
    +
    +

    Returns the diversity entropy (Div) estimated from the data +sequence (Sig) using the specified ‘keyword’ arguments:

    +
    +
    +
    m:
    +
      +

    • Embedding Dimension, an integer > 1

      • +
    +
    +
    tau:
    +
      +

    • Time Delay, a positive integer

      • +
    +
    +
    r:
    +
      +

    • Histogram bins #: either

      • +
    +
      +

    • an integer [1 < r] representing the number of bins

      • +

    • a list/numpy array of 3 or more increasing values in range [-1 1] representing the bin edges including the rightmost edge.

      • +
    +
    +
    Logx:
    +
      +

    • Logarithm base, a positive scalar (enter 0 for natural log)

      • +
    +
    +
    +
    +
    +
    See also:
    +

    CoSiEn, PhasEn, SlopEn, GridEn, MSEn

    +
    +
    References:
    +
    +
    [1] X. Wang, S. Si and Y. Li,

    “Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault +Diagnosis of Rotating Machinery,” +IEEE Transactions on Industrial Informatics, +vol. 17, no. 8, pp. 5419-5429, Aug. 2021

    +
    +
    [2] Y. Wang, M. Liu, Y. Guo, F. Shu, C. Chen and W. Chen,

    “Cumulative Diversity Pattern Entropy (CDEn): A High-Performance, +Almost-Parameter-Free Complexity Estimator for Nonstationary Time Series,” +IEEE Transactions on Industrial Informatics +vol. 19, no. 9, pp. 9642-9653, Sept. 2023

    +
    +
    +
    +
    +
    +
    EnofEn(Sig, tau=10, S=10, Xrange=None, Logx=numpy.exp)
    @@ -692,7 +757,7 @@

    Functions for estimating the entropy of a single univariate time series.
    -FuzzEn(Sig, m=2, tau=1, r=(0.2, 2), Fx='default', Logx=numpy.exp)
    +FuzzEn(Sig, m=2, tau=1, r=(0.2, 2.0), Fx='default', Logx=numpy.exp)

    FuzzEn estimates the fuzzy entropy of a univariate data sequence.

    Fuzz, Ps1, Ps2 = FuzzEn(Sig)
    @@ -721,35 +786,36 @@

    Functions for estimating the entropy of a single univariate time series.
    Fx:
      -

    • Fuzzy function name, one of the following strings: {'sigmoid', 'modsampen', 'default', 'gudermannian', 'linear'}

      • +

    • Fuzzy function name, one of the following strings:

    +

    {'sigmoid', 'modsampen', 'default', 'gudermannian', 'bell', 'triangular', 'trapezoidal1', 'trapezoidal2', 'z_shaped', 'gaussian', 'constgaussian'}

    r:
      -

    • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values. The r parameters for each fuzzy

      • +

    • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values.

    -

    function are defined as follows: [default: (.2 2)]

    -

    -
    +
    Logx:

    • Logarithm base, a positive scalar [default: natural]

    +

    For further information on the keyword arguments, see the EntropyHub guide.

    See also:
    @@ -794,6 +911,11 @@

    Functions for estimating the entropy of a single univariate time series. +
    [3] Hamed Azami, et al.

    “Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: +Assessment and Comparison” +IEEE Access +7 (2019): 104833-104847

    +

    @@ -1061,7 +1183,7 @@

    Functions for estimating the entropy of a single univariate time series.

    Permutation entropy variation, one of the following:

    -
    {'uniquant', 'finegrain', 'modified', 'ampaware', 'weighted', 'edge'}

    See the EntropyHub guide for more info on PermEn variants.

    +
    {'uniquant', 'finegrain', 'modified', 'ampaware', 'weighted', 'edge', 'phase'}

    See the EntropyHub guide for more info on PermEn variants.

    @@ -1125,6 +1247,11 @@

    Functions for estimating the entropy of a single univariate time series. +
    [9] Kang Huan, Xiaofeng Zhang, and Guangbin Zhang,

    “Phase permutation entropy: A complexity measure for nonlinear time +series incorporating phase information.” +Physica A: Statistical Mechanics and its Applications +568 (2021): 125686.

    +
    @@ -1197,9 +1324,74 @@

    Functions for estimating the entropy of a single univariate time series. +
    +
    +RangEn(Sig, m=2, tau=1, r=0.2, Methodx='SampEn', Logx=numpy.exp)
    +

    RangEn estimates the range entropy of a univariate data sequence.

    +
    Rangx, A, B = RangEn(Sig) 
+
    +
    +

    Returns the range entropy estimate (Rangx) and the number of matched state +vectors (m: B, m+1: A) estimated from the data sequence (Sig) +using the sample entropy algorithm and the following default parameters: +embedding dimension = 2, time delay = 1, radius threshold = 0.2, logarithm = natural.

    +
    Rangx, A, B = RangEn(Sig, keyword = value, ...)
+
    +
    +

    Returns the range entropy estimates (Rangx) for dimensions = m +estimated for the data sequence (Sig) using the specified keyword arguments:

    +
    +
    +
    m:
    +
      +

    • Embedding Dimension, a positive integer

      • +
    +
    +
    tau:
    +
      +

    • Time Delay, a positive integer

      • +
    +
    +
    r:
    +
      +

    • Radius Distance Threshold, a positive value between 0 and 1

      • +
    +
    +
    Methodx:
    +
      +

    • Base entropy method, either ‘SampEn’ [default] or ‘ApEn’

      • +
    +
    +
    Logx:
    +
      +

    • Logarithm base, a positive scalar

      • +
    +
    +
    +
    +
    +
    See also:
    +

    ApEn, SampEn, FuzzEn, MSEn

    +
    +
    References:
    +
    +
    [1] Omidvarnia, Amir, et al.

    “Range entropy: A bridge between signal complexity and self-similarity” +Entropy +20.12 (2018): 962.

    +
    +
    [2] Joshua S Richman and J. Randall Moorman.

    “Physiological time-series analysis using approximate entropy +and sample entropy.” +American Journal of Physiology-Heart and Circulatory Physiology +2000

    +
    +
    +
    +
    +
    +
    -SampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
    +SampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)

    SampEn estimates the sample entropy of a univariate data sequence.

    Samp, A, B = SampEn(Sig)
    @@ -1207,7 +1399,18 @@

    Functions for estimating the entropy of a single univariate time series.Returns the sample entropy estimates (Samp) and the number of matched state vectors (m: B, m+1: A) for m = [0, 1, 2] estimated from the data sequence (Sig) using the default parameters: embedding dimension = 2, time delay = 1, -radius threshold = 0.2*SD(Sig), logarithm = natural

    +radius threshold = 0.2*SD(Sig), logarithm = natural.

    +
    Samp, A, B, (Vcp, Ka, Kb) = SampEn(Sig, ..., Vcp = True) 
+
    +
    +

    If Vcp == True, an additional tuple (Vcp, Ka, Kb) is returned with +the sample entropy estimates (Samp) and the number of matched state +vectors (m: B, m+1: A). (Vcp, Ka, Kb) contains the variance of the conditional +probabilities (Vcp), i.e. CP = A/B, and the number of overlapping +matching vector pairs of lengths m+1 (Ka) and m (Kb), +respectively. Note Vcp is undefined for the zeroth embedding dimension (m = 0) +and due to the computational demand, will take substantially more time to return function outputs. +See Appendix B in [2] for more info.

    Samp, A, B = SampEn(Sig, keyword = value, ...)
    @@ -1235,6 +1438,11 @@

    Functions for estimating the entropy of a single univariate time series.

    Logarithm base, a positive scalar

    +
    Vcp:
    +
      +

    • Option to return variance of conditional probabilities and the number of overlapping matching vector pairs, a boolean [default: False]

      • +
    +
    @@ -1248,6 +1456,10 @@

    Functions for estimating the entropy of a single univariate time series. +
    [2] Douglas E Lake, Joshua S Richman, M.P. Griffin, J. Randall Moorman

    “Sample entropy analysis of neonatal heart rate variability.” +American Journal of Physiology-Regulatory, Integrative and Comparative Physiology +283, no. 3 (2002): R789-R797.

    +

    @@ -1484,7 +1696,7 @@

    Functions for estimating the entropy of a single univariate time series.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Functions/Bidimensional.html b/docs/_build/html/python/Functions/Bidimensional.html index b0e457f..f267fbc 100644 --- a/docs/_build/html/python/Functions/Bidimensional.html +++ b/docs/_build/html/python/Functions/Bidimensional.html @@ -4,7 +4,7 @@ - Bidimensional Entropies — EntropyHub 0.2 documentation + Bidimensional Entropies — EntropyHub 1.0 documentation @@ -162,7 +162,7 @@

    Bidimensional Entropies

    Functions for estimating the entropy of a two-dimensional univariate matrix.

