Metadata-Version: 2.1
Name: EntropyHub
Version: 0.2
Summary: An open-source toolkit for entropic time series analysis.
Home-page: https://www.EntropyHub.xyz
Author: Matthew W. Flood
Author-email: info@entropyhub.xyz, help@entropyhub.xyz
License: Apache 2.0
Project-URL: Contact, https://www.entropyhub.xyz/#contact
Project-URL: Examples, https://www.entropyhub.xyz/python/pyexamples.html
Project-URL: Collaboration, https://www.researchgate.net/profile/Matthew-Flood-3
Project-URL: Citation, https://doi.org/10.1371/journal.pone.0259448
Project-URL: Bug Reports, https://github.com/MattWillFlood/EntropyHub/issues
Project-URL: Source, https://www.EntropyHub.xyz
Project-URL: Documentation, https://github.com/MattWillFlood/EntropyHub/blob/main/EntropyHub%20Guide.pdf
Description: # EntropyHub: An open-source toolkit for entropic time series analysis
        
        __*Python Edition*__
        
        
                
                 ___  _   _  _____  _____  ____  ____  _     _          
                |  _|| \ | ||_   _||     \|    ||    || \   / |   ___________ 
                | \_ |  \| |  | |  |   __/|    ||  __| \ \_/ /   /  _______  \
                |  _|| \ \ |  | |  |   \  |    || |     \   /   |  /  ___  \  |
                | \_ | |\  |  | |  | |\ \ |    || |      | |    | |  /   \  | | 
                |___||_| \_|  |_|  |_| \_||____||_|      |_|   _|_|__\___/  | | 
                 _   _  _   _  ____                           / |__\______\/  | 
                | | | || | | ||    \     An open-source      |  /\______\__|_/ 
                | |_| || | | ||    |     toolkit for         | |  /   \  | | 
                |  _  || | | ||    \     entropic time-      | |  \___/  | |          
                | | | || |_| ||     \    series analysis     |  \_______/  |
                |_| |_|\_____/|_____/                         \___________/ 
                
                
        
        ## 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](http://www.scholarpedia.org/article/Entropy "Scholarpedia")
        under the second law of thermodynamics, a law 
        [considered to underpin our fundamental understanding](https://www.penguin.co.uk/books/301539/the-order-of-time/9780141984964.html "Rovelli") 
        of [time in physics](https://en.wikipedia.org/wiki/Time_in_physics "Wiki Time"). 
        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 
        best capture the uncertainty of the system under examination. This has resulted 
        in many entropy statistics from approximate entropy and sample entropy, to
        multiscale sample entropy and refined-composite multiscale cross-sample entropy.
        
        
        As the number of statisitcal entropy measures grows, it becomes more difficult
        to identify, contrast and compare the performance of each measure. 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 probability theory and information theory as
        defined by Shannon, and not thermodynamic or other entropies from classical physics.***
        
        
        ## Installation
        
        There are two ways to install EntropyHub for Python. Method 1 is strongly recommended.
        
        #### Method 1:
           1. Using `pip` in your python IDE, type:
                `pip install EntropyHub`
        	
        #### Method 2:
           1. Download the folder above (EntropyHub.*x.x.x*.tar.gz) and unzip it.
           2. Open a command terminal (__*cmd*__ on Windows, __*terminal*__ on Mac) or __use the Anaconda prompt
              if you use Anaconda as your python package distribution__. 
           3. In the command prompt/terminal, navigate to the directory where you saved and extracted the .tar.gz folder.
           4. Enter the following in the command line:
                 `python setup.py install`
               
        ### System Requirements & Dependencies
          There are several package dependencies which will be installed alongside EntropyHub: Numpy, Scipy, Matplotlib, PyEMD
          
          EntropyHub was designed using Python 3 and thus is not intended for use with Python 2.
          Python versions > 3.6 are required for using EntropyHub. 
          
        
        
        ## Documentation & Help 
        
        A key advantage of EntropyHub is the comprehensive documentation available to help users to make the most of the toolkit.
        One can simply access the docstrings of a function (like any Python function) by typing `help FunctionName` in the command line, 
        which will print the docstrings.
        
        All information on the EntropyHub package is detailed in the *EntropyHub Guide*, a .pdf document available [here](https://github.com/MattWillFlood/EntropyHub/blob/main/EntropyHub%20Guide.pdf).
          
        	
        ## Functions
        
        EntropyHub functions fall into 5 categories: 
        
            * Base                functions for estimating the entropy of a single univariate time series.
            * Cross               functions for estimating the entropy between two univariate time series.
            * Bidimensional       functions for estimating the entropy of a two-dimensional univariate matrix.
            * Multiscale          functions for estimating the multiscale entropy of a single univariate time series using any of the Base entropy functions.
            * Multiscale Cross    functions for estimating the multiscale entropy between two univariate time series using any of the Cross-entropy functions.
        
