diff --git a/docs/_build/doctrees/EHupdates.doctree b/docs/_build/doctrees/EHupdates.doctree index 16b0890..acfe268 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 363e8ae..a367e10 100644 Binary files a/docs/_build/doctrees/Home.doctree and b/docs/_build/doctrees/Home.doctree differ diff --git a/docs/_build/doctrees/Publications.doctree b/docs/_build/doctrees/Publications.doctree index 36c56eb..af98b36 100644 Binary files a/docs/_build/doctrees/Publications.doctree and b/docs/_build/doctrees/Publications.doctree differ diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle index 336e685..d9288b4 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 a682208..9faeb2b 100644 Binary files a/docs/_build/doctrees/index.doctree and b/docs/_build/doctrees/index.doctree differ diff --git a/docs/_build/doctrees/julia/EHjulia.doctree b/docs/_build/doctrees/julia/EHjulia.doctree index 2a11923..b3ba96b 100644 Binary files a/docs/_build/doctrees/julia/EHjulia.doctree and b/docs/_build/doctrees/julia/EHjulia.doctree differ diff --git a/docs/_build/doctrees/julia/jlexamples.doctree b/docs/_build/doctrees/julia/jlexamples.doctree index 032d03b..9321bee 100644 Binary files a/docs/_build/doctrees/julia/jlexamples.doctree and b/docs/_build/doctrees/julia/jlexamples.doctree differ diff --git a/docs/_build/doctrees/matlab/EHmatlab.doctree b/docs/_build/doctrees/matlab/EHmatlab.doctree index a7d5882..068958f 100644 Binary files a/docs/_build/doctrees/matlab/EHmatlab.doctree and b/docs/_build/doctrees/matlab/EHmatlab.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex1.doctree b/docs/_build/doctrees/matlab/Examples/Ex1.doctree index 0527f98..c0f0f7d 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex1.doctree and b/docs/_build/doctrees/matlab/Examples/Ex1.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex10.doctree b/docs/_build/doctrees/matlab/Examples/Ex10.doctree index 66e270e..b0abd96 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex10.doctree and b/docs/_build/doctrees/matlab/Examples/Ex10.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex11.doctree b/docs/_build/doctrees/matlab/Examples/Ex11.doctree index 3bb5603..cc2b29b 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex11.doctree and b/docs/_build/doctrees/matlab/Examples/Ex11.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex12.doctree b/docs/_build/doctrees/matlab/Examples/Ex12.doctree index 7bc6c35..b09ef1c 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex12.doctree and b/docs/_build/doctrees/matlab/Examples/Ex12.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex13.doctree b/docs/_build/doctrees/matlab/Examples/Ex13.doctree index 9eacff7..a748033 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex13.doctree and b/docs/_build/doctrees/matlab/Examples/Ex13.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex2.doctree b/docs/_build/doctrees/matlab/Examples/Ex2.doctree index 1d8c3a4..1cf0f50 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex2.doctree and b/docs/_build/doctrees/matlab/Examples/Ex2.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex3.doctree b/docs/_build/doctrees/matlab/Examples/Ex3.doctree index d870f08..2e72724 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex3.doctree and b/docs/_build/doctrees/matlab/Examples/Ex3.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex4.doctree b/docs/_build/doctrees/matlab/Examples/Ex4.doctree index 7b44ac3..6168adb 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex4.doctree and b/docs/_build/doctrees/matlab/Examples/Ex4.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex5.doctree b/docs/_build/doctrees/matlab/Examples/Ex5.doctree index f842629..a4f34bc 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex5.doctree and b/docs/_build/doctrees/matlab/Examples/Ex5.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex6.doctree b/docs/_build/doctrees/matlab/Examples/Ex6.doctree index cf86391..fb4718d 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex6.doctree and b/docs/_build/doctrees/matlab/Examples/Ex6.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex7.doctree b/docs/_build/doctrees/matlab/Examples/Ex7.doctree index 3df90cd..756bda3 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex7.doctree and b/docs/_build/doctrees/matlab/Examples/Ex7.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex8.doctree b/docs/_build/doctrees/matlab/Examples/Ex8.doctree index 517c96b..5acef91 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex8.doctree and b/docs/_build/doctrees/matlab/Examples/Ex8.doctree differ diff --git a/docs/_build/doctrees/matlab/Examples/Ex9.doctree b/docs/_build/doctrees/matlab/Examples/Ex9.doctree index 34d6da7..854fd63 100644 Binary files a/docs/_build/doctrees/matlab/Examples/Ex9.doctree and b/docs/_build/doctrees/matlab/Examples/Ex9.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matBase.doctree b/docs/_build/doctrees/matlab/Functions/matBase.doctree index e7c4c89..260d551 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matBase.doctree and b/docs/_build/doctrees/matlab/Functions/matBase.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matBidimensional.doctree b/docs/_build/doctrees/matlab/Functions/matBidimensional.doctree index 3bc5f65..c4b58bc 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matBidimensional.doctree and b/docs/_build/doctrees/matlab/Functions/matBidimensional.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matCross.doctree b/docs/_build/doctrees/matlab/Functions/matCross.doctree index 27f2fca..d2ad21e 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matCross.doctree and b/docs/_build/doctrees/matlab/Functions/matCross.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matMultiscale.doctree b/docs/_build/doctrees/matlab/Functions/matMultiscale.doctree index 8ae7772..a3fe73b 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matMultiscale.doctree and b/docs/_build/doctrees/matlab/Functions/matMultiscale.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matMultiscaleCross.doctree b/docs/_build/doctrees/matlab/Functions/matMultiscaleCross.doctree index af93303..3283e70 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matMultiscaleCross.doctree and b/docs/_build/doctrees/matlab/Functions/matMultiscaleCross.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matMultivariate.doctree b/docs/_build/doctrees/matlab/Functions/matMultivariate.doctree index d66afb7..1a352ca 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matMultivariate.doctree and b/docs/_build/doctrees/matlab/Functions/matMultivariate.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matMultivariateMultiscale.doctree b/docs/_build/doctrees/matlab/Functions/matMultivariateMultiscale.doctree index 62be284..89a1974 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matMultivariateMultiscale.doctree and b/docs/_build/doctrees/matlab/Functions/matMultivariateMultiscale.doctree differ diff --git a/docs/_build/doctrees/matlab/Functions/matOther.doctree b/docs/_build/doctrees/matlab/Functions/matOther.doctree index 4742acb..23ab5dc 100644 Binary files a/docs/_build/doctrees/matlab/Functions/matOther.doctree and b/docs/_build/doctrees/matlab/Functions/matOther.doctree differ diff --git a/docs/_build/doctrees/matlab/matAPI.doctree b/docs/_build/doctrees/matlab/matAPI.doctree index e32177c..1949d63 100644 Binary files a/docs/_build/doctrees/matlab/matAPI.doctree and b/docs/_build/doctrees/matlab/matAPI.doctree differ diff --git a/docs/_build/doctrees/matlab/matexamples.doctree b/docs/_build/doctrees/matlab/matexamples.doctree index d188691..fc23263 100644 Binary files a/docs/_build/doctrees/matlab/matexamples.doctree and b/docs/_build/doctrees/matlab/matexamples.doctree differ diff --git a/docs/_build/doctrees/python/EHpython.doctree b/docs/_build/doctrees/python/EHpython.doctree index 5e9c224..7a2f471 100644 Binary files a/docs/_build/doctrees/python/EHpython.doctree and b/docs/_build/doctrees/python/EHpython.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex1.doctree b/docs/_build/doctrees/python/Examples/Ex1.doctree index 34811ad..96d23cd 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex1.doctree and b/docs/_build/doctrees/python/Examples/Ex1.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex10.doctree b/docs/_build/doctrees/python/Examples/Ex10.doctree index 7be7e99..b85f0cf 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex10.doctree and b/docs/_build/doctrees/python/Examples/Ex10.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex11.doctree b/docs/_build/doctrees/python/Examples/Ex11.doctree index d3ed12d..01d1b31 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex11.doctree and b/docs/_build/doctrees/python/Examples/Ex11.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex12.doctree b/docs/_build/doctrees/python/Examples/Ex12.doctree index a3e5629..cf74789 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex12.doctree and b/docs/_build/doctrees/python/Examples/Ex12.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex13.doctree b/docs/_build/doctrees/python/Examples/Ex13.doctree index 71febca..5c3fc42 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex13.doctree and b/docs/_build/doctrees/python/Examples/Ex13.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex2.doctree b/docs/_build/doctrees/python/Examples/Ex2.doctree index 21388d4..2b9358e 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex2.doctree and b/docs/_build/doctrees/python/Examples/Ex2.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex3.doctree b/docs/_build/doctrees/python/Examples/Ex3.doctree index d1df49f..807c99b 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex3.doctree and b/docs/_build/doctrees/python/Examples/Ex3.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex4.doctree b/docs/_build/doctrees/python/Examples/Ex4.doctree index 650250b..7cea945 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex4.doctree and b/docs/_build/doctrees/python/Examples/Ex4.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex5.doctree b/docs/_build/doctrees/python/Examples/Ex5.doctree index b036902..7c5bb39 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex5.doctree and b/docs/_build/doctrees/python/Examples/Ex5.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex6.doctree b/docs/_build/doctrees/python/Examples/Ex6.doctree index a94130b..2d60760 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex6.doctree and b/docs/_build/doctrees/python/Examples/Ex6.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex7.doctree b/docs/_build/doctrees/python/Examples/Ex7.doctree index 268c703..095c2cc 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex7.doctree and b/docs/_build/doctrees/python/Examples/Ex7.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex8.doctree b/docs/_build/doctrees/python/Examples/Ex8.doctree index 3efad2e..d8d4b2f 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex8.doctree and b/docs/_build/doctrees/python/Examples/Ex8.doctree differ diff --git a/docs/_build/doctrees/python/Examples/Ex9.doctree b/docs/_build/doctrees/python/Examples/Ex9.doctree index 4361204..f411c17 100644 Binary files a/docs/_build/doctrees/python/Examples/Ex9.doctree and b/docs/_build/doctrees/python/Examples/Ex9.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Base.doctree b/docs/_build/doctrees/python/Functions/Base.doctree index 45d6f69..053ffdc 100644 Binary files a/docs/_build/doctrees/python/Functions/Base.doctree and b/docs/_build/doctrees/python/Functions/Base.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Bidimensional.doctree b/docs/_build/doctrees/python/Functions/Bidimensional.doctree index f9db941..ccf87ee 100644 Binary files a/docs/_build/doctrees/python/Functions/Bidimensional.doctree and b/docs/_build/doctrees/python/Functions/Bidimensional.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Cross.doctree b/docs/_build/doctrees/python/Functions/Cross.doctree index cd8d01b..4675349 100644 Binary files a/docs/_build/doctrees/python/Functions/Cross.doctree and b/docs/_build/doctrees/python/Functions/Cross.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Multiscale.doctree b/docs/_build/doctrees/python/Functions/Multiscale.doctree index 3a0603d..b8fec06 100644 Binary files a/docs/_build/doctrees/python/Functions/Multiscale.doctree and b/docs/_build/doctrees/python/Functions/Multiscale.doctree differ diff --git a/docs/_build/doctrees/python/Functions/MultiscaleCross.doctree b/docs/_build/doctrees/python/Functions/MultiscaleCross.doctree index bd6df59..1ab2ea9 100644 Binary files a/docs/_build/doctrees/python/Functions/MultiscaleCross.doctree and b/docs/_build/doctrees/python/Functions/MultiscaleCross.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Multivariate.doctree b/docs/_build/doctrees/python/Functions/Multivariate.doctree index 6916e8f..e1cea61 100644 Binary files a/docs/_build/doctrees/python/Functions/Multivariate.doctree and b/docs/_build/doctrees/python/Functions/Multivariate.doctree differ diff --git a/docs/_build/doctrees/python/Functions/MultivariateMultiscale.doctree b/docs/_build/doctrees/python/Functions/MultivariateMultiscale.doctree index 6e0733c..0c69d12 100644 Binary files a/docs/_build/doctrees/python/Functions/MultivariateMultiscale.doctree and b/docs/_build/doctrees/python/Functions/MultivariateMultiscale.doctree differ diff --git a/docs/_build/doctrees/python/Functions/Other.doctree b/docs/_build/doctrees/python/Functions/Other.doctree index 4f18e93..d5498f1 100644 Binary files a/docs/_build/doctrees/python/Functions/Other.doctree and b/docs/_build/doctrees/python/Functions/Other.doctree differ diff --git a/docs/_build/doctrees/python/pyAPI.doctree b/docs/_build/doctrees/python/pyAPI.doctree index 49b344d..38c0c54 100644 Binary files a/docs/_build/doctrees/python/pyAPI.doctree and b/docs/_build/doctrees/python/pyAPI.doctree differ diff --git a/docs/_build/doctrees/python/pyexamples.doctree b/docs/_build/doctrees/python/pyexamples.doctree index 808d18a..5cd0ce9 100644 Binary files a/docs/_build/doctrees/python/pyexamples.doctree and b/docs/_build/doctrees/python/pyexamples.doctree differ diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo index a802741..914fee2 100644 --- a/docs/_build/html/.buildinfo +++ b/docs/_build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 4c42f4feeefbbc77a5eae3d957aaf51a +config: d13bd84c1df75ff52c57f0be8ce17402 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/EHupdates.html b/docs/_build/html/EHupdates.html index 363b52b..6b423a4 100644 --- a/docs/_build/html/EHupdates.html +++ b/docs/_build/html/EHupdates.html @@ -1,23 +1,32 @@ - + Latest Updates — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -95,7 +104,7 @@
  • Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
  • Ex. 10 - Bidimensional Fuzzy Entropy
  • Ex. 11 - Multivariate Dispersion Entropy
    • -
  • Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • +
  • Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
  • Ex. 13 - Windowing data with WindowData()
    • @@ -181,14 +190,14 @@ https://raw.githubusercontent.com/MattWillFlood/EntropyHub/main/metrics.entropyhub.svg
    -

    Latest Updates

    +

    Latest Updates

    -

    April 2024

    +

    April 2024

    -

    Version 2.0 is out now!

    +

    Version 2.0 is out now!

    Since the introduction of multivariate entropy methods over a decade ago, the utilization of multivariate and multivariate multiscale entropy methods has seen a notable increase in recent years.
    Systems comprising multiple dynamically related components are ubiquitous in many research fields and multivariate entropies provide a powerful method for estimating the complexity of such systems.
    @@ -232,9 +241,9 @@

    Version 2.0 is out now!

    -

    March 2024

    +

    March 2024

    -

    Version 1.0 is here!

    +

    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.
    @@ -305,7 +314,7 @@

    Version 1.0 is here!

    -

    More to come!

    +

    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!
    @@ -315,7 +324,7 @@

    More to come!
    -

    November 2022

    +

    November 2022

    EntropyHub is 1 year old!

    There has been great interest in EntropyHub in the first year since its original publication in Plos One.
    @@ -332,7 +341,7 @@

    November 2022
    -

    July 2022

    +

    July 2022

    IEEE EMBC Symposium on Entropy Algorithms

    _images/EMBC1.jpg
    @@ -344,7 +353,7 @@

    July 2022
    -

    February 2022

    +

    February 2022

    EntropyHub Presentation at IEEE EMBC Glasgow 2022

    _images/EMBC22.png
    @@ -356,7 +365,7 @@

    February 2022
    -

    December 2021

    +

    December 2021

    Version Update - EntropyHub v0.2

    + EntropyHub v0.2 includes two new bidimensional entropy methods:

    @@ -368,7 +377,7 @@

    December 2021
    -

    November 2021

    +

    November 2021

    Publication of paper on EntropyHub in PLoS One.

    _images/PLOS.png
    @@ -390,7 +399,7 @@

    November 2021
    -

    June 2021

    +

    June 2021

    First release of EntropyHub (v0.1).

    The initial release of the EntropyHub toolkit on all platforms including:

      diff --git a/docs/_build/html/Home.html b/docs/_build/html/Home.html index d4820d3..b8e8fb1 100644 --- a/docs/_build/html/Home.html +++ b/docs/_build/html/Home.html @@ -1,23 +1,32 @@ - + EntropyHub — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -183,9 +192,9 @@

    -

    EntropyHub

    +

    EntropyHub

    -

    An open-source toolkit for entropic data analysis

    +

    An open-source toolkit for entropic data analysis

    _images/EntropyHub_Profiler.png

    Available in:

    @@ -197,7 +206,7 @@

    An open-source toolkit for entropic data analysis

    -

    Welcome!

    +

    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.
    @@ -206,7 +215,7 @@

    Welcome!
    -

    About

    +

    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 dynamical systems and information theory, Entropy quantifies that uncertainty.

    @@ -227,7 +236,7 @@

    About

    -

    Documentation & Help

    +

    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.
    @@ -236,7 +245,7 @@

    Documentation & Help
    -

    Citation and Licensing

    +

    Citation and Licensing

    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:

    @@ -270,7 +279,7 @@

    Citation and Licensing
    -

    Contact

    +

    Contact

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

    diff --git a/docs/_build/html/Publications.html b/docs/_build/html/Publications.html index 4f5acc8..e90b085 100644 --- a/docs/_build/html/Publications.html +++ b/docs/_build/html/Publications.html @@ -1,23 +1,32 @@ - + Publications — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -180,7 +189,7 @@
    -