    -

    While EntropyHub functions primarily apply to time series data, with the following +

    While EntropyHub functions primarily apply to univarite data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis.

    @@ -441,7 +441,7 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix.

    Returns the bidimensional fuzzy entropy estimate (Fuzz2D) estimated for the data matrix (Mat) using the default parameters: time delay = 1, -fuzzy function (Fx) = 'default', fuzzy function parameters (r) = (0.2, 2, +fuzzy function (Fx) = 'default', fuzzy function parameters (r) = (0.2*SD(Mat), 2), logarithm = natural, matrix template size = [floor(H/10) floor(W/10)] (where H and W represent the height (rows) and width (columns) of the data matrix 'Mat') ** The minimum number of rows and columns of Mat must be > 10.

    @@ -464,49 +464,107 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix.
    Fx:
      -

    • Fuzzy funtion name, one of the following:

      • +

    • Fuzzy function name, one of the following strings:

    -

    {'sigmoid', 'modsampen', 'default', 'gudermannian', 'linear'}

    +

    {'sigmoid', 'modsampen', 'default', 'gudermannian', 'bell', 'triangular', 'trapezoidal1', 'trapezoidal2', 'z_shaped', 'gaussian', 'constgaussian'}

    r:

    • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values.

    -

    The r parameters for each fuzzy function are defined as follows:

    -
    -
      +
      +
      The r parameters for each fuzzy function are defined as follows: [default: (0.2*SD(Mat), 2)]
        +
      • +
        default: [Tuple]
          +

        • r(1) = divisor of the exponential argument

          • +

        • r(2) = argument exponent (pre-division)

          • +
        +
        +
        +
        • +
      • +
        sigmoid: [Tuple]
          +

        • r(1) = divisor of the exponential argument

          • +

        • r(2) = value subtracted from argument (pre-division)

          • +
        +
        +
        +
        • +
      • +
        modsampen: [Tuple]
          +

        • r(1) = divisor of the exponential argument

          • +

        • r(2) = value subtracted from argument (pre-division)

          • +
        +
        +
        +
        • +
      • +
        gudermannian:
          +

        • r = a scalar whose value is the numerator of argument to gudermannian function: GD(x) = atan(tanh(r/x)). GD(x) is normalised to have a maximum value of 1.

          • +
        +
        +
        +
        • +
      • +
        triangular:
          +

        • r = a scalar whose value is the threshold (corner point) of the triangular function.

          • +
        +
        +
        +
        • +
      • +
        trapezoidal1:
          +

        • r = a scalar whose value corresponds to the upper (2r) and lower (r) corner points of the trapezoid.

          • +
        +
        +
        +
        • +
      • +
        trapezoidal2: [Tuple]
          +

        • r(1) = a value corresponding to the upper corner point of the trapezoid.

          • +

        • r(2) = a value corresponding to the lower corner point of the trapezoid.

          • +
        +
        +
        +
      • -
        sigmoid:

        r(1) = divisor of the exponential argument -r(2) = value subtracted from argument (pre-division)

        +
        z_shaped:
          +

        • r = a scalar whose value corresponds to the upper (2r) and lower (r) corner points of the z-shape.

          • +
      • -
        modsampen:

        r(1) = divisor of the exponential argument -r(2) = value subtracted from argument (pre-division)

        +
        bell:
          +

        • r(1) = divisor of the distance value

          • +

        • r(2) = exponent of generalized bell-shaped function

          • +
      • -
        default:

        r(1) = divisor of the exponential argument -r(2) = argument exponent (pre-division)

        +
        gaussian:
          +

        • r = a scalar whose value scales the slope of the Gaussian curve.

          • +
      • -
        gudermannian:

        r = a scalar whose value is the numerator of argument to gudermannian function: -GD(x) = atan(tanh(r/x)). GD(x) is normalised to have a maximum value of 1.

        +
        constgaussian:
          +

        • r = a scalar whose value defines the lower threshod and shape of the Gaussian curve.

          • +
      • -
        linear:

        r = an integer value. When r = 0, the argument of the exponential function is +

        [DEPRICATED] linear:

        r = an integer value. When r = 0, the argument of the exponential function is normalised between [0 1]. When r = 1, the minimuum value of the exponential argument is set to 0.

      -
    +
    +
    Logx:
      @@ -540,6 +598,11 @@

      Functions for estimating the entropy of a two-dimensional univariate matrix. 41st Annual International Conference of the IEEE (EMBC) Society 2019.

    +
    [3] Hamed Azami, et al.

    “Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: +Assessment and Comparison” +IEEE Access +7 (2019): 104833-104847

    +
    @@ -599,16 +662,16 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix.
    -
    NOTE - ``The original bidimensional permutation entropy algorithms

    [1][2] do not account for equal-valued elements of the embedding -matrices. `` +

    NOTE:

    The original bidimensional permutation entropy algorithms [1][2] +do not account for equal-valued elements of the embedding matrices. To overcome this, PermEn2D uses the lowest common rank for such instances. For example, given an embedding matrix A where, A = [3.4 5.5 7.3]

    -

    |2.1 6 9.9| +

    [2.1 6 9.9] [7.3 1.1 2.1]

    -

    would normally be mapped to an ordinal pattern like so, +

    would normally be mapped to an ordinal pattern like so, [3.4 5.5 7.3 2.1 6 9.9 7.3 1.1 2.1] => [ 8 4 9 1 2 5 3 7 6 ] However, indices 4 & 9, and 3 & 7 have the same values, 2.1 @@ -728,7 +791,7 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Functions/Cross.html b/docs/_build/html/python/Functions/Cross.html index 9b580ac..61fbd04 100644 --- a/docs/_build/html/python/Functions/Cross.html +++ b/docs/_build/html/python/Functions/Cross.html @@ -4,7 +4,7 @@ - Cross Entropies — EntropyHub 0.2 documentation + Cross Entropies — EntropyHub 1.0 documentation @@ -164,12 +164,6 @@

    Functions for estimating the entropy between two univariate time series.

    The following functions also form the cross-entropy method used by multiscale cross-entropy functions.

    -
    -

    Attention

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately. -We are currently working to enable different signal lengths for cross-entropy estimation.

    -

    These functions are directly available when EntropyHub is imported:

    import EntropyHub as EH
 
@@ -179,24 +173,24 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
 
    
-XApEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
    
+XApEn(*Sig, m=2, tau=1, r=None, Logx=numpy.exp)
 
    XApEn  estimates the cross-approximate entropy between two univariate data sequences.
    
-
    XAp, Phi = XApEn(Sig)
+
    XAp, Phi = XApEn(Sig1, Sig2)
 
    
 
    
 
    Returns the cross-approximate entropy estimates (XAp) and the average
 number of matched vectors (Phi) for m = [0,1,2], estimated for the data
-sequences contained in ‘Sig’ using the default parameters:
+sequences contained in Sig1 and Sig2 using the default parameters:
 embedding dimension = 2, time delay = 1, 
-radius distance threshold = 0.2*SD(Sig),  logarithm = natural
    
-
    **NOTE: XApEn is direction-dependent. Thus, the first row/column of Sig is used as the template data sequence, and the second row/column is the matching sequence.
    
+radius threshold = 0.2*SDpooled(Sig1,``Sig2``),  logarithm = natural
    
+
    **NOTE: XApEn is direction-dependent. Thus, Sig1 is used as the template data sequence, and Sig2 is the matching sequence.
    
 
    
-
    XAp, Phi = XApEn(Sig, keyword = value, ...)
+
    XAp, Phi = XApEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    
 
    Returns the cross-approximate entropy estimates (XAp) between the data
-sequences contained in Sig using the specified ‘keyword’ arguments:
    
+sequences contained in Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
 
    
 
    m:
    
@@ -211,7 +205,7 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    r:
    
 
      
-
    • Radius Distance Threshold, a positive scalar [default: 0.2*SD(Sig)]
      • 
+
    • Radius Distance Threshold, a positive scalar [default: 0.2*SDpooled(Sig1,``Sig2``)]
      • 
 
    
 
    
 
    Logx:
    
@@ -242,25 +236,25 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XCondEn(Sig, m=2, tau=1, c=6, Logx=numpy.exp, Norm=False)
    
+XCondEn(*Sig, m=2, tau=1, c=6, Logx=numpy.exp, Norm=False)
 
    XCondEn  estimates the corrected cross-conditional entropy between two univariate data sequences.
    
-
    XCond, SEw, SEz = XCondEn(Sig) 
+
    XCond, SEw, SEz = XCondEn(Sig1, Sig2) 
 
    
 
    
 
    Returns the corrected cross-conditional entropy estimates (XCond) and the
 corresponding Shannon entropies (m: SEw, m+1: SEz) for m = [1,2] 
-estimated for the data sequences contained in Sig using the default
+estimated for the data sequences contained in Sig1 and Sig2 using the default
 parameters:  embedding dimension = 2, time delay = 1, 
 number of symbols = 6, logarithm = natural
 ** Note: XCondEn is direction-dependent. Therefore, the order of the
-data sequences in Sig matters. If the first row/column of Sig is the
-sequence ‘y’, and the second row/column is the sequence ‘u’, then XCond is
+data sequences Sig1 and Sig2 matters. If Sig1 is the
+sequence ‘y’, and Sig2 is the sequence ‘u’, then XCond is
 the amount of information carried by y(i) when the pattern u(i) is found.
    