        #### The following tables outline the functions available in the EntropyHub package.
        
        *When new entropies are published in the scientific literature, efforts will
        be made to incorporate them in future releases.*
        
        ### Base Entropies:
        
        Entropy Type   |  Function Name 
        ---|---
        Approximate Entropy                               	  |	ApEn
        Sample Entropy                                		  |	SampEn
        Fuzzy Entropy                                 		  |	FuzzEn
        Kolmogorov Entropy                            		  |	K2En
        Permutation Entropy                           		  |	PermEn
        Conditional Entropy                           		  |	CondEn
        Distribution Entropy                          		  |	DistEn
        Spectral Entropy                              		  |	SpecEn
        Dispersion Entropy                            		  |	DispEn
        Symbolic Dynamic Entropy                          	  |	SyDyEn
        Increment Entropy                                 	  |	IncrEn
        Cosine Similarity Entropy                         	  |	CoSiEn
        Phase Entropy                                         |	PhasEn
        Slope Entropy                                      	  |	SlopEn
        Bubble Entropy                                		  |	BubbEn
        Gridded Distribution Entropy                          |	GridEn
        Entropy of Entropy                            	      |	EnofEn
        Attention Entropy                                     |	AttnEn
        
        _______________________________________________________________________
        
        ### Cross Entropies:
        
        Entropy Type   |  Function Name 
        ---|---
        Cross Sample Entropy                                  |	XSampEn
        Cross Approximate Entropy                             |	XApEn
        Cross Fuzzy Entropy                                   |	XFuzzEn
        Cross Permutation Entropy                             |	XPermEn
        Cross Conditional Entropy                             |	XCondEn
        Cross Distribution Entropy                            |	XDistEn
        Cross Spectral Entropy                          	  |	XSpecEn
        Cross Kolmogorov Entropy                              |	XK2En
        	
        _______________________________________________________________________
        
        ### Bidimensional Entropies
        
        Entropy Type   |  Function Name 
        ---|---
        Bidimensional Sample Entropy                         |	SampEn2D
        Bidimensional Fuzzy Entropy                          |	FuzzEn2D
        Bidimensional Distribution Entropy                   |	DistEn2D
        Bidimensional Dispersion Entropy                     |	DispEn2D
        Bidimensional Permutation Entropy                    |	PermEn2D
        Bidimensional Espinosa Entropy                       |	EspEn2D
        	
        _________________________________________________________________________
        
        ### Multiscale Entropy Functions
        
        Entropy Type   |  Function Name 
        ---|---
        Multiscale Entropy                                    | MSEn
        Composite/Refined-Composite Multiscale Entropy        | cMSEn
        Refined Multiscale Entropy                            | rMSEn
        Hierarchical Multiscale Entropy                       | hMSEn
        	
        _________________________________________________________________________
        
        ### Multiscale Cross-Entropy Functions
        Entropy Type   |  Function Name 
        ---|---
        Multiscale Cross-Entropy                              |   XMSEn
        Composite/Refined-Composite Multiscale Cross-Entropy  |   cXMSEn
        Refined Multiscale Cross-Entropy                      |   rXMSEn
        Hierarchical Multiscale Cross-Entropy                 |   hXMSEn
        
        
        
        
        
        ## License and Terms of Use
        EntropyHub is licensed under the Apache License (Version 2.0) and is free to
        use by all on condition that the following reference be included on any outputs
        realized using the software:
         
                Matthew W. Flood and Bernd Grimm (2021), 
                EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,
                PLoS ONE 16(11):e0259448
                DOI: 10.1371/journal.pone.0259448
                www.EntropyHub.xyz
        
        __________________________________________________________________
        
        
                © Copyright 2021 Matthew W. Flood, EntropyHub
                Licensed under the Apache License, Version 2.0 (the "License");
                you may not use this file except in compliance with the License.
                You may obtain a copy of the License at
                
                         http://www.apache.org/licenses/LICENSE-2.0
                
                Unless required by applicable law or agreed to in writing, software
                distributed under the License is distributed on an "AS IS" BASIS,
                WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
                See the License for the specific language governing permissions and
                limitations under the License.
                
                For Terms of Use see https://www.EntropyHub.xyz
        
        
        
        ## Contact
        
        If you find this package useful, please consider starring it on GitHub, 
        MatLab File Exchange, PyPI or Julia Packages as this helps us to gauge user 
        satisfaction.
        
        For general queries and information about EntropyHub, contact:    info@entropyhub.xyz
        If you have any questions or need help using the package, please contact us at:    help@entropyhub.xyz
        If you notice or identify any issues, please do not hesitate to contact us at:    fix@entropyhub.xyz
        
        
        __Thank you__ for using EntropyHub.
        
        Yours in research,
        
        Matt
        
                
Keywords: entropy,nonlinear,time series,statistics,physics,mathematics,signal processing,statistical physics,entropic,toolkit,research,multiscale,regularity,periodic,sample entropy,approximate entropy,fuzzy entropy,permutation entropy,uncertainty,dispersion entropy,kolmogorov,conditional entropy,composite,refined,randomness,random,signal analysis,nonlinearity,julia,matlab,open-source,refined-composite,hierarchical entropy,information theory,shannon entropy,complexity
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/markdown