    Publications

    +

    Publications

    @@ -189,9 +198,9 @@

    Publications

    -

    List of research articles citing EntropyHub

    +

    List of research articles citing EntropyHub

    -

    2024

    +

    2024

    -

    2023

    +

    2023

    -

    2022

    +

    2022

    • The complexity analysis of cerebral oxygen saturation during pneumoperitoneum and Trendelenburg position: a retrospective cohort study
      diff --git a/docs/_build/html/_sources/Publications.rst.txt b/docs/_build/html/_sources/Publications.rst.txt index f9f2659..d3d1915 100644 --- a/docs/_build/html/_sources/Publications.rst.txt +++ b/docs/_build/html/_sources/Publications.rst.txt @@ -12,7 +12,7 @@ Publications .. image:: https://raw.githubusercontent.com/MattWillFlood/EntropyHub/main/metrics.entropyhub.svg :align: left :target: https://github.com/MattWillFlood/EntropyHub - :scale: 50 % + :scale: 50% .. raw:: html diff --git a/docs/_build/html/_sources/matlab/EHmatlab.rst.txt b/docs/_build/html/_sources/matlab/EHmatlab.rst.txt index 93843c9..bfe5331 100644 --- a/docs/_build/html/_sources/matlab/EHmatlab.rst.txt +++ b/docs/_build/html/_sources/matlab/EHmatlab.rst.txt @@ -35,77 +35,81 @@ There are 2 ways to install EntropyHub for MatLab. Method 1: ********* +.. toggle:: + + 1. In MatLab, open the Add-Ons browser under the home tab by clicking 'Get Add-Ons'. -1. In MatLab, open the Add-Ons browser under the home tab by clicking 'Get Add-Ons'. + .. image:: ../_images/MATLAB_README3.png + :width: 800px + :align: center + :height: 400px - .. image:: ../_images/MATLAB_README3.png - :width: 800px - :align: center - :height: 400px + 2. In the search bar, search for *'EntropyHub'*. -2. In the search bar, search for *'EntropyHub'*. + .. image:: ../_images/matscreen2.png + :width: 800px + :align: center + :height: 400px - .. image:: ../_images/matscreen2.png - :width: 800px - :align: center - :height: 400px + .. image:: ../_images/matscreen3.png + :width: 800px + :align: center + :height: 250px - .. image:: ../_images/matscreen3.png - :width: 800px - :align: center - :height: 250px + 3. Open the resulting link, and click '*add*' in the top-right corner. -3. Open the resulting link, and click '*add*' in the top-right corner. + .. image:: ../_images/matscreen4.png + :width: 600px + :align: center + :height: 500px - .. image:: ../_images/matscreen4.png - :width: 600px - :align: center - :height: 500px + 4. Follow the instructions to install the toolbox. **Note: You must be logged in to your MathWorks account**. -4. Follow the instructions to install the toolbox. **Note: You must be logged in to your MathWorks account**. - - .. image:: ../_images/matscreen5.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen5.png + :width: 600px + :align: center + :height: 500px Method 2: ********* -1. Go to the `MatLab folder in the EntropyHub repository `_ on GitHub. +.. toggle:: + + + 1. Go to the `MatLab folder in the EntropyHub repository `_ on GitHub. - .. image:: ../_images/MATLAB_README4.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/MATLAB_README4.png + :width: 600px + :align: center + :height: 500px -2. Open the link to the MatLab toolbox file (**EntropyHub.mltbx**) file. + 2. Open the link to the MatLab toolbox file (**EntropyHub.mltbx**) file. - .. image:: ../_images/matscreen8.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen8.png + :width: 600px + :align: center + :height: 500px -3. Download the toolbox file. + 3. Download the toolbox file. - .. image:: ../_images/matscreen9.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen9.png + :width: 600px + :align: center + :height: 500px -4. Open matlab and change the current folder to the directory where the .mltbx file is saved. + 4. Open matlab and change the current folder to the directory where the .mltbx file is saved. - .. image:: ../_images/MATLAB_README1.png - :width: 600px - :align: center - :height: 600px + .. image:: ../_images/MATLAB_README1.png + :width: 600px + :align: center + :height: 600px -5. Double-click the .mltbx file to open it and click install. + 5. Double-click the .mltbx file to open it and click install. - .. image:: ../_images/MATLAB_README2.png - :align: center + .. image:: ../_images/MATLAB_README2.png + :align: center diff --git a/docs/_build/html/_sources/matlab/matAPI.rst.txt b/docs/_build/html/_sources/matlab/matAPI.rst.txt index e10ef51..0d73879 100644 --- a/docs/_build/html/_sources/matlab/matAPI.rst.txt +++ b/docs/_build/html/_sources/matlab/matAPI.rst.txt @@ -46,166 +46,176 @@ EntropyHub functions fall into 8 categories: 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 | -+------------------------------+----------------+ -|Diversity Entropy | DivEn | -+------------------------------+----------------+ -|Range Entropy | RangEn | -+------------------------------+----------------+ - +.. toggle:: + + +------------------------------+----------------+ + |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 | + +------------------------------+----------------+ + |Diversity Entropy | DivEn | + +------------------------------+----------------+ + |Range Entropy | RangEn | + +------------------------------+----------------+ Cross Entropies: **************** - -+------------------------------------+----------------+ -|Entropy Type | Function Name | -+====================================+================+ -|Cross-Approximate Entropy | XApEn | -+------------------------------------+----------------+ -|Cross-Sample Entropy | XSampEn | -+------------------------------------+----------------+ -|Cross-Fuzzy Entropy | XFuzzEn | -+------------------------------------+----------------+ -|Cross-Kolmogorov Entropy | XK2En | -+------------------------------------+----------------+ -|Cross-Permutation Entropy | XPermEn | -+------------------------------------+----------------+ -|Cross-Conditional Entropy | XCondEn | -+------------------------------------+----------------+ -|Cross-Distribution Entropy | XDistEn | -+------------------------------------+----------------+ -|Cross-Spectral Entropy | XSpecEn | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Cross-Approximate Entropy | XApEn | + +------------------------------------+----------------+ + |Cross-Sample Entropy | XSampEn | + +------------------------------------+----------------+ + |Cross-Fuzzy Entropy | XFuzzEn | + +------------------------------------+----------------+ + |Cross-Kolmogorov Entropy | XK2En | + +------------------------------------+----------------+ + |Cross-Permutation Entropy | XPermEn | + +------------------------------------+----------------+ + |Cross-Conditional Entropy | XCondEn | + +------------------------------------+----------------+ + |Cross-Distribution Entropy | XDistEn | + +------------------------------------+----------------+ + |Cross-Spectral Entropy | XSpecEn | + +------------------------------------+----------------+ Multivariate Entropies: *********************** +.. toggle:: + + +----------------------------------------+----------------+ + | 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 | + +----------------------------------------+----------------+ + -+----------------------------------------+----------------+ -| 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 | -+------------------------------------+----------------+ -|Bidimensioanl Distribution Entropy | DistEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Dispersion Entropy | DispEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Permutation Entropy | PermEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Espinosa Entropy | EspEn2D | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Bidimensional Sample Entropy | SampEn2D | + +------------------------------------+----------------+ + |Bidimensional Fuzzy Entropy | FuzzEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Distribution Entropy | DistEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Dispersion Entropy | DispEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Permutation Entropy | PermEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Espinosa Entropy | EspEn2D | + +------------------------------------+----------------+ Multiscale Entropies: ********************** - -+------------------------------------------+----------------+ -|Entropy Type | Function Name | -+==========================================+================+ -|Multiscale Entropy | MSEn | -+------------------------------------------+----------------+ -|Composite Multiscale Entropy | cMSEn | -|(+ Refined-Composite Multiscale Entropy) | | -+------------------------------------------+----------------+ -|Refined Multiscale Entropy | rMSEn | -+------------------------------------------+----------------+ -|Hierarchical Multiscale Entropy | hMSEn | -+------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------+----------------+ + |Entropy Type | Function Name | + +==========================================+================+ + |Multiscale Entropy | MSEn | + +------------------------------------------+----------------+ + |Composite Multiscale Entropy | cMSEn | + |(+ Refined-Composite Multiscale Entropy) | | + +------------------------------------------+----------------+ + |Refined Multiscale Entropy | rMSEn | + +------------------------------------------+----------------+ + |Hierarchical Multiscale Entropy | hMSEn | + +------------------------------------------+----------------+ Multiscale Cross-Entropies: *************************** +.. toggle:: + + +------------------------------------------------+----------------+ + |Entropy Type | Function Name | + +================================================+================+ + |Multiscale Cross-Entropy | XMSEn | + +------------------------------------------------+----------------+ + |Composite Multiscale Cross-Entropy | cXMSEn | + |(+ Refined-Composite Multiscale Cross-Entropy) | | + +------------------------------------------------+----------------+ + |Refined Multiscale Cross-Entropy | rXMSEn | + +------------------------------------------------+----------------+ + |Hierarchical Multiscale Cross-Entropy | hXMSEn | + +------------------------------------------------+----------------+ -+------------------------------------------------+----------------+ -|Entropy Type | Function Name | -+================================================+================+ -|Multiscale Cross-Entropy | XMSEn | -+------------------------------------------------+----------------+ -|Composite Multiscale Cross-Entropy | cXMSEn | -|(+ Refined-Composite Multiscale Cross-Entropy) | | -+------------------------------------------------+----------------+ -|Refined Multiscale Cross-Entropy | rXMSEn | -+------------------------------------------------+----------------+ -|Hierarchical Multiscale Cross-Entropy | hXMSEn | -+------------------------------------------------+----------------+ Multivariate Multiscale Entropies: *********************************** +.. toggle:: -+------------------------------------------+----------------+ -| Entropy Type | Function Name | -+==========================================+================+ -| Multivariate Multiscale Entropy | MvMSEn | -+------------------------------------------+----------------+ -| Composite (+ Refined-Composite) | cMvMSEn | -| Multivariate Multiscale Entropy | | -+------------------------------------------+----------------+ + +------------------------------------------+----------------+ + | Entropy Type | Function Name | + +==========================================+================+ + | Multivariate Multiscale Entropy | MvMSEn | + +------------------------------------------+----------------+ + | Composite (+ Refined-Composite) | cMvMSEn | + | Multivariate Multiscale Entropy | | + +------------------------------------------+----------------+ Other Functions: **************** - -+------------------------------------------+----------------+ -| Function Description | Function Name | -+==========================================+================+ -| Example dataset import tool | ExampleData | -+------------------------------------------+----------------+ -| Windowing tool | WindowData | -| (for data segmentation) | | -+------------------------------------------+----------------+ \ No newline at end of file +.. toggle:: + + +------------------------------------------+----------------+ + | Function Description | Function Name | + +==========================================+================+ + | Example dataset import tool | ExampleData | + +------------------------------------------+----------------+ + | Windowing tool | WindowData | + | (for data segmentation) | | + +------------------------------------------+----------------+ \ No newline at end of file diff --git a/docs/_build/html/_sources/python/EHpython.rst.txt b/docs/_build/html/_sources/python/EHpython.rst.txt index b952ffa..700762d 100644 --- a/docs/_build/html/_sources/python/EHpython.rst.txt +++ b/docs/_build/html/_sources/python/EHpython.rst.txt @@ -41,6 +41,9 @@ Method 1: Method 2: ********* + +.. toggle:: + 1. Download the ``EntropyHub.x.x.x.tar.gz`` folder from the `EntropyHub PyPI repo `_ (or the `EntropyHub GitHub repo `_) and unzip it. diff --git a/docs/_build/html/_sources/python/pyAPI.rst.txt b/docs/_build/html/_sources/python/pyAPI.rst.txt index e9a7fed..db32a83 100644 --- a/docs/_build/html/_sources/python/pyAPI.rst.txt +++ b/docs/_build/html/_sources/python/pyAPI.rst.txt @@ -61,168 +61,176 @@ EntropyHub functions fall into 8 categories: 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 | -+------------------------------+----------------+ -|Diversity Entropy | DivEn | -+------------------------------+----------------+ -|Range Entropy | RangEn | -+------------------------------+----------------+ +.. toggle:: + + +------------------------------+----------------+ + |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 | + +------------------------------+----------------+ + |Diversity Entropy | DivEn | + +------------------------------+----------------+ + |Range Entropy | RangEn | + +------------------------------+----------------+ Cross Entropies: **************** - -+------------------------------------+----------------+ -|Entropy Type | Function Name | -+====================================+================+ -|Cross-Approximate Entropy | XApEn | -+------------------------------------+----------------+ -|Cross-Sample Entropy | XSampEn | -+------------------------------------+----------------+ -|Cross-Fuzzy Entropy | XFuzzEn | -+------------------------------------+----------------+ -|Cross-Kolmogorov Entropy | XK2En | -+------------------------------------+----------------+ -|Cross-Permutation Entropy | XPermEn | -+------------------------------------+----------------+ -|Cross-Conditional Entropy | XCondEn | -+------------------------------------+----------------+ -|Cross-Distribution Entropy | XDistEn | -+------------------------------------+----------------+ -|Cross-Spectral Entropy | XSpecEn | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Cross-Approximate Entropy | XApEn | + +------------------------------------+----------------+ + |Cross-Sample Entropy | XSampEn | + +------------------------------------+----------------+ + |Cross-Fuzzy Entropy | XFuzzEn | + +------------------------------------+----------------+ + |Cross-Kolmogorov Entropy | XK2En | + +------------------------------------+----------------+ + |Cross-Permutation Entropy | XPermEn | + +------------------------------------+----------------+ + |Cross-Conditional Entropy | XCondEn | + +------------------------------------+----------------+ + |Cross-Distribution Entropy | XDistEn | + +------------------------------------+----------------+ + |Cross-Spectral Entropy | XSpecEn | + +------------------------------------+----------------+ Multivariate Entropies: *********************** +.. toggle:: -+----------------------------------------+----------------+ -| 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 | -+----------------------------------------+----------------+ + +----------------------------------------+----------------+ + | 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 | -+------------------------------------+----------------+ -|Bidimensioanl Distribution Entropy | DistEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Dispersion Entropy | DispEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Permutation Entropy | PermEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Espinosa Entropy | EspEn2D | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Bidimensional Sample Entropy | SampEn2D | + +------------------------------------+----------------+ + |Bidimensional Fuzzy Entropy | FuzzEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Distribution Entropy | DistEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Dispersion Entropy | DispEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Permutation Entropy | PermEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Espinosa Entropy | EspEn2D | + +------------------------------------+----------------+ Multiscale Entropies: ********************** - -+------------------------------------------+----------------+ -|Entropy Type | Function Name | -+==========================================+================+ -|Multiscale Entropy | MSEn | -+------------------------------------------+----------------+ -|Composite Multiscale Entropy | cMSEn | -|(+ Refined-Composite Multiscale Entropy) | | -+------------------------------------------+----------------+ -|Refined Multiscale Entropy | rMSEn | -+------------------------------------------+----------------+ -|Hierarchical Multiscale Entropy | hMSEn | -+------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------+----------------+ + |Entropy Type | Function Name | + +==========================================+================+ + |Multiscale Entropy | MSEn | + +------------------------------------------+----------------+ + |Composite Multiscale Entropy | cMSEn | + |(+ Refined-Composite Multiscale Entropy) | | + +------------------------------------------+----------------+ + |Refined Multiscale Entropy | rMSEn | + +------------------------------------------+----------------+ + |Hierarchical Multiscale Entropy | hMSEn | + +------------------------------------------+----------------+ Multiscale Cross-Entropies: *************************** - -+------------------------------------------------+----------------+ -|Entropy Type | Function Name | -+================================================+================+ -|Multiscale Cross-Entropy | XMSEn | -+------------------------------------------------+----------------+ -|Composite Multiscale Cross-Entropy | cXMSEn | -|(+ Refined-Composite Multiscale Cross-Entropy) | | -+------------------------------------------------+----------------+ -|Refined Multiscale Cross-Entropy | rXMSEn | -+------------------------------------------------+----------------+ -|Hierarchical Multiscale Cross-Entropy | hXMSEn | -+------------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------------+----------------+ + |Entropy Type | Function Name | + +================================================+================+ + |Multiscale Cross-Entropy | XMSEn | + +------------------------------------------------+----------------+ + |Composite Multiscale Cross-Entropy | cXMSEn | + |(+ Refined-Composite Multiscale Cross-Entropy) | | + +------------------------------------------------+----------------+ + |Refined Multiscale Cross-Entropy | rXMSEn | + +------------------------------------------------+----------------+ + |Hierarchical Multiscale Cross-Entropy | hXMSEn | + +------------------------------------------------+----------------+ Multivariate Multiscale Entropies: *********************************** +.. toggle:: -+------------------------------------------+----------------+ -| Entropy Type | Function Name | -+==========================================+================+ -| Multivariate Multiscale Entropy | MvMSEn | -+------------------------------------------+----------------+ -| Composite (+ Refined-Composite) | cMvMSEn | -| Multivariate Multiscale Entropy | | -+------------------------------------------+----------------+ + +------------------------------------------+----------------+ + | Entropy Type | Function Name | + +==========================================+================+ + | Multivariate Multiscale Entropy | MvMSEn | + +------------------------------------------+----------------+ + | Composite (+ Refined-Composite) | cMvMSEn | + | Multivariate Multiscale Entropy | | + +------------------------------------------+----------------+ Other Functions: **************** - -+------------------------------------------+----------------+ -| Function Description | Function Name | -+==========================================+================+ -| Example dataset import tool | ExampleData | -+------------------------------------------+----------------+ -| Windowing tool | WindowData | -| (for data segmentation) | | -+------------------------------------------+----------------+ \ No newline at end of file +.. toggle:: + + +------------------------------------------+----------------+ + | Function Description | Function Name | + +==========================================+================+ + | Example dataset import tool | ExampleData | + +------------------------------------------+----------------+ + | Windowing tool | WindowData | + | (for data segmentation) | | + +------------------------------------------+----------------+ \ No newline at end of file diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js index 8549469..8141580 100644 --- a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js +++ b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js @@ -1,20 +1,9 @@ -/* - * _sphinx_javascript_frameworks_compat.js - * ~~~~~~~~~~ - * - * Compatability shim for jQuery and underscores.js. - * - * WILL BE REMOVED IN Sphinx 6.0 - * xref RemovedInSphinx60Warning +/* Compatability shim for jQuery and underscores.js. * + * Copyright Sphinx contributors + * Released under the two clause BSD licence */ -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - - /** * small helper function to urldecode strings * diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css index 0889677..f316efc 100644 --- a/docs/_build/html/_static/basic.css +++ b/docs/_build/html/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -237,6 +237,10 @@ a.headerlink { visibility: hidden; } +a:visited { + color: #551A8B; +} + h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -324,17 +328,17 @@ aside.sidebar { p.sidebar-title { font-weight: bold; } + nav.contents, aside.topic, - div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ + nav.contents, aside.topic, - div.topic { border: 1px solid #ccc; padding: 7px; @@ -375,7 +379,6 @@ div.sidebar > :last-child, aside.sidebar > :last-child, nav.contents > :last-child, aside.topic > :last-child, - div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -385,7 +388,6 @@ div.sidebar::after, aside.sidebar::after, nav.contents::after, aside.topic::after, - div.topic::after, div.admonition::after, blockquote::after { @@ -611,25 +613,6 @@ ul.simple p { margin-bottom: 0; } -/* Docutils 0.17 and older (footnotes & citations) */ -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -/* Docutils 0.18+ (footnotes & citations) */ aside.footnote > span, div.citation > span { float: left; @@ -654,8 +637,6 @@ div.citation > p:last-of-type:after { clear: both; } -/* Footnotes & citations ends */ - dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; @@ -668,10 +649,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -697,6 +674,16 @@ dd { margin-left: 30px; } +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + dl > dd:last-child, dl > dd:last-child > :last-child { margin-bottom: 0; @@ -765,6 +752,14 @@ abbr, acronym { cursor: help; } +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + /* -- code displays --------------------------------------------------------- */ pre { diff --git a/docs/_build/html/_static/css/theme.css b/docs/_build/html/_static/css/theme.css index c03c88f..19a446a 100644 --- a/docs/_build/html/_static/css/theme.css +++ b/docs/_build/html/_static/css/theme.css @@ -1,4 +1,4 @@ html{box-sizing:border-box}*,:after,:before{box-sizing:inherit}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}[hidden],audio:not([controls]){display:none}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}blockquote{margin:0}dfn{font-style:italic}ins{background:#ff9;text-decoration:none}ins,mark{color:#000}mark{background:#ff0;font-style:italic;font-weight:700}.rst-content code,.rst-content tt,code,kbd,pre,samp{font-family:monospace,serif;_font-family:courier new,monospace;font-size:1em}pre{white-space:pre}q{quotes:none}q:after,q:before{content:"";content:none}small{font-size:85%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}dl,ol,ul{margin:0;padding:0;list-style:none;list-style-image:none}li{list-style:none}dd{margin:0}img{border:0;-ms-interpolation-mode:bicubic;vertical-align:middle;max-width:100%}svg:not(:root){overflow:hidden}figure,form{margin:0}label{cursor:pointer}button,input,select,textarea{font-size:100%;margin:0;vertical-align:baseline;*vertical-align:middle}button,input{line-height:normal}button,input[type=button],input[type=reset],input[type=submit]{cursor:pointer;-webkit-appearance:button;*overflow:visible}button[disabled],input[disabled]{cursor:default}input[type=search]{-webkit-appearance:textfield;-moz-box-sizing:content-box;-webkit-box-sizing:content-box;box-sizing:content-box}textarea{resize:vertical}table{border-collapse:collapse;border-spacing:0}td{vertical-align:top}.chromeframe{margin:.2em 0;background:#ccc;color:#000;padding:.2em 0}.ir{display:block;border:0;text-indent:-999em;overflow:hidden;background-color:transparent;background-repeat:no-repeat;text-align:left;direction:ltr;*line-height:0}.ir br{display:none}.hidden{display:none!important;visibility:hidden}.visuallyhidden{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.visuallyhidden.focusable:active,.visuallyhidden.focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static;width:auto}.invisible{visibility:hidden}.relative{position:relative}big,small{font-size:100%}@media print{body,html,section{background:none!important}*{box-shadow:none!important;text-shadow:none!important;filter:none!important;-ms-filter:none!important}a,a:visited{text-decoration:underline}.ir a:after,a[href^="#"]:after,a[href^="javascript:"]:after{content:""}blockquote,pre{page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}@page{margin:.5cm}.rst-content .toctree-wrapper>p.caption,h2,h3,p{orphans:3;widows:3}.rst-content .toctree-wrapper>p.caption,h2,h3{page-break-after:avoid}}.btn,.fa:before,.icon:before,.rst-content .admonition,.rst-content .admonition-title:before,.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .code-block-caption .headerlink:before,.rst-content .danger,.rst-content .eqno .headerlink:before,.rst-content .error,.rst-content .hint,.rst-content .important,.rst-content .note,.rst-content .seealso,.rst-content .tip,.rst-content .warning,.rst-content code.download span:first-child:before,.rst-content dl dt .headerlink:before,.rst-content h1 .headerlink:before,.rst-content h2 .headerlink:before,.rst-content h3 .headerlink:before,.rst-content h4 .headerlink:before,.rst-content h5 .headerlink:before,.rst-content h6 .headerlink:before,.rst-content p.caption .headerlink:before,.rst-content p .headerlink:before,.rst-content table>caption .headerlink:before,.rst-content tt.download span:first-child:before,.wy-alert,.wy-dropdown .caret:before,.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,.wy-menu-vertical li.current>a button.toctree-expand:before,.wy-menu-vertical li.on a button.toctree-expand:before,.wy-menu-vertical li button.toctree-expand:before,input[type=color],input[type=date],input[type=datetime-local],input[type=datetime],input[type=email],input[type=month],input[type=number],input[type=password],input[type=search],input[type=tel],input[type=text],input[type=time],input[type=url],input[type=week],select,textarea{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}/*! * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) - */@font-face{font-family:FontAwesome;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713);src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix&v=4.7.0) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#fontawesomeregular) format("svg");font-weight:400;font-style:normal}.fa,.icon,.rst-content .admonition-title,.