-
    XCond, SEw, SEz = XCondEn(Sig, keyword = value, ...)
+
    XCond, SEw, SEz = XCondEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the corrected cross-conditional entropy estimates (XCond) for
-the data sequences contained in Sig using the specified ‘keyword’ arguments:
    
+the data sequences contained in Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
 
    
 
    m:
    
@@ -312,21 +306,21 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XDistEn(Sig, m=2, tau=1, Bins='Sturges', Logx=2, Norm=True)
    
+XDistEn(*Sig, m=2, tau=1, Bins='Sturges', Logx=2, Norm=True)
 
    XDistEn  estimates the cross-distribution entropy between two univariate data sequences.
    
-
    XDist, Ppi = XDistEn(Sig) 
+
    XDist, Ppi = XDistEn(Sig1, Sig2) 
 
    
 
    
 
    Returns the cross-distribution entropy estimate (XDist) and the
 corresponding distribution probabilities (Ppi) estimated between the data 
-sequences contained in Sig using the default parameters: 
+sequences contained in Sig1 and Sig2 using the default parameters: 
 embedding dimension = 2, time delay = 1, binning method = 'Sturges',
 logarithm = base 2, normalisation = w.r.t # of histogram bins
    
-
    XDist, Ppi = XDistEn(Sig, keyword = value, ...)
+
    XDist, Ppi = XDistEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the cross-distribution entropy estimate (XDist) estimated between the 
-data sequences contained in ‘Sig’ using the specified ‘keyword’ = arguments:
    
+data sequences contained in Sig1 and Sig2 using the specified ‘keyword’ = arguments:
    
 
    
 
    
 
    m:
    
@@ -382,23 +376,23 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XFuzzEn(Sig, m=2, tau=1, r=(0.2, 2), Fx='default', Logx=numpy.exp)
    
+XFuzzEn(*Sig, m=2, tau=1, r=(0.2, 2.0), Fx='default', Logx=numpy.exp)
 
    XFuzzEn  estimates the cross-fuzzy entropy between two univariate data sequences.
    
-
    XFuzz, Ps1, Ps2 = XFuzzEn(Sig) 
+
    XFuzz, Ps1, Ps2 = XFuzzEn(Sig1,Sig2) 
 
    
 
    
 
    Returns the cross-fuzzy entropy estimates (XFuzz) and the average fuzzy
 distances (m: Ps1, m+1: Ps2) for m = [1,2] estimated for the data sequences
-contained in Sig, using the default parameters: embedding dimension = 2,
+contained in Sig1 and Sig2, using the default parameters: embedding dimension = 2,
 time delay = 1, fuzzy function (Fx) = ‘default’, 
 fuzzy function parameters (r) = (0.2, 2), logarithm = natural
    
-
    XFuzz, Ps1, Ps2 = XFuzzEn(Sig, keyword = value, ...)
+
    XFuzz, Ps1, Ps2 = XFuzzEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the cross-fuzzy entropy estimates (XFuzz) for dimensions = [1, …, m]
-estimated for the data sequences in ‘Sig’ using the specified ‘keyword’ arguments:
    
+estimated for the data sequences in Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
-
    
+
    
 
    m:
    
 
    
-
    
+
    
 
    Logx:
    
 
      
 
    • Logarithm base, a positive scalar  [default: natural]
      • 
 
    
 
    
 
    
+
    
 
    For further information on the keyword arguments, see the EntropyHub guide.
    
 
    
 
    See also:
    
@@ -477,6 +526,11 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
+
    [2] Hamed Azami, et al.
    “Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: 
+Assessment and Comparison”
+IEEE Access
+7 (2019): 104833-104847
    
+
    
 
    
 
    
 
    
@@ -484,21 +538,21 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XK2En(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
    
+XK2En(*Sig, m=2, tau=1, r=None, Logx=numpy.exp)
 
    XK2En  estimates the cross-Kolmogorov entropy between two univariate  data sequences.
    
-
    XK2, Ci = XK2En(Sig) 
+
    XK2, Ci = XK2En(Sig1, Sig2) 
 
    
 
    
 
    Returns the cross-Kolmogorov entropy estimates (XK2) and the correlation
 integrals (Ci) for m = [1, 2] estimated between the data sequences 
-contained in Sig using the default parameters: 
-embedding dimension = 2, time delay = 1, distance threshold (r) = 0.2*SD(Sig),
-logarithm = natural
    
-
    XK2, Ci = XK2En(Sig, keyword = value, ...)
+contained in Sig1 and``Sig2`` using the default parameters: 
+embedding dimension = 2, time delay = 1, 
+distance threshold (r) = 0.2*SDpooled(Sig1,``Sig2``), logarithm = natural
    
+
    XK2, Ci = XK2En(Sig1,Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the cross-Kolmogorov entropy estimates (XK2) estimated between
-the data sequences contained in Sig using the specified ‘keyword’ arguments:
    
+the data sequences contained in Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
 
    
 
    m:
    
@@ -513,7 +567,7 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    r:
    
 
      
-
    • Radius Distance Threshold, a positive scalar [default: 0.2*SD(Sig)]
      • 
+
    • Radius Distance Threshold, a positive scalar [default: 0.2*SDpooled(Sig1,``Sig2``),]
      • 
 
    
 
    
 
    Logx:
    
@@ -539,19 +593,19 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XPermEn(Sig, m=3, tau=1, Logx=numpy.exp)
    
+XPermEn(*Sig, m=3, tau=1, Logx=numpy.exp)
 
    XPermEn  estimates the cross-permutation entropy between two univariate data sequences.
    
-
    XPerm = XPermEn(Sig) 
+
    XPerm = XPermEn(Sig1, Sig2) 
 
    
 
    
 
    Returns the cross-permuation entropy estimates (XPerm) estimated betweeen
-the data sequences contained in Sig using the default parameters:
+the data sequences contained in Sig1 and Sig2 using the default parameters:
 embedding dimension = 3, time delay = 1, logarithm = base 2,
    
-
    XPerm = XPermEn(Sig, keyword = value, ...)
+
    XPerm = XPermEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the permutation entropy estimates (Perm) estimated between the data
-sequences contained in Sig using the specified ‘keyword’ arguments:
    
+sequences contained in Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
 
    
 
    m:
    
@@ -590,20 +644,32 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XSampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
    
+XSampEn(*Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)
 
    XSampEn  Estimates the cross-sample entropy between two univariate data sequences.
    
-
    XSamp, A, B = XSampEn(Sig) 
+
    XSamp, A, B = XSampEn(Sig1, Sig2) 
 
    
 
    
 
    Returns the cross-sample entropy estimates (XSamp) and the number of 
 matched vectors (m: B, m+1: A) for m = [0,1,2] estimated for the two 
-univariate data sequences contained in Sig using the default parameters:
-embedding dimension = 2, time delay = 1, radius = 0.2*SD(Sig), logarithm = natural
    
-
    XSamp, A, B = XSampEn(Sig, keyword = value, ...)
+univariate data sequences contained in Sig1 and Sig2 using the default parameters:
+embedding dimension = 2, time delay = 1, radius = 0.2*SDpooled(Sig1,``Sig2``),
+logarithm = natural
    
+
    XSamp, A, B, (Vcp, Ka, Kb) = XSampEn(Sig1, Sig2, ..., Vcp = True) 
+
    
+
    
+
    If Vcp == True, an additional tuple (Vcp, Ka, Kb) is returned with    
+the cross-sample entropy estimates (XSamp) and the number of matched state
+vectors (m: B, m+1: A). (Vcp, Ka, Kb)  contains the variance of the conditional
+probabilities (Vcp, i.e. CP = A/B),  and the number of overlapping
+matching vector pairs of lengths m+1 (Ka) and m (Kb),
+respectively.  Note Vcp is undefined for the zeroth embedding dimension (m = 0) 
+and due to computational demand, will take substantially more time to return function outputs.
+See Appendix B in [2] for more info.
    
+
    XSamp, A, B = XSampEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the cross-sample entropy estimates (XSamp) for dimensions [0,1,…, m]
-estimated between the data sequences in Sig using the specified ‘keyword’ arguments:
    
+estimated between the data sequences Sig1 and Sig2 using the specified ‘keyword’ arguments:
    
 
    
 
    
+
    [2]  Douglas E Lake, Joshua S Richman, M.P. Griffin, J. Randall Moorman
    “Sample entropy analysis of neonatal heart rate variability.”
+American Journal of Physiology-Regulatory, Integrative and Comparative Physiology
+283, no. 3 (2002): R789-R797.
    
