| Name | EntropyHub JSON |
| Version |
2.0
JSON |
| download |
| home_page | None |
| Summary | An open-source toolkit for entropic data analysis. |
| upload_time | 2024-04-24 22:52:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] 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. |
| 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
multivariate
randomness
random
signal analysis
nonlinearity
julia
matlab
open-source
refined-composite
hierarchical entropy
information theory
shannon entropy
complexity
dynamical
systems
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# EntropyHub: An open-source toolkit for entropic data analysis
__*Python Edition*__
## 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 8 categories:
* Base functions for estimating the entropy of a single univariate time series.
* Cross functions for estimating the entropy between two univariate time series.
* Multivariate functions for estimating the entropy of a multivariate dataset.
* Bidimensional functions for estimating the entropy of a two-dimensional univariate matrix.
* Multiscale functions for estimating the multiscale entropy of a single univariate time series using any of the Base entropy functions.
* Multiscale Cross functions for estimating the multiscale entropy between two univariate time series using any of the Cross-entropy functions.
* Multivariate Multiscale functions for estimating the multivariate multiscale entropy of multivariate dataset using any of the Multivariate-entropy functions.
* Other Supplementary functions for various tasks related to EntropyHub and signal processing.
#### The following tables outline the functions available in the EntropyHub package.
*When new entropies are published in the scientific literature, efforts will
be made to incorporate them in future releases.*
### Base Entropies:
Entropy Type | Function Name
---|---
Approximate Entropy | ApEn
Sample Entropy | SampEn
Fuzzy Entropy | FuzzEn
Kolmogorov Entropy | K2En
Permutation Entropy | PermEn
Conditional Entropy | CondEn
Distribution Entropy | DistEn
Range Entropy | RangEn
Diversity Entropy | DivEn
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
_______________________________________________________________________
### Multivariate Entropies:
Entropy Type | Function Name
--|--
Multivariate Sample Entropy | MvSampEn
Multivariate Fuzzy Entropy | MvFuzzEn
Multivariate Permutation Entropy | MvPermEn
Multivariate Dispersion Entropy | MvDispEn
Multivariate Cosine Similarity Entropy | MvCoSiEn
_______________________________________________________________________
### Bidimensional Entropies
Entropy Type | Function Name
---|---
Bidimensional Sample Entropy | SampEn2D
Bidimensional Fuzzy Entropy | FuzzEn2D
Bidimensional Distribution Entropy | DistEn2D
Bidimensional Dispersion Entropy | DispEn2D
Bidimensional Permutation Entropy | PermEn2D
Bidimensional Espinosa Entropy | EspEn2D
_________________________________________________________________________
### Multiscale Entropy Functions
Entropy Type | Function Name
---|---
Multiscale Entropy | MSEn
Composite/Refined-Composite Multiscale Entropy | cMSEn
Refined Multiscale Entropy | rMSEn
Hierarchical Multiscale Entropy | hMSEn
_________________________________________________________________________
### Multiscale Cross-Entropy Functions
Entropy Type | Function Name
---|---
Multiscale Cross-Entropy | XMSEn
Composite/Refined-Composite Multiscale Cross-Entropy | cXMSEn
Refined Multiscale Cross-Entropy | rXMSEn
Hierarchical Multiscale Cross-Entropy | hXMSEn
_________________________________________________________________________
### Multivariate Multiscale Entropy Functions
Entropy Type | Function Name
--|--
Multivariate Multiscale Entropy | MvMSEn
Composite/Refined-Composite Multivariate Multiscale Entropy | cMvMSEn
_________________________________________________________________________
### Other Functions
Entropy Type | Function Name
--|--
Example Data Import Tool | ExampleData
Window Data Tool | WindowData
Multiscale Entropy Object | MSobject
## License and Terms of Use
EntropyHub is licensed under the Apache License (Version 2.0) and is free to
use by all on condition that the following reference be included on any outputs
realized using the software:
Matthew W. Flood (2021),
EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,
PLoS ONE 16(11):e0259448
DOI: 10.1371/journal.pone.0259448
www.EntropyHub.xyz
__________________________________________________________________
© Copyright 2024 Matthew W. Flood, EntropyHub
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
For Terms of Use see https://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.