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content code.download span:first-child,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink,.rst-content tt.download span:first-child,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li button.toctree-expand{display:inline-block;font:normal normal normal 14px/1 FontAwesome;font-size:inherit;text-rendering:auto;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.fa-lg{font-size:1.33333em;line-height:.75em;vertical-align:-15%}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-fw{width:1.28571em;text-align:center}.fa-ul{padding-left:0;margin-left:2.14286em;list-style-type:none}.fa-ul>li{position:relative}.fa-li{position:absolute;left:-2.14286em;width:2.14286em;top:.14286em;text-align:center}.fa-li.fa-lg{left:-1.85714em}.fa-border{padding:.2em .25em .15em;border:.08em solid #eee;border-radius:.1em}.fa-pull-left{float:left}.fa-pull-right{float:right}.fa-pull-left.icon,.fa.fa-pull-left,.rst-content .code-block-caption .fa-pull-left.headerlink,.rst-content .eqno .fa-pull-left.headerlink,.rst-content .fa-pull-left.admonition-title,.rst-content code.download span.fa-pull-left:first-child,.rst-content dl dt .fa-pull-left.headerlink,.rst-content h1 .fa-pull-left.headerlink,.rst-content h2 .fa-pull-left.headerlink,.rst-content h3 .fa-pull-left.headerlink,.rst-content h4 .fa-pull-left.headerlink,.rst-content h5 .fa-pull-left.headerlink,.rst-content h6 .fa-pull-left.headerlink,.rst-content p .fa-pull-left.headerlink,.rst-content table>caption .fa-pull-left.headerlink,.rst-content tt.download span.fa-pull-left:first-child,.wy-menu-vertical li.current>a button.fa-pull-left.toctree-expand,.wy-menu-vertical li.on a button.fa-pull-left.toctree-expand,.wy-menu-vertical li button.fa-pull-left.toctree-expand{margin-right:.3em}.fa-pull-right.icon,.fa.fa-pull-right,.rst-content .code-block-caption .fa-pull-right.headerlink,.rst-content .eqno .fa-pull-right.headerlink,.rst-content .fa-pull-right.admonition-title,.rst-content code.download span.fa-pull-right:first-child,.rst-content dl dt .fa-pull-right.headerlink,.rst-content h1 .fa-pull-right.headerlink,.rst-content h2 .fa-pull-right.headerlink,.rst-content h3 .fa-pull-right.headerlink,.rst-content h4 .fa-pull-right.headerlink,.rst-content h5 .fa-pull-right.headerlink,.rst-content h6 .fa-pull-right.headerlink,.rst-content p .fa-pull-right.headerlink,.rst-content table>caption .fa-pull-right.headerlink,.rst-content tt.download span.fa-pull-right:first-child,.wy-menu-vertical li.current>a button.fa-pull-right.toctree-expand,.wy-menu-vertical li.on a button.fa-pull-right.toctree-expand,.wy-menu-vertical li button.fa-pull-right.toctree-expand{margin-left:.3em}.pull-right{float:right}.pull-left{float:left}.fa.pull-left,.pull-left.icon,.rst-content .code-block-caption .pull-left.headerlink,.rst-content .eqno .pull-left.headerlink,.rst-content .pull-left.admonition-title,.rst-content code.download span.pull-left:first-child,.rst-content dl dt .pull-left.headerlink,.rst-content h1 .pull-left.headerlink,.rst-content h2 .pull-left.headerlink,.rst-content h3 .pull-left.headerlink,.rst-content h4 .pull-left.headerlink,.rst-content h5 .pull-left.headerlink,.rst-content h6 .pull-left.headerlink,.rst-content p .pull-left.headerlink,.rst-content table>caption .pull-left.headerlink,.rst-content tt.download span.pull-left:first-child,.wy-menu-vertical li.current>a button.pull-left.toctree-expand,.wy-menu-vertical li.on a button.pull-left.toctree-expand,.wy-menu-vertical li button.pull-left.toctree-expand{margin-right:.3em}.fa.pull-right,.pull-right.icon,.rst-content .code-block-caption .pull-right.headerlink,.rst-content .eqno .pull-right.headerlink,.rst-content .pull-right.admonition-title,.rst-content code.download span.pull-right:first-child,.rst-content dl dt .pull-right.headerlink,.rst-content h1 .pull-right.headerlink,.rst-content h2 .pull-right.headerlink,.rst-content h3 .pull-right.headerlink,.rst-content h4 .pull-right.headerlink,.rst-content h5 .pull-right.headerlink,.rst-content h6 .pull-right.headerlink,.rst-content p .pull-right.headerlink,.rst-content table>caption .pull-right.headerlink,.rst-content tt.download span.pull-right:first-child,.wy-menu-vertical li.current>a button.pull-right.toctree-expand,.wy-menu-vertical li.on a button.pull-right.toctree-expand,.wy-menu-vertical li button.pull-right.toctree-expand{margin-left:.3em}.fa-spin{-webkit-animation:fa-spin 2s linear infinite;animation:fa-spin 2s linear infinite}.fa-pulse{-webkit-animation:fa-spin 1s steps(8) infinite;animation:fa-spin 1s steps(8) infinite}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.fa-rotate-90{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";-webkit-transform:scaleX(-1);-ms-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";-webkit-transform:scaleY(-1);-ms-transform:scaleY(-1);transform:scaleY(-1)}:root .fa-flip-horizontal,:root .fa-flip-vertical,:root .fa-rotate-90,:root .fa-rotate-180,:root .fa-rotate-270{filter:none}.fa-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:middle}.fa-stack-1x,.fa-stack-2x{position:absolute;left:0;width:100%;text-align:center}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:#fff}.fa-glass:before{content:""}.fa-music:before{content:""}.fa-search:before,.icon-search:before{content:""}.fa-envelope-o:before{content:""}.fa-heart:before{content:""}.fa-star:before{content:""}.fa-star-o:before{content:""}.fa-user:before{content:""}.fa-film:before{content:""}.fa-th-large:before{content:""}.fa-th:before{content:""}.fa-th-list:before{content:""}.fa-check:before{content:""}.fa-close:before,.fa-remove:before,.fa-times:before{content:""}.fa-search-plus:before{content:""}.fa-search-minus:before{content:""}.fa-power-off:before{content:""}.fa-signal:before{content:""}.fa-cog:before,.fa-gear:before{content:""}.fa-trash-o:before{content:""}.fa-home:before,.icon-home:before{content:""}.fa-file-o:before{content:""}.fa-clock-o:before{content:""}.fa-road:before{content:""}.fa-download:before,.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{content:""}.fa-arrow-circle-o-down:before{content:""}.fa-arrow-circle-o-up:before{content:""}.fa-inbox:before{content:""}.fa-play-circle-o:before{content:""}.fa-repeat:before,.fa-rotate-right:before{content:""}.fa-refresh:before{content:""}.fa-list-alt:before{content:""}.fa-lock:before{content:""}.fa-flag:before{content:""}.fa-headphones:before{content:""}.fa-volume-off:before{content:""}.fa-volume-down:before{content:""}.fa-volume-up:before{content:""}.fa-qrcode:before{content:""}.fa-barcode:before{content:""}.fa-tag:before{content:""}.fa-tags:before{content:""}.fa-book:before,.icon-book:before{content:""}.fa-bookmark:before{content:""}.fa-print:before{content:""}.fa-camera:before{content:""}.fa-font:before{content:""}.fa-bold:before{content:""}.fa-italic:before{content:""}.fa-text-height:before{content:""}.fa-text-width:before{content:""}.fa-align-left:before{content:""}.fa-align-center:before{content:""}.fa-align-right:before{content:""}.fa-align-justify:before{content:""}.fa-list:before{content:""}.fa-dedent:before,.fa-outdent:before{content:""}.fa-indent:before{content:""}.fa-video-camera:before{content:""}.fa-image:before,.fa-photo:before,.fa-picture-o:before{content:""}.fa-pencil:before{content:""}.fa-map-marker:before{content:""}.fa-adjust:before{content:""}.fa-tint:before{content:""}.fa-edit:before,.fa-pencil-square-o:before{content:""}.fa-share-square-o:before{content:""}.fa-check-square-o:before{content:""}.fa-arrows:before{content:""}.fa-step-backward:before{content:""}.fa-fast-backward:before{content:""}.fa-backward:before{content:""}.fa-play:before{content:""}.fa-pause:before{content:""}.fa-stop:before{content:""}.fa-forward:before{content:""}.fa-fast-forward:before{content:""}.fa-step-forward:before{content:""}.fa-eject:before{content:""}.fa-chevron-left:before{content:""}.fa-chevron-right:before{content:""}.fa-plus-circle:before{content:""}.fa-minus-circle:before{content:""}.fa-times-circle:before,.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before{content:""}.fa-check-circle:before,.wy-inline-validate.wy-inline-validate-success .wy-input-context:before{content:""}.fa-question-circle:before{content:""}.fa-info-circle:before{content:""}.fa-crosshairs:before{content:""}.fa-times-circle-o:before{content:""}.fa-check-circle-o:before{content:""}.fa-ban:before{content:""}.fa-arrow-left:before{content:""}.fa-arrow-right:before{content:""}.fa-arrow-up:before{content:""}.fa-arrow-down:before{content:""}.fa-mail-forward:before,.fa-share:before{content:""}.fa-expand:before{content:""}.fa-compress:before{content:""}.fa-plus:before{content:""}.fa-minus:before{content:""}.fa-asterisk:before{content:""}.fa-exclamation-circle:before,.rst-content .admonition-title:before,.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before{content:""}.fa-gift:before{content:""}.fa-leaf:before{content:""}.fa-fire:before,.icon-fire:before{content:""}.fa-eye:before{content:""}.fa-eye-slash:before{content:""}.fa-exclamation-triangle:before,.fa-warning:before{content:""}.fa-plane:before{content:""}.fa-calendar:before{content:""}.fa-random:before{content:""}.fa-comment:before{content:""}.fa-magnet:before{content:""}.fa-chevron-up:before{content:""}.fa-chevron-down:before{content:""}.fa-retweet:before{content:""}.fa-shopping-cart:before{content:""}.fa-folder:before{content:""}.fa-folder-open:before{content:""}.fa-arrows-v:before{content:""}.fa-arrows-h:before{content:""}.fa-bar-chart-o:before,.fa-bar-chart:before{content:""}.fa-twitter-square:before{content:""}.fa-facebook-square:before{content:""}.fa-camera-retro:before{content:""}.fa-key:before{content:""}.fa-cogs:before,.fa-gears:before{content:""}.fa-comments:before{content:""}.fa-thumbs-o-up:before{content:""}.fa-thumbs-o-down:before{content:""}.fa-star-half:before{content:""}.fa-heart-o:before{content:""}.fa-sign-out:before{content:""}.fa-linkedin-square:before{content:""}.fa-thumb-tack:before{content:""}.fa-external-link:before{content:""}.fa-sign-in:before{content:""}.fa-trophy:before{content:""}.fa-github-square:before{content:""}.fa-upload:before{content:""}.fa-lemon-o:before{content:""}.fa-phone:before{content:""}.fa-square-o:before{content:""}.fa-bookmark-o:before{content:""}.fa-phone-square:before{content:""}.fa-twitter:before{content:""}.fa-facebook-f:before,.fa-facebook:before{content:""}.fa-github:before,.icon-github:before{content:""}.fa-unlock:before{content:""}.fa-credit-card:before{content:""}.fa-feed:before,.fa-rss:before{content:""}.fa-hdd-o:before{content:""}.fa-bullhorn:before{content:""}.fa-bell:before{content:""}.fa-certificate:before{content:""}.fa-hand-o-right:before{content:""}.fa-hand-o-left:before{content:""}.fa-hand-o-up:before{content:""}.fa-hand-o-down:before{content:""}.fa-arrow-circle-left:before,.icon-circle-arrow-left:before{content:""}.fa-arrow-circle-right:before,.icon-circle-arrow-right:before{content:""}.fa-arrow-circle-up:before{content:""}.fa-arrow-circle-down:before{content:""}.fa-globe:before{content:""}.fa-wrench:before{content:""}.fa-tasks:before{content:""}.fa-filter:before{content:""}.fa-briefcase:before{content:""}.fa-arrows-alt:before{content:""}.fa-group:before,.fa-users:before{content:""}.fa-chain:before,.fa-link:before,.icon-link:before{content:""}.fa-cloud:before{content:""}.fa-flask:before{content:""}.fa-cut:before,.fa-scissors:before{content:""}.fa-copy:before,.fa-files-o:before{content:""}.fa-paperclip:before{content:""}.fa-floppy-o:before,.fa-save:before{content:""}.fa-square:before{content:""}.fa-bars:before,.fa-navicon:before,.fa-reorder:before{content:""}.fa-list-ul:before{content:""}.fa-list-ol:before{content:""}.fa-strikethrough:before{content:""}.fa-underline:before{content:""}.fa-table:before{content:""}.fa-magic:before{content:""}.fa-truck:before{content:""}.fa-pinterest:before{content:""}.fa-pinterest-square:before{content:""}.fa-google-plus-square:before{content:""}.fa-google-plus:before{content:""}.fa-money:before{content:""}.fa-caret-down:before,.icon-caret-down:before,.wy-dropdown .caret:before{content:""}.fa-caret-up:before{content:""}.fa-caret-left:before{content:""}.fa-caret-right:before{content:""}.fa-columns:before{content:""}.fa-sort:before,.fa-unsorted:before{content:""}.fa-sort-desc:before,.fa-sort-down:before{content:""}.fa-sort-asc:before,.fa-sort-up:before{content:""}.fa-envelope:before{content:""}.fa-linkedin:before{content:""}.fa-rotate-left:before,.fa-undo:before{content:""}.fa-gavel:before,.fa-legal:before{content:""}.fa-dashboard:before,.fa-tachometer:before{content:""}.fa-comment-o:before{content:""}.fa-comments-o:before{content:""}.fa-bolt:before,.fa-flash:before{content:""}.fa-sitemap:before{content:""}.fa-umbrella:before{content:""}.fa-clipboard:before,.fa-paste:before{content:""}.fa-lightbulb-o:before{content:""}.fa-exchange:before{content:""}.fa-cloud-download:before{content:""}.fa-cloud-upload:before{content:""}.fa-user-md:before{content:""}.fa-stethoscope:before{content:""}.fa-suitcase:before{content:""}.fa-bell-o:before{content:""}.fa-coffee:before{content:""}.fa-cutlery:before{content:""}.fa-file-text-o:before{content:""}.fa-building-o:before{content:""}.fa-hospital-o:before{content:""}.fa-ambulance:before{content:""}.fa-medkit:before{content:""}.fa-fighter-jet:before{content:""}.fa-beer:before{content:""}.fa-h-square:before{content:""}.fa-plus-square:before{content:""}.fa-angle-double-left:before{content:""}.fa-angle-double-right:before{content:""}.fa-angle-double-up:before{content:""}.fa-angle-double-down:before{content:""}.fa-angle-left:before{content:""}.fa-angle-right:before{content:""}.fa-angle-up:before{content:""}.fa-angle-down:before{content:""}.fa-desktop:before{content:""}.fa-laptop:before{content:""}.fa-tablet:before{content:""}.fa-mobile-phone:before,.fa-mobile:before{content:""}.fa-circle-o:before{content:""}.fa-quote-left:before{content:""}.fa-quote-right:before{content:""}.fa-spinner:before{content:""}.fa-circle:before{content:""}.fa-mail-reply:before,.fa-reply:before{content:""}.fa-github-alt:before{content:""}.fa-folder-o:before{content:""}.fa-folder-open-o:before{content:""}.fa-smile-o:before{content:""}.fa-frown-o:before{content:""}.fa-meh-o:before{content:""}.fa-gamepad:before{content:""}.fa-keyboard-o:before{content:""}.fa-flag-o:before{content:""}.fa-flag-checkered:before{content:""}.fa-terminal:before{content:""}.fa-code:before{content:""}.fa-mail-reply-all:before,.fa-reply-all:before{content:""}.fa-star-half-empty:before,.fa-star-half-full:before,.fa-star-half-o:before{content:""}.fa-location-arrow:before{content:""}.fa-crop:before{content:""}.fa-code-fork:before{content:""}.fa-chain-broken:before,.fa-unlink:before{content:""}.fa-question:before{content:""}.fa-info:before{content:""}.fa-exclamation:before{content:""}.fa-superscript:before{content:""}.fa-subscript:before{content:""}.fa-eraser:before{content:""}.fa-puzzle-piece:before{content:""}.fa-microphone:before{content:""}.fa-microphone-slash:before{content:""}.fa-shield:before{content:""}.fa-calendar-o:before{content:""}.fa-fire-extinguisher:before{content:""}.fa-rocket:before{content:""}.fa-maxcdn:before{content:""}.fa-chevron-circle-left:before{content:""}.fa-chevron-circle-right:before{content:""}.fa-chevron-circle-up:before{content:""}.fa-chevron-circle-down:before{content:""}.fa-html5:before{content:""}.fa-css3:before{content:""}.fa-anchor:before{content:""}.fa-unlock-alt:before{content:""}.fa-bullseye:before{content:""}.fa-ellipsis-h:before{content:""}.fa-ellipsis-v:before{content:""}.fa-rss-square:before{content:""}.fa-play-circle:before{content:""}.fa-ticket:before{content:""}.fa-minus-square:before{content:""}.fa-minus-square-o:before,.wy-menu-vertical li.current>a button.toctree-expand:before,.wy-menu-vertical li.on a button.toctree-expand:before{content:""}.fa-level-up:before{content:""}.fa-level-down:before{content:""}.fa-check-square:before{content:""}.fa-pencil-square:before{content:""}.fa-external-link-square:before{content:""}.fa-share-square:before{content:""}.fa-compass:before{content:""}.fa-caret-square-o-down:before,.fa-toggle-down:before{content:""}.fa-caret-square-o-up:before,.fa-toggle-up:before{content:""}.fa-caret-square-o-right:before,.fa-toggle-right:before{content:""}.fa-eur:before,.fa-euro:before{content:""}.fa-gbp:before{content:""}.fa-dollar:before,.fa-usd:before{content:""}.fa-inr:before,.fa-rupee:before{content:""}.fa-cny:before,.fa-jpy:before,.fa-rmb:before,.fa-yen:before{content:""}.fa-rouble:before,.fa-rub:before,.fa-ruble:before{content:""}.fa-krw:before,.fa-won:before{content:""}.fa-bitcoin:before,.fa-btc:before{content:""}.fa-file:before{content:""}.fa-file-text:before{content:""}.fa-sort-alpha-asc:before{content:""}.fa-sort-alpha-desc:before{content:""}.fa-sort-amount-asc:before{content:""}.fa-sort-amount-desc:before{content:""}.fa-sort-numeric-asc:before{content:""}.fa-sort-numeric-desc:before{content:""}.fa-thumbs-up:before{content:""}.fa-thumbs-down:before{content:""}.fa-youtube-square:before{content:""}.fa-youtube:before{content:""}.fa-xing:before{content:""}.fa-xing-square:before{content:""}.fa-youtube-play:before{content:""}.fa-dropbox:before{content:""}.fa-stack-overflow:before{content:""}.fa-instagram:before{content:""}.fa-flickr:before{content:""}.fa-adn:before{content:""}.fa-bitbucket:before,.icon-bitbucket:before{content:""}.fa-bitbucket-square:before{content:""}.fa-tumblr:before{content:""}.fa-tumblr-square:before{content:""}.fa-long-arrow-down:before{content:""}.fa-long-arrow-up:before{content:""}.fa-long-arrow-left:before{content:""}.fa-long-arrow-right:before{content:""}.fa-apple:before{content:""}.fa-windows:before{content:""}.fa-android:before{content:""}.fa-linux:before{content:""}.fa-dribbble:before{content:""}.fa-skype:before{content:""}.fa-foursquare:before{content:""}.fa-trello:before{content:""}.fa-female:before{content:""}.fa-male:before{content:""}.fa-gittip:before,.fa-gratipay:before{content:""}.fa-sun-o:before{content:""}.fa-moon-o:before{content:""}.fa-archive:before{content:""}.fa-bug:before{content:""}.fa-vk:before{content:""}.fa-weibo:before{content:""}.fa-renren:before{content:""}.fa-pagelines:before{content:""}.fa-stack-exchange:before{content:""}.fa-arrow-circle-o-right:before{content:""}.fa-arrow-circle-o-left:before{content:""}.fa-caret-square-o-left:before,.fa-toggle-left:before{content:""}.fa-dot-circle-o:before{content:""}.fa-wheelchair:before{content:""}.fa-vimeo-square:before{content:""}.fa-try:before,.fa-turkish-lira:before{content:""}.fa-plus-square-o:before,.wy-menu-vertical li button.toctree-expand:before{content:""}.fa-space-shuttle:before{content:""}.fa-slack:before{content:""}.fa-envelope-square:before{content:""}.fa-wordpress:before{content:""}.fa-openid:before{content:""}.fa-bank:before,.fa-institution:before,.fa-university:before{content:""}.fa-graduation-cap:before,.fa-mortar-board:before{content:""}.fa-yahoo:before{content:""}.fa-google:before{content:""}.fa-reddit:before{content:""}.fa-reddit-square:before{content:""}.fa-stumbleupon-circle:before{content:""}.fa-stumbleupon:before{content:""}.fa-delicious:before{content:""}.fa-digg:before{content:""}.fa-pied-piper-pp:before{content:""}.fa-pied-piper-alt:before{content:""}.fa-drupal:before{content:""}.fa-joomla:before{content:""}.fa-language:before{content:""}.fa-fax:before{content:""}.fa-building:before{content:""}.fa-child:before{content:""}.fa-paw:before{content:""}.fa-spoon:before{content:""}.fa-cube:before{content:""}.fa-cubes:before{content:""}.fa-behance:before{content:""}.fa-behance-square:before{content:""}.fa-steam:before{content:""}.fa-steam-square:before{content:""}.fa-recycle:before{content:""}.fa-automobile:before,.fa-car:before{content:""}.fa-cab:before,.fa-taxi:before{content:""}.fa-tree:before{content:""}.fa-spotify:before{content:""}.fa-deviantart:before{content:""}.fa-soundcloud:before{content:""}.fa-database:before{content:""}.fa-file-pdf-o:before{content:""}.fa-file-word-o:before{content:""}.fa-file-excel-o:before{content:""}.fa-file-powerpoint-o:before{content:""}.fa-file-image-o:before,.fa-file-photo-o:before,.fa-file-picture-o:before{content:""}.fa-file-archive-o:before,.fa-file-zip-o:before{content:""}.fa-file-audio-o:before,.fa-file-sound-o:before{content:""}.fa-file-movie-o:before,.fa-file-video-o:before{content:""}.fa-file-code-o:before{content:""}.fa-vine:before{content:""}.fa-codepen:before{content:""}.fa-jsfiddle:before{content:""}.fa-life-bouy:before,.fa-life-buoy:before,.fa-life-ring:before,.fa-life-saver:before,.fa-support:before{content:""}.fa-circle-o-notch:before{content:""}.fa-ra:before,.fa-rebel:before,.fa-resistance:before{content:""}.fa-empire:before,.fa-ge:before{content:""}.fa-git-square:before{content:""}.fa-git:before{content:""}.fa-hacker-news:before,.fa-y-combinator-square:before,.fa-yc-square:before{content:""}.fa-tencent-weibo:before{content:""}.fa-qq:before{content:""}.fa-wechat:before,.fa-weixin:before{content:""}.fa-paper-plane:before,.fa-send:before{content:""}.fa-paper-plane-o:before,.fa-send-o:before{content:""}.fa-history:before{content:""}.fa-circle-thin:before{content:""}.fa-header:before{content:""}.fa-paragraph:before{content:""}.fa-sliders:before{content:""}.fa-share-alt:before{content:""}.fa-share-alt-square:before{content:""}.fa-bomb:before{content:""}.fa-futbol-o:before,.fa-soccer-ball-o:before{content:""}.fa-tty:before{content:""}.fa-binoculars:before{content:""}.fa-plug:before{content:""}.fa-slideshare:before{content:""}.fa-twitch:before{content:""}.fa-yelp:before{content:""}.fa-newspaper-o:before{content:""}.fa-wifi:before{content:""}.fa-calculator:before{content:""}.fa-paypal:before{content:""}.fa-google-wallet:before{content:""}.fa-cc-visa:before{content:""}.fa-cc-mastercard:before{content:""}.fa-cc-discover:before{content:""}.fa-cc-amex:before{content:""}.fa-cc-paypal:before{content:""}.fa-cc-stripe:before{content:""}.fa-bell-slash:before{content:""}.fa-bell-slash-o:before{content:""}.fa-trash:before{content:""}.fa-copyright:before{content:""}.fa-at:before{content:""}.fa-eyedropper:before{content:""}.fa-paint-brush:before{content:""}.fa-birthday-cake:before{content:""}.fa-area-chart:before{content:""}.fa-pie-chart:before{content:""}.fa-line-chart:before{content:""}.fa-lastfm:before{content:""}.fa-lastfm-square:before{content:""}.fa-toggle-off:before{content:""}.fa-toggle-on:before{content:""}.fa-bicycle:before{content:""}.fa-bus:before{content:""}.fa-ioxhost:before{content:""}.fa-angellist:before{content:""}.fa-cc:before{content:""}.fa-ils:before,.fa-shekel:before,.fa-sheqel:before{content:""}.fa-meanpath:before{content:""}.fa-buysellads:before{content:""}.fa-connectdevelop:before{content:""}.fa-dashcube:before{content:""}.fa-forumbee:before{content:""}.fa-leanpub:before{content:""}.fa-sellsy:before{content:""}.fa-shirtsinbulk:before{content:""}.fa-simplybuilt:before{content:""}.fa-skyatlas:before{content:""}.fa-cart-plus:before{content:""}.fa-cart-arrow-down:before{content:""}.fa-diamond:before{content:""}.fa-ship:before{content:""}.fa-user-secret:before{content:""}.fa-motorcycle:before{content:""}.fa-street-view:before{content:""}.fa-heartbeat:before{content:""}.fa-venus:before{content:""}.fa-mars:before{content:""}.fa-mercury:before{content:""}.fa-intersex:before,.fa-transgender:before{content:""}.fa-transgender-alt:before{content:""}.fa-venus-double:before{content:""}.fa-mars-double:before{content:""}.fa-venus-mars:before{content:""}.fa-mars-stroke:before{content:""}.fa-mars-stroke-v:before{content:""}.fa-mars-stroke-h:before{content:""}.fa-neuter:before{content:""}.fa-genderless:before{content:""}.fa-facebook-official:before{content:""}.fa-pinterest-p:before{content:""}.fa-whatsapp:before{content:""}.fa-server:before{content:""}.fa-user-plus:before{content:""}.fa-user-times:before{content:""}.fa-bed:before,.fa-hotel:before{content:""}.fa-viacoin:before{content:""}.fa-train:before{content:""}.fa-subway:before{content:""}.fa-medium:before{content:""}.fa-y-combinator:before,.fa-yc:before{content:""}.fa-optin-monster:before{content:""}.fa-opencart:before{content:""}.fa-expeditedssl:before{content:""}.fa-battery-4:before,.fa-battery-full:before,.fa-battery:before{content:""}.fa-battery-3:before,.fa-battery-three-quarters:before{content:""}.fa-battery-2:before,.fa-battery-half:before{content:""}.fa-battery-1:before,.fa-battery-quarter:before{content:""}.fa-battery-0:before,.fa-battery-empty:before{content:""}.fa-mouse-pointer:before{content:""}.fa-i-cursor:before{content:""}.fa-object-group:before{content:""}.fa-object-ungroup:before{content:""}.fa-sticky-note:before{content:""}.fa-sticky-note-o:before{content:""}.fa-cc-jcb:before{content:""}.fa-cc-diners-club:before{content:""}.fa-clone:before{content:""}.fa-balance-scale:before{content:""}.fa-hourglass-o:before{content:""}.fa-hourglass-1:before,.fa-hourglass-start:before{content:""}.fa-hourglass-2:before,.fa-hourglass-half:before{content:""}.fa-hourglass-3:before,.fa-hourglass-end:before{content:""}.fa-hourglass:before{content:""}.fa-hand-grab-o:before,.fa-hand-rock-o:before{content:""}.fa-hand-paper-o:before,.fa-hand-stop-o:before{content:""}.fa-hand-scissors-o:before{content:""}.fa-hand-lizard-o:before{content:""}.fa-hand-spock-o:before{content:""}.fa-hand-pointer-o:before{content:""}.fa-hand-peace-o:before{content:""}.fa-trademark:before{content:""}.fa-registered:before{content:""}.fa-creative-commons:before{content:""}.fa-gg:before{content:""}.fa-gg-circle:before{content:""}.fa-tripadvisor:before{content:""}.fa-odnoklassniki:before{content:""}.fa-odnoklassniki-square:before{content:""}.fa-get-pocket:before{content:""}.fa-wikipedia-w:before{content:""}.fa-safari:before{content:""}.fa-chrome:before{content:""}.fa-firefox:before{content:""}.fa-opera:before{content:""}.fa-internet-explorer:before{content:""}.fa-television:before,.fa-tv:before{content:""}.fa-contao:before{content:""}.fa-500px:before{content:""}.fa-amazon:before{content:""}.fa-calendar-plus-o:before{content:""}.fa-calendar-minus-o:before{content:""}.fa-calendar-times-o:before{content:""}.fa-calendar-check-o:before{content:""}.fa-industry:before{content:""}.fa-map-pin:before{content:""}.fa-map-signs:before{content:""}.fa-map-o:before{content:""}.fa-map:before{content:""}.fa-commenting:before{content:""}.fa-commenting-o:before{content:""}.fa-houzz:before{content:""}.fa-vimeo:before{content:""}.fa-black-tie:before{content:""}.fa-fonticons:before{content:""}.fa-reddit-alien:before{content:""}.fa-edge:before{content:""}.fa-credit-card-alt:before{content:""}.fa-codiepie:before{content:""}.fa-modx:before{content:""}.fa-fort-awesome:before{content:""}.fa-usb:before{content:""}.fa-product-hunt:before{content:""}.fa-mixcloud:before{content:""}.fa-scribd:before{content:""}.fa-pause-circle:before{content:""}.fa-pause-circle-o:before{content:""}.fa-stop-circle:before{content:""}.fa-stop-circle-o:before{content:""}.fa-shopping-bag:before{content:""}.fa-shopping-basket:before{content:""}.fa-hashtag:before{content:""}.fa-bluetooth:before{content:""}.fa-bluetooth-b:before{content:""}.fa-percent:before{content:""}.fa-gitlab:before,.icon-gitlab:before{content:""}.fa-wpbeginner:before{content:""}.fa-wpforms:before{content:""}.fa-envira:before{content:""}.fa-universal-access:before{content:""}.fa-wheelchair-alt:before{content:""}.fa-question-circle-o:before{content:""}.fa-blind:before{content:""}.fa-audio-description:before{content:""}.fa-volume-control-phone:before{content:""}.fa-braille:before{content:""}.fa-assistive-listening-systems:before{content:""}.fa-american-sign-language-interpreting:before,.fa-asl-interpreting:before{content:""}.fa-deaf:before,.fa-deafness:before,.fa-hard-of-hearing:before{content:""}.fa-glide:before{content:""}.fa-glide-g:before{content:""}.fa-sign-language:before,.fa-signing:before{content:""}.fa-low-vision:before{content:""}.fa-viadeo:before{content:""}.fa-viadeo-square:before{content:""}.fa-snapchat:before{content:""}.fa-snapchat-ghost:before{content:""}.fa-snapchat-square:before{content:""}.fa-pied-piper:before{content:""}.fa-first-order:before{content:""}.fa-yoast:before{content:""}.fa-themeisle:before{content:""}.fa-google-plus-circle:before,.fa-google-plus-official:before{content:""}.fa-fa:before,.fa-font-awesome:before{content:""}.fa-handshake-o:before{content:""}.fa-envelope-open:before{content:""}.fa-envelope-open-o:before{content:""}.fa-linode:before{content:""}.fa-address-book:before{content:""}.fa-address-book-o:before{content:""}.fa-address-card:before,.fa-vcard:before{content:""}.fa-address-card-o:before,.fa-vcard-o:before{content:""}.fa-user-circle:before{content:""}.fa-user-circle-o:before{content:""}.fa-user-o:before{content:""}.fa-id-badge:before{content:""}.fa-drivers-license:before,.fa-id-card:before{content:""}.fa-drivers-license-o:before,.fa-id-card-o:before{content:""}.fa-quora:before{content:""}.fa-free-code-camp:before{content:""}.fa-telegram:before{content:""}.fa-thermometer-4:before,.fa-thermometer-full:before,.fa-thermometer:before{content:""}.fa-thermometer-3:before,.fa-thermometer-three-quarters:before{content:""}.fa-thermometer-2:before,.fa-thermometer-half:before{content:""}.fa-thermometer-1:before,.fa-thermometer-quarter:before{content:""}.fa-thermometer-0:before,.fa-thermometer-empty:before{content:""}.fa-shower:before{content:""}.fa-bath:before,.fa-bathtub:before,.fa-s15:before{content:""}.fa-podcast:before{content:""}.fa-window-maximize:before{content:""}.fa-window-minimize:before{content:""}.fa-window-restore:before{content:""}.fa-times-rectangle:before,.fa-window-close:before{content:""}.fa-times-rectangle-o:before,.fa-window-close-o:before{content:""}.fa-bandcamp:before{content:""}.fa-grav:before{content:""}.fa-etsy:before{content:""}.fa-imdb:before{content:""}.fa-ravelry:before{content:""}.fa-eercast:before{content:""}.fa-microchip:before{content:""}.fa-snowflake-o:before{content:""}.fa-superpowers:before{content:""}.fa-wpexplorer:before{content:""}.fa-meetup:before{content:""}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}.fa,.icon,.rst-content .admonition-title,.