+
    
 
    
 
    
 
    
@@ -646,23 +716,23 @@ 
    Functions for estimating the entropy between two univariate time series.
 
    
-XSpecEn(Sig, N=None, Freqs=(0, 1), Logx=numpy.exp, Norm=True)
    
+XSpecEn(*Sig, N=None, Freqs=(0, 1), Logx=numpy.exp, Norm=True)
 
    XSpecEn  estimates the cross-spectral entropy between two univariate data sequences.
    
-
    XSpec, BandEn = XSpecEn(Sig) 
+
    XSpec, BandEn = XSpecEn(Sig1, Sig2) 
 
    
 
    
 
    Returns the cross-spectral entropy estimate (XSpec) of the full cross-
 spectrum and the within-band entropy (BandEn) estimated for the data 
-sequences contained in Sig using the default  parameters: 
-N-point FFT = length of Sig, normalised band edge frequencies = [0 1],
-logarithm = base 2, normalisation = w.r.t # of spectrum/band frequency 
-values.
    
-
    XSpec, BandEn = XSpecEn(Sig, keyword = value, ...)
+sequences contained in Sig1 and Sig2 using the default  parameters: 
+N-point FFT = 2 * max length of Sig1/Sig2, 
+normalised band edge frequencies = [0 1], logarithm = base 2, 
+normalisation = w.r.t # of spectrum/band frequency  values.
    
+
    XSpec, BandEn = XSpecEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
 
    Returns the cross-spectral entropy (XSpec) and the within-band entropy 
-(BandEn) estimate for the data sequences contained in Sig using the
-following specified ‘keyword’ arguments:
    
+(BandEn) estimate for the data sequences contained in Sig1 and Sig2 
+using the  following specified ‘keyword’ arguments:
    
 
    
 
    
 
    N:
    
@@ -721,7 +791,7 @@ 
    Functions for estimating the entropy between two univariate time series.
 
   
    
-    
    © Copyright 2023, Matthew W. Flood.
    
+    
    © Copyright 2024, Matthew W. Flood.
    
   
    
 
    
diff --git a/docs/_build/html/python/Functions/Multiscale.html b/docs/_build/html/python/Functions/Multiscale.html
index a1efac4..aa045cc 100644
--- a/docs/_build/html/python/Functions/Multiscale.html
+++ b/docs/_build/html/python/Functions/Multiscale.html
@@ -4,7 +4,7 @@
   
 
   
-  Multiscale Entropies — EntropyHub 0.2 documentation
+  Multiscale Entropies — EntropyHub 1.0 documentation
       
       
       
@@ -164,13 +164,13 @@ 
    Multiscale EntropiesFunctions for estimating the multiscale entropy of a univariate time series.
    
 
    Multiscale entropy can be calculated using any of the Base Entropies:
 ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn,
-DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En,
-PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn.
    
+DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En,
+PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn.
    
 
    
 
    Important
    
 
    Multiscale cross-entropy functions have two positional arguments:
    
 
      
-
    1. the time series signal, Sig (a vector > 10 elements),
      2. 
+
    2. the data sequence, Sig (a vector > 10 elements),
      3. 
 
    3. the multiscale entropy object, Mobj.
      4. 
 
    
 
    
@@ -201,7 +201,7 @@ 
    Functions for estimating the multiscale entropy of a univariate time series.
 To see the default parameters for a particular entropy method,          
 type:   help(EnType)   (e.g.  help(SampEn))
    
 
    EnType can be any of the following (case sensitive) string names:
    
-
    
+
    
 
    Base Entropies:
    
 
    
 
    'ApEn':
    
@@ -295,6 +295,8 @@ 
    Functions for estimating the multiscale entropy of a univariate time series.
 
 
    
 
    
+
    ['DivEn']:    - Diversity Entropy
+['RangEn']:   - Range Entropy
    
 
    
 
    Cross Entropies:
    
 
    
@@ -340,8 +342,42 @@ 
    Functions for estimating the multiscale entropy of a univariate time series.
    -
    See also:
    -

    MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

    +
    Bidimensional Entropies:
    +
    +
    'SampEn2D':
    +
      +

    • Bidimensional Sample Entropy

      • +
    +
    +
    'FuzzEn2D':
    +
      +

    • Bidimensional Fuzzy Entropy

      • +
    +
    +
    'DispEn2D':
    +
      +

    • Bidimensional Dispersion Entropy

      • +
    +
    +
    'DistEn2D':
    +
      +

    • Bidimensional Distribution Entropy

      • +
    +
    +
    'PermEn2D':
    +
      +

    • Bidimensional Permutation Entropy

      • +
    +
    +
    'EspEn2D':
    +
      +

    • Bidimensional Espinosa Entropy

      • +
    +
    +
    +
    +
    See also:
    +

    MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

    @@ -375,7 +411,7 @@

    Functions for estimating the multiscale entropy of a univariate time series.
    Methodx:
      -

    • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift'}

      • +

    • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift' , 'generalized'}

    RadNew:
    @@ -458,6 +494,10 @@

    Functions for estimating the multiscale entropy of a univariate time series. and dispersion entropy.” Entropy 20.2 (2018): 138.

    +
    [12] Madalena Costa and Ary L. Goldberger,

    “Generalized multiscale entropy analysis: Application to quantifying +the complex volatility of human heartbeat time series.” +Entropy 17.3 (2015): 1197-1203.

    +
    @@ -466,7 +506,7 @@

    Functions for estimating the multiscale entropy of a univariate time series.
    cMSEn(Sig, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)
    -

    cMSEn Returns the composite multiscale entropy of a univariate data sequence.

    +

    cMSEn Returns the composite (or refined-composite) multiscale entropy of a univariate data sequence.

    MSx, CI = cMSEn(Sig, Mobj)
    @@ -474,6 +514,15 @@

    Functions for estimating the multiscale entropy of a univariate time series. sequence (Sig) using the parameters specified by the multiscale object (Mobj) using the composite multiscale entropy method (cMSE) over 3 temporal scales.

    +
    MSx, CI = cMSEn(Sig, Mobj, Refined = True) 
+
    +
    +

    Returns a vector of refined-composite multiscale entropy values (MSx) for the data +sequence (Sig) using the parameters specified by the multiscale object +(Mobj) using the refined-composite multiscale entropy method (rcMSE) over 3 temporal +scales. When Refined == True, the base entropy method must be SampEn or FuzzEn. +If the entropy method is SampEn, cMSEn employs the method described in [5]. +If the entropy method is FuzzEn, cMSEn employs the method described in [6].

    MSx, CI = cMSEn(Sig, Mobj, keyword = value, ...)
    @@ -505,7 +554,7 @@

    Functions for estimating the multiscale entropy of a univariate time series.

    Refined:
      -

    • Refined-composite MSEn method. When Refined == True and the entropy function specified by Mobj is SampEn,

      • +

    • Refined-composite MSEn method. When Refined == True and the entropy function specified by Mobj is SampEn or FuzzEn,

    cMSEn returns the refined-composite multiscale entropy (rcMSEn) [default: False]

    @@ -544,6 +593,11 @@

    Functions for estimating the multiscale entropy of a univariate time series. Physics Letters A 378.20 (2014): 1369-1374.

    +
    [6] Hamed Azami et al.,

    “Refined multiscale fuzzy entropy based on standard deviation +for biomedical signal analysis” +Med Biol Eng Comput +55 (2017):2037–2052

    +

    @@ -729,7 +783,7 @@

    Functions for estimating the multiscale entropy of a univariate time series.
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/Functions/MultiscaleCross.html b/docs/_build/html/python/Functions/MultiscaleCross.html index 21aa791..3a224ec 100644 --- a/docs/_build/html/python/Functions/MultiscaleCross.html +++ b/docs/_build/html/python/Functions/MultiscaleCross.html @@ -4,7 +4,7 @@ - Multiscale Cross-Entropies — EntropyHub 0.2 documentation + Multiscale Cross-Entropies — EntropyHub 1.0 documentation @@ -167,17 +167,13 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    To do so, we again use the MSobject function to pass a multiscale object (Mobj) to the multiscale cross-entropy functions.

    Important

    -

    Multiscale cross-entropy functions have two positional arguments:

    +

    Multiscale cross-entropy functions have three positional arguments:

      -

    1. the time series signals, Sig (an Nx2 matrix),

      2. +

    2. the first data sequence, Sig1 (a vector > 10 elements),

      3. +

    3. the second data sequence, Sig2 (a vector > 10 elements),

    4. the multiscale entropy object, Mobj.

    -
    -

    Important

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately.