Matt
Raw data
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"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, multivariate, randomness, random, signal analysis, nonlinearity, julia, matlab, open-source, refined-composite, hierarchical entropy, information theory, shannon entropy, complexity, dynamical, systems",
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"description": "# EntropyHub: An open-source toolkit for entropic data analysis\r\n\r\n__*Python Edition*__\r\n\r\n\r\n\r\n## Installation\r\n\r\nThere are two ways to install EntropyHub for Python. Method 1 is strongly recommended.\r\n\r\n#### Method 1:\r\n 1. Using `pip` in your python IDE, type:\r\n `pip install EntropyHub`\r\n\t\r\n#### Method 2:\r\n 1. Download the folder above (EntropyHub.*x.x.x*.tar.gz) and unzip it.\r\n 2. Open a command terminal (__*cmd*__ on Windows, __*terminal*__ on Mac) or __use the Anaconda prompt\r\n if you use Anaconda as your python package distribution__. \r\n 3. In the command prompt/terminal, navigate to the directory where you saved and extracted the .tar.gz folder.\r\n 4. Enter the following in the command line:\r\n `python setup.py install`\r\n \r\n### System Requirements & Dependencies\r\n There are several package dependencies which will be installed alongside EntropyHub: \r\n Numpy, Scipy, Matplotlib, PyEMD\r\n \r\n EntropyHub was designed using Python 3 and thus is not intended for use with Python 2.\r\n Python versions > 3.6 are required for using EntropyHub. \r\n \r\n\r\n\r\n## Documentation & Help \r\n\r\nA key advantage of EntropyHub is the comprehensive documentation available to help users to make the most of the toolkit.\r\nOne can simply access the docstrings of a function (like any Python function) by typing `help FunctionName` in the command line, \r\nwhich will print the docstrings.\r\n\r\nAll 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).\r\n \r\n\t\r\n## Functions\r\n\r\nEntropyHub functions fall into 8 categories: \r\n\r\n * Base functions for estimating the entropy of a single univariate time series.\r\n * Cross functions for estimating the entropy between two univariate time series.\r\n * Multivariate functions for estimating the entropy of a multivariate dataset.\r\n * Bidimensional functions for estimating the entropy of a two-dimensional univariate matrix.\r\n * Multiscale functions for estimating the multiscale entropy of a single univariate time series using any of the Base entropy functions.\r\n * Multiscale Cross functions for estimating the multiscale entropy between two univariate time series using any of the Cross-entropy functions.\r\n * Multivariate Multiscale functions for estimating the multivariate multiscale entropy of multivariate dataset using any of the Multivariate-entropy functions.\r\n * Other Supplementary functions for various tasks related to EntropyHub and signal processing.\r\n\r\n\r\n#### The following tables outline the functions available in the EntropyHub package.\r\n\r\n*When new entropies are published in the scientific literature, efforts will\r\nbe made to incorporate them in future releases.*\r\n\r\n### Base Entropies:\r\n\r\nEntropy Type | Function Name \r\n---|---\r\nApproximate Entropy \t |\tApEn\r\nSample Entropy \t\t |\tSampEn\r\nFuzzy Entropy \t\t |\tFuzzEn\r\nKolmogorov Entropy \t\t |\tK2En\r\nPermutation Entropy \t\t |\tPermEn\r\nConditional Entropy \t\t |\tCondEn\r\nDistribution Entropy \t\t |\tDistEn\r\nRange Entropy | RangEn\r\nDiversity Entropy | DivEn\r\nSpectral Entropy \t\t |\tSpecEn\r\nDispersion Entropy \t\t |\tDispEn\r\nSymbolic Dynamic Entropy \t |\tSyDyEn\r\nIncrement Entropy \t |\tIncrEn\r\nCosine Similarity Entropy \t |\tCoSiEn\r\nPhase Entropy |\tPhasEn\r\nSlope Entropy \t |\tSlopEn\r\nBubble Entropy \t\t |\tBubbEn\r\nGridded Distribution Entropy |\tGridEn\r\nEntropy of Entropy \t |\tEnofEn\r\nAttention Entropy |\tAttnEn\r\n\r\n_______________________________________________________________________\r\n\r\n### Cross Entropies:\r\n\r\nEntropy Type | Function Name \r\n---|---\r\nCross Sample Entropy |\tXSampEn\r\nCross Approximate Entropy |\tXApEn\r\nCross Fuzzy Entropy |\tXFuzzEn\r\nCross