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content code.download span:first-child,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink,.rst-content tt.download span:first-child,.wy-dropdown .caret,.wy-inline-validate.wy-inline-validate-danger .wy-input-context,.wy-inline-validate.wy-inline-validate-info .wy-input-context,.wy-inline-validate.wy-inline-validate-success .wy-input-context,.wy-inline-validate.wy-inline-validate-warning .wy-input-context,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li button.toctree-expand{font-family:inherit}.fa:before,.icon:before,.rst-content .admonition-title:before,.rst-content .code-block-caption .headerlink:before,.rst-content .eqno .headerlink:before,.rst-content code.download span:first-child:before,.rst-content dl dt .headerlink:before,.rst-content h1 .headerlink:before,.rst-content h2 .headerlink:before,.rst-content h3 .headerlink:before,.rst-content h4 .headerlink:before,.rst-content h5 .headerlink:before,.rst-content h6 .headerlink:before,.rst-content p.caption .headerlink:before,.rst-content p .headerlink:before,.rst-content table>caption .headerlink:before,.rst-content tt.download span:first-child:before,.wy-dropdown .caret:before,.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,.wy-menu-vertical li.current>a button.toctree-expand:before,.wy-menu-vertical li.on a button.toctree-expand:before,.wy-menu-vertical li button.toctree-expand:before{font-family:FontAwesome;display:inline-block;font-style:normal;font-weight:400;line-height:1;text-decoration:inherit}.rst-content .code-block-caption a .headerlink,.rst-content .eqno a .headerlink,.rst-content a .admonition-title,.rst-content code.download a span:first-child,.rst-content dl dt a .headerlink,.rst-content h1 a .headerlink,.rst-content h2 a .headerlink,.rst-content h3 a .headerlink,.rst-content h4 a .headerlink,.rst-content h5 a .headerlink,.rst-content h6 a .headerlink,.rst-content p.caption a .headerlink,.rst-content p a .headerlink,.rst-content table>caption a .headerlink,.rst-content tt.download a span:first-child,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li a button.toctree-expand,a .fa,a .icon,a .rst-content .admonition-title,a .rst-content .code-block-caption .headerlink,a .rst-content .eqno .headerlink,a .rst-content code.download span:first-child,a .rst-content dl dt .headerlink,a .rst-content h1 .headerlink,a .rst-content h2 .headerlink,a .rst-content h3 .headerlink,a .rst-content h4 .headerlink,a .rst-content h5 .headerlink,a .rst-content h6 .headerlink,a .rst-content p.caption .headerlink,a .rst-content p .headerlink,a .rst-content table>caption .headerlink,a .rst-content tt.download span:first-child,a .wy-menu-vertical li button.toctree-expand{display:inline-block;text-decoration:inherit}.btn .fa,.btn .icon,.btn .rst-content .admonition-title,.btn .rst-content .code-block-caption .headerlink,.btn .rst-content .eqno .headerlink,.btn .rst-content code.download span:first-child,.btn .rst-content dl dt .headerlink,.btn .rst-content h1 .headerlink,.btn .rst-content h2 .headerlink,.btn .rst-content h3 .headerlink,.btn .rst-content h4 .headerlink,.btn .rst-content h5 .headerlink,.btn .rst-content h6 .headerlink,.btn .rst-content p .headerlink,.btn .rst-content table>caption .headerlink,.btn .rst-content tt.download span:first-child,.btn .wy-menu-vertical li.current>a button.toctree-expand,.btn .wy-menu-vertical li.on a button.toctree-expand,.btn .wy-menu-vertical li button.toctree-expand,.nav .fa,.nav .icon,.nav .rst-content .admonition-title,.nav .rst-content .code-block-caption .headerlink,.nav .rst-content .eqno .headerlink,.nav .rst-content code.download span:first-child,.nav .rst-content dl dt .headerlink,.nav .rst-content h1 .headerlink,.nav .rst-content h2 .headerlink,.nav .rst-content h3 .headerlink,.nav .rst-content h4 .headerlink,.nav .rst-content h5 .headerlink,.nav .rst-content h6 .headerlink,.nav .rst-content p .headerlink,.nav .rst-content table>caption .headerlink,.nav .rst-content tt.download span:first-child,.nav .wy-menu-vertical li.current>a button.toctree-expand,.nav .wy-menu-vertical li.on a button.toctree-expand,.nav .wy-menu-vertical li button.toctree-expand,.rst-content .btn .admonition-title,.rst-content .code-block-caption .btn .headerlink,.rst-content .code-block-caption .nav .headerlink,.rst-content .eqno .btn .headerlink,.rst-content .eqno .nav .headerlink,.rst-content .nav .admonition-title,.rst-content code.download .btn span:first-child,.rst-content code.download .nav span:first-child,.rst-content dl dt .btn .headerlink,.rst-content dl dt .nav .headerlink,.rst-content h1 .btn .headerlink,.rst-content h1 .nav .headerlink,.rst-content h2 .btn .headerlink,.rst-content h2 .nav .headerlink,.rst-content h3 .btn .headerlink,.rst-content h3 .nav .headerlink,.rst-content h4 .btn .headerlink,.rst-content h4 .nav .headerlink,.rst-content h5 .btn .headerlink,.rst-content h5 .nav .headerlink,.rst-content h6 .btn .headerlink,.rst-content h6 .nav .headerlink,.rst-content p .btn .headerlink,.rst-content p .nav .headerlink,.rst-content table>caption .btn .headerlink,.rst-content table>caption .nav .headerlink,.rst-content tt.download .btn span:first-child,.rst-content tt.download .nav span:first-child,.wy-menu-vertical li .btn button.toctree-expand,.wy-menu-vertical li.current>a .btn button.toctree-expand,.wy-menu-vertical li.current>a .nav button.toctree-expand,.wy-menu-vertical li .nav button.toctree-expand,.wy-menu-vertical li.on a .btn button.toctree-expand,.wy-menu-vertical li.on a .nav button.toctree-expand{display:inline}.btn .fa-large.icon,.btn .fa.fa-large,.btn .rst-content .code-block-caption .fa-large.headerlink,.btn .rst-content .eqno .fa-large.headerlink,.btn .rst-content .fa-large.admonition-title,.btn .rst-content code.download span.fa-large:first-child,.btn .rst-content dl dt .fa-large.headerlink,.btn .rst-content h1 .fa-large.headerlink,.btn .rst-content h2 .fa-large.headerlink,.btn .rst-content h3 .fa-large.headerlink,.btn .rst-content h4 .fa-large.headerlink,.btn .rst-content h5 .fa-large.headerlink,.btn .rst-content h6 .fa-large.headerlink,.btn .rst-content p .fa-large.headerlink,.btn .rst-content table>caption .fa-large.headerlink,.btn .rst-content tt.download span.fa-large:first-child,.btn .wy-menu-vertical li button.fa-large.toctree-expand,.nav .fa-large.icon,.nav .fa.fa-large,.nav .rst-content .code-block-caption .fa-large.headerlink,.nav .rst-content .eqno .fa-large.headerlink,.nav .rst-content .fa-large.admonition-title,.nav .rst-content code.download span.fa-large:first-child,.nav .rst-content dl dt .fa-large.headerlink,.nav .rst-content h1 .fa-large.headerlink,.nav .rst-content h2 .fa-large.headerlink,.nav .rst-content h3 .fa-large.headerlink,.nav .rst-content h4 .fa-large.headerlink,.nav .rst-content h5 .fa-large.headerlink,.nav .rst-content h6 .fa-large.headerlink,.nav .rst-content p .fa-large.headerlink,.nav .rst-content table>caption .fa-large.headerlink,.nav .rst-content tt.download span.fa-large:first-child,.nav .wy-menu-vertical li button.fa-large.toctree-expand,.rst-content .btn .fa-large.admonition-title,.rst-content .code-block-caption .btn .fa-large.headerlink,.rst-content .code-block-caption .nav .fa-large.headerlink,.rst-content .eqno .btn .fa-large.headerlink,.rst-content .eqno .nav .fa-large.headerlink,.rst-content .nav .fa-large.admonition-title,.rst-content code.download .btn span.fa-large:first-child,.rst-content code.download .nav span.fa-large:first-child,.rst-content dl dt .btn .fa-large.headerlink,.rst-content dl dt .nav .fa-large.headerlink,.rst-content h1 .btn .fa-large.headerlink,.rst-content h1 .nav .fa-large.headerlink,.rst-content h2 .btn .fa-large.headerlink,.rst-content h2 .nav .fa-large.headerlink,.rst-content h3 .btn .fa-large.headerlink,.rst-content h3 .nav .fa-large.headerlink,.rst-content h4 .btn .fa-large.headerlink,.rst-content h4 .nav .fa-large.headerlink,.rst-content h5 .btn .fa-large.headerlink,.rst-content h5 .nav .fa-large.headerlink,.rst-content h6 .btn .fa-large.headerlink,.rst-content h6 .nav .fa-large.headerlink,.rst-content p .btn .fa-large.headerlink,.rst-content p .nav .fa-large.headerlink,.rst-content table>caption .btn .fa-large.headerlink,.rst-content table>caption .nav .fa-large.headerlink,.rst-content tt.download .btn span.fa-large:first-child,.rst-content tt.download .nav span.fa-large:first-child,.wy-menu-vertical li .btn button.fa-large.toctree-expand,.wy-menu-vertical li .nav button.fa-large.toctree-expand{line-height:.9em}.btn .fa-spin.icon,.btn .fa.fa-spin,.btn .rst-content .code-block-caption .fa-spin.headerlink,.btn .rst-content .eqno .fa-spin.headerlink,.btn .rst-content .fa-spin.admonition-title,.btn .rst-content code.download span.fa-spin:first-child,.btn .rst-content dl dt .fa-spin.headerlink,.btn .rst-content h1 .fa-spin.headerlink,.btn .rst-content h2 .fa-spin.headerlink,.btn .rst-content h3 .fa-spin.headerlink,.btn .rst-content h4 .fa-spin.headerlink,.btn .rst-content h5 .fa-spin.headerlink,.btn .rst-content h6 .fa-spin.headerlink,.btn .rst-content p .fa-spin.headerlink,.btn .rst-content table>caption .fa-spin.headerlink,.btn .rst-content tt.download span.fa-spin:first-child,.btn .wy-menu-vertical li button.fa-spin.toctree-expand,.nav .fa-spin.icon,.nav .fa.fa-spin,.nav .rst-content .code-block-caption .fa-spin.headerlink,.nav .rst-content .eqno .fa-spin.headerlink,.nav .rst-content .fa-spin.admonition-title,.nav .rst-content code.download span.fa-spin:first-child,.nav .rst-content dl dt .fa-spin.headerlink,.nav .rst-content h1 .fa-spin.headerlink,.nav .rst-content h2 .fa-spin.headerlink,.nav .rst-content h3 .fa-spin.headerlink,.nav .rst-content h4 .fa-spin.headerlink,.nav .rst-content h5 .fa-spin.headerlink,.nav .rst-content h6 .fa-spin.headerlink,.nav .rst-content p .fa-spin.headerlink,.nav .rst-content table>caption .fa-spin.headerlink,.nav .rst-content tt.download span.fa-spin:first-child,.nav .wy-menu-vertical li button.fa-spin.toctree-expand,.rst-content .btn .fa-spin.admonition-title,.rst-content .code-block-caption .btn .fa-spin.headerlink,.rst-content .code-block-caption .nav .fa-spin.headerlink,.rst-content .eqno .btn .fa-spin.headerlink,.rst-content .eqno .nav .fa-spin.headerlink,.rst-content .nav .fa-spin.admonition-title,.rst-content code.download .btn span.fa-spin:first-child,.rst-content code.download .nav span.fa-spin:first-child,.rst-content dl dt .btn .fa-spin.headerlink,.rst-content dl dt .nav .fa-spin.headerlink,.rst-content h1 .btn .fa-spin.headerlink,.rst-content h1 .nav .fa-spin.headerlink,.rst-content h2 .btn .fa-spin.headerlink,.rst-content h2 .nav .fa-spin.headerlink,.rst-content h3 .btn .fa-spin.headerlink,.rst-content h3 .nav .fa-spin.headerlink,.rst-content h4 .btn .fa-spin.headerlink,.rst-content h4 .nav .fa-spin.headerlink,.rst-content h5 .btn .fa-spin.headerlink,.rst-content h5 .nav .fa-spin.headerlink,.rst-content h6 .btn .fa-spin.headerlink,.rst-content h6 .nav .fa-spin.headerlink,.rst-content p .btn .fa-spin.headerlink,.rst-content p .nav .fa-spin.headerlink,.rst-content table>caption .btn .fa-spin.headerlink,.rst-content table>caption .nav .fa-spin.headerlink,.rst-content tt.download .btn span.fa-spin:first-child,.rst-content tt.download .nav span.fa-spin:first-child,.wy-menu-vertical li .btn button.fa-spin.toctree-expand,.wy-menu-vertical li .nav button.fa-spin.toctree-expand{display:inline-block}.btn.fa:before,.btn.icon:before,.rst-content .btn.admonition-title:before,.rst-content .code-block-caption .btn.headerlink:before,.rst-content .eqno .btn.headerlink:before,.rst-content code.download span.btn:first-child:before,.rst-content dl dt .btn.headerlink:before,.rst-content h1 .btn.headerlink:before,.rst-content h2 .btn.headerlink:before,.rst-content h3 .btn.headerlink:before,.rst-content h4 .btn.headerlink:before,.rst-content h5 .btn.headerlink:before,.rst-content h6 .btn.headerlink:before,.rst-content p .btn.headerlink:before,.rst-content table>caption .btn.headerlink:before,.rst-content tt.download span.btn:first-child:before,.wy-menu-vertical li button.btn.toctree-expand:before{opacity:.5;-webkit-transition:opacity .05s ease-in;-moz-transition:opacity .05s ease-in;transition:opacity .05s ease-in}.btn.fa:hover:before,.btn.icon:hover:before,.rst-content .btn.admonition-title:hover:before,.rst-content .code-block-caption .btn.headerlink:hover:before,.rst-content .eqno .btn.headerlink:hover:before,.rst-content code.download span.btn:first-child:hover:before,.rst-content dl dt .btn.headerlink:hover:before,.rst-content h1 .btn.headerlink:hover:before,.rst-content h2 .btn.headerlink:hover:before,.rst-content h3 .btn.headerlink:hover:before,.rst-content h4 .btn.headerlink:hover:before,.rst-content h5 .btn.headerlink:hover:before,.rst-content h6 .btn.headerlink:hover:before,.rst-content p .btn.headerlink:hover:before,.rst-content table>caption .btn.headerlink:hover:before,.rst-content tt.download span.btn:first-child:hover:before,.wy-menu-vertical li button.btn.toctree-expand:hover:before{opacity:1}.btn-mini .fa:before,.btn-mini .icon:before,.btn-mini .rst-content .admonition-title:before,.btn-mini .rst-content .code-block-caption .headerlink:before,.btn-mini .rst-content .eqno .headerlink:before,.btn-mini .rst-content code.download span:first-child:before,.btn-mini .rst-content dl dt .headerlink:before,.btn-mini .rst-content h1 .headerlink:before,.btn-mini .rst-content h2 .headerlink:before,.btn-mini .rst-content h3 .headerlink:before,.btn-mini .rst-content h4 .headerlink:before,.btn-mini .rst-content h5 .headerlink:before,.btn-mini .rst-content h6 .headerlink:before,.btn-mini .rst-content p .headerlink:before,.btn-mini .rst-content table>caption .headerlink:before,.btn-mini .rst-content tt.download span:first-child:before,.btn-mini .wy-menu-vertical li button.toctree-expand:before,.rst-content .btn-mini .admonition-title:before,.rst-content .code-block-caption .btn-mini .headerlink:before,.rst-content .eqno .btn-mini .headerlink:before,.rst-content code.download .btn-mini span:first-child:before,.rst-content dl dt .btn-mini .headerlink:before,.rst-content h1 .btn-mini .headerlink:before,.rst-content h2 .btn-mini .headerlink:before,.rst-content h3 .btn-mini .headerlink:before,.rst-content h4 .btn-mini .headerlink:before,.rst-content h5 .btn-mini .headerlink:before,.rst-content h6 .btn-mini .headerlink:before,.rst-content p .btn-mini .headerlink:before,.rst-content table>caption .btn-mini .headerlink:before,.rst-content tt.download .btn-mini span:first-child:before,.wy-menu-vertical li .btn-mini button.toctree-expand:before{font-size:14px;vertical-align:-15%}.rst-content .admonition,.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .danger,.rst-content .error,.rst-content .hint,.rst-content .important,.rst-content .note,.rst-content .seealso,.rst-content .tip,.rst-content .warning,.wy-alert{padding:12px;line-height:24px;margin-bottom:24px;background:#e7f2fa}.rst-content .admonition-title,.wy-alert-title{font-weight:700;display:block;color:#fff;background:#6ab0de;padding:6px 12px;margin:-12px -12px 12px}.rst-content .danger,.rst-content .error,.rst-content .wy-alert-danger.admonition,.rst-content .wy-alert-danger.admonition-todo,.rst-content .wy-alert-danger.attention,.rst-content .wy-alert-danger.caution,.rst-content .wy-alert-danger.hint,.rst-content .wy-alert-danger.important,.rst-content .wy-alert-danger.note,.rst-content .wy-alert-danger.seealso,.rst-content .wy-alert-danger.tip,.rst-content .wy-alert-danger.warning,.wy-alert.wy-alert-danger{background:#fdf3f2}.rst-content .danger .admonition-title,.rst-content .danger .wy-alert-title,.rst-content .error .admonition-title,.rst-content .error .wy-alert-title,.rst-content .wy-alert-danger.admonition-todo .admonition-title,.rst-content .wy-alert-danger.admonition-todo .wy-alert-title,.rst-content .wy-alert-danger.admonition .admonition-title,.rst-content .wy-alert-danger.admonition .wy-alert-title,.rst-content .wy-alert-danger.attention .admonition-title,.rst-content .wy-alert-danger.attention .wy-alert-title,.rst-content .wy-alert-danger.caution .admonition-title,.rst-content .wy-alert-danger.caution .wy-alert-title,.rst-content .wy-alert-danger.hint .admonition-title,.rst-content .wy-alert-danger.hint .wy-alert-title,.rst-content .wy-alert-danger.important .admonition-title,.rst-content .wy-alert-danger.important .wy-alert-title,.rst-content .wy-alert-danger.note .admonition-title,.rst-content .wy-alert-danger.note .wy-alert-title,.rst-content .wy-alert-danger.seealso .admonition-title,.rst-content .wy-alert-danger.seealso .wy-alert-title,.rst-content .wy-alert-danger.tip .admonition-title,.rst-content .wy-alert-danger.tip .wy-alert-title,.rst-content .wy-alert-danger.warning .admonition-title,.rst-content .wy-alert-danger.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-danger .admonition-title,.wy-alert.wy-alert-danger .rst-content .admonition-title,.wy-alert.wy-alert-danger .wy-alert-title{background:#f29f97}.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .warning,.rst-content .wy-alert-warning.admonition,.rst-content .wy-alert-warning.danger,.rst-content .wy-alert-warning.error,.rst-content .wy-alert-warning.hint,.rst-content .wy-alert-warning.important,.rst-content .wy-alert-warning.note,.rst-content .wy-alert-warning.seealso,.rst-content .wy-alert-warning.tip,.wy-alert.wy-alert-warning{background:#ffedcc}.rst-content .admonition-todo .admonition-title,.rst-content .admonition-todo .wy-alert-title,.rst-content .attention .admonition-title,.rst-content .attention .wy-alert-title,.rst-content .caution .admonition-title,.rst-content .caution .wy-alert-title,.rst-content .warning .admonition-title,.rst-content .warning .wy-alert-title,.rst-content .wy-alert-warning.admonition .admonition-title,.rst-content .wy-alert-warning.admonition .wy-alert-title,.rst-content .wy-alert-warning.danger .admonition-title,.rst-content .wy-alert-warning.danger .wy-alert-title,.rst-content .wy-alert-warning.error .admonition-title,.rst-content .wy-alert-warning.error .wy-alert-title,.rst-content .wy-alert-warning.hint .admonition-title,.rst-content .wy-alert-warning.hint .wy-alert-title,.rst-content .wy-alert-warning.important .admonition-title,.rst-content .wy-alert-warning.important .wy-alert-title,.rst-content .wy-alert-warning.note .admonition-title,.rst-content .wy-alert-warning.note .wy-alert-title,.rst-content .wy-alert-warning.seealso .admonition-title,.rst-content .wy-alert-warning.seealso .wy-alert-title,.rst-content .wy-alert-warning.tip .admonition-title,.rst-content .wy-alert-warning.tip .wy-alert-title,.rst-content .wy-alert.wy-alert-warning .admonition-title,.wy-alert.wy-alert-warning .rst-content .admonition-title,.wy-alert.wy-alert-warning .wy-alert-title{background:#f0b37e}.rst-content .note,.rst-content .seealso,.rst-content .wy-alert-info.admonition,.rst-content .wy-alert-info.admonition-todo,.rst-content .wy-alert-info.attention,.rst-content .wy-alert-info.caution,.rst-content .wy-alert-info.danger,.rst-content .wy-alert-info.error,.rst-content .wy-alert-info.hint,.rst-content .wy-alert-info.important,.rst-content .wy-alert-info.tip,.rst-content .wy-alert-info.warning,.wy-alert.wy-alert-info{background:#e7f2fa}.rst-content .note .admonition-title,.rst-content .note .wy-alert-title,.rst-content .seealso .admonition-title,.rst-content .seealso .wy-alert-title,.rst-content .wy-alert-info.admonition-todo .admonition-title,.rst-content .wy-alert-info.admonition-todo .wy-alert-title,.rst-content .wy-alert-info.admonition .admonition-title,.rst-content .wy-alert-info.admonition .wy-alert-title,.rst-content .wy-alert-info.attention .admonition-title,.rst-content .wy-alert-info.attention .wy-alert-title,.rst-content .wy-alert-info.caution .admonition-title,.rst-content .wy-alert-info.caution .wy-alert-title,.rst-content .wy-alert-info.danger .admonition-title,.rst-content .wy-alert-info.danger .wy-alert-title,.rst-content .wy-alert-info.error .admonition-title,.rst-content .wy-alert-info.error .wy-alert-title,.rst-content .wy-alert-info.hint .admonition-title,.rst-content .wy-alert-info.hint .wy-alert-title,.rst-content .wy-alert-info.important .admonition-title,.rst-content .wy-alert-info.important .wy-alert-title,.rst-content .wy-alert-info.tip .admonition-title,.rst-content .wy-alert-info.tip .wy-alert-title,.rst-content .wy-alert-info.warning .admonition-title,.rst-content .wy-alert-info.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-info .admonition-title,.wy-alert.wy-alert-info .rst-content .admonition-title,.wy-alert.wy-alert-info .wy-alert-title{background:#6ab0de}.rst-content .hint,.rst-content .important,.rst-content .tip,.rst-content .wy-alert-success.admonition,.rst-content .wy-alert-success.admonition-todo,.rst-content .wy-alert-success.attention,.rst-content .wy-alert-success.caution,.rst-content .wy-alert-success.danger,.rst-content .wy-alert-success.error,.rst-content .wy-alert-success.note,.rst-content .wy-alert-success.seealso,.rst-content .wy-alert-success.warning,.wy-alert.wy-alert-success{background:#dbfaf4}.rst-content .hint .admonition-title,.rst-content .hint .wy-alert-title,.rst-content .important .admonition-title,.rst-content .important .wy-alert-title,.rst-content .tip .admonition-title,.rst-content .tip .wy-alert-title,.rst-content .wy-alert-success.admonition-todo .admonition-title,.rst-content .wy-alert-success.admonition-todo .wy-alert-title,.rst-content .wy-alert-success.admonition .admonition-title,.rst-content .wy-alert-success.admonition .wy-alert-title,.rst-content .wy-alert-success.attention .admonition-title,.rst-content .wy-alert-success.attention .wy-alert-title,.rst-content .wy-alert-success.caution .admonition-title,.rst-content .wy-alert-success.caution .wy-alert-title,.rst-content .wy-alert-success.danger .admonition-title,.rst-content .wy-alert-success.danger .wy-alert-title,.rst-content .wy-alert-success.error .admonition-title,.rst-content .wy-alert-success.error .wy-alert-title,.rst-content .wy-alert-success.note .admonition-title,.rst-content .wy-alert-success.note .wy-alert-title,.rst-content .wy-alert-success.seealso .admonition-title,.rst-content .wy-alert-success.seealso .wy-alert-title,.rst-content .wy-alert-success.warning .admonition-title,.rst-content .wy-alert-success.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-success .admonition-title,.wy-alert.wy-alert-success .rst-content .admonition-title,.wy-alert.wy-alert-success .wy-alert-title{background:#1abc9c}.rst-content .wy-alert-neutral.admonition,.rst-content .wy-alert-neutral.admonition-todo,.rst-content .wy-alert-neutral.attention,.rst-content .wy-alert-neutral.caution,.rst-content .wy-alert-neutral.danger,.rst-content .wy-alert-neutral.error,.rst-content .wy-alert-neutral.hint,.rst-content .wy-alert-neutral.important,.rst-content .wy-alert-neutral.note,.rst-content .wy-alert-neutral.seealso,.rst-content .wy-alert-neutral.tip,.rst-content .wy-alert-neutral.warning,.wy-alert.wy-alert-neutral{background:#f3f6f6}.rst-content .wy-alert-neutral.admonition-todo .admonition-title,.rst-content .wy-alert-neutral.admonition-todo .wy-alert-title,.rst-content .wy-alert-neutral.admonition .admonition-title,.rst-content .wy-alert-neutral.admonition .wy-alert-title,.rst-content .wy-alert-neutral.attention .admonition-title,.rst-content .wy-alert-neutral.attention .wy-alert-title,.rst-content .wy-alert-neutral.caution .admonition-title,.rst-content .wy-alert-neutral.caution .wy-alert-title,.rst-content .wy-alert-neutral.danger .admonition-title,.rst-content .wy-alert-neutral.danger .wy-alert-title,.rst-content .wy-alert-neutral.error .admonition-title,.rst-content .wy-alert-neutral.error .wy-alert-title,.rst-content .wy-alert-neutral.hint .admonition-title,.rst-content .wy-alert-neutral.hint .wy-alert-title,.rst-content .wy-alert-neutral.important .admonition-title,.rst-content .wy-alert-neutral.important .wy-alert-title,.rst-content .wy-alert-neutral.note .admonition-title,.rst-content .wy-alert-neutral.note .wy-alert-title,.rst-content .wy-alert-neutral.seealso .admonition-title,.rst-content .wy-alert-neutral.seealso .wy-alert-title,.rst-content .wy-alert-neutral.tip .admonition-title,.rst-content .wy-alert-neutral.tip .wy-alert-title,.rst-content .wy-alert-neutral.warning .admonition-title,.rst-content .wy-alert-neutral.