    -
    @@ -205,7 +201,7 @@

    Functions for estimating the multiscale cross-entropy between two univariate To see the default parameters for a particular entropy method, type: help(EnType) (e.g. help(SampEn))

    EnType can be any of the following (case sensitive) string names:

    -
    +
    Base Entropies:
    'ApEn':
    @@ -299,6 +295,8 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +

    ['DivEn']: - Diversity Entropy +['RangEn']: - Range Entropy

    Cross Entropies:
    @@ -344,8 +342,42 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    -
    See also:
    -

    MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

    +
    Bidimensional Entropies:
    +
    +
    'SampEn2D':
    +
      +

    • Bidimensional Sample Entropy

      • +
    +
    +
    'FuzzEn2D':
    +
      +

    • Bidimensional Fuzzy Entropy

      • +
    +
    +
    'DispEn2D':
    +
      +

    • Bidimensional Dispersion Entropy

      • +
    +
    +
    'DistEn2D':
    +
      +

    • Bidimensional Distribution Entropy

      • +
    +
    +
    'PermEn2D':
    +
      +

    • Bidimensional Permutation Entropy

      • +
    +
    +
    'EspEn2D':
    +
      +

    • Bidimensional Espinosa Entropy

      • +
    +
    +
    +
    +
    See also:
    +

    MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

    @@ -355,22 +387,22 @@

    Functions for estimating the multiscale cross-entropy between two univariate
    -XMSEn(Sig, Mbjx, Scales=3, Methodx='coarse', RadNew=0, Plotx=False)
    +XMSEn(Sig1, Sig2, Mbjx, Scales=3, Methodx='coarse', RadNew=0, Plotx=False)

    XMSEn returns the multiscale cross-entropy between two univariate data sequences.

    -
    MSx, CI = XMSEn(Sig, Mobj) 
+
    MSx, CI = XMSEn(Sig1, Sig2, Mobj) 
 
    
 
    
 
    Returns a vector of multiscale cross-entropy values (MSx) and the
-complexity index (CI) between the data sequences contained in Sig using 
+complexity index (CI) between the data sequences contained in Sig1 and Sig2 using 
 the parameters specified by the multiscale object (Mobj) over 3 temporal
 scales with coarse-graining (default).
    
-
    MSx, CI = XMSEn(Sig, Mobj, keyword = value, ...)
+
    MSx, CI = XMSEn(Sig1, Sig2, Mobj, keyword = value, ...)
 
    
 
    
 
    Returns a vector of multiscale cross-entropy values (MSx) and the
-complexity index (CI) between the data sequences contained in ‘Sig’ 
+complexity index (CI) between the data sequences contained in Sig1 and Sig2 
 using the parameters specified by the multiscale object (Mobj) and the
-following ‘keywrod’ arguments:
    
+following ‘keyword’ arguments:
    
 
    
 
    
 
    Scales:
    
@@ -380,7 +412,7 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
    Methodx:
      -

    • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift'}

      • +

    • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift', 'generalized'}

    RadNew:
    @@ -392,8 +424,8 @@

    Functions for estimating the multiscale cross-entropy between two univariate it is set to 0.2 (default). The value of RadNew specifies one of the following methods:

      -

    • [1] Standard Deviation - r*std(Ykj)

      • -

    • [2] Variance - r*var(Ykj)

      • +

    • [1] Pooled Standard Deviation - r*std(Ykj)

      • +

    • [2] Pooled Variance - r*var(Ykj)

    • [3] Mean Absolute Deviation - r*mad(Ykj)

    • [4] Median Absolute Deviation - r*mad(Ykj,1)

    @@ -446,20 +478,29 @@

    Functions for estimating the multiscale cross-entropy between two univariate
    -cXMSEn(Sig, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)
    -

    cXMSEn returns the composite multiscale cross-entropy between two univariate data sequences.

    -
    MSx, CI = cXMSEn(Sig, Mobj) 
+cXMSEn(Sig1, Sig2, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)
+
    cXMSEn  returns the composite (or refined-composite) multiscale cross-entropy between two univariate data sequences.
    
+
    MSx, CI = cXMSEn(Sig1, Sig2, Mobj) 
 
    
 
    
 
    Returns a vector of composite multiscale cross-entropy values (MSx) 
-between two univariate data sequences contained in Sig using the 
+between two univariate data sequences contained in Sig1 and Sig2 using the 
 parameters specified by the multiscale object (Mobj) using the composite 
 multiscale method (cMSE) over 3 temporal scales.
    
-
    MSx, CI = cXMSEn(Sig, Mobj, keyword = value, ...)
+
    MSx, CI = cXMSEn(Sig1, Sig2, Mobj, Refined = True) 
+
    
+
    
+
    Returns a vector of refined-composite multiscale cross-entropy values (MSx) for the data 
+sequences (Sig1, Sig2) using the parameters specified by the multiscale object 
+(Mobj) using the refined-composite multiscale entropy method (rcMSE) over 3 temporal
+scales. When Refined == True, the base entropy method must be XSampEn or XFuzzEn.
+If the entropy method is XSampEn, cXMSEn employs the method described in [7]. 
+If the entropy method is XFuzzEn, cXMSEn employs the method described in [8].
    
+
    MSx, CI = cXMSEn(Sig1, Sig2, Mobj, keyword = value, ...)
 
    
 
    
 
    Returns a vector of composite multiscale cross-entropy values (MSx) 
-between the data sequences contained in ‘Sig’ using the parameters
+between the data sequences contained in Sig1 and Sig2 using the parameters
 specified by the multiscale object (Mobj) and the following ‘keyword’ arguments:
    
 
    
 
    
@@ -477,8 +518,8 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 it is set to 0.2 (default). The value of RadNew specifies one of the following methods:
    
 
    
 
      
-
    • [1] Standard Deviation          - r*std(Ykj)
      • 
-
    • [2] Variance                    - r*var(Ykj)
      • 
+
    • [1] Pooled Standard Deviation   - r*std(Ykj)
      • 
+
    • [2] Pooled Variance             - r*var(Ykj)
      • 
 
    • [3] Mean Absolute Deviation     - r*mad(Ykj)
      • 
 
    • [4] Median Absolute Deviation   - r*mad(Ykj,1)
      • 
 
    
@@ -486,8 +527,9 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 
    
 
    Refined:
    
 
      
-
    • Refined-composite XMSEn method. When Refined == True and the cross-entropy function specified by Mobj is XSampEn,  cXMSEn returns the refined-composite multiscale entropy (rcXMSEn) [default: False]
      • 
+
    • Refined-composite XMSEn method. When Refined == True and the cross-entropy function specified by
      • 
 
    
+
    Mobj is XSampEn or XFuzzEn,  cXMSEn returns the refined-composite multiscale entropy (rcXMSEn) [default: False]
    
 
    
 
    Plotx:
    
 
      
@@ -529,6 +571,16 @@ 
      Functions for estimating the multiscale cross-entropy between two univariate
 Entropy 
 15.3 (2013): 1069-1084.
      
 
    
+
    [7] Shuen-De Wu, et al.,
    “Analysis of complex time series using refined composite 
+multiscale entropy.” 
+Physics Letters A 
+378.20 (2014): 1369-1374.
    
+
    
+
    [8] Hamed Azami et al.,
    “Refined multiscale fuzzy entropy based on standard deviation 
+for biomedical signal analysis”
+Med Biol Eng Comput 
+55 (2017):2037–2052
    
+

    @@ -536,28 +588,28 @@

    Functions for estimating the multiscale cross-entropy between two univariate
    -hXMSEn(Sig, Mbjx, Scales=3, RadNew=0, Plotx=False)
    +hXMSEn(Sig1, Sig2, Mbjx, Scales=3, RadNew=0, Plotx=False)

    hXMSEn returns the hierarchical cross-entropy between two univariate data sequences.

    -
    MSx, Sn, CI = hXMSEn(Sig, Mobj) 
+
    MSx, Sn, CI = hXMSEn(Sig1, Sig2, Mobj) 
 
    
 
    
 
    Returns a vector of cross-entropy values (MSx) calculated at each node 
 in the hierarchical tree, the average cross-entropy value across all 
 nodes at each scale (Sn), and the complexity index (CI) of the hierarchical 
-tree (i.e. sum(Sn)) between the data sequences contained in Sig using
+tree (i.e. sum(Sn)) between the data sequences contained in Sig1 and``Sig2`` using
 the parameters specified by the multiscale object (Mobj) over 3 temporal scales (default).
 The entropy values in MSx are ordered from the root node (S_00) to the
 Nth subnode at scale T (S_TN): i.e. S_00, S_10, S_11, S_20, S_21, S_22,
 S_23, S_30, S_31, S_32, S_33, S_34, S_35, S_36, S_37, S_40, … , S_TN.
 The average cross-entropy values in Sn are ordered in the same way, with the
 value of the root node given first: i.e. S0, S1, S2, …, ST
    
-
    MSx, Sn, CI = hXMSEn(Sig, Mobj, Keyword = value, ...)
+
    MSx, Sn, CI = hXMSEn(Sig1, Sig2, Mobj, Keyword = value, ...)
 