Permutation Entropy |\tXPermEn\r\nCross Conditional Entropy |\tXCondEn\r\nCross Distribution Entropy |\tXDistEn\r\nCross Spectral Entropy \t |\tXSpecEn\r\nCross Kolmogorov Entropy |\tXK2En\r\n\t\r\n_______________________________________________________________________\r\n\r\n\r\n### Multivariate Entropies:\r\n\r\nEntropy Type | Function Name \r\n--|--\r\nMultivariate Sample Entropy |\tMvSampEn\r\nMultivariate Fuzzy Entropy |\tMvFuzzEn\r\nMultivariate Permutation Entropy |\tMvPermEn\r\nMultivariate Dispersion Entropy |\tMvDispEn\r\nMultivariate Cosine Similarity Entropy |\tMvCoSiEn\r\n\r\n_______________________________________________________________________\r\n\r\n### Bidimensional Entropies\r\n\r\nEntropy Type | Function Name \r\n---|---\r\nBidimensional Sample Entropy |\tSampEn2D\r\nBidimensional Fuzzy Entropy |\tFuzzEn2D\r\nBidimensional Distribution Entropy |\tDistEn2D\r\nBidimensional Dispersion Entropy |\tDispEn2D\r\nBidimensional Permutation Entropy |\tPermEn2D\r\nBidimensional Espinosa Entropy |\tEspEn2D\r\n\t\r\n_________________________________________________________________________\r\n\r\n### Multiscale Entropy Functions\r\n\r\nEntropy Type | Function Name \r\n---|---\r\nMultiscale Entropy | MSEn\r\nComposite/Refined-Composite Multiscale Entropy | cMSEn\r\nRefined Multiscale Entropy | rMSEn\r\nHierarchical Multiscale Entropy | hMSEn\r\n\t\r\n_________________________________________________________________________\r\n\r\n### Multiscale Cross-Entropy Functions\r\nEntropy Type | Function Name \r\n---|---\r\nMultiscale Cross-Entropy | XMSEn\r\nComposite/Refined-Composite Multiscale Cross-Entropy | cXMSEn\r\nRefined Multiscale Cross-Entropy | rXMSEn\r\nHierarchical Multiscale Cross-Entropy | hXMSEn\r\n\r\n_________________________________________________________________________\r\n\r\n### Multivariate Multiscale Entropy Functions\r\n\r\nEntropy Type | Function Name \r\n--|--\r\nMultivariate Multiscale Entropy | MvMSEn\r\nComposite/Refined-Composite Multivariate Multiscale Entropy | cMvMSEn\r\n\r\n_________________________________________________________________________\r\n\r\n### Other Functions\r\n\r\nEntropy Type | Function Name \r\n--|--\r\nExample Data Import Tool | ExampleData\r\nWindow Data Tool | WindowData\r\nMultiscale Entropy Object | MSobject\r\n\r\n\r\n\r\n## License and Terms of Use\r\nEntropyHub is licensed under the Apache License (Version 2.0) and is free to\r\nuse by all on condition that the following reference be included on any outputs\r\nrealized using the software:\r\n \r\n Matthew W. Flood (2021), \r\n EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,\r\n PLoS ONE 16(11):e0259448\r\n DOI: 10.1371/journal.pone.0259448\r\n www.EntropyHub.xyz\r\n\r\n__________________________________________________________________\r\n\r\n\r\n \u00a9 Copyright 2024 Matthew W. Flood, EntropyHub\r\n Licensed under the Apache License, Version 2.0 (the \"License\");\r\n you may not use this file except in compliance with the License.\r\n You may obtain a copy of the License at\r\n \r\n http://www.apache.org/licenses/LICENSE-2.0\r\n \r\n Unless required by applicable law or agreed to in writing, software\r\n distributed under the License is distributed on an \"AS IS\" BASIS,\r\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n See the License for the specific language governing permissions and\r\n limitations under the License.\r\n \r\n For Terms of Use see https://www.EntropyHub.xyz\r\n\r\n\r\n\r\n## Contact\r\n\r\nIf you find this package useful, please consider starring it on GitHub, \r\nMatLab File Exchange, PyPI or Julia Packages as this helps us to gauge user \r\nsatisfaction.\r\n\r\nFor general queries and information about EntropyHub, contact: info@entropyhub.xyz\r\nIf you have any questions or need help using the package, please contact us at: help@entropyhub.xyz\r\nIf you notice or identify any issues, please do not hesitate to contact us at: fix@entropyhub.xyz\r\n\r\n__Thank you__ for using EntropyHub.\r\n\r\nMatt\r\n\r\n\r\n\r\n \r\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. 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