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-neutral .admonition-title,.wy-alert.wy-alert-neutral .rst-content .admonition-title,.wy-alert.wy-alert-neutral .wy-alert-title{color:#404040;background:#e1e4e5}.rst-content .wy-alert-neutral.admonition-todo a,.rst-content .wy-alert-neutral.admonition a,.rst-content .wy-alert-neutral.attention a,.rst-content .wy-alert-neutral.caution a,.rst-content .wy-alert-neutral.danger a,.rst-content .wy-alert-neutral.error a,.rst-content .wy-alert-neutral.hint a,.rst-content .wy-alert-neutral.important a,.rst-content .wy-alert-neutral.note a,.rst-content .wy-alert-neutral.seealso a,.rst-content .wy-alert-neutral.tip a,.rst-content .wy-alert-neutral.warning a,.wy-alert.wy-alert-neutral a{color:#2980b9}.rst-content .admonition-todo p:last-child,.rst-content .admonition p:last-child,.rst-content .attention p:last-child,.rst-content .caution p:last-child,.rst-content .danger p:last-child,.rst-content .error p:last-child,.rst-content .hint p:last-child,.rst-content .important p:last-child,.rst-content .note p:last-child,.rst-content .seealso p:last-child,.rst-content .tip p:last-child,.rst-content .warning p:last-child,.wy-alert p:last-child{margin-bottom:0}.wy-tray-container{position:fixed;bottom:0;left:0;z-index:600}.wy-tray-container li{display:block;width:300px;background:transparent;color:#fff;text-align:center;box-shadow:0 5px 5px 0 rgba(0,0,0,.1);padding:0 24px;min-width:20%;opacity:0;height:0;line-height:56px;overflow:hidden;-webkit-transition:all .3s ease-in;-moz-transition:all .3s ease-in;transition:all .3s ease-in}.wy-tray-container li.wy-tray-item-success{background:#27ae60}.wy-tray-container li.wy-tray-item-info{background:#2980b9}.wy-tray-container li.wy-tray-item-warning{background:#e67e22}.wy-tray-container li.wy-tray-item-danger{background:#e74c3c}.wy-tray-container li.on{opacity:1;height:56px}@media screen and (max-width:768px){.wy-tray-container{bottom:auto;top:0;width:100%}.wy-tray-container li{width:100%}}button{font-size:100%;margin:0;vertical-align:baseline;*vertical-align:middle;cursor:pointer;line-height:normal;-webkit-appearance:button;*overflow:visible}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}button[disabled]{cursor:default}.btn{display:inline-block;border-radius:2px;line-height:normal;white-space:nowrap;text-align:center;cursor:pointer;font-size:100%;padding:6px 12px 8px;color:#fff;border:1px solid rgba(0,0,0,.1);background-color:#27ae60;text-decoration:none;font-weight:400;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;box-shadow:inset 0 1px 2px -1px hsla(0,0%,100%,.5),inset 0 -2px 0 0 rgba(0,0,0,.1);outline-none:false;vertical-align:middle;*display:inline;zoom:1;-webkit-user-drag:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;-webkit-transition:all .1s linear;-moz-transition:all .1s linear;transition:all .1s linear}.btn-hover{background:#2e8ece;color:#fff}.btn:hover{background:#2cc36b;color:#fff}.btn:focus{background:#2cc36b;outline:0}.btn:active{box-shadow:inset 0 -1px 0 0 rgba(0,0,0,.05),inset 0 2px 0 0 rgba(0,0,0,.1);padding:8px 12px 6px}.btn:visited{color:#fff}.btn-disabled,.btn-disabled:active,.btn-disabled:focus,.btn-disabled:hover,.btn:disabled{background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);filter:alpha(opacity=40);opacity:.4;cursor:not-allowed;box-shadow:none}.btn::-moz-focus-inner{padding:0;border:0}.btn-small{font-size:80%}.btn-info{background-color:#2980b9!important}.btn-info:hover{background-color:#2e8ece!important}.btn-neutral{background-color:#f3f6f6!important;color:#404040!important}.btn-neutral:hover{background-color:#e5ebeb!important;color:#404040}.btn-neutral:visited{color:#404040!important}.btn-success{background-color:#27ae60!important}.btn-success:hover{background-color:#295!important}.btn-danger{background-color:#e74c3c!important}.btn-danger:hover{background-color:#ea6153!important}.btn-warning{background-color:#e67e22!important}.btn-warning:hover{background-color:#e98b39!important}.btn-invert{background-color:#222}.btn-invert:hover{background-color:#2f2f2f!important}.btn-link{background-color:transparent!important;color:#2980b9;box-shadow:none;border-color:transparent!important}.btn-link:active,.btn-link:hover{background-color:transparent!important;color:#409ad5!important;box-shadow:none}.btn-link:visited{color:#9b59b6}.wy-btn-group .btn,.wy-control .btn{vertical-align:middle}.wy-btn-group{margin-bottom:24px;*zoom:1}.wy-btn-group:after,.wy-btn-group:before{display:table;content:""}.wy-btn-group:after{clear:both}.wy-dropdown{position:relative;display:inline-block}.wy-dropdown-active .wy-dropdown-menu{display:block}.wy-dropdown-menu{position:absolute;left:0;display:none;float:left;top:100%;min-width:100%;background:#fcfcfc;z-index:100;border:1px solid #cfd7dd;box-shadow:0 2px 2px 0 rgba(0,0,0,.1);padding:12px}.wy-dropdown-menu>dd>a{display:block;clear:both;color:#404040;white-space:nowrap;font-size:90%;padding:0 12px;cursor:pointer}.wy-dropdown-menu>dd>a:hover{background:#2980b9;color:#fff}.wy-dropdown-menu>dd.divider{border-top:1px solid #cfd7dd;margin:6px 0}.wy-dropdown-menu>dd.search{padding-bottom:12px}.wy-dropdown-menu>dd.search input[type=search]{width:100%}.wy-dropdown-menu>dd.call-to-action{background:#e3e3e3;text-transform:uppercase;font-weight:500;font-size:80%}.wy-dropdown-menu>dd.call-to-action:hover{background:#e3e3e3}.wy-dropdown-menu>dd.call-to-action .btn{color:#fff}.wy-dropdown.wy-dropdown-up .wy-dropdown-menu{bottom:100%;top:auto;left:auto;right:0}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu{background:#fcfcfc;margin-top:2px}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a{padding:6px 12px}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a:hover{background:#2980b9;color:#fff}.wy-dropdown.wy-dropdown-left .wy-dropdown-menu{right:0;left:auto;text-align:right}.wy-dropdown-arrow:before{content:" ";border-bottom:5px solid #f5f5f5;border-left:5px solid transparent;border-right:5px solid transparent;position:absolute;display:block;top:-4px;left:50%;margin-left:-3px}.wy-dropdown-arrow.wy-dropdown-arrow-left:before{left:11px}.wy-form-stacked select{display:block}.wy-form-aligned .wy-help-inline,.wy-form-aligned input,.wy-form-aligned label,.wy-form-aligned select,.wy-form-aligned textarea{display:inline-block;*display:inline;*zoom:1;vertical-align:middle}.wy-form-aligned .wy-control-group>label{display:inline-block;vertical-align:middle;width:10em;margin:6px 12px 0 0;float:left}.wy-form-aligned .wy-control{float:left}.wy-form-aligned .wy-control label{display:block}.wy-form-aligned .wy-control select{margin-top:6px}fieldset{margin:0}fieldset,legend{border:0;padding:0}legend{width:100%;white-space:normal;margin-bottom:24px;font-size:150%;*margin-left:-7px}label,legend{display:block}label{margin:0 0 .3125em;color:#333;font-size:90%}input,select,textarea{font-size:100%;margin:0;vertical-align:baseline;*vertical-align:middle}.wy-control-group{margin-bottom:24px;max-width:1200px;margin-left:auto;margin-right:auto;*zoom:1}.wy-control-group:after,.wy-control-group:before{display:table;content:""}.wy-control-group:after{clear:both}.wy-control-group.wy-control-group-required>label:after{content:" *";color:#e74c3c}.wy-control-group .wy-form-full,.wy-control-group .wy-form-halves,.wy-control-group .wy-form-thirds{padding-bottom:12px}.wy-control-group .wy-form-full input[type=color],.wy-control-group .wy-form-full input[type=date],.wy-control-group .wy-form-full input[type=datetime-local],.wy-control-group .wy-form-full input[type=datetime],.wy-control-group .wy-form-full input[type=email],.wy-control-group .wy-form-full input[type=month],.wy-control-group .wy-form-full input[type=number],.wy-control-group .wy-form-full input[type=password],.wy-control-group .wy-form-full input[type=search],.wy-control-group .wy-form-full input[type=tel],.wy-control-group .wy-form-full input[type=text],.wy-control-group .wy-form-full input[type=time],.wy-control-group .wy-form-full input[type=url],.wy-control-group .wy-form-full input[type=week],.wy-control-group .wy-form-full select,.wy-control-group .wy-form-halves input[type=color],.wy-control-group .wy-form-halves input[type=date],.wy-control-group .wy-form-halves input[type=datetime-local],.wy-control-group .wy-form-halves input[type=datetime],.wy-control-group .wy-form-halves input[type=email],.wy-control-group .wy-form-halves input[type=month],.wy-control-group .wy-form-halves input[type=number],.wy-control-group .wy-form-halves input[type=password],.wy-control-group .wy-form-halves input[type=search],.wy-control-group .wy-form-halves input[type=tel],.wy-control-group .wy-form-halves input[type=text],.wy-control-group .wy-form-halves input[type=time],.wy-control-group .wy-form-halves input[type=url],.wy-control-group .wy-form-halves input[type=week],.wy-control-group .wy-form-halves select,.wy-control-group .wy-form-thirds input[type=color],.wy-control-group .wy-form-thirds input[type=date],.wy-control-group .wy-form-thirds input[type=datetime-local],.wy-control-group .wy-form-thirds input[type=datetime],.wy-control-group .wy-form-thirds input[type=email],.wy-control-group .wy-form-thirds input[type=month],.wy-control-group .wy-form-thirds input[type=number],.wy-control-group .wy-form-thirds input[type=password],.wy-control-group .wy-form-thirds input[type=search],.wy-control-group .wy-form-thirds input[type=tel],.wy-control-group .wy-form-thirds input[type=text],.wy-control-group .wy-form-thirds input[type=time],.wy-control-group .wy-form-thirds input[type=url],.wy-control-group .wy-form-thirds input[type=week],.wy-control-group .wy-form-thirds select{width:100%}.wy-control-group .wy-form-full{float:left;display:block;width:100%;margin-right:0}.wy-control-group .wy-form-full:last-child{margin-right:0}.wy-control-group .wy-form-halves{float:left;display:block;margin-right:2.35765%;width:48.82117%}.wy-control-group .wy-form-halves:last-child,.wy-control-group .wy-form-halves:nth-of-type(2n){margin-right:0}.wy-control-group .wy-form-halves:nth-of-type(odd){clear:left}.wy-control-group .wy-form-thirds{float:left;display:block;margin-right:2.35765%;width:31.76157%}.wy-control-group .wy-form-thirds:last-child,.wy-control-group .wy-form-thirds:nth-of-type(3n){margin-right:0}.wy-control-group .wy-form-thirds:nth-of-type(3n+1){clear:left}.wy-control-group.wy-control-group-no-input .wy-control,.wy-control-no-input{margin:6px 0 0;font-size:90%}.wy-control-no-input{display:inline-block}.wy-control-group.fluid-input input[type=color],.wy-control-group.fluid-input input[type=date],.wy-control-group.fluid-input input[type=datetime-local],.wy-control-group.fluid-input input[type=datetime],.wy-control-group.fluid-input input[type=email],.wy-control-group.fluid-input input[type=month],.wy-control-group.fluid-input input[type=number],.wy-control-group.fluid-input input[type=password],.wy-control-group.fluid-input input[type=search],.wy-control-group.fluid-input input[type=tel],.wy-control-group.fluid-input input[type=text],.wy-control-group.fluid-input input[type=time],.wy-control-group.fluid-input input[type=url],.wy-control-group.fluid-input input[type=week]{width:100%}.wy-form-message-inline{padding-left:.3em;color:#666;font-size:90%}.wy-form-message{display:block;color:#999;font-size:70%;margin-top:.3125em;font-style:italic}.wy-form-message p{font-size:inherit;font-style:italic;margin-bottom:6px}.wy-form-message p:last-child{margin-bottom:0}input{line-height:normal}input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;*overflow:visible}input[type=color],input[type=date],input[type=datetime-local],input[type=datetime],input[type=email],input[type=month],input[type=number],input[type=password],input[type=search],input[type=tel],input[type=text],input[type=time],input[type=url],input[type=week]{-webkit-appearance:none;padding:6px;display:inline-block;border:1px solid #ccc;font-size:80%;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;box-shadow:inset 0 1px 3px #ddd;border-radius:0;-webkit-transition:border .3s linear;-moz-transition:border .3s linear;transition:border .3s linear}input[type=datetime-local]{padding:.34375em .625em}input[disabled]{cursor:default}input[type=checkbox],input[type=radio]{padding:0;margin-right:.3125em;*height:13px;*width:13px}input[type=checkbox],input[type=radio],input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}input[type=color]:focus,input[type=date]:focus,input[type=datetime-local]:focus,input[type=datetime]:focus,input[type=email]:focus,input[type=month]:focus,input[type=number]:focus,input[type=password]:focus,input[type=search]:focus,input[type=tel]:focus,input[type=text]:focus,input[type=time]:focus,input[type=url]:focus,input[type=week]:focus{outline:0;outline:thin dotted\9;border-color:#333}input.no-focus:focus{border-color:#ccc!important}input[type=checkbox]:focus,input[type=file]:focus,input[type=radio]:focus{outline:thin dotted #333;outline:1px auto #129fea}input[type=color][disabled],input[type=date][disabled],input[type=datetime-local][disabled],input[type=datetime][disabled],input[type=email][disabled],input[type=month][disabled],input[type=number][disabled],input[type=password][disabled],input[type=search][disabled],input[type=tel][disabled],input[type=text][disabled],input[type=time][disabled],input[type=url][disabled],input[type=week][disabled]{cursor:not-allowed;background-color:#fafafa}input:focus:invalid,select:focus:invalid,textarea:focus:invalid{color:#e74c3c;border:1px solid #e74c3c}input:focus:invalid:focus,select:focus:invalid:focus,textarea:focus:invalid:focus{border-color:#e74c3c}input[type=checkbox]:focus:invalid:focus,input[type=file]:focus:invalid:focus,input[type=radio]:focus:invalid:focus{outline-color:#e74c3c}input.wy-input-large{padding:12px;font-size:100%}textarea{overflow:auto;vertical-align:top;width:100%;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif}select,textarea{padding:.5em .625em;display:inline-block;border:1px solid #ccc;font-size:80%;box-shadow:inset 0 1px 3px #ddd;-webkit-transition:border .3s linear;-moz-transition:border .3s linear;transition:border .3s linear}select{border:1px solid #ccc;background-color:#fff}select[multiple]{height:auto}select:focus,textarea:focus{outline:0}input[readonly],select[disabled],select[readonly],textarea[disabled],textarea[readonly]{cursor:not-allowed;background-color:#fafafa}input[type=checkbox][disabled],input[type=radio][disabled]{cursor:not-allowed}.wy-checkbox,.wy-radio{margin:6px 0;color:#404040;display:block}.wy-checkbox input,.wy-radio input{vertical-align:baseline}.wy-form-message-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle}.wy-input-prefix,.wy-input-suffix{white-space:nowrap;padding:6px}.wy-input-prefix .wy-input-context,.wy-input-suffix .wy-input-context{line-height:27px;padding:0 8px;display:inline-block;font-size:80%;background-color:#f3f6f6;border:1px solid #ccc;color:#999}.wy-input-suffix .wy-input-context{border-left:0}.wy-input-prefix .wy-input-context{border-right:0}.wy-switch{position:relative;display:block;height:24px;margin-top:12px;cursor:pointer}.wy-switch:before{left:0;top:0;width:36px;height:12px;background:#ccc}.wy-switch:after,.wy-switch:before{position:absolute;content:"";display:block;border-radius:4px;-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.wy-switch:after{width:18px;height:18px;background:#999;left:-3px;top:-3px}.wy-switch span{position:absolute;left:48px;display:block;font-size:12px;color:#ccc;line-height:1}.wy-switch.active:before{background:#1e8449}.wy-switch.active:after{left:24px;background:#27ae60}.wy-switch.disabled{cursor:not-allowed;opacity:.8}.wy-control-group.wy-control-group-error .wy-form-message,.wy-control-group.wy-control-group-error>label{color:#e74c3c}.wy-control-group.wy-control-group-error input[type=color],.wy-control-group.wy-control-group-error input[type=date],.wy-control-group.wy-control-group-error input[type=datetime-local],.wy-control-group.wy-control-group-error input[type=datetime],.wy-control-group.wy-control-group-error input[type=email],.wy-control-group.wy-control-group-error input[type=month],.wy-control-group.wy-control-group-error input[type=number],.wy-control-group.wy-control-group-error input[type=password],.wy-control-group.wy-control-group-error input[type=search],.wy-control-group.wy-control-group-error input[type=tel],.wy-control-group.wy-control-group-error input[type=text],.wy-control-group.wy-control-group-error input[type=time],.wy-control-group.wy-control-group-error input[type=url],.wy-control-group.wy-control-group-error input[type=week],.wy-control-group.wy-control-group-error textarea{border:1px solid #e74c3c}.wy-inline-validate{white-space:nowrap}.wy-inline-validate .wy-input-context{padding:.5em .625em;display:inline-block;font-size:80%}.wy-inline-validate.wy-inline-validate-success .wy-input-context{color:#27ae60}.wy-inline-validate.wy-inline-validate-danger .wy-input-context{color:#e74c3c}.wy-inline-validate.wy-inline-validate-warning .wy-input-context{color:#e67e22}.wy-inline-validate.wy-inline-validate-info .wy-input-context{color:#2980b9}.rotate-90{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg)}.rotate-180{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg)}.rotate-270{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg)}.mirror{-webkit-transform:scaleX(-1);-moz-transform:scaleX(-1);-ms-transform:scaleX(-1);-o-transform:scaleX(-1);transform:scaleX(-1)}.mirror.rotate-90{-webkit-transform:scaleX(-1) rotate(90deg);-moz-transform:scaleX(-1) rotate(90deg);-ms-transform:scaleX(-1) rotate(90deg);-o-transform:scaleX(-1) rotate(90deg);transform:scaleX(-1) rotate(90deg)}.mirror.rotate-180{-webkit-transform:scaleX(-1) rotate(180deg);-moz-transform:scaleX(-1) rotate(180deg);-ms-transform:scaleX(-1) rotate(180deg);-o-transform:scaleX(-1) rotate(180deg);transform:scaleX(-1) rotate(180deg)}.mirror.rotate-270{-webkit-transform:scaleX(-1) rotate(270deg);-moz-transform:scaleX(-1) rotate(270deg);-ms-transform:scaleX(-1) rotate(270deg);-o-transform:scaleX(-1) rotate(270deg);transform:scaleX(-1) rotate(270deg)}@media only screen and (max-width:480px){.wy-form button[type=submit]{margin:.7em 0 0}.wy-form input[type=color],.wy-form input[type=date],.wy-form input[type=datetime-local],.wy-form input[type=datetime],.wy-form input[type=email],.wy-form input[type=month],.wy-form input[type=number],.wy-form input[type=password],.wy-form input[type=search],.wy-form input[type=tel],.wy-form input[type=text],.wy-form input[type=time],.wy-form input[type=url],.wy-form input[type=week],.wy-form label{margin-bottom:.3em;display:block}.wy-form input[type=color],.wy-form input[type=date],.wy-form input[type=datetime-local],.wy-form input[type=datetime],.wy-form input[type=email],.wy-form input[type=month],.wy-form input[type=number],.wy-form input[type=password],.wy-form input[type=search],.wy-form input[type=tel],.wy-form input[type=time],.wy-form input[type=url],.wy-form input[type=week]{margin-bottom:0}.wy-form-aligned .wy-control-group label{margin-bottom:.3em;text-align:left;display:block;width:100%}.wy-form-aligned .wy-control{margin:1.5em 0 0}.wy-form-message,.wy-form-message-inline,.wy-form .wy-help-inline{display:block;font-size:80%;padding:6px 0}}@media screen and (max-width:768px){.tablet-hide{display:none}}@media screen and (max-width:480px){.mobile-hide{display:none}}.float-left{float:left}.float-right{float:right}.full-width{width:100%}.rst-content table.docutils,.rst-content table.field-list,.wy-table{border-collapse:collapse;border-spacing:0;empty-cells:show;margin-bottom:24px}.rst-content table.docutils caption,.rst-content table.field-list caption,.wy-table caption{color:#000;font:italic 85%/1 arial,sans-serif;padding:1em 0;text-align:center}.rst-content table.docutils td,.rst-content table.docutils th,.rst-content table.field-list td,.rst-content table.field-list th,.wy-table td,.wy-table th{font-size:90%;margin:0;overflow:visible;padding:8px 16px}.rst-content table.docutils td:first-child,.rst-content table.docutils th:first-child,.rst-content table.field-list td:first-child,.rst-content table.field-list th:first-child,.wy-table td:first-child,.wy-table th:first-child{border-left-width:0}.rst-content table.docutils thead,.rst-content table.field-list thead,.wy-table thead{color:#000;text-align:left;vertical-align:bottom;white-space:nowrap}.rst-content table.docutils thead th,.rst-content table.field-list thead th,.wy-table thead th{font-weight:700;border-bottom:2px solid #e1e4e5}.rst-content table.docutils td,.rst-content table.field-list td,.wy-table td{background-color:transparent;vertical-align:middle}.rst-content table.docutils td p,.rst-content table.field-list td p,.wy-table td p{line-height:18px}.rst-content table.docutils td p:last-child,.rst-content table.field-list td p:last-child,.wy-table td p:last-child{margin-bottom:0}.rst-content table.docutils .wy-table-cell-min,.rst-content table.field-list .wy-table-cell-min,.wy-table .wy-table-cell-min{width:1%;padding-right:0}.rst-content table.docutils .wy-table-cell-min input[type=checkbox],.rst-content table.field-list .wy-table-cell-min input[type=checkbox],.wy-table .wy-table-cell-min input[type=checkbox]{margin:0}.wy-table-secondary{color:grey;font-size:90%}.wy-table-tertiary{color:grey;font-size:80%}.rst-content table.docutils:not(.field-list) tr:nth-child(2n-1) td,.wy-table-backed,.wy-table-odd td,.wy-table-striped tr:nth-child(2n-1) td{background-color:#f3f6f6}.rst-content table.docutils,.wy-table-bordered-all{border:1px solid #e1e4e5}.rst-content table.docutils td,.wy-table-bordered-all td{border-bottom:1px solid #e1e4e5;border-left:1px solid #e1e4e5}.rst-content table.docutils tbody>tr:last-child td,.wy-table-bordered-all tbody>tr:last-child td{border-bottom-width:0}.wy-table-bordered{border:1px solid #e1e4e5}.wy-table-bordered-rows td{border-bottom:1px solid #e1e4e5}.wy-table-bordered-rows tbody>tr:last-child td{border-bottom-width:0}.wy-table-horizontal td,.wy-table-horizontal th{border-width:0 0 1px;border-bottom:1px solid #e1e4e5}.wy-table-horizontal tbody>tr:last-child td{border-bottom-width:0}.wy-table-responsive{margin-bottom:24px;max-width:100%;overflow:auto}.wy-table-responsive table{margin-bottom:0!important}.wy-table-responsive table td,.wy-table-responsive table th{white-space:nowrap}a{color:#2980b9;text-decoration:none;cursor:pointer}a:hover{color:#3091d1}a:visited{color:#9b59b6}html{height:100%}body,html{overflow-x:hidden}body{font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;font-weight:400;color:#404040;min-height:100%;background:#edf0f2}.wy-text-left{text-align:left}.wy-text-center{text-align:center}.wy-text-right{text-align:right}.wy-text-large{font-size:120%}.wy-text-normal{font-size:100%}.wy-text-small,small{font-size:80%}.wy-text-strike{text-decoration:line-through}.wy-text-warning{color:#e67e22!important}a.wy-text-warning:hover{color:#eb9950!important}.wy-text-info{color:#2980b9!important}a.wy-text-info:hover{color:#409ad5!important}.wy-text-success{color:#27ae60!important}a.wy-text-success:hover{color:#36d278!important}.wy-text-danger{color:#e74c3c!important}a.wy-text-danger:hover{color:#ed7669!important}.wy-text-neutral{color:#404040!important}a.wy-text-neutral:hover{color:#595959!important}.rst-content .toctree-wrapper>p.caption,h1,h2,h3,h4,h5,h6,legend{margin-top:0;font-weight:700;font-family:Roboto Slab,ff-tisa-web-pro,Georgia,Arial,sans-serif}p{line-height:24px;font-size:16px;margin:0 0 24px}h1{font-size:175%}.rst-content .toctree-wrapper>p.caption,h2{font-size:150%}h3{font-size:125%}h4{font-size:115%}h5{font-size:110%}h6{font-size:100%}hr{display:block;height:1px;border:0;border-top:1px solid #e1e4e5;margin:24px 0;padding:0}.rst-content code,.rst-content tt,code{white-space:nowrap;max-width:100%;background:#fff;border:1px solid #e1e4e5;font-size:75%;padding:0 5px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#e74c3c;overflow-x:auto}.rst-content tt.code-large,code.code-large{font-size:90%}.rst-content .section ul,.rst-content .toctree-wrapper ul,.rst-content section ul,.wy-plain-list-disc,article ul{list-style:disc;line-height:24px;margin-bottom:24px}.rst-content .section ul li,.rst-content .toctree-wrapper ul li,.rst-content section ul li,.wy-plain-list-disc li,article ul li{list-style:disc;margin-left:24px}.rst-content .section ul li p:last-child,.rst-content .section ul li ul,.rst-content .toctree-wrapper ul li p:last-child,.rst-content .toctree-wrapper ul li ul,.rst-content section ul li p:last-child,.rst-content section ul li ul,.wy-plain-list-disc li p:last-child,.wy-plain-list-disc li ul,article ul li p:last-child,article ul li ul{margin-bottom:0}.rst-content .section ul li li,.rst-content .toctree-wrapper ul li li,.rst-content section ul li li,.wy-plain-list-disc li li,article ul li li{list-style:circle}.rst-content .section ul li li li,.rst-content .toctree-wrapper ul li li li,.rst-content section ul li li li,.wy-plain-list-disc li li li,article ul li li li{list-style:square}.rst-content .section ul li ol li,.rst-content .toctree-wrapper ul li ol li,.rst-content section ul li ol li,.wy-plain-list-disc li ol li,article ul li ol li{list-style:decimal}.rst-content .section ol,.rst-content .section ol.arabic,.rst-content .toctree-wrapper ol,.rst-content .toctree-wrapper ol.arabic,.rst-content section ol,.rst-content section ol.arabic,.wy-plain-list-decimal,article ol{list-style:decimal;line-height:24px;margin-bottom:24px}.rst-content .section ol.arabic li,.rst-content .section ol li,.rst-content .toctree-wrapper ol.arabic li,.rst-content .toctree-wrapper ol li,.rst-content section ol.arabic li,.rst-content section ol li,.wy-plain-list-decimal li,article ol li{list-style:decimal;margin-left:24px}.rst-content .section ol.arabic li ul,.rst-content .section ol li p:last-child,.rst-content .section ol li ul,.rst-content .toctree-wrapper ol.arabic li ul,.rst-content .toctree-wrapper ol li p:last-child,.rst-content .toctree-wrapper ol li ul,.rst-content section ol.arabic li ul,.rst-content section ol li p:last-child,.rst-content section ol li ul,.wy-plain-list-decimal li p:last-child,.wy-plain-list-decimal li ul,article ol li p:last-child,article ol li ul{margin-bottom:0}.rst-content .section ol.arabic li ul li,.rst-content .section ol li ul li,.rst-content .toctree-wrapper ol.arabic li ul li,.rst-content .toctree-wrapper ol li ul li,.rst-content section ol.arabic li ul li,.rst-content section ol li ul li,.wy-plain-list-decimal li ul li,article ol li ul li{list-style:disc}.wy-breadcrumbs{*zoom:1}.wy-breadcrumbs:after,.wy-breadcrumbs:before{display:table;content:""}.wy-breadcrumbs:after{clear:both}.wy-breadcrumbs>li{display:inline-block;padding-top:5px}.wy-breadcrumbs>li.wy-breadcrumbs-aside{float:right}.rst-content .wy-breadcrumbs>li code,.rst-content .wy-breadcrumbs>li tt,.wy-breadcrumbs>li .rst-content tt,.wy-breadcrumbs>li code{all:inherit;color:inherit}.breadcrumb-item:before{content:"/";color:#bbb;font-size:13px;padding:0 6px 0 3px}.wy-breadcrumbs-extra{margin-bottom:0;color:#b3b3b3;font-size:80%;display:inline-block}@media screen and (max-width:480px){.wy-breadcrumbs-extra,.wy-breadcrumbs li.wy-breadcrumbs-aside{display:none}}@media print{.wy-breadcrumbs li.wy-breadcrumbs-aside{display:none}}html{font-size:16px}.wy-affix{position:fixed;top:1.618em}.wy-menu a:hover{text-decoration:none}.wy-menu-horiz{*zoom:1}.wy-menu-horiz:after,.wy-menu-horiz:before{display:table;content:""}.wy-menu-horiz:after{clear:both}.wy-menu-horiz li,.wy-menu-horiz ul{display:inline-block}.wy-menu-horiz li:hover{background:hsla(0,0%,100%,.1)}.wy-menu-horiz li.divide-left{border-left:1px solid #404040}.wy-menu-horiz li.divide-right{border-right:1px solid #404040}.wy-menu-horiz a{height:32px;display:inline-block;line-height:32px;padding:0 16px}.wy-menu-vertical{width:300px}.wy-menu-vertical header,.wy-menu-vertical p.caption{color:#55a5d9;height:32px;line-height:32px;padding:0 1.618em;margin:12px 0 0;display:block;font-weight:700;text-transform:uppercase;font-size:85%;white-space:nowrap}.wy-menu-vertical ul{margin-bottom:0}.wy-menu-vertical li.divide-top{border-top:1px solid #404040}.wy-menu-vertical li.divide-bottom{border-bottom:1px solid #404040}.wy-menu-vertical li.current{background:#e3e3e3}.wy-menu-vertical li.current a{color:grey;border-right:1px solid #c9c9c9;padding:.4045em 2.427em}.wy-menu-vertical li.current a:hover{background:#d6d6d6}.rst-content .wy-menu-vertical li tt,.wy-menu-vertical li .rst-content tt,.wy-menu-vertical li code{border:none;background:inherit;color:inherit;padding-left:0;padding-right:0}.wy-menu-vertical li button.toctree-expand{display:block;float:left;margin-left:-1.2em;line-height:18px;color:#4d4d4d;border:none;background:none;padding:0}.wy-menu-vertical li.current>a,.wy-menu-vertical li.on a{color:#404040;font-weight:700;position:relative;background:#fcfcfc;border:none;padding:.4045em 1.618em}.wy-menu-vertical li.current>a:hover,.wy-menu-vertical li.on a:hover{background:#fcfcfc}.wy-menu-vertical li.current>a:hover button.toctree-expand,.wy-menu-vertical li.on a:hover button.toctree-expand{color:grey}.