    
 
    
 
    Returns a vector of cross-entropy values (MSx) calculated at each node 
 in the hierarchical tree, the average cross-entropy value across all
 nodes at each scale (Sn), and the complexity index (CI) of the entire
-hierarchical tree between the data sequences contained in Sig using 
+hierarchical tree between the data sequences contained in Sig1 and Sig2 using 
 the following keyword arguments:
    
 
    
 
    
@@ -575,8 +627,8 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 it is set to 0.2 (default). The value of RadNew specifies one of the following methods:
    
 
    
 
      
-
    • [1] Standard Deviation          - r*std(Ykj)
      • 
-
    • [2] Variance                    - r*var(Ykj)
      • 
+
    • [1] Pooled Standard Deviation   - r*std(Ykj)
      • 
+
    • [2] Pooled Variance             - r*var(Ykj)
      • 
 
    • [3] Mean Absolute Deviation     - r*mad(Ykj)
      • 
 
    • [4] Median Absolute Deviation   - r*mad(Ykj,1)
      • 
 
    
@@ -614,25 +666,25 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 
 
    
 
    
-rXMSEn(Sig, Mbjx, Scales=3, F_Order=6, F_Num=0.5, RadNew=0, Plotx=False)
    
+rXMSEn(Sig1, Sig2, Mbjx, Scales=3, F_Order=6, F_Num=0.5, RadNew=0, Plotx=False)
 
    rXMSEn  returns the refined multiscale cross-entropy between two   univariate data sequences.
    
-
    MSx, CI = rXMSEn(Sig, Mobj) 
+
    MSx, CI = rXMSEn(Sig1, Sig2, Mobj) 
 
    
 
    
 
    Returns a vector of refined multiscale cross-entropy values (MSx) and
-the complexity index (CI) between the data sequences contained in Sig
+the complexity index (CI) between the data sequences contained in Sig1 and Sig2
 using the parameters specified by the multiscale object (Mobj) and the
-following default parameters:   Scales = 3, Butterworth LPF Order = 6,
+following default parameters:   Scales = 3, Butterworth LPF Order = 6, 
 Butterworth LPF cutoff frequency at scale (T): Fc = 0.5/T. 
-If the entropy function specified by Mobj is XSampEn or XApEn, rMSEn
-updates the threshold radius of the data sequences (Xt) at each scale
-to 0.2*std(Xt) if no r value is provided by Mobj, or r*std(Xt) if r
+If the entropy function specified by Mobj is XSampEn or XApEn, rXMSEn
+updates the threshold radius of the data sequences at each scale
+to 0.2*SDpooled(Sig1,Sig2) if no r value is provided by Mobj, or r*SDpooled(Sig1,Sig2) if r
 is specified.
    
-
    MSx, CI = rXMSEn(Sig, Mobj, keyword = value, ...)
+
    MSx, CI = rXMSEn(Sig1, Sig2, Mobj, keyword = value, ...)
 
    
 
    
 
    Returns a vector of refined multiscale cross-entropy values (MSx) and 
-the complexity index (CI) between the data sequences contained in Sig
+the complexity index (CI) between the data sequences contained in Sig1 and Sig2 
 using the parameters specified by the multiscale object (Mobj) and the
 following ‘keyword’ arguments:
    
 
    
@@ -661,8 +713,8 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 it is set to 0.2 (default). The value of RadNew specifies one of the following methods:
    
 
    
 
      
-
    • [1] Standard Deviation          - r*std(Ykj)
      • 
-
    • [2] Variance                    - r*var(Ykj)
      • 
+
    • [1] Pooled Standard Deviation   - r*std(Ykj)
      • 
+
    • [2] Pooled Variance             - r*var(Ykj)
      • 
 
    • [3] Mean Absolute Deviation     - r*mad(Ykj)
      • 
 
    • [4] Median Absolute Deviation   - r*mad(Ykj,1)
      • 
 
    
@@ -682,7 +734,7 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 
    References:
    
 
    
 
    [1] Matthew W. Flood,
    “rXMSEn - EntropyHub Project”
-2021, https://github.com/MattWillFlood/EntropyHub
    
+2024, https://github.com/MattWillFlood/EntropyHub
    
 
    
 
    [2] Rui Yan, Zhuo Yang, and Tao Zhang,
    “Multiscale cross entropy: a novel algorithm for analyzing two
 time series.” 
@@ -695,7 +747,7 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
 IEEE Transactions on Biomedical Engineering 
 56.9 (2009): 2202-2213.
    
 
    
-
    [4] Puneeta Marwaha and Ramesh Kumar Sunkaria,
    “Optimal selection of threshold value ‘r’for refined multiscale
+
    [4] Puneeta Marwaha and Ramesh Kumar Sunkaria,
    “Optimal selection of threshold value ‘r’ for refined multiscale
 entropy.” 
 Cardiovascular engineering and technology 
 6.4 (2015): 557-576.
    
@@ -733,7 +785,7 @@ 
    Functions for estimating the multiscale cross-entropy between two univariate
   
    
 
   
    
-    
    © Copyright 2023, Matthew W. Flood.
    
+    
    © Copyright 2024, Matthew W. Flood.
    
   
    
 
    
diff --git a/docs/_build/html/python/pyAPI.html b/docs/_build/html/python/pyAPI.html
index e6f375d..9e896fb 100644
--- a/docs/_build/html/python/pyAPI.html
+++ b/docs/_build/html/python/pyAPI.html
@@ -4,7 +4,7 @@
   
 
   
-  Python Functions: — EntropyHub 0.2 documentation
+  Python Functions: — EntropyHub 1.0 documentation
       
       
       
@@ -252,6 +252,12 @@ 
    Base Entropies:
    Attention Entropy
    
 
    AttnEn
    
 
+
    Diversity Entropy
    
+
    DivEn
    
+
+
    Range Entropy
    
+
    RangEn
    
+

    @@ -385,7 +391,7 @@

    Multiscale Cross-Entropies: -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/python/pyexamples.html b/docs/_build/html/python/pyexamples.html index 9f51af6..e597e66 100644 --- a/docs/_build/html/python/pyexamples.html +++ b/docs/_build/html/python/pyexamples.html @@ -4,7 +4,7 @@ - Python Examples: — EntropyHub 0.2 documentation + Python Examples: — EntropyHub 1.0 documentation @@ -202,7 +202,7 @@

    Python Examples:

    vector of uniformly distributed pseudorandom integers in range [1 8]

    'chirp':
    -

    vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025

    +

    vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025

    'lorenz':

    3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10]

    @@ -237,12 +237,10 @@

    Python Examples:

    THINGS TO REMEMBER

    -

    For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. -At present, it is not possible to pass signals of different lengths separately.

    Parameters of the base or cross- entropy methods are passed to multiscale and multiscale cross- entropy functions using the multiscale entropy object given by MSobject(). Base and cross- entropy methods are declared with MSobject() using a string of the function name.

    -

    Each bidimensional entropy function (SampEn2D, FuzzEn2D, DistEn2D, DispEn2D) has +

    Each bidimensional entropy function (SampEn2D, FuzzEn2D, DistEn2D, DispEn2D, EspEn2D) has an important keyword argument - Lock. Bidimensional entropy functions are “locked” by default (Lock == True) to only permit matrices with a maximum size of 128 x 128.

    In hierarchical multiscale entropy (hMSEn()) and hierarchical multiscale cross- @@ -263,7 +261,7 @@

    Python Examples: -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html index 0caa06c..8b54552 100644 --- a/docs/_build/html/search.html +++ b/docs/_build/html/search.html @@ -3,7 +3,7 @@ - Search — EntropyHub 0.2 documentation + Search — EntropyHub 1.0 documentation @@ -173,7 +173,7 @@
    -

    © Copyright 2023, Matthew W. Flood.