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand{display:block;line-height:18px;color:#333}.wy-menu-vertical li.toctree-l1.current>a{border-bottom:1px solid #c9c9c9;border-top:1px solid #c9c9c9}.wy-menu-vertical .toctree-l1.current .toctree-l2>ul,.wy-menu-vertical .toctree-l2.current .toctree-l3>ul,.wy-menu-vertical .toctree-l3.current .toctree-l4>ul,.wy-menu-vertical .toctree-l4.current .toctree-l5>ul,.wy-menu-vertical .toctree-l5.current .toctree-l6>ul,.wy-menu-vertical .toctree-l6.current .toctree-l7>ul,.wy-menu-vertical .toctree-l7.current .toctree-l8>ul,.wy-menu-vertical .toctree-l8.current .toctree-l9>ul,.wy-menu-vertical .toctree-l9.current .toctree-l10>ul,.wy-menu-vertical .toctree-l10.current .toctree-l11>ul{display:none}.wy-menu-vertical .toctree-l1.current .current.toctree-l2>ul,.wy-menu-vertical .toctree-l2.current .current.toctree-l3>ul,.wy-menu-vertical .toctree-l3.current .current.toctree-l4>ul,.wy-menu-vertical .toctree-l4.current .current.toctree-l5>ul,.wy-menu-vertical .toctree-l5.current .current.toctree-l6>ul,.wy-menu-vertical .toctree-l6.current .current.toctree-l7>ul,.wy-menu-vertical .toctree-l7.current .current.toctree-l8>ul,.wy-menu-vertical .toctree-l8.current .current.toctree-l9>ul,.wy-menu-vertical .toctree-l9.current .current.toctree-l10>ul,.wy-menu-vertical .toctree-l10.current .current.toctree-l11>ul{display:block}.wy-menu-vertical li.toctree-l3,.wy-menu-vertical li.toctree-l4{font-size:.9em}.wy-menu-vertical li.toctree-l2 a,.wy-menu-vertical li.toctree-l3 a,.wy-menu-vertical li.toctree-l4 a,.wy-menu-vertical li.toctree-l5 a,.wy-menu-vertical li.toctree-l6 a,.wy-menu-vertical li.toctree-l7 a,.wy-menu-vertical li.toctree-l8 a,.wy-menu-vertical li.toctree-l9 a,.wy-menu-vertical li.toctree-l10 a{color:#404040}.wy-menu-vertical li.toctree-l2 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l3 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l4 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l5 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l6 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l7 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l8 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l9 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l10 a:hover button.toctree-expand{color:grey}.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a,.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a,.wy-menu-vertical li.toctree-l4.current li.toctree-l5>a,.wy-menu-vertical li.toctree-l5.current li.toctree-l6>a,.wy-menu-vertical li.toctree-l6.current li.toctree-l7>a,.wy-menu-vertical li.toctree-l7.current li.toctree-l8>a,.wy-menu-vertical li.toctree-l8.current li.toctree-l9>a,.wy-menu-vertical li.toctree-l9.current li.toctree-l10>a,.wy-menu-vertical li.toctree-l10.current li.toctree-l11>a{display:block}.wy-menu-vertical li.toctree-l2.current>a{padding:.4045em 2.427em}.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a{padding:.4045em 1.618em .4045em 4.045em}.wy-menu-vertical li.toctree-l3.current>a{padding:.4045em 4.045em}.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{padding:.4045em 1.618em .4045em 5.663em}.wy-menu-vertical li.toctree-l4.current>a{padding:.4045em 5.663em}.wy-menu-vertical li.toctree-l4.current li.toctree-l5>a{padding:.4045em 1.618em .4045em 7.281em}.wy-menu-vertical li.toctree-l5.current>a{padding:.4045em 7.281em}.wy-menu-vertical li.toctree-l5.current li.toctree-l6>a{padding:.4045em 1.618em .4045em 8.899em}.wy-menu-vertical li.toctree-l6.current>a{padding:.4045em 8.899em}.wy-menu-vertical li.toctree-l6.current li.toctree-l7>a{padding:.4045em 1.618em .4045em 10.517em}.wy-menu-vertical li.toctree-l7.current>a{padding:.4045em 10.517em}.wy-menu-vertical li.toctree-l7.current li.toctree-l8>a{padding:.4045em 1.618em .4045em 12.135em}.wy-menu-vertical li.toctree-l8.current>a{padding:.4045em 12.135em}.wy-menu-vertical li.toctree-l8.current li.toctree-l9>a{padding:.4045em 1.618em .4045em 13.753em}.wy-menu-vertical li.toctree-l9.current>a{padding:.4045em 13.753em}.wy-menu-vertical li.toctree-l9.current li.toctree-l10>a{padding:.4045em 1.618em .4045em 15.371em}.wy-menu-vertical li.toctree-l10.current>a{padding:.4045em 15.371em}.wy-menu-vertical li.toctree-l10.current li.toctree-l11>a{padding:.4045em 1.618em .4045em 16.989em}.wy-menu-vertical li.toctree-l2.current>a,.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a{background:#c9c9c9}.wy-menu-vertical li.toctree-l2 button.toctree-expand{color:#a3a3a3}.wy-menu-vertical li.toctree-l3.current>a,.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{background:#bdbdbd}.wy-menu-vertical li.toctree-l3 button.toctree-expand{color:#969696}.wy-menu-vertical li.current ul{display:block}.wy-menu-vertical li ul{margin-bottom:0;display:none}.wy-menu-vertical li ul li a{margin-bottom:0;color:#d9d9d9;font-weight:400}.wy-menu-vertical a{line-height:18px;padding:.4045em 1.618em;display:block;position:relative;font-size:90%;color:#d9d9d9}.wy-menu-vertical a:hover{background-color:#4e4a4a;cursor:pointer}.wy-menu-vertical a:hover button.toctree-expand{color:#d9d9d9}.wy-menu-vertical a:active{background-color:#2980b9;cursor:pointer;color:#fff}.wy-menu-vertical a:active button.toctree-expand{color:#fff}.wy-side-nav-search{display:block;width:300px;padding:.809em;margin-bottom:.809em;z-index:200;background-color:#2980b9;text-align:center;color:#fcfcfc}.wy-side-nav-search input[type=text]{width:100%;border-radius:50px;padding:6px 12px;border-color:#2472a4}.wy-side-nav-search img{display:block;margin:auto auto .809em;height:45px;width:45px;background-color:#2980b9;padding:5px;border-radius:100%}.wy-side-nav-search .wy-dropdown>a,.wy-side-nav-search>a{color:#fcfcfc;font-size:100%;font-weight:700;display:inline-block;padding:4px 6px;margin-bottom:.809em;max-width:100%}.wy-side-nav-search .wy-dropdown>a:hover,.wy-side-nav-search>a:hover{background:hsla(0,0%,100%,.1)}.wy-side-nav-search .wy-dropdown>a img.logo,.wy-side-nav-search>a img.logo{display:block;margin:0 auto;height:auto;width:auto;border-radius:0;max-width:100%;background:transparent}.wy-side-nav-search .wy-dropdown>a.icon img.logo,.wy-side-nav-search>a.icon img.logo{margin-top:.85em}.wy-side-nav-search>div.version{margin-top:-.4045em;margin-bottom:.809em;font-weight:400;color:hsla(0,0%,100%,.3)}.wy-nav .wy-menu-vertical header{color:#2980b9}.wy-nav .wy-menu-vertical a{color:#b3b3b3}.wy-nav .wy-menu-vertical a:hover{background-color:#2980b9;color:#fff}[data-menu-wrap]{-webkit-transition:all .2s ease-in;-moz-transition:all .2s ease-in;transition:all .2s ease-in;position:absolute;opacity:1;width:100%;opacity:0}[data-menu-wrap].move-center{left:0;right:auto;opacity:1}[data-menu-wrap].move-left{right:auto;left:-100%;opacity:0}[data-menu-wrap].move-right{right:-100%;left:auto;opacity:0}.wy-body-for-nav{background:#fcfcfc}.wy-grid-for-nav{position:absolute;width:100%;height:100%}.wy-nav-side{position:fixed;top:0;bottom:0;left:0;padding-bottom:2em;width:300px;overflow-x:hidden;overflow-y:hidden;min-height:100%;color:#9b9b9b;background:#343131;z-index:200}.wy-side-scroll{width:320px;position:relative;overflow-x:hidden;overflow-y:scroll;height:100%}.wy-nav-top{display:none;background:#2980b9;color:#fff;padding:.4045em .809em;position:relative;line-height:50px;text-align:center;font-size:100%;*zoom:1}.wy-nav-top:after,.wy-nav-top:before{display:table;content:""}.wy-nav-top:after{clear:both}.wy-nav-top a{color:#fff;font-weight:700}.wy-nav-top img{margin-right:12px;height:45px;width:45px;background-color:#2980b9;padding:5px;border-radius:100%}.wy-nav-top i{font-size:30px;float:left;cursor:pointer;padding-top:inherit}.wy-nav-content-wrap{margin-left:300px;background:#fcfcfc;min-height:100%}.wy-nav-content{padding:1.618em 3.236em;height:100%;max-width:800px;margin:auto}.wy-body-mask{position:fixed;width:100%;height:100%;background:rgba(0,0,0,.2);display:none;z-index:499}.wy-body-mask.on{display:block}footer{color:grey}footer p{margin-bottom:12px}.rst-content footer span.commit tt,footer span.commit .rst-content tt,footer span.commit code{padding:0;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;font-size:1em;background:none;border:none;color:grey}.rst-footer-buttons{*zoom:1}.rst-footer-buttons:after,.rst-footer-buttons:before{width:100%;display:table;content:""}.rst-footer-buttons:after{clear:both}.rst-breadcrumbs-buttons{margin-top:12px;*zoom:1}.rst-breadcrumbs-buttons:after,.rst-breadcrumbs-buttons:before{display:table;content:""}.rst-breadcrumbs-buttons:after{clear:both}#search-results .search li{margin-bottom:24px;border-bottom:1px solid #e1e4e5;padding-bottom:24px}#search-results .search li:first-child{border-top:1px solid #e1e4e5;padding-top:24px}#search-results .search li a{font-size:120%;margin-bottom:12px;display:inline-block}#search-results .context{color:grey;font-size:90%}.genindextable li>ul{margin-left:24px}@media screen and (max-width:768px){.wy-body-for-nav{background:#fcfcfc}.wy-nav-top{display:block}.wy-nav-side{left:-300px}.wy-nav-side.shift{width:85%;left:0}.wy-menu.wy-menu-vertical,.wy-side-nav-search,.wy-side-scroll{width:auto}.wy-nav-content-wrap{margin-left:0}.wy-nav-content-wrap .wy-nav-content{padding:1.618em}.wy-nav-content-wrap.shift{position:fixed;min-width:100%;left:85%;top:0;height:100%;overflow:hidden}}@media screen and (min-width:1100px){.wy-nav-content-wrap{background:rgba(0,0,0,.05)}.wy-nav-content{margin:0;background:#fcfcfc}}@media print{.rst-versions,.wy-nav-side,footer{display:none}.wy-nav-content-wrap{margin-left:0}}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60;*zoom:1}.rst-versions .rst-current-version:after,.rst-versions .rst-current-version:before{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-content .code-block-caption .rst-versions .rst-current-version .headerlink,.rst-content .eqno .rst-versions .rst-current-version .headerlink,.rst-content .rst-versions .rst-current-version .admonition-title,.rst-content code.download .rst-versions .rst-current-version span:first-child,.rst-content dl dt .rst-versions .rst-current-version .headerlink,.rst-content h1 .rst-versions .rst-current-version .headerlink,.rst-content h2 .rst-versions .rst-current-version .headerlink,.rst-content h3 .rst-versions .rst-current-version .headerlink,.rst-content h4 .rst-versions .rst-current-version .headerlink,.rst-content h5 .rst-versions .rst-current-version .headerlink,.rst-content h6 .rst-versions .rst-current-version .headerlink,.rst-content p .rst-versions .rst-current-version .headerlink,.rst-content table>caption .rst-versions .rst-current-version .headerlink,.rst-content tt.download .rst-versions .rst-current-version span:first-child,.rst-versions .rst-current-version .fa,.rst-versions .rst-current-version .icon,.rst-versions .rst-current-version .rst-content .admonition-title,.rst-versions .rst-current-version .rst-content .code-block-caption .headerlink,.rst-versions .rst-current-version .rst-content .eqno .headerlink,.rst-versions .rst-current-version .rst-content code.download span:first-child,.rst-versions .rst-current-version .rst-content dl dt .headerlink,.rst-versions .rst-current-version .rst-content h1 .headerlink,.rst-versions .rst-current-version .rst-content h2 .headerlink,.rst-versions .rst-current-version .rst-content h3 .headerlink,.rst-versions .rst-current-version .rst-content h4 .headerlink,.rst-versions .rst-current-version .rst-content h5 .headerlink,.rst-versions .rst-current-version .rst-content h6 .headerlink,.rst-versions .rst-current-version .rst-content p .headerlink,.rst-versions .rst-current-version .rst-content table>caption .headerlink,.rst-versions .rst-current-version .rst-content tt.download span:first-child,.rst-versions .rst-current-version .wy-menu-vertical li button.toctree-expand,.wy-menu-vertical li .rst-versions .rst-current-version button.toctree-expand{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}.rst-content .toctree-wrapper>p.caption,.rst-content h1,.rst-content h2,.rst-content h3,.rst-content h4,.rst-content h5,.rst-content h6{margin-bottom:24px}.rst-content img{max-width:100%;height:auto}.rst-content div.figure,.rst-content figure{margin-bottom:24px}.rst-content div.figure .caption-text,.rst-content figure .caption-text{font-style:italic}.rst-content div.figure p:last-child.caption,.rst-content figure p:last-child.caption{margin-bottom:0}.rst-content div.figure.align-center,.rst-content figure.align-center{text-align:center}.rst-content .section>a>img,.rst-content .section>img,.rst-content section>a>img,.rst-content section>img{margin-bottom:24px}.rst-content abbr[title]{text-decoration:none}.rst-content.style-external-links a.reference.external:after{font-family:FontAwesome;content:"\f08e";color:#b3b3b3;vertical-align:super;font-size:60%;margin:0 .2em}.rst-content blockquote{margin-left:24px;line-height:24px;margin-bottom:24px}.rst-content pre.literal-block{white-space:pre;margin:0;padding:12px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;display:block;overflow:auto}.rst-content div[class^=highlight],.rst-content pre.literal-block{border:1px solid #e1e4e5;overflow-x:auto;margin:1px 0 24px}.rst-content div[class^=highlight] div[class^=highlight],.rst-content pre.literal-block div[class^=highlight]{padding:0;border:none;margin:0}.rst-content div[class^=highlight] td.code{width:100%}.rst-content .linenodiv pre{border-right:1px solid #e6e9ea;margin:0;padding:12px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;user-select:none;pointer-events:none}.rst-content div[class^=highlight] pre{white-space:pre;margin:0;padding:12px;display:block;overflow:auto}.rst-content div[class^=highlight] pre .hll{display:block;margin:0 -12px;padding:0 12px}.rst-content .linenodiv pre,.rst-content div[class^=highlight] pre,.rst-content pre.literal-block{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;font-size:12px;line-height:1.4}.rst-content div.highlight .gp,.rst-content div.highlight span.linenos{user-select:none;pointer-events:none}.rst-content div.highlight span.linenos{display:inline-block;padding-left:0;padding-right:12px;margin-right:12px;border-right:1px solid #e6e9ea}.rst-content .code-block-caption{font-style:italic;font-size:85%;line-height:1;padding:1em 0;text-align:center}@media print{.rst-content .codeblock,.rst-content div[class^=highlight],.rst-content div[class^=highlight] pre{white-space:pre-wrap}}.rst-content .admonition,.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .danger,.rst-content .error,.rst-content .hint,.rst-content .important,.rst-content .note,.rst-content .seealso,.rst-content .tip,.rst-content .warning{clear:both}.rst-content .admonition-todo .last,.rst-content .admonition-todo>:last-child,.rst-content .admonition .last,.rst-content .admonition>:last-child,.rst-content .attention .last,.rst-content .attention>:last-child,.rst-content .caution .last,.rst-content .caution>:last-child,.rst-content .danger .last,.rst-content .danger>:last-child,.rst-content .error .last,.rst-content .error>:last-child,.rst-content .hint .last,.rst-content .hint>:last-child,.rst-content .important .last,.rst-content .important>:last-child,.rst-content .note .last,.rst-content .note>:last-child,.rst-content .seealso .last,.rst-content .seealso>:last-child,.rst-content .tip .last,.rst-content .tip>:last-child,.rst-content .warning .last,.rst-content .warning>:last-child{margin-bottom:0}.rst-content .admonition-title:before{margin-right:4px}.rst-content .admonition table{border-color:rgba(0,0,0,.1)}.rst-content .admonition table td,.rst-content .admonition table th{background:transparent!important;border-color:rgba(0,0,0,.1)!important}.rst-content .section ol.loweralpha,.rst-content .section ol.loweralpha>li,.rst-content .toctree-wrapper ol.loweralpha,.rst-content .toctree-wrapper ol.loweralpha>li,.rst-content section ol.loweralpha,.rst-content section ol.loweralpha>li{list-style:lower-alpha}.rst-content .section ol.upperalpha,.rst-content .section ol.upperalpha>li,.rst-content .toctree-wrapper ol.upperalpha,.rst-content .toctree-wrapper ol.upperalpha>li,.rst-content section ol.upperalpha,.rst-content section ol.upperalpha>li{list-style:upper-alpha}.rst-content .section ol li>*,.rst-content .section ul li>*,.rst-content .toctree-wrapper ol li>*,.rst-content .toctree-wrapper ul li>*,.rst-content section ol li>*,.rst-content section ul li>*{margin-top:12px;margin-bottom:12px}.rst-content .section ol li>:first-child,.rst-content .section ul li>:first-child,.rst-content .toctree-wrapper ol li>:first-child,.rst-content .toctree-wrapper ul li>:first-child,.rst-content section ol li>:first-child,.rst-content section ul li>:first-child{margin-top:0}.rst-content .section ol li>p,.rst-content .section ol li>p:last-child,.rst-content .section ul li>p,.rst-content .section ul li>p:last-child,.rst-content .toctree-wrapper ol li>p,.rst-content .toctree-wrapper ol li>p:last-child,.rst-content .toctree-wrapper ul li>p,.rst-content .toctree-wrapper ul li>p:last-child,.rst-content section ol li>p,.rst-content section ol li>p:last-child,.rst-content section ul li>p,.rst-content section ul li>p:last-child{margin-bottom:12px}.rst-content .section ol li>p:only-child,.rst-content .section ol li>p:only-child:last-child,.rst-content .section ul li>p:only-child,.rst-content .section ul li>p:only-child:last-child,.rst-content .toctree-wrapper ol li>p:only-child,.rst-content .toctree-wrapper ol li>p:only-child:last-child,.rst-content .toctree-wrapper ul li>p:only-child,.rst-content .toctree-wrapper ul li>p:only-child:last-child,.rst-content section ol li>p:only-child,.rst-content section ol li>p:only-child:last-child,.rst-content section ul li>p:only-child,.rst-content section ul li>p:only-child:last-child{margin-bottom:0}.rst-content .section ol li>ol,.rst-content .section ol li>ul,.rst-content .section ul li>ol,.rst-content .section ul li>ul,.rst-content .toctree-wrapper ol li>ol,.rst-content .toctree-wrapper ol li>ul,.rst-content .toctree-wrapper ul li>ol,.rst-content .toctree-wrapper ul li>ul,.rst-content section ol li>ol,.rst-content section ol li>ul,.rst-content section ul li>ol,.rst-content section ul li>ul{margin-bottom:12px}.rst-content .section ol.simple li>*,.rst-content .section ol.simple li ol,.rst-content .section ol.simple li ul,.rst-content .section ul.simple li>*,.rst-content .section ul.simple li ol,.rst-content .section ul.simple li ul,.rst-content .toctree-wrapper ol.simple li>*,.rst-content .toctree-wrapper ol.simple li ol,.rst-content .toctree-wrapper ol.simple li ul,.rst-content .toctree-wrapper ul.simple li>*,.rst-content .toctree-wrapper ul.simple li ol,.rst-content .toctree-wrapper ul.simple li ul,.rst-content section ol.simple li>*,.rst-content section ol.simple li ol,.rst-content section ol.simple li ul,.rst-content section ul.simple li>*,.rst-content section ul.simple li ol,.rst-content section ul.simple li ul{margin-top:0;margin-bottom:0}.rst-content .line-block{margin-left:0;margin-bottom:24px;line-height:24px}.rst-content .line-block .line-block{margin-left:24px;margin-bottom:0}.rst-content .topic-title{font-weight:700;margin-bottom:12px}.rst-content .toc-backref{color:#404040}.rst-content .align-right{float:right;margin:0 0 24px 24px}.rst-content .align-left{float:left;margin:0 24px 24px 0}.rst-content .align-center{margin:auto}.rst-content .align-center:not(table){display:block}.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content .toctree-wrapper>p.caption .headerlink,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink{opacity:0;font-size:14px;font-family:FontAwesome;margin-left:.5em}.rst-content .code-block-caption .headerlink:focus,.rst-content .code-block-caption:hover .headerlink,.rst-content .eqno .headerlink:focus,.rst-content .eqno:hover .headerlink,.rst-content .toctree-wrapper>p.caption .headerlink:focus,.rst-content .toctree-wrapper>p.caption:hover .headerlink,.rst-content dl dt .headerlink:focus,.rst-content dl dt:hover .headerlink,.rst-content h1 .headerlink:focus,.rst-content h1:hover .headerlink,.rst-content h2 .headerlink:focus,.rst-content h2:hover .headerlink,.rst-content h3 .headerlink:focus,.rst-content h3:hover .headerlink,.rst-content h4 .headerlink:focus,.rst-content h4:hover .headerlink,.rst-content h5 .headerlink:focus,.rst-content h5:hover .headerlink,.rst-content h6 .headerlink:focus,.rst-content h6:hover .headerlink,.rst-content p.caption .headerlink:focus,.rst-content p.caption:hover .headerlink,.rst-content p .headerlink:focus,.rst-content p:hover .headerlink,.rst-content table>caption .headerlink:focus,.rst-content table>caption:hover .headerlink{opacity:1}.rst-content p a{overflow-wrap:anywhere}.rst-content .wy-table td p,.rst-content .wy-table td ul,.rst-content .wy-table th p,.rst-content .wy-table th ul,.rst-content table.docutils td p,.rst-content table.docutils td ul,.rst-content table.docutils th p,.rst-content table.docutils th ul,.rst-content table.field-list td p,.rst-content table.field-list td ul,.rst-content table.field-list th p,.rst-content table.field-list th ul{font-size:inherit}.rst-content .btn:focus{outline:2px solid}.rst-content table>caption .headerlink:after{font-size:12px}.rst-content .centered{text-align:center}.rst-content .sidebar{float:right;width:40%;display:block;margin:0 0 24px 24px;padding:24px;background:#f3f6f6;border:1px solid #e1e4e5}.rst-content .sidebar dl,.rst-content .sidebar p,.rst-content .sidebar ul{font-size:90%}.rst-content .sidebar .last,.rst-content .sidebar>:last-child{margin-bottom:0}.rst-content .sidebar .sidebar-title{display:block;font-family:Roboto Slab,ff-tisa-web-pro,Georgia,Arial,sans-serif;font-weight:700;background:#e1e4e5;padding:6px 12px;margin:-24px -24px 24px;font-size:100%}.rst-content .highlighted{background:#f1c40f;box-shadow:0 0 0 2px #f1c40f;display:inline;font-weight:700}.rst-content .citation-reference,.rst-content .footnote-reference{vertical-align:baseline;position:relative;top:-.4em;line-height:0;font-size:90%}.rst-content .citation-reference>span.fn-bracket,.rst-content .footnote-reference>span.fn-bracket{display:none}.rst-content .hlist{width:100%}.rst-content dl dt span.classifier:before{content:" : "}.rst-content dl dt span.classifier-delimiter{display:none!important}html.writer-html4 .rst-content table.docutils.citation,html.writer-html4 .rst-content table.docutils.footnote{background:none;border:none}html.writer-html4 .rst-content table.docutils.citation td,html.writer-html4 .rst-content table.docutils.citation tr,html.writer-html4 .rst-content table.docutils.footnote td,html.writer-html4 .rst-content table.docutils.footnote tr{border:none;background-color:transparent!important;white-space:normal}html.writer-html4 .rst-content table.docutils.citation td.label,html.writer-html4 .rst-content table.docutils.footnote td.label{padding-left:0;padding-right:0;vertical-align:top}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.field-list,html.writer-html5 .rst-content dl.footnote{display:grid;grid-template-columns:auto minmax(80%,95%)}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dt{display:inline-grid;grid-template-columns:max-content auto}html.writer-html5 .rst-content aside.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content div.citation{display:grid;grid-template-columns:auto auto minmax(.65rem,auto) minmax(40%,95%)}html.writer-html5 .rst-content aside.citation>span.label,html.writer-html5 .rst-content aside.footnote>span.label,html.writer-html5 .rst-content div.citation>span.label{grid-column-start:1;grid-column-end:2}html.writer-html5 .rst-content aside.citation>span.backrefs,html.writer-html5 .rst-content aside.footnote>span.backrefs,html.writer-html5 .rst-content div.citation>span.backrefs{grid-column-start:2;grid-column-end:3;grid-row-start:1;grid-row-end:3}html.writer-html5 .rst-content aside.citation>p,html.writer-html5 .rst-content aside.footnote>p,html.writer-html5 .rst-content div.citation>p{grid-column-start:4;grid-column-end:5}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.field-list,html.writer-html5 .rst-content dl.footnote{margin-bottom:24px}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dt{padding-left:1rem}html.writer-html5 .rst-content dl.citation>dd,html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dd,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dd,html.writer-html5 .rst-content dl.footnote>dt{margin-bottom:0}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.footnote{font-size:.9rem}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.footnote>dt{margin:0 .5rem .5rem 0;line-height:1.2rem;word-break:break-all;font-weight:400}html.writer-html5 .rst-content dl.citation>dt>span.brackets:before,html.writer-html5 .rst-content dl.footnote>dt>span.brackets:before{content:"["}html.writer-html5 .rst-content dl.citation>dt>span.brackets:after,html.writer-html5 .rst-content dl.footnote>dt>span.brackets:after{content:"]"}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref{text-align:left;font-style:italic;margin-left:.65rem;word-break:break-word;word-spacing:-.1rem;max-width:5rem}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref>a,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref>a{word-break:keep-all}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref>a:not(:first-child):before,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref>a:not(:first-child):before{content:" "}html.writer-html5 .rst-content dl.citation>dd,html.writer-html5 .rst-content dl.footnote>dd{margin:0 0 .5rem;line-height:1.2rem}html.writer-html5 .rst-content dl.citation>dd p,html.writer-html5 .rst-content dl.footnote>dd p{font-size:.9rem}html.writer-html5 .rst-content aside.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content div.citation{padding-left:1rem;padding-right:1rem;font-size:.9rem;line-height:1.2rem}html.writer-html5 .rst-content aside.citation p,html.writer-html5 .rst-content aside.footnote p,html.writer-html5 .rst-content div.citation p{font-size:.9rem;line-height:1.2rem;margin-bottom:12px}html.writer-html5 .rst-content aside.citation span.backrefs,html.writer-html5 .rst-content aside.footnote span.backrefs,html.writer-html5 .rst-content div.citation span.backrefs{text-align:left;font-style:italic;margin-left:.65rem;word-break:break-word;word-spacing:-.1rem;max-width:5rem}html.writer-html5 .rst-content aside.citation span.backrefs>a,html.writer-html5 .rst-content aside.footnote span.backrefs>a,html.writer-html5 .rst-content div.citation span.backrefs>a{word-break:keep-all}html.writer-html5 .rst-content aside.citation span.backrefs>a:not(:first-child):before,html.writer-html5 .rst-content aside.footnote span.backrefs>a:not(:first-child):before,html.writer-html5 .rst-content div.citation span.backrefs>a:not(:first-child):before{content:" "}html.writer-html5 .rst-content aside.citation span.label,html.writer-html5 .rst-content aside.footnote span.label,html.writer-html5 .rst-content div.citation span.label{line-height:1.2rem}html.writer-html5 .rst-content aside.citation-list,html.writer-html5 .rst-content aside.footnote-list,html.writer-html5 .rst-content div.citation-list{margin-bottom:24px}html.writer-html5 .rst-content dl.option-list kbd{font-size:.9rem}.rst-content table.docutils.footnote,html.writer-html4 .rst-content table.docutils.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content aside.footnote-list aside.footnote,html.writer-html5 .rst-content div.citation-list>div.citation,html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.footnote{color:grey}.rst-content table.docutils.footnote code,.rst-content table.docutils.footnote tt,html.writer-html4 .rst-content table.docutils.citation code,html.writer-html4 .rst-content table.docutils.citation tt,html.writer-html5 .rst-content aside.footnote-list aside.footnote code,html.writer-html5 .rst-content aside.footnote-list aside.footnote tt,html.writer-html5 .rst-content aside.footnote code,html.writer-html5 .rst-content aside.footnote tt,html.writer-html5 .rst-content div.citation-list>div.citation code,html.writer-html5 .rst-content div.citation-list>div.citation tt,html.writer-html5 .rst-content dl.citation code,html.writer-html5 .rst-content dl.citation tt,html.writer-html5 .rst-content dl.footnote code,html.writer-html5 .rst-content dl.footnote tt{color:#555}.rst-content .wy-table-responsive.citation,.rst-content .wy-table-responsive.footnote{margin-bottom:0}.rst-content .wy-table-responsive.citation+:not(.citation),.rst-content .wy-table-responsive.footnote+:not(.footnote){margin-top:24px}.rst-content .wy-table-responsive.citation:last-child,.rst-content .wy-table-responsive.footnote:last-child{margin-bottom:24px}.rst-content table.docutils th{border-color:#e1e4e5}html.writer-html5 .rst-content table.docutils th{border:1px solid #e1e4e5}html.writer-html5 .rst-content table.docutils td>p,html.writer-html5 .rst-content table.docutils th>p{line-height:1rem;margin-bottom:0;font-size:.9rem}.rst-content table.docutils td .last,.rst-content table.docutils td .last>:last-child{margin-bottom:0}.rst-content table.field-list,.rst-content table.field-list td{border:none}.rst-content table.field-list td p{line-height:inherit}.rst-content table.field-list td>strong{display:inline-block}.rst-content table.field-list .field-name{padding-right:10px;text-align:left;white-space:nowrap}.rst-content table.field-list .field-body{text-align:left}.rst-content code,.rst-content tt{color:#000;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;padding:2px 5px}.rst-content code big,.rst-content code em,.rst-content tt big,.rst-content tt em{font-size:100%!important;line-height:normal}.rst-content code.literal,.rst-content tt.literal{color:#e74c3c;white-space:normal}.rst-content code.xref,.rst-content tt.xref,a .rst-content code,a .rst-content tt{font-weight:700;color:#404040;overflow-wrap:normal}.rst-content kbd,.rst-content pre,.rst-content samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace}.rst-content a code,.rst-content a tt{color:#2980b9}.rst-content dl{margin-bottom:24px}.rst-content dl dt{font-weight:700;margin-bottom:12px}.rst-content dl ol,.rst-content dl p,.rst-content dl table,.rst-content dl ul{margin-bottom:12px}.rst-content dl dd{margin:0 0 12px 24px;line-height:24px}.rst-content dl dd>ol:last-child,.rst-content dl dd>p:last-child,.rst-content dl dd>table:last-child,.rst-content dl dd>ul:last-child{margin-bottom:0}html.writer-html4 .rst-content dl:not(.docutils),html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple){margin-bottom:24px}html.writer-html4 .rst-content dl:not(.docutils)>dt,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{display:table;margin:6px 0;font-size:90%;line-height:normal;background:#e7f2fa;color:#2980b9;border-top:3px solid #6ab0de;padding:6px;position:relative}html.writer-html4 .rst-content dl:not(.docutils)>dt:before,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:before{color:#6ab0de}html.writer-html4 .rst-content dl:not(.docutils)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{margin-bottom:6px;border:none;border-left:3px solid #ccc;background:#f0f0f0;color:#555}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils)>dt:first-child,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .property{display:inline-block;padding-right:8px;max-width:100%}html.writer-html4 .rst-content dl:not(.docutils) .k,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel{border:1px solid #7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block} \ No newline at end of file + */@font-face{font-family:FontAwesome;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713);src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix&v=4.7.0) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#fontawesomeregular) format("svg");font-weight:400;font-style:normal}.fa,.icon,.rst-content .admonition-title,.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content code.download span:first-child,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink,.rst-content tt.download span:first-child,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li button.toctree-expand{display:inline-block;font:normal normal normal 14px/1 FontAwesome;font-size:inherit;text-rendering:auto;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.fa-lg{font-size:1.33333em;line-height:.75em;vertical-align:-15%}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-fw{width:1.28571em;text-align:center}.fa-ul{padding-left:0;margin-left:2.14286em;list-style-type:none}.fa-ul>li{position:relative}.fa-li{position:absolute;left:-2.14286em;width:2.14286em;top:.14286em;text-align:center}.fa-li.fa-lg{left:-1.85714em}.fa-border{padding:.2em .25em .15em;border:.08em solid #eee;border-radius:.1em}.fa-pull-left{float:left}.fa-pull-right{float:right}.fa-pull-left.icon,.fa.fa-pull-left,.rst-content .code-block-caption .fa-pull-left.headerlink,.rst-content .eqno .fa-pull-left.headerlink,.rst-content .fa-pull-left.admonition-title,.rst-content code.download span.fa-pull-left:first-child,.rst-content dl dt .fa-pull-left.headerlink,.rst-content h1 .fa-pull-left.headerlink,.rst-content h2 .fa-pull-left.headerlink,.rst-content h3 .fa-pull-left.headerlink,.rst-content h4 .fa-pull-left.headerlink,.rst-content h5 .fa-pull-left.headerlink,.rst-content h6 .fa-pull-left.headerlink,.rst-content p .fa-pull-left.headerlink,.rst-content table>caption .fa-pull-left.headerlink,.rst-content tt.download span.fa-pull-left:first-child,.wy-menu-vertical li.current>a button.fa-pull-left.toctree-expand,.wy-menu-vertical li.on a button.fa-pull-left.toctree-expand,.wy-menu-vertical li button.fa-pull-left.toctree-expand{margin-right:.3em}.fa-pull-right.icon,.fa.fa-pull-right,.rst-content .code-block-caption .fa-pull-right.headerlink,.rst-content .eqno .fa-pull-right.headerlink,.rst-content .fa-pull-right.admonition-title,.rst-content code.download span.fa-pull-right:first-child,.rst-content dl dt .fa-pull-right.headerlink,.rst-content h1 .fa-pull-right.headerlink,.rst-content h2 .fa-pull-right.headerlink,.rst-content h3 .fa-pull-right.headerlink,.rst-content h4 .fa-pull-right.headerlink,.rst-content h5 .fa-pull-right.headerlink,.rst-content h6 .fa-pull-right.headerlink,.rst-content p .fa-pull-right.headerlink,.rst-content table>caption .fa-pull-right.headerlink,.rst-content tt.download span.fa-pull-right:first-child,.wy-menu-vertical li.current>a button.fa-pull-right.toctree-expand,.wy-menu-vertical li.on a button.fa-pull-right.toctree-expand,.wy-menu-vertical li button.fa-pull-right.toctree-expand{margin-left:.3em}.pull-right{float:right}.pull-left{float:left}.fa.pull-left,.pull-left.icon,.rst-content .code-block-caption .pull-left.headerlink,.rst-content .eqno .pull-left.headerlink,.rst-content .pull-left.admonition-title,.rst-content code.download span.pull-left:first-child,.rst-content dl dt .pull-left.headerlink,.rst-content h1 .pull-left.headerlink,.rst-content h2 .pull-left.headerlink,.rst-content h3 .pull-left.headerlink,.rst-content h4 .pull-left.headerlink,.rst-content h5 .pull-left.headerlink,.rst-content h6 .pull-left.headerlink,.rst-content p .pull-left.headerlink,.rst-content table>caption .pull-left.headerlink,.rst-content tt.download span.pull-left:first-child,.wy-menu-vertical li.current>a button.pull-left.toctree-expand,.wy-menu-vertical li.on a button.pull-left.toctree-expand,.wy-menu-vertical li button.pull-left.toctree-expand{margin-right:.3em}.fa.pull-right,.pull-right.icon,.rst-content .code-block-caption .pull-right.headerlink,.rst-content .eqno .pull-right.headerlink,.rst-content .pull-right.admonition-title,.rst-content code.download span.pull-right:first-child,.rst-content dl dt .pull-right.headerlink,.rst-content h1 .pull-right.headerlink,.rst-content h2 .pull-right.headerlink,.rst-content h3 .pull-right.headerlink,.rst-content h4 .pull-right.headerlink,.rst-content h5 .pull-right.headerlink,.rst-content h6 .pull-right.headerlink,.rst-content p .pull-right.headerlink,.rst-content table>caption .pull-right.headerlink,.rst-content tt.download span.pull-right:first-child,.wy-menu-vertical li.current>a button.pull-right.toctree-expand,.wy-menu-vertical li.on a button.pull-right.toctree-expand,.wy-menu-vertical li button.pull-right.toctree-expand{margin-left:.3em}.fa-spin{-webkit-animation:fa-spin 2s linear infinite;animation:fa-spin 2s linear infinite}.fa-pulse{-webkit-animation:fa-spin 1s steps(8) infinite;animation:fa-spin 1s steps(8) infinite}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.fa-rotate-90{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";-webkit-transform:scaleX(-1);-ms-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-ms-filter:"progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";-webkit-transform:scaleY(-1);-ms-transform:scaleY(-1);transform:scaleY(-1)}:root .fa-flip-horizontal,:root .fa-flip-vertical,:root .fa-rotate-90,:root .fa-rotate-180,:root .fa-rotate-270{filter:none}.fa-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:middle}.fa-stack-1x,.fa-stack-2x{position:absolute;left:0;width:100%;text-align:center}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:#fff}.fa-glass:before{content:""}.fa-music:before{content:""}.fa-search:before,.icon-search:before{content:""}.fa-envelope-o:before{content:""}.fa-heart:before{content:""}.fa-star:before{content:""}.fa-star-o:before{content:""}.fa-user:before{content:""}.fa-film:before{content:""}.fa-th-large:before{content:""}.fa-th:before{content:""}.fa-th-list:before{content:""}.fa-check:before{content:""}.fa-close:before,.fa-remove:before,.fa-times:before{content:""}.fa-search-plus:before{content:""}.fa-search-minus:before{content:""}.fa-power-off:before{content:""}.fa-signal:before{content:""}.fa-cog:before,.fa-gear:before{content:""}.fa-trash-o:before{content:""}.fa-home:before,.icon-home:before{content:""}.fa-file-o:before{content:""}.fa-clock-o:before{content:""}.fa-road:before{content:""}.fa-download:before,.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{content:""}.fa-arrow-circle-o-down:before{content:""}.fa-arrow-circle-o-up:before{content:""}.fa-inbox:before{content:""}.fa-play-circle-o:before{content:""}.fa-repeat:before,.fa-rotate-right:before{content:""}.fa-refresh:before{content:""}.fa-list-alt:before{content:""}.fa-lock:before{content:""}.fa-flag:before{content:""}.fa-headphones:before{content:""}.fa-volume-off:before{content:""}.fa-volume-down:before{content:""}.fa-volume-up:before{content:""}.fa-qrcode:before{content:""}.fa-barcode:before{content:""}.fa-tag:before{content:""}.fa-tags:before{content:""}.fa-book:before,.icon-book:before{content:""}.fa-bookmark:before{content:""}.fa-print:before{content:""}.fa-camera:before{content:""}.fa-font:before{content:""}.fa-bold:before{content:""}.fa-italic:before{content:""}.fa-text-height:before{content:""}.fa-text-width:before{content:""}.fa-align-left:before{content:""}.fa-align-center:before{content:""}.fa-align-right:before{content:""}.fa-align-justify:before{content:""}.fa-list:before{content:""}.fa-dedent:before,.fa-outdent:before{content:""}.fa-indent:before{content:""}.fa-video-camera:before{content:""}.fa-image:before,.fa-photo:before,.fa-picture-o:before{content:""}.fa-pencil:before{content:""}.fa-map-marker:before{content:""}.fa-adjust:before{content:""}.fa-tint:before{content:""}.fa-edit:before,.fa-pencil-square-o:before{content:""}.fa-share-square-o:before{content:""}.fa-check-square-o:before{content:""}.fa-arrows:before{content:""}.fa-step-backward:before{content:""}.fa-fast-backward:before{content:""}.fa-backward:before{content:""}.fa-play:before{content:""}.fa-pause:before{content:""}.fa-stop:before{content:""}.fa-forward:before{content:""}.fa-fast-forward:before{content:""}.fa-step-forward:before{content:""}.fa-eject:before{content:""}.fa-chevron-left:before{content:""}.fa-chevron-right:before{content:""}.fa-plus-circle:before{content:""}.fa-minus-circle:before{content:""}.fa-times-circle:before,.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before{content:""}.fa-check-circle:before,.wy-inline-validate.wy-inline-validate-success .wy-input-context:before{content:""}.fa-question-circle:before{content:""}.fa-info-circle:before{content:""}.fa-crosshairs:before{content:""}.fa-times-circle-o:before{content:""}.fa-check-circle-o:before{content:""}.fa-ban:before{content:""}.fa-arrow-left:before{content:""}.fa-arrow-right:before{content:""}.fa-arrow-up:before{content:""}.fa-arrow-down:before{content:""}.fa-mail-forward:before,.fa-share:before{content:""}.fa-expand:before{content:""}.fa-compress:before{content:""}.fa-plus:before{content:""}.fa-minus:before{content:""}.fa-asterisk:before{content:""}.fa-exclamation-circle:before,.rst-content .admonition-title:before,.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before{content:""}.fa-gift:before{content:""}.fa-leaf:before{content:""}.fa-fire:before,.icon-fire:before{content:""}.fa-eye:before{content:""}.fa-eye-slash:before{content:""}.fa-exclamation-triangle:before,.fa-warning:before{content:""}.fa-plane:before{content:""}.fa-calendar:before{content:""}.fa-random:before{content:""}.fa-comment:before{content:""}.fa-magnet:before{content:""}.fa-chevron-up:before{content:""}.fa-chevron-down:before{content:""}.fa-retweet:before{content:""}.fa-shopping-cart:before{content:""}.fa-folder:before{content:""}.fa-folder-open:before{content:""}.fa-arrows-v:before{content:""}.fa-arrows-h:before{content:""}.fa-bar-chart-o:before,.fa-bar-chart:before{content:""}.fa-twitter-square:before{content:""}.fa-facebook-square:before{content:""}.fa-camera-retro:before{content:""}.fa-key:before{content:""}.fa-cogs:before,.fa-gears:before{content:""}.fa-comments:before{content:""}.fa-thumbs-o-up:before{content:""}.fa-thumbs-o-down:before{content:""}.fa-star-half:before{content:""}.fa-heart-o:before{content:""}.fa-sign-out:before{content:""}.fa-linkedin-square:before{content:""}.fa-thumb-tack:before{content:""}.fa-external-link:before{content:""}.fa-sign-in:before{content:""}.fa-trophy:before{content:""}.fa-github-square:before{content:""}.fa-upload:before{content:""}.fa-lemon-o:before{content:""}.fa-phone:before{content:""}.fa-square-o:before{content:""}.fa-bookmark-o:before{content:""}.fa-phone-square:before{content:""}.fa-twitter:before{content:""}.fa-facebook-f:before,.fa-facebook:before{content:""}.fa-github:before,.icon-github:before{content:""}.fa-unlock:before{content:""}.fa-credit-card:before{content:""}.fa-feed:before,.fa-rss:before{content:""}.fa-hdd-o:before{content:""}.fa-bullhorn:before{content:""}.fa-bell:before{content:""}.fa-certificate:before{content:""}.fa-hand-o-right:before{content:""}.fa-hand-o-left:before{content:""}.fa-hand-o-up:before{content:""}.fa-hand-o-down:before{content:""}.fa-arrow-circle-left:before,.icon-circle-arrow-left:before{content:""}.fa-arrow-circle-right:before,.icon-circle-arrow-right:before{content:""}.fa-arrow-circle-up:before{content:""}.fa-arrow-circle-down:before{content:""}.fa-globe:before{content:""}.fa-wrench:before{content:""}.fa-tasks:before{content:""}.fa-filter:before{content:""}.fa-briefcase:before{content:""}.fa-arrows-alt:before{content:""}.fa-group:before,.fa-users:before{content:""}.fa-chain:before,.fa-link:before,.icon-link:before{content:""}.fa-cloud:before{content:""}.fa-flask:before{content:""}.fa-cut:before,.fa-scissors:before{content:""}.fa-copy:before,.fa-files-o:before{content:""}.fa-paperclip:before{content:""}.fa-floppy-o:before,.fa-save:before{content:""}.fa-square:before{content:""}.fa-bars:before,.fa-navicon:before,.fa-reorder:before{content:""}.fa-list-ul:before{content:""}.fa-list-ol:before{content:""}.fa-strikethrough:before{content:""}.fa-underline:before{content:""}.fa-table:before{content:""}.fa-magic:before{content:""}.fa-truck:before{content:""}.fa-pinterest:before{content:""}.fa-pinterest-square:before{content:""}.fa-google-plus-square:before{content:""}.fa-google-plus:before{content:""}.fa-money:before{content:""}.fa-caret-down:before,.icon-caret-down:before,.wy-dropdown .caret:before{content:""}.fa-caret-up:before{content:""}.fa-caret-left:before{content:""}.fa-caret-right:before{content:""}.fa-columns:before{content:""}.fa-sort:before,.fa-unsorted:before{content:""}.fa-sort-desc:before,.fa-sort-down:before{content:""}.fa-sort-asc:before,.fa-sort-up:before{content:""}.fa-envelope:before{content:""}.fa-linkedin:before{content:""}.fa-rotate-left:before,.fa-undo:before{content:""}.fa-gavel:before,.fa-legal:before{content:""}.fa-dashboard:before,.fa-tachometer:before{content:""}.fa-comment-o:before{content:""}.fa-comments-o:before{content:""}.fa-bolt:before,.fa-flash:before{content:""}.fa-sitemap:before{content:""}.fa-umbrella:before{content:""}.fa-clipboard:before,.fa-paste:before{content:""}.fa-lightbulb-o:before{content:""}.fa-exchange:before{content:""}.fa-cloud-download:before{content:""}.fa-cloud-upload:before{content:""}.fa-user-md:before{content:""}.fa-stethoscope:before{content:""}.fa-suitcase:before{content:""}.fa-bell-o:before{content:""}.fa-coffee:before{content:""}.fa-cutlery:before{content:""}.fa-file-text-o:before{content:""}.fa-building-o:before{content:""}.fa-hospital-o:before{content:""}.fa-ambulance:before{content:""}.fa-medkit:before{content:""}.fa-fighter-jet:before{content:""}.fa-beer:before{content:""}.fa-h-square:before{content:""}.fa-plus-square:before{content:""}.fa-angle-double-left:before{content:""}.fa-angle-double-right:before{content:""}.fa-angle-double-up:before{content:""}.fa-angle-double-down:before{content:""}.fa-angle-left:before{content:""}.fa-angle-right:before{content:""}.fa-angle-up:before{content:""}.fa-angle-down:before{content:""}.fa-desktop:before{content:""}.fa-laptop:before{content:""}.fa-tablet:before{content:""}.fa-mobile-phone:before,.fa-mobile:before{content:""}.fa-circle-o:before{content:""}.fa-quote-left:before{content:""}.fa-quote-right:before{content:""}.fa-spinner:before{content:""}.fa-circle:before{content:""}.fa-mail-reply:before,.fa-reply:before{content:""}.fa-github-alt:before{content:""}.fa-folder-o:before{content:""}.fa-folder-open-o:before{content:""}.fa-smile-o:before{content:""}.fa-frown-o:before{content:""}.fa-meh-o:before{content:""}.fa-gamepad:before{content:""}.fa-keyboard-o:before{content:""}.fa-flag-o:before{content:""}.fa-flag-checkered:before{content:""}.fa-terminal:before{content:""}.fa-code:before{content:""}.fa-mail-reply-all:before,.fa-reply-all:before{content:""}.fa-star-half-empty:before,.fa-star-half-full:before,.fa-star-half-o:before{content:""}.fa-location-arrow:before{content:""}.fa-crop:before{content:""}.fa-code-fork:before{content:""}.fa-chain-broken:before,.fa-unlink:before{content:""}.fa-question:before{content:""}.fa-info:before{content:""}.fa-exclamation:before{content:""}.fa-superscript:before{content:""}.fa-subscript:before{content:""}.fa-eraser:before{content:""}.fa-puzzle-piece:before{content:""}.fa-microphone:before{content:""}.fa-microphone-slash:before{content:""}.fa-shield:before{content:""}.fa-calendar-o:before{content:""}.fa-fire-extinguisher:before{content:""}.fa-rocket:before{content:""}.fa-maxcdn:before{content:""}.fa-chevron-circle-left:before{content:""}.fa-chevron-circle-right:before{content:""}.fa-chevron-circle-up:before{content:""}.fa-chevron-circle-down:before{content:""}.fa-html5:before{content:""}.fa-css3:before{content:""}.fa-anchor:before{content:""}.fa-unlock-alt:before{content:""}.fa-bullseye:before{content:""}.fa-ellipsis-h:before{content:""}.fa-ellipsis-v:before{content:""}.fa-rss-square:before{content:""}.fa-play-circle:before{content:""}.fa-ticket:before{content:""}.fa-minus-square:before{content:""}.fa-minus-square-o:before,.wy-menu-vertical li.current>a button.toctree-expand:before,.wy-menu-vertical li.on a button.toctree-expand:before{content:""}.fa-level-up:before{content:""}.fa-level-down:before{content:""}.fa-check-square:before{content:""}.fa-pencil-square:before{content:""}.fa-external-link-square:before{content:""}.fa-share-square:before{content:""}.fa-compass:before{content:""}.fa-caret-square-o-down:before,.fa-toggle-down:before{content:""}.fa-caret-square-o-up:before,.fa-toggle-up:before{content:""}.fa-caret-square-o-right:before,.fa-toggle-right:before{content:""}.fa-eur:before,.fa-euro:before{content:""}.fa-gbp:before{content:""}.fa-dollar:before,.fa-usd:before{content:""}.fa-inr:before,.fa-rupee:before{content:""}.fa-cny:before,.fa-jpy:before,.fa-rmb:before,.fa-yen:before{content:""}.fa-rouble:before,.fa-rub:before,.fa-ruble:before{content:""}.fa-krw:before,.fa-won:before{content:""}.fa-bitcoin:before,.fa-btc:before{content:""}.fa-file:before{content:""}.fa-file-text:before{content:""}.fa-sort-alpha-asc:before{content:""}.fa-sort-alpha-desc:before{content:""}.fa-sort-amount-asc:before{content:""}.fa-sort-amount-desc:before{content:""}.fa-sort-numeric-asc:before{content:""}.fa-sort-numeric-desc:before{content:""}.fa-thumbs-up:before{content:""}.fa-thumbs-down:before{content:""}.fa-youtube-square:before{content:""}.fa-youtube:before{content:""}.fa-xing:before{content:""}.fa-xing-square:before{content:""}.fa-youtube-play:before{content:""}.fa-dropbox:before{content:""}.fa-stack-overflow:before{content:""}.fa-instagram:before{content:""}.fa-flickr:before{content:""}.fa-adn:before{content:""}.fa-bitbucket:before,.icon-bitbucket:before{content:""}.fa-bitbucket-square:before{content:""}.fa-tumblr:before{content:""}.fa-tumblr-square:before{content:""}.fa-long-arrow-down:before{content:""}.fa-long-arrow-up:before{content:""}.fa-long-arrow-left:before{content:""}.fa-long-arrow-right:before{content:""}.fa-apple:before{content:""}.fa-windows:before{content:""}.fa-android:before{content:""}.fa-linux:before{content:""}.fa-dribbble:before{content:""}.fa-skype:before{content:""}.fa-foursquare:before{content:""}.fa-trello:before{content:""}.fa-female:before{content:""}.fa-male:before{content:""}.fa-gittip:before,.fa-gratipay:before{content:""}.fa-sun-o:before{content:""}.fa-moon-o:before{content:""}.fa-archive:before{content:""}.fa-bug:before{content:""}.fa-vk:before{content:""}.fa-weibo:before{content:""}.fa-renren:before{content:""}.fa-pagelines:before{content:""}.fa-stack-exchange:before{content:""}.fa-arrow-circle-o-right:before{content:""}.fa-arrow-circle-o-left:before{content:""}.fa-caret-square-o-left:before,.fa-toggle-left:before{content:""}.fa-dot-circle-o:before{content:""}.fa-wheelchair:before{content:""}.fa-vimeo-square:before{content:""}.fa-try:before,.fa-turkish-lira:before{content:""}.fa-plus-square-o:before,.wy-menu-vertical li button.toctree-expand:before{content:""}.fa-space-shuttle:before{content:""}.fa-slack:before{content:""}.fa-envelope-square:before{content:""}.fa-wordpress:before{content:""}.fa-openid:before{content:""}.fa-bank:before,.fa-institution:before,.fa-university:before{content:""}.fa-graduation-cap:before,.fa-mortar-board:before{content:""}.fa-yahoo:before{content:""}.fa-google:before{content:""}.fa-reddit:before{content:""}.fa-reddit-square:before{content:""}.fa-stumbleupon-circle:before{content:""}.fa-stumbleupon:before{content:""}.fa-delicious:before{content:""}.fa-digg:before{content:""}.fa-pied-piper-pp:before{content:""}.fa-pied-piper-alt:before{content:""}.fa-drupal:before{content:""}.fa-joomla:before{content:""}.fa-language:before{content:""}.fa-fax:before{content:""}.fa-building:before{content:""}.fa-child:before{content:""}.fa-paw:before{content:""}.fa-spoon:before{content:""}.fa-cube:before{content:""}.fa-cubes:before{content:""}.fa-behance:before{content:""}.fa-behance-square:before{content:""}.fa-steam:before{content:""}.fa-steam-square:before{content:""}.fa-recycle:before{content:""}.fa-automobile:before,.fa-car:before{content:""}.fa-cab:before,.fa-taxi:before{content:""}.fa-tree:before{content:""}.fa-spotify:before{content:""}.fa-deviantart:before{content:""}.fa-soundcloud:before{content:""}.fa-database:before{content:""}.fa-file-pdf-o:before{content:""}.fa-file-word-o:before{content:""}.fa-file-excel-o:before{content:""}.fa-file-powerpoint-o:before{content:""}.fa-file-image-o:before,.fa-file-photo-o:before,.fa-file-picture-o:before{content:""}.fa-file-archive-o:before,.fa-file-zip-o:before{content:""}.fa-file-audio-o:before,.fa-file-sound-o:before{content:""}.fa-file-movie-o:before,.fa-file-video-o:before{content:""}.fa-file-code-o:before{content:""}.fa-vine:before{content:""}.fa-codepen:before{content:""}.fa-jsfiddle:before{content:""}.fa-life-bouy:before,.fa-life-buoy:before,.fa-life-ring:before,.fa-life-saver:before,.fa-support:before{content:""}.fa-circle-o-notch:before{content:""}.fa-ra:before,.fa-rebel:before,.fa-resistance:before{content:""}.fa-empire:before,.fa-ge:before{content:""}.fa-git-square:before{content:""}.fa-git:before{content:""}.fa-hacker-news:before,.fa-y-combinator-square:before,.fa-yc-square:before{content:""}.fa-tencent-weibo:before{content:""}.fa-qq:before{content:""}.fa-wechat:before,.fa-weixin:before{content:""}.fa-paper-plane:before,.fa-send:before{content:""}.fa-paper-plane-o:before,.fa-send-o:before{content:""}.fa-history:before{content:""}.fa-circle-thin:before{content:""}.fa-header:before{content:""}.fa-paragraph:before{content:""}.fa-sliders:before{content:""}.fa-share-alt:before{content:""}.fa-share-alt-square:before{content:""}.fa-bomb:before{content:""}.fa-futbol-o:before,.fa-soccer-ball-o:before{content:""}.fa-tty:before{content:""}.fa-binoculars:before{content:""}.fa-plug:before{content:""}.fa-slideshare:before{content:""}.fa-twitch:before{content:""}.fa-yelp:before{content:""}.fa-newspaper-o:before{content:""}.fa-wifi:before{content:""}.fa-calculator:before{content:""}.fa-paypal:before{content:""}.fa-google-wallet:before{content:""}.fa-cc-visa:before{content:""}.fa-cc-mastercard:before{content:""}.fa-cc-discover:before{content:""}.fa-cc-amex:before{content:""}.fa-cc-paypal:before{content:""}.fa-cc-stripe:before{content:""}.fa-bell-slash:before{content:""}.fa-bell-slash-o:before{content:""}.fa-trash:before{content:""}.fa-copyright:before{content:""}.fa-at:before{content:""}.fa-eyedropper:before{content:""}.fa-paint-brush:before{content:""}.fa-birthday-cake:before{content:""}.fa-area-chart:before{content:""}.fa-pie-chart:before{content:""}.fa-line-chart:before{content:""}.fa-lastfm:before{content:""}.fa-lastfm-square:before{content:""}.fa-toggle-off:before{content:""}.fa-toggle-on:before{content:""}.fa-bicycle:before{content:""}.fa-bus:before{content:""}.fa-ioxhost:before{content:""}.fa-angellist:before{content:""}.fa-cc:before{content:""}.fa-ils:before,.fa-shekel:before,.fa-sheqel:before{content:""}.fa-meanpath:before{content:""}.fa-buysellads:before{content:""}.fa-connectdevelop:before{content:""}.fa-dashcube:before{content:""}.fa-forumbee:before{content:""}.fa-leanpub:before{content:""}.fa-sellsy:before{content:""}.fa-shirtsinbulk:before{content:""}.fa-simplybuilt:before{content:""}.fa-skyatlas:before{content:""}.fa-cart-plus:before{content:""}.fa-cart-arrow-down:before{content:""}.fa-diamond:before{content:""}.fa-ship:before{content:""}.fa-user-secret:before{content:""}.fa-motorcycle:before{content:""}.fa-street-view:before{content:""}.fa-heartbeat:before{content:""}.fa-venus:before{content:""}.fa-mars:before{content:""}.fa-mercury:before{content:""}.fa-intersex:before,.fa-transgender:before{content:""}.fa-transgender-alt:before{content:""}.fa-venus-double:before{content:""}.fa-mars-double:before{content:""}.fa-venus-mars:before{content:""}.fa-mars-stroke:before{content:""}.fa-mars-stroke-v:before{content:""}.fa-mars-stroke-h:before{content:""}.fa-neuter:before{content:""}.fa-genderless:before{content:""}.fa-facebook-official:before{content:""}.fa-pinterest-p:before{content:""}.fa-whatsapp:before{content:""}.fa-server:before{content:""}.fa-user-plus:before{content:""}.fa-user-times:before{content:""}.fa-bed:before,.fa-hotel:before{content:""}.fa-viacoin:before{content:""}.fa-train:before{content:""}.fa-subway:before{content:""}.fa-medium:before{content:""}.fa-y-combinator:before,.fa-yc:before{content:""}.fa-optin-monster:before{content:""}.fa-opencart:before{content:""}.fa-expeditedssl:before{content:""}.fa-battery-4:before,.fa-battery-full:before,.fa-battery:before{content:""}.fa-battery-3:before,.fa-battery-three-quarters:before{content:""}.fa-battery-2:before,.fa-battery-half:before{content:""}.fa-battery-1:before,.fa-battery-quarter:before{content:""}.fa-battery-0:before,.fa-battery-empty:before{content:""}.fa-mouse-pointer:before{content:""}.fa-i-cursor:before{content:""}.fa-object-group:before{content:""}.fa-object-ungroup:before{content:""}.fa-sticky-note:before{content:""}.fa-sticky-note-o:before{content:""}.fa-cc-jcb:before{content:""}.fa-cc-diners-club:before{content:""}.fa-clone:before{content:""}.fa-balance-scale:before{content:""}.fa-hourglass-o:before{content:""}.fa-hourglass-1:before,.fa-hourglass-start:before{content:""}.fa-hourglass-2:before,.fa-hourglass-half:before{content:""}.fa-hourglass-3:before,.fa-hourglass-end:before{content:""}.fa-hourglass:before{content:""}.fa-hand-grab-o:before,.fa-hand-rock-o:before{content:""}.fa-hand-paper-o:before,.fa-hand-stop-o:before{content:""}.fa-hand-scissors-o:before{content:""}.fa-hand-lizard-o:before{content:""}.fa-hand-spock-o:before{content:""}.fa-hand-pointer-o:before{content:""}.fa-hand-peace-o:before{content:""}.fa-trademark:before{content:""}.fa-registered:before{content:""}.fa-creative-commons:before{content:""}.fa-