    +

    © Copyright 2024, Matthew W. Flood.

    diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js index 4f72262..5fe4bbe 100644 --- a/docs/_build/html/searchindex.js +++ b/docs/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["EHupdates", "Home", "Publications", "index", "julia/EHjulia", "julia/jlexamples", "matlab/EHmatlab", "matlab/Examples/Ex1", "matlab/Examples/Ex10", "matlab/Examples/Ex2", "matlab/Examples/Ex3", "matlab/Examples/Ex4", "matlab/Examples/Ex5", "matlab/Examples/Ex6", "matlab/Examples/Ex7", "matlab/Examples/Ex8", "matlab/Examples/Ex9", "matlab/Functions/matBase", "matlab/Functions/matBidimensional", "matlab/Functions/matCross", "matlab/Functions/matMultiscale", "matlab/Functions/matMultiscaleCross", "matlab/matAPI", "matlab/matexamples", "python/EHpython", "python/Examples/Ex1", "python/Examples/Ex10", "python/Examples/Ex2", "python/Examples/Ex3", "python/Examples/Ex4", "python/Examples/Ex5", "python/Examples/Ex6", "python/Examples/Ex7", "python/Examples/Ex8", 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Composite Multiscale Cross-[Approximate] Entropy", "Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy", "Base Entropies", "Bidimensional Entropies", "Cross Entropies", "Multiscale Entropies", "Multiscale Cross-Entropies", "MatLab Functions:", "MatLab Examples:", "EntropyHub: Python", "Example 1: Sample Entropy", "Example 10: Bidimensional Fuzzy Entropy", "Example 2: (Fine-Grained) Permutation Entropy", "Example 3: Phase Entropy w/ Pioncare Plot", "Example 4: Cross-Distribution Entropy w/ Different Binning Methods", "Example 5: Multiscale Entropy Object [MSobject()]", "Example 6: Multiscale [Increment] Entropy", "Example 7: Refined Multiscale [Sample] Entropy", "Example 8: Composite Multiscale Cross-[Approximate] Entropy", "Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy", "Base Entropies", "Bidimensional Entropies", "Cross Entropies", "Multiscale Entropies", "Multiscale Cross-Entropies", "Python Functions:", "Python Examples:"], "terms": 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"exampledata": [23, 41], "browser": 23, "thing": [23, 41], "TO": [23, 41], "rememb": [23, 41], "python": [24, 40, 41], "2024": [0, 2], "march": 0, "data": [1, 3], "new": [], "version": 0, "0": 0, "here": 0, "more": 0, "come": 0}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 56}}) \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 6dbad3f..2e9035f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,10 +14,10 @@ def setup(app): # -- Project information ----------------------------------------------------- project = 'EntropyHub' -copyright = '2023, Matthew W. Flood' +copyright = '2024, Matthew W. Flood' author = 'Matthew W. Flood' # The full version, including alpha/beta/rc tags -release = '0.2' +release = '1.0' # -- General configuration --------------------------------------------------- extensions = [ 'sphinx_rtd_theme', 'sphinx.ext.autodoc', diff --git a/docs/index.rst b/docs/index.rst index 1af4e5b..a10bc2f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -9,8 +9,9 @@ EntropyHub ********** -An open-source toolkit for entropic time series analysis +An open-source toolkit for entropic data analysis ######################################################## + .. image:: ./_images/EntropyHub_Profiler.png :width: 150px :align: center @@ -33,30 +34,22 @@ An open-source toolkit for entropic time series analysis * `Take part in our user survey `_ -Welcome -####### - -Welcome to EntropyHub! - -This toolkit provides a wide range of functions to calculate different entropy statistics. - -There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. -Although many functions for estimating these entropies can be found in various corners of the internet, -there is currently no toolkit to perform entropic time-series analysis in MatLab, Python and Julia, all with an extensive documentation and consistent syntax. +Welcome! +######## -The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +| This toolkit provides a wide range of functions to calculate different entropy statistics. +| There is an ever-growing range of information-theoretic and dynamical systems entropy measures presented in the scientific literature. +| The goal of EntropyHub is to integrate the many established entropy methods in one open-source package. +......................................................................................................... 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. +In the context of dynamical systems and information theory, **Entropy** quantifies that uncertainty. -The concept of entropy has its origins in `classical physics `_ under the second law of thermodynamics, -a law `considered to underpin our fundamental understanding `_ -of `time in physics `_. In the context of nonlinear dynamics, entropy is central in quantifying the degree of uncertainty or information gain, and is therefore widely used to explain complex nonlinear behaviour in real-world systems. Attempting to analyse the analog world around us requires that we measure time in discrete steps, but doing so compromises @@ -69,25 +62,21 @@ To overcome this, we have developed EntropyHub - an open-source toolkit designed 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 nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics.** +.. note:: -......................................................................................................... + It is important to clarify that the entropy functions herein described estimate entropy in the context of nonlinear dynamics, probability theory and information theory, and not thermodynamic or other entropies from classical physics. +......................................................................................................... Documentation & Help #################### -The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively -(available for :download:`download here <./_static/EntropyHubGuide.pdf>`). - -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.* - +| The `EntropyHub Guide `_ is a .pdf booklet written to help you use the toolkit effectively (available for :download:`download here <./_static/EntropyHubGuide.pdf>`). +| 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 MatLab help browser under* **Supplementary Software**. ......................................................................................................... - Citation and Licensing ###################### @@ -95,7 +84,7 @@ 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 scientific outputs realized using the software: - | **Matthew W. Flood and Bernd Grimm,** + | **Matthew W. Flood** | **EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,** | **PLoS One 16(11):e0259448 (2021),** | **DOI: 10.1371/journal.pone.0259448** @@ -105,7 +94,7 @@ realized using the software: __________________________________________________________________ - © Copyright 2021 Matthew W. Flood, EntropyHub + © 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. diff --git a/docs/julia/EHjulia.rst b/docs/julia/EHjulia.rst index cf0d870..6f17ba1 100644 --- a/docs/julia/EHjulia.rst +++ b/docs/julia/EHjulia.rst @@ -33,7 +33,7 @@ There are several package dependencies which will be installed alongside Entropy - *Plots* - *StatsBase*, *StatsFuns*, *Statistics* - *GroupSlices*, *Combinatorics*, *Clustering* - - *LinearAlgebra*, *Dierckx* + - *LinearAlgebra*, *DataInterpolations* EntropyHub was designed using Julia 1.5 and is intended for use with Julia versions >= 1.2. diff --git a/docs/julia/jlexamples.rst b/docs/julia/jlexamples.rst index 532c7c6..7490da7 100644 --- a/docs/julia/jlexamples.rst +++ b/docs/julia/jlexamples.rst @@ -4,7 +4,7 @@ EntropyHub: Julia **EntropyHub.jl** is the EntropyHub package for *Julia*. -Examples in the Julia language can be found `here `_ +Examples in the Julia language can be found `here `_ .. image:: ../_images/EntropyHubJuliaLogo.png :width: 250px diff --git a/docs/matlab/Functions/matBase.rst b/docs/matlab/Functions/matBase.rst index d7f247f..56a5d47 100644 --- a/docs/matlab/Functions/matBase.rst +++ b/docs/matlab/Functions/matBase.rst @@ -17,5 +17,5 @@ Functions for estimating the entropy of a single univariate time series. ................................................................................ .. mat:automodule:: EntropyHub - :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn + :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn diff --git a/docs/matlab/Functions/matBidimensional.rst b/docs/matlab/Functions/matBidimensional.rst index 4479914..a462f3c 100644 --- a/docs/matlab/Functions/matBidimensional.rst +++ b/docs/matlab/Functions/matBidimensional.rst @@ -12,7 +12,7 @@ Bidimensional Entropies Functions for estimating the entropy of a two-dimensional univariate matrix. **************************************************************************** -While EntropyHub functions primarily apply to time series data, with the following +While EntropyHub functions primarily apply to univariate data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis. diff --git a/docs/matlab/Functions/matCross.rst b/docs/matlab/Functions/matCross.rst index 0d4baf1..a1ac356 100644 --- a/docs/matlab/Functions/matCross.rst +++ b/docs/matlab/Functions/matCross.rst @@ -14,13 +14,6 @@ Functions for estimating the entropy between two univariate time series. *The following functions also form the cross-entropy method used by* **multiscale cross-entropy** *functions.* -..................................................................................................... - -.. attention:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - We are currently working to enable different signal lengths for cross-entropy estimation. ..................................................................................................... diff --git a/docs/matlab/Functions/matMultiscale.rst b/docs/matlab/Functions/matMultiscale.rst index 300ebd6..0821cec 100644 --- a/docs/matlab/Functions/matMultiscale.rst +++ b/docs/matlab/Functions/matMultiscale.rst @@ -13,14 +13,14 @@ Functions for estimating the multiscale entropy of a univariate time series. Multiscale entropy can be calculated using any of the :ref:`matBase`: ``ApEn``, ``AttnEn``, ``BubbEn``, ``CondEn``, ``CoSiEn``, ``DistEn``, -``DispEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, -``PermEn``, ``PhasEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. +``DispEn``, ``DivEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, +``PermEn``, ``PhasEn``, ``RangEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. .. important:: Multiscale cross-entropy functions have two positional arguments: - 1. the time series signal, ``Sig`` (a vector > 10 elements), + 1. the data sequence, ``Sig`` (a vector > 10 elements), 2. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/matlab/Functions/matMultiscaleCross.rst b/docs/matlab/Functions/matMultiscaleCross.rst index 697bf06..f9b3402 100644 --- a/docs/matlab/Functions/matMultiscaleCross.rst +++ b/docs/matlab/Functions/matMultiscaleCross.rst @@ -18,15 +18,11 @@ To do so, we again use the ``MSobject`` function to pass a multiscale object (`` .. important:: - Multiscale cross-entropy functions have two positional arguments: + Multiscale cross-entropy functions have three positional arguments: - 1. the time series signals, ``Sig`` (an Nx2 matrix), - 2. the multiscale entropy object, ``Mobj``. - -.. important:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. + 1. the first data sequence, ``Sig1`` (a vector > 10 elements), + 2. the second data sequence, ``Sig2`` (a vector > 10 elements), + 3. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/matlab/matAPI.rst b/docs/matlab/matAPI.rst index 9f0b163..2c3580d 100644 --- a/docs/matlab/matAPI.rst +++ b/docs/matlab/matAPI.rst @@ -77,6 +77,10 @@ Base Entropies: +------------------------------+----------------+ |Attention Entropy | AttnEn | +------------------------------+----------------+ +|Diversity Entropy | DivEn | ++------------------------------+----------------+ +|Range Entropy | RangEn | ++------------------------------+----------------+ Cross Entropies: diff --git a/docs/matlab/matexamples.rst b/docs/matlab/matexamples.rst index 7e15b33..6850142 100644 --- a/docs/matlab/matexamples.rst +++ b/docs/matlab/matexamples.rst @@ -46,7 +46,7 @@ These examples are merely a snippet of the full range of EntropyHub functionalit :``'uniform'``: vector of uniformly distributed random numbers in range [0 1] :``'gaussian'``: vector of normally distributed random numbers with mean = 0; SD = 1 :``'randintegers'``: vector of uniformly distributed pseudorandom integers in range [1 8] -:``'chirp'``: vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025 +:``'chirp'``: vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025 :``'lorenz'``: 3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10] :``'henon'``: 2-column matrix: X, Y components of the Henon attractor (alpha = 1.4; beta = 0.3); [Xo = 0; Yo = 0] :``'uniform2'``: 2-column matrix: uniformly distributed random numbers in range [0 1] @@ -61,14 +61,11 @@ These examples are merely a snippet of the full range of EntropyHub functionalit .. admonition:: THINGS TO REMEMBER - For *cross-entropy* and *multiscale cross-entropy* functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - Parameters of the *base* or *cross-* entropy methods are passed to *multiscale* and *multiscale cross-* entropy functions using the multiscale entropy object given by ``MSobject()``. *Base* and *cross-* entropy methods are declared with ``MSobject()`` using a string of the function name. - Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*) has + Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*, *EspEn2D*) has an important keyword argument - ``Lock``. *Bidimensional* entropy functions are "locked" by default (``Lock == true``) to only permit matrices with a maximum size of 128 x 128. diff --git a/docs/python/Functions/Base.rst b/docs/python/Functions/Base.rst index 6bd3f80..ae1c0b9 100644 --- a/docs/python/Functions/Base.rst +++ b/docs/python/Functions/Base.rst @@ -27,4 +27,4 @@ These functions are directly available when EntropyHub is imported: .. automodule:: EntropyHub - :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, SampEn, SlopEn, SpecEn, SyDyEn + :members: ApEn, AttnEn, BubbEn, CondEn, CoSiEn, DistEn, DispEn, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, PermEn, PhasEn, RangEn, SampEn, SlopEn, SpecEn, SyDyEn diff --git a/docs/python/Functions/Bidimensional.rst b/docs/python/Functions/Bidimensional.rst index 31e7cfd..d6bc753 100644 --- a/docs/python/Functions/Bidimensional.rst +++ b/docs/python/Functions/Bidimensional.rst @@ -11,7 +11,7 @@ Bidimensional Entropies Functions for estimating the entropy of a two-dimensional univariate matrix. **************************************************************************** -While EntropyHub functions primarily apply to time series data, with the following +While EntropyHub functions primarily apply to univarite data sequences, with the following bidimensional entropy functions one can estimate the entropy of two-dimensional (2D) matrices. Hence, bidimensional entropy functions are useful for applications such as image/texture analysis. diff --git a/docs/python/Functions/Cross.rst b/docs/python/Functions/Cross.rst index 615204f..2fe9606 100644 --- a/docs/python/Functions/Cross.rst +++ b/docs/python/Functions/Cross.rst @@ -15,13 +15,6 @@ Functions for estimating the entropy between two univariate time series. ..................................................................................................... -.. attention:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - We are currently working to enable different signal lengths for cross-entropy estimation. - - These functions are directly available when EntropyHub is imported: .. code-block:: python diff --git a/docs/python/Functions/Multiscale.rst b/docs/python/Functions/Multiscale.rst index 747e3fd..75037b0 100644 --- a/docs/python/Functions/Multiscale.rst +++ b/docs/python/Functions/Multiscale.rst @@ -11,14 +11,14 @@ Functions for estimating the multiscale entropy of a univariate time series. Multiscale entropy can be calculated using any of the :ref:`pyBase`: ``ApEn``, ``AttnEn``, ``BubbEn``, ``CondEn``, ``CoSiEn``, ``DistEn``, -``DispEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, -``PermEn``, ``PhasEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. +``DispEn``, ``DivEn``, ``EnofEn``, ``FuzzEn``, ``GridEn``, ``IncrEn``, ``K2En``, +``PermEn``, ``PhasEn``, ``RangEn``, ``SampEn``, ``SlopEn``, ``SpecEn``, ``SyDyEn``. .. important:: Multiscale cross-entropy functions have two positional arguments: - 1. the time series signal, ``Sig`` (a vector > 10 elements), + 1. the data sequence, ``Sig`` (a vector > 10 elements), 2. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/python/Functions/MultiscaleCross.rst b/docs/python/Functions/MultiscaleCross.rst index 6d60ef1..2e7aa33 100644 --- a/docs/python/Functions/MultiscaleCross.rst +++ b/docs/python/Functions/MultiscaleCross.rst @@ -16,15 +16,11 @@ To do so, we again use the ``MSobject`` function to pass a multiscale object (`` .. important:: - Multiscale cross-entropy functions have two positional arguments: + Multiscale cross-entropy functions have three positional arguments: - 1. the time series signals, ``Sig`` (an Nx2 matrix), - 2. the multiscale entropy object, ``Mobj``. - -.. important:: - - For cross-entropy and multiscale cross-entropy functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. + 1. the first data sequence, ``Sig1`` (a vector > 10 elements), + 2. the second data sequence, ``Sig2`` (a vector > 10 elements), + 3. the multiscale entropy object, ``Mobj``. .......................................................................... diff --git a/docs/python/pyAPI.rst b/docs/python/pyAPI.rst index 8f5b206..aec16ee 100644 --- a/docs/python/pyAPI.rst +++ b/docs/python/pyAPI.rst @@ -90,7 +90,10 @@ Base Entropies: +------------------------------+----------------+ |Attention Entropy | AttnEn | +------------------------------+----------------+ - +|Diversity Entropy | DivEn | ++------------------------------+----------------+ +|Range Entropy | RangEn | ++------------------------------+----------------+ Cross Entropies: **************** diff --git a/docs/python/pyexamples.rst b/docs/python/pyexamples.rst index 41e388b..8bb43df 100644 --- a/docs/python/pyexamples.rst +++ b/docs/python/pyexamples.rst @@ -47,7 +47,7 @@ These examples are merely a snippet of the full range of EntropyHub functionalit :``'uniform'``: vector of uniformly distributed random numbers in range [0 1] :``'gaussian'``: vector of normally distributed random numbers with mean = 0; SD = 1 :``'randintegers'``: vector of uniformly distributed pseudorandom integers in range [1 8] -:``'chirp'``: vector of chirp signal with the following parameters, f0 = :01; t1 = 4000; f1 = :025 +:``'chirp'``: vector of chirp signal with the following parameters, f0 = .01; t1 = 4000; f1 = .025 :``'lorenz'``: 3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10] :``'henon'``: 2-column matrix: X, Y components of the Henon attractor (alpha = 1.4; beta = 0.3); [Xo = 0; Yo = 0] :``'uniform2'``: 2-column matrix: uniformly distributed random numbers in range [0 1] @@ -62,14 +62,11 @@ These examples are merely a snippet of the full range of EntropyHub functionalit .. admonition:: THINGS TO REMEMBER - For *cross-entropy* and *multiscale cross-entropy* functions, the two time series signals are passed as a two-column or two-row matrix. - At present, it is not possible to pass signals of different lengths separately. - Parameters of the *base* or *cross-* entropy methods are passed to *multiscale* and *multiscale cross-* entropy functions using the multiscale entropy object given by ``MSobject()``. *Base* and *cross-* entropy methods are declared with ``MSobject()`` using a string of the function name. - Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*) has + Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*, *EspEn2D*) has an important keyword argument - ``Lock``. *Bidimensional* entropy functions are "locked" by default (``Lock == True``) to only permit matrices with a maximum size of 128 x 128.