gg:before{content:""}.fa-gg-circle:before{content:""}.fa-tripadvisor:before{content:""}.fa-odnoklassniki:before{content:""}.fa-odnoklassniki-square:before{content:""}.fa-get-pocket:before{content:""}.fa-wikipedia-w:before{content:""}.fa-safari:before{content:""}.fa-chrome:before{content:""}.fa-firefox:before{content:""}.fa-opera:before{content:""}.fa-internet-explorer:before{content:""}.fa-television:before,.fa-tv:before{content:""}.fa-contao:before{content:""}.fa-500px:before{content:""}.fa-amazon:before{content:""}.fa-calendar-plus-o:before{content:""}.fa-calendar-minus-o:before{content:""}.fa-calendar-times-o:before{content:""}.fa-calendar-check-o:before{content:""}.fa-industry:before{content:""}.fa-map-pin:before{content:""}.fa-map-signs:before{content:""}.fa-map-o:before{content:""}.fa-map:before{content:""}.fa-commenting:before{content:""}.fa-commenting-o:before{content:""}.fa-houzz:before{content:""}.fa-vimeo:before{content:""}.fa-black-tie:before{content:""}.fa-fonticons:before{content:""}.fa-reddit-alien:before{content:""}.fa-edge:before{content:""}.fa-credit-card-alt:before{content:""}.fa-codiepie:before{content:""}.fa-modx:before{content:""}.fa-fort-awesome:before{content:""}.fa-usb:before{content:""}.fa-product-hunt:before{content:""}.fa-mixcloud:before{content:""}.fa-scribd:before{content:""}.fa-pause-circle:before{content:""}.fa-pause-circle-o:before{content:""}.fa-stop-circle:before{content:""}.fa-stop-circle-o:before{content:""}.fa-shopping-bag:before{content:""}.fa-shopping-basket:before{content:""}.fa-hashtag:before{content:""}.fa-bluetooth:before{content:""}.fa-bluetooth-b:before{content:""}.fa-percent:before{content:""}.fa-gitlab:before,.icon-gitlab:before{content:""}.fa-wpbeginner:before{content:""}.fa-wpforms:before{content:""}.fa-envira:before{content:""}.fa-universal-access:before{content:""}.fa-wheelchair-alt:before{content:""}.fa-question-circle-o:before{content:""}.fa-blind:before{content:""}.fa-audio-description:before{content:""}.fa-volume-control-phone:before{content:""}.fa-braille:before{content:""}.fa-assistive-listening-systems:before{content:""}.fa-american-sign-language-interpreting:before,.fa-asl-interpreting:before{content:""}.fa-deaf:before,.fa-deafness:before,.fa-hard-of-hearing:before{content:""}.fa-glide:before{content:""}.fa-glide-g:before{content:""}.fa-sign-language:before,.fa-signing:before{content:""}.fa-low-vision:before{content:""}.fa-viadeo:before{content:""}.fa-viadeo-square:before{content:""}.fa-snapchat:before{content:""}.fa-snapchat-ghost:before{content:""}.fa-snapchat-square:before{content:""}.fa-pied-piper:before{content:""}.fa-first-order:before{content:""}.fa-yoast:before{content:""}.fa-themeisle:before{content:""}.fa-google-plus-circle:before,.fa-google-plus-official:before{content:""}.fa-fa:before,.fa-font-awesome:before{content:""}.fa-handshake-o:before{content:""}.fa-envelope-open:before{content:""}.fa-envelope-open-o:before{content:""}.fa-linode:before{content:""}.fa-address-book:before{content:""}.fa-address-book-o:before{content:""}.fa-address-card:before,.fa-vcard:before{content:""}.fa-address-card-o:before,.fa-vcard-o:before{content:""}.fa-user-circle:before{content:""}.fa-user-circle-o:before{content:""}.fa-user-o:before{content:""}.fa-id-badge:before{content:""}.fa-drivers-license:before,.fa-id-card:before{content:""}.fa-drivers-license-o:before,.fa-id-card-o:before{content:""}.fa-quora:before{content:""}.fa-free-code-camp:before{content:""}.fa-telegram:before{content:""}.fa-thermometer-4:before,.fa-thermometer-full:before,.fa-thermometer:before{content:""}.fa-thermometer-3:before,.fa-thermometer-three-quarters:before{content:""}.fa-thermometer-2:before,.fa-thermometer-half:before{content:""}.fa-thermometer-1:before,.fa-thermometer-quarter:before{content:""}.fa-thermometer-0:before,.fa-thermometer-empty:before{content:""}.fa-shower:before{content:""}.fa-bath:before,.fa-bathtub:before,.fa-s15:before{content:""}.fa-podcast:before{content:""}.fa-window-maximize:before{content:""}.fa-window-minimize:before{content:""}.fa-window-restore:before{content:""}.fa-times-rectangle:before,.fa-window-close:before{content:""}.fa-times-rectangle-o:before,.fa-window-close-o:before{content:""}.fa-bandcamp:before{content:""}.fa-grav:before{content:""}.fa-etsy:before{content:""}.fa-imdb:before{content:""}.fa-ravelry:before{content:""}.fa-eercast:before{content:""}.fa-microchip:before{content:""}.fa-snowflake-o:before{content:""}.fa-superpowers:before{content:""}.fa-wpexplorer:before{content:""}.fa-meetup:before{content:""}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}.fa,.icon,.rst-content .admonition-title,.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content code.download span:first-child,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink,.rst-content tt.download span:first-child,.wy-dropdown .caret,.wy-inline-validate.wy-inline-validate-danger .wy-input-context,.wy-inline-validate.wy-inline-validate-info .wy-input-context,.wy-inline-validate.wy-inline-validate-success .wy-input-context,.wy-inline-validate.wy-inline-validate-warning .wy-input-context,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li button.toctree-expand{font-family:inherit}.fa:before,.icon:before,.rst-content .admonition-title:before,.rst-content .code-block-caption .headerlink:before,.rst-content .eqno .headerlink:before,.rst-content code.download span:first-child:before,.rst-content dl dt .headerlink:before,.rst-content h1 .headerlink:before,.rst-content h2 .headerlink:before,.rst-content h3 .headerlink:before,.rst-content h4 .headerlink:before,.rst-content h5 .headerlink:before,.rst-content h6 .headerlink:before,.rst-content p.caption .headerlink:before,.rst-content p .headerlink:before,.rst-content table>caption .headerlink:before,.rst-content tt.download span:first-child:before,.wy-dropdown .caret:before,.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,.wy-menu-vertical li.current>a button.toctree-expand:before,.wy-menu-vertical li.on a button.toctree-expand:before,.wy-menu-vertical li button.toctree-expand:before{font-family:FontAwesome;display:inline-block;font-style:normal;font-weight:400;line-height:1;text-decoration:inherit}.rst-content .code-block-caption a .headerlink,.rst-content .eqno a .headerlink,.rst-content a .admonition-title,.rst-content code.download a span:first-child,.rst-content dl dt a .headerlink,.rst-content h1 a .headerlink,.rst-content h2 a .headerlink,.rst-content h3 a .headerlink,.rst-content h4 a .headerlink,.rst-content h5 a .headerlink,.rst-content h6 a .headerlink,.rst-content p.caption a .headerlink,.rst-content p a .headerlink,.rst-content table>caption a .headerlink,.rst-content tt.download a span:first-child,.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li a button.toctree-expand,a .fa,a .icon,a .rst-content .admonition-title,a .rst-content .code-block-caption .headerlink,a .rst-content .eqno .headerlink,a .rst-content code.download span:first-child,a .rst-content dl dt .headerlink,a .rst-content h1 .headerlink,a .rst-content h2 .headerlink,a .rst-content h3 .headerlink,a .rst-content h4 .headerlink,a .rst-content h5 .headerlink,a .rst-content h6 .headerlink,a .rst-content p.caption .headerlink,a .rst-content p .headerlink,a .rst-content table>caption .headerlink,a .rst-content tt.download span:first-child,a .wy-menu-vertical li button.toctree-expand{display:inline-block;text-decoration:inherit}.btn .fa,.btn .icon,.btn .rst-content .admonition-title,.btn .rst-content .code-block-caption .headerlink,.btn .rst-content .eqno .headerlink,.btn .rst-content code.download span:first-child,.btn .rst-content dl dt .headerlink,.btn .rst-content h1 .headerlink,.btn .rst-content h2 .headerlink,.btn .rst-content h3 .headerlink,.btn .rst-content h4 .headerlink,.btn .rst-content h5 .headerlink,.btn .rst-content h6 .headerlink,.btn .rst-content p .headerlink,.btn .rst-content table>caption .headerlink,.btn .rst-content tt.download span:first-child,.btn .wy-menu-vertical li.current>a button.toctree-expand,.btn .wy-menu-vertical li.on a button.toctree-expand,.btn .wy-menu-vertical li button.toctree-expand,.nav .fa,.nav .icon,.nav .rst-content .admonition-title,.nav .rst-content .code-block-caption .headerlink,.nav .rst-content .eqno .headerlink,.nav .rst-content code.download span:first-child,.nav .rst-content dl dt .headerlink,.nav .rst-content h1 .headerlink,.nav .rst-content h2 .headerlink,.nav .rst-content h3 .headerlink,.nav .rst-content h4 .headerlink,.nav .rst-content h5 .headerlink,.nav .rst-content h6 .headerlink,.nav .rst-content p .headerlink,.nav .rst-content table>caption .headerlink,.nav .rst-content tt.download span:first-child,.nav .wy-menu-vertical li.current>a button.toctree-expand,.nav .wy-menu-vertical li.on a button.toctree-expand,.nav .wy-menu-vertical li button.toctree-expand,.rst-content .btn .admonition-title,.rst-content .code-block-caption .btn .headerlink,.rst-content .code-block-caption .nav .headerlink,.rst-content .eqno .btn .headerlink,.rst-content .eqno .nav .headerlink,.rst-content .nav .admonition-title,.rst-content code.download .btn span:first-child,.rst-content code.download .nav span:first-child,.rst-content dl dt .btn .headerlink,.rst-content dl dt .nav .headerlink,.rst-content h1 .btn .headerlink,.rst-content h1 .nav .headerlink,.rst-content h2 .btn .headerlink,.rst-content h2 .nav .headerlink,.rst-content h3 .btn .headerlink,.rst-content h3 .nav .headerlink,.rst-content h4 .btn .headerlink,.rst-content h4 .nav .headerlink,.rst-content h5 .btn .headerlink,.rst-content h5 .nav .headerlink,.rst-content h6 .btn .headerlink,.rst-content h6 .nav .headerlink,.rst-content p .btn .headerlink,.rst-content p .nav .headerlink,.rst-content table>caption .btn .headerlink,.rst-content table>caption .nav .headerlink,.rst-content tt.download .btn span:first-child,.rst-content tt.download .nav span:first-child,.wy-menu-vertical li .btn button.toctree-expand,.wy-menu-vertical li.current>a .btn button.toctree-expand,.wy-menu-vertical li.current>a .nav button.toctree-expand,.wy-menu-vertical li .nav button.toctree-expand,.wy-menu-vertical li.on a .btn button.toctree-expand,.wy-menu-vertical li.on a .nav button.toctree-expand{display:inline}.btn .fa-large.icon,.btn .fa.fa-large,.btn .rst-content .code-block-caption .fa-large.headerlink,.btn .rst-content .eqno .fa-large.headerlink,.btn .rst-content .fa-large.admonition-title,.btn .rst-content code.download span.fa-large:first-child,.btn .rst-content dl dt .fa-large.headerlink,.btn .rst-content h1 .fa-large.headerlink,.btn .rst-content h2 .fa-large.headerlink,.btn .rst-content h3 .fa-large.headerlink,.btn .rst-content h4 .fa-large.headerlink,.btn .rst-content h5 .fa-large.headerlink,.btn .rst-content h6 .fa-large.headerlink,.btn .rst-content p .fa-large.headerlink,.btn .rst-content table>caption .fa-large.headerlink,.btn .rst-content tt.download span.fa-large:first-child,.btn .wy-menu-vertical li button.fa-large.toctree-expand,.nav .fa-large.icon,.nav .fa.fa-large,.nav .rst-content .code-block-caption .fa-large.headerlink,.nav .rst-content .eqno .fa-large.headerlink,.nav .rst-content .fa-large.admonition-title,.nav .rst-content code.download span.fa-large:first-child,.nav .rst-content dl dt .fa-large.headerlink,.nav .rst-content h1 .fa-large.headerlink,.nav .rst-content h2 .fa-large.headerlink,.nav .rst-content h3 .fa-large.headerlink,.nav .rst-content h4 .fa-large.headerlink,.nav .rst-content h5 .fa-large.headerlink,.nav .rst-content h6 .fa-large.headerlink,.nav .rst-content p .fa-large.headerlink,.nav .rst-content table>caption .fa-large.headerlink,.nav .rst-content tt.download span.fa-large:first-child,.nav .wy-menu-vertical li button.fa-large.toctree-expand,.rst-content .btn .fa-large.admonition-title,.rst-content .code-block-caption .btn .fa-large.headerlink,.rst-content .code-block-caption .nav .fa-large.headerlink,.rst-content .eqno .btn .fa-large.headerlink,.rst-content .eqno .nav .fa-large.headerlink,.rst-content .nav .fa-large.admonition-title,.rst-content code.download .btn span.fa-large:first-child,.rst-content code.download .nav span.fa-large:first-child,.rst-content dl dt .btn .fa-large.headerlink,.rst-content dl dt .nav .fa-large.headerlink,.rst-content h1 .btn .fa-large.headerlink,.rst-content h1 .nav .fa-large.headerlink,.rst-content h2 .btn .fa-large.headerlink,.rst-content h2 .nav .fa-large.headerlink,.rst-content h3 .btn .fa-large.headerlink,.rst-content h3 .nav .fa-large.headerlink,.rst-content h4 .btn .fa-large.headerlink,.rst-content h4 .nav .fa-large.headerlink,.rst-content h5 .btn .fa-large.headerlink,.rst-content h5 .nav .fa-large.headerlink,.rst-content h6 .btn .fa-large.headerlink,.rst-content h6 .nav .fa-large.headerlink,.rst-content p .btn .fa-large.headerlink,.rst-content p .nav .fa-large.headerlink,.rst-content table>caption .btn .fa-large.headerlink,.rst-content table>caption .nav .fa-large.headerlink,.rst-content tt.download .btn span.fa-large:first-child,.rst-content tt.download .nav span.fa-large:first-child,.wy-menu-vertical li .btn button.fa-large.toctree-expand,.wy-menu-vertical li .nav button.fa-large.toctree-expand{line-height:.9em}.btn .fa-spin.icon,.btn .fa.fa-spin,.btn .rst-content .code-block-caption .fa-spin.headerlink,.btn .rst-content .eqno .fa-spin.headerlink,.btn .rst-content .fa-spin.admonition-title,.btn .rst-content code.download span.fa-spin:first-child,.btn .rst-content dl dt .fa-spin.headerlink,.btn .rst-content h1 .fa-spin.headerlink,.btn .rst-content h2 .fa-spin.headerlink,.btn .rst-content h3 .fa-spin.headerlink,.btn .rst-content h4 .fa-spin.headerlink,.btn .rst-content h5 .fa-spin.headerlink,.btn .rst-content h6 .fa-spin.headerlink,.btn .rst-content p .fa-spin.headerlink,.btn .rst-content table>caption .fa-spin.headerlink,.btn .rst-content tt.download span.fa-spin:first-child,.btn .wy-menu-vertical li button.fa-spin.toctree-expand,.nav .fa-spin.icon,.nav .fa.fa-spin,.nav .rst-content .code-block-caption .fa-spin.headerlink,.nav .rst-content .eqno .fa-spin.headerlink,.nav .rst-content .fa-spin.admonition-title,.nav .rst-content code.download span.fa-spin:first-child,.nav .rst-content dl dt .fa-spin.headerlink,.nav .rst-content h1 .fa-spin.headerlink,.nav .rst-content h2 .fa-spin.headerlink,.nav .rst-content h3 .fa-spin.headerlink,.nav .rst-content h4 .fa-spin.headerlink,.nav .rst-content h5 .fa-spin.headerlink,.nav .rst-content h6 .fa-spin.headerlink,.nav .rst-content p .fa-spin.headerlink,.nav .rst-content table>caption .fa-spin.headerlink,.nav .rst-content tt.download span.fa-spin:first-child,.nav .wy-menu-vertical li button.fa-spin.toctree-expand,.rst-content .btn .fa-spin.admonition-title,.rst-content .code-block-caption .btn .fa-spin.headerlink,.rst-content .code-block-caption .nav .fa-spin.headerlink,.rst-content .eqno .btn .fa-spin.headerlink,.rst-content .eqno .nav .fa-spin.headerlink,.rst-content .nav .fa-spin.admonition-title,.rst-content code.download .btn span.fa-spin:first-child,.rst-content code.download .nav span.fa-spin:first-child,.rst-content dl dt .btn .fa-spin.headerlink,.rst-content dl dt .nav .fa-spin.headerlink,.rst-content h1 .btn .fa-spin.headerlink,.rst-content h1 .nav .fa-spin.headerlink,.rst-content h2 .btn .fa-spin.headerlink,.rst-content h2 .nav .fa-spin.headerlink,.rst-content h3 .btn .fa-spin.headerlink,.rst-content h3 .nav .fa-spin.headerlink,.rst-content h4 .btn .fa-spin.headerlink,.rst-content h4 .nav .fa-spin.headerlink,.rst-content h5 .btn .fa-spin.headerlink,.rst-content h5 .nav .fa-spin.headerlink,.rst-content h6 .btn .fa-spin.headerlink,.rst-content h6 .nav .fa-spin.headerlink,.rst-content p .btn .fa-spin.headerlink,.rst-content p .nav .fa-spin.headerlink,.rst-content table>caption .btn .fa-spin.headerlink,.rst-content table>caption .nav .fa-spin.headerlink,.rst-content tt.download .btn span.fa-spin:first-child,.rst-content tt.download .nav span.fa-spin:first-child,.wy-menu-vertical li .btn button.fa-spin.toctree-expand,.wy-menu-vertical li .nav button.fa-spin.toctree-expand{display:inline-block}.btn.fa:before,.btn.icon:before,.rst-content .btn.admonition-title:before,.rst-content .code-block-caption .btn.headerlink:before,.rst-content .eqno .btn.headerlink:before,.rst-content code.download span.btn:first-child:before,.rst-content dl dt .btn.headerlink:before,.rst-content h1 .btn.headerlink:before,.rst-content h2 .btn.headerlink:before,.rst-content h3 .btn.headerlink:before,.rst-content h4 .btn.headerlink:before,.rst-content h5 .btn.headerlink:before,.rst-content h6 .btn.headerlink:before,.rst-content p .btn.headerlink:before,.rst-content table>caption .btn.headerlink:before,.rst-content tt.download span.btn:first-child:before,.wy-menu-vertical li button.btn.toctree-expand:before{opacity:.5;-webkit-transition:opacity .05s ease-in;-moz-transition:opacity .05s ease-in;transition:opacity .05s ease-in}.btn.fa:hover:before,.btn.icon:hover:before,.rst-content .btn.admonition-title:hover:before,.rst-content .code-block-caption .btn.headerlink:hover:before,.rst-content .eqno .btn.headerlink:hover:before,.rst-content code.download span.btn:first-child:hover:before,.rst-content dl dt .btn.headerlink:hover:before,.rst-content h1 .btn.headerlink:hover:before,.rst-content h2 .btn.headerlink:hover:before,.rst-content h3 .btn.headerlink:hover:before,.rst-content h4 .btn.headerlink:hover:before,.rst-content h5 .btn.headerlink:hover:before,.rst-content h6 .btn.headerlink:hover:before,.rst-content p .btn.headerlink:hover:before,.rst-content table>caption .btn.headerlink:hover:before,.rst-content tt.download span.btn:first-child:hover:before,.wy-menu-vertical li button.btn.toctree-expand:hover:before{opacity:1}.btn-mini .fa:before,.btn-mini .icon:before,.btn-mini .rst-content .admonition-title:before,.btn-mini .rst-content .code-block-caption .headerlink:before,.btn-mini .rst-content .eqno .headerlink:before,.btn-mini .rst-content code.download span:first-child:before,.btn-mini .rst-content dl dt .headerlink:before,.btn-mini .rst-content h1 .headerlink:before,.btn-mini .rst-content h2 .headerlink:before,.btn-mini .rst-content h3 .headerlink:before,.btn-mini .rst-content h4 .headerlink:before,.btn-mini .rst-content h5 .headerlink:before,.btn-mini .rst-content h6 .headerlink:before,.btn-mini .rst-content p .headerlink:before,.btn-mini .rst-content table>caption .headerlink:before,.btn-mini .rst-content tt.download span:first-child:before,.btn-mini .wy-menu-vertical li button.toctree-expand:before,.rst-content .btn-mini .admonition-title:before,.rst-content .code-block-caption .btn-mini .headerlink:before,.rst-content .eqno .btn-mini .headerlink:before,.rst-content code.download .btn-mini span:first-child:before,.rst-content dl dt .btn-mini .headerlink:before,.rst-content h1 .btn-mini .headerlink:before,.rst-content h2 .btn-mini .headerlink:before,.rst-content h3 .btn-mini .headerlink:before,.rst-content h4 .btn-mini .headerlink:before,.rst-content h5 .btn-mini .headerlink:before,.rst-content h6 .btn-mini .headerlink:before,.rst-content p .btn-mini .headerlink:before,.rst-content table>caption .btn-mini .headerlink:before,.rst-content tt.download .btn-mini span:first-child:before,.wy-menu-vertical li .btn-mini button.toctree-expand:before{font-size:14px;vertical-align:-15%}.rst-content .admonition,.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .danger,.rst-content .error,.rst-content .hint,.rst-content .important,.rst-content .note,.rst-content .seealso,.rst-content .tip,.rst-content .warning,.wy-alert{padding:12px;line-height:24px;margin-bottom:24px;background:#e7f2fa}.rst-content .admonition-title,.wy-alert-title{font-weight:700;display:block;color:#fff;background:#6ab0de;padding:6px 12px;margin:-12px -12px 12px}.rst-content .danger,.rst-content .error,.rst-content .wy-alert-danger.admonition,.rst-content .wy-alert-danger.admonition-todo,.rst-content .wy-alert-danger.attention,.rst-content .wy-alert-danger.caution,.rst-content .wy-alert-danger.hint,.rst-content .wy-alert-danger.important,.rst-content .wy-alert-danger.note,.rst-content .wy-alert-danger.seealso,.rst-content .wy-alert-danger.tip,.rst-content .wy-alert-danger.warning,.wy-alert.wy-alert-danger{background:#fdf3f2}.rst-content .danger .admonition-title,.rst-content .danger .wy-alert-title,.rst-content .error .admonition-title,.rst-content .error .wy-alert-title,.rst-content .wy-alert-danger.admonition-todo .admonition-title,.rst-content .wy-alert-danger.admonition-todo .wy-alert-title,.rst-content .wy-alert-danger.admonition .admonition-title,.rst-content .wy-alert-danger.admonition .wy-alert-title,.rst-content .wy-alert-danger.attention .admonition-title,.rst-content .wy-alert-danger.attention .wy-alert-title,.rst-content .wy-alert-danger.caution .admonition-title,.rst-content .wy-alert-danger.caution .wy-alert-title,.rst-content .wy-alert-danger.hint .admonition-title,.rst-content .wy-alert-danger.hint .wy-alert-title,.rst-content .wy-alert-danger.important .admonition-title,.rst-content .wy-alert-danger.important .wy-alert-title,.rst-content .wy-alert-danger.note .admonition-title,.rst-content .wy-alert-danger.note .wy-alert-title,.rst-content .wy-alert-danger.seealso .admonition-title,.rst-content .wy-alert-danger.seealso .wy-alert-title,.rst-content .wy-alert-danger.tip .admonition-title,.rst-content .wy-alert-danger.tip .wy-alert-title,.rst-content .wy-alert-danger.warning .admonition-title,.rst-content .wy-alert-danger.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-danger .admonition-title,.wy-alert.wy-alert-danger .rst-content .admonition-title,.wy-alert.wy-alert-danger .wy-alert-title{background:#f29f97}.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .warning,.rst-content .wy-alert-warning.admonition,.rst-content .wy-alert-warning.danger,.rst-content .wy-alert-warning.error,.rst-content .wy-alert-warning.hint,.rst-content .wy-alert-warning.important,.rst-content .wy-alert-warning.note,.rst-content .wy-alert-warning.seealso,.rst-content .wy-alert-warning.tip,.wy-alert.wy-alert-warning{background:#ffedcc}.rst-content .admonition-todo .admonition-title,.rst-content .admonition-todo .wy-alert-title,.rst-content .attention .admonition-title,.rst-content .attention .wy-alert-title,.rst-content .caution .admonition-title,.rst-content .caution .wy-alert-title,.rst-content .warning .admonition-title,.rst-content .warning .wy-alert-title,.rst-content .wy-alert-warning.admonition .admonition-title,.rst-content .wy-alert-warning.admonition .wy-alert-title,.rst-content .wy-alert-warning.danger .admonition-title,.rst-content .wy-alert-warning.danger .wy-alert-title,.rst-content .wy-alert-warning.error .admonition-title,.rst-content .wy-alert-warning.error .wy-alert-title,.rst-content .wy-alert-warning.hint .admonition-title,.rst-content .wy-alert-warning.hint .wy-alert-title,.rst-content .wy-alert-warning.important .admonition-title,.rst-content .wy-alert-warning.important .wy-alert-title,.rst-content .wy-alert-warning.note .admonition-title,.rst-content .wy-alert-warning.note .wy-alert-title,.rst-content .wy-alert-warning.seealso .admonition-title,.rst-content .wy-alert-warning.seealso .wy-alert-title,.rst-content .wy-alert-warning.tip .admonition-title,.rst-content .wy-alert-warning.tip .wy-alert-title,.rst-content .wy-alert.wy-alert-warning .admonition-title,.wy-alert.wy-alert-warning .rst-content .admonition-title,.wy-alert.wy-alert-warning .wy-alert-title{background:#f0b37e}.rst-content .note,.rst-content .seealso,.rst-content .wy-alert-info.admonition,.rst-content .wy-alert-info.admonition-todo,.rst-content .wy-alert-info.attention,.rst-content .wy-alert-info.caution,.rst-content .wy-alert-info.danger,.rst-content .wy-alert-info.error,.rst-content .wy-alert-info.hint,.rst-content .wy-alert-info.important,.rst-content .wy-alert-info.tip,.rst-content .wy-alert-info.warning,.wy-alert.wy-alert-info{background:#e7f2fa}.rst-content .note .admonition-title,.rst-content .note .wy-alert-title,.rst-content .seealso .admonition-title,.rst-content .seealso .wy-alert-title,.rst-content .wy-alert-info.admonition-todo .admonition-title,.rst-content .wy-alert-info.admonition-todo .wy-alert-title,.rst-content .wy-alert-info.admonition .admonition-title,.rst-content .wy-alert-info.admonition .wy-alert-title,.rst-content .wy-alert-info.attention .admonition-title,.rst-content .wy-alert-info.attention .wy-alert-title,.rst-content .wy-alert-info.caution .admonition-title,.rst-content .wy-alert-info.caution .wy-alert-title,.rst-content .wy-alert-info.danger .admonition-title,.rst-content .wy-alert-info.danger .wy-alert-title,.rst-content .wy-alert-info.error .admonition-title,.rst-content .wy-alert-info.error .wy-alert-title,.rst-content .wy-alert-info.hint .admonition-title,.rst-content .wy-alert-info.hint .wy-alert-title,.rst-content .wy-alert-info.important .admonition-title,.rst-content .wy-alert-info.important .wy-alert-title,.rst-content .wy-alert-info.tip .admonition-title,.rst-content .wy-alert-info.tip .wy-alert-title,.rst-content .wy-alert-info.warning .admonition-title,.rst-content .wy-alert-info.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-info .admonition-title,.wy-alert.wy-alert-info .rst-content .admonition-title,.wy-alert.wy-alert-info .wy-alert-title{background:#6ab0de}.rst-content .hint,.rst-content .important,.rst-content .tip,.rst-content .wy-alert-success.admonition,.rst-content .wy-alert-success.admonition-todo,.rst-content .wy-alert-success.attention,.rst-content .wy-alert-success.caution,.rst-content .wy-alert-success.danger,.rst-content .wy-alert-success.error,.rst-content .wy-alert-success.note,.rst-content .wy-alert-success.seealso,.rst-content .wy-alert-success.warning,.wy-alert.wy-alert-success{background:#dbfaf4}.rst-content .hint .admonition-title,.rst-content .hint .wy-alert-title,.rst-content .important .admonition-title,.rst-content .important .wy-alert-title,.rst-content .tip .admonition-title,.rst-content .tip .wy-alert-title,.rst-content .wy-alert-success.admonition-todo .admonition-title,.rst-content .wy-alert-success.admonition-todo .wy-alert-title,.rst-content .wy-alert-success.admonition .admonition-title,.rst-content .wy-alert-success.admonition .wy-alert-title,.rst-content .wy-alert-success.attention .admonition-title,.rst-content .wy-alert-success.attention .wy-alert-title,.rst-content .wy-alert-success.caution .admonition-title,.rst-content .wy-alert-success.caution .wy-alert-title,.rst-content .wy-alert-success.danger .admonition-title,.rst-content .wy-alert-success.danger .wy-alert-title,.rst-content .wy-alert-success.error .admonition-title,.rst-content .wy-alert-success.error .wy-alert-title,.rst-content .wy-alert-success.note .admonition-title,.rst-content .wy-alert-success.note .wy-alert-title,.rst-content .wy-alert-success.seealso .admonition-title,.rst-content .wy-alert-success.seealso .wy-alert-title,.rst-content .wy-alert-success.warning .admonition-title,.rst-content .wy-alert-success.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-success .admonition-title,.wy-alert.wy-alert-success .rst-content .admonition-title,.wy-alert.wy-alert-success .wy-alert-title{background:#1abc9c}.rst-content .wy-alert-neutral.admonition,.rst-content .wy-alert-neutral.admonition-todo,.rst-content .wy-alert-neutral.attention,.rst-content .wy-alert-neutral.caution,.rst-content .wy-alert-neutral.danger,.rst-content .wy-alert-neutral.error,.rst-content .wy-alert-neutral.hint,.rst-content .wy-alert-neutral.important,.rst-content .wy-alert-neutral.note,.rst-content .wy-alert-neutral.seealso,.rst-content .wy-alert-neutral.tip,.rst-content .wy-alert-neutral.warning,.wy-alert.wy-alert-neutral{background:#f3f6f6}.rst-content .wy-alert-neutral.admonition-todo .admonition-title,.rst-content .wy-alert-neutral.admonition-todo .wy-alert-title,.rst-content .wy-alert-neutral.admonition .admonition-title,.rst-content .wy-alert-neutral.admonition .wy-alert-title,.rst-content .wy-alert-neutral.attention .admonition-title,.rst-content .wy-alert-neutral.attention .wy-alert-title,.rst-content .wy-alert-neutral.caution .admonition-title,.rst-content .wy-alert-neutral.caution .wy-alert-title,.rst-content .wy-alert-neutral.danger .admonition-title,.rst-content .wy-alert-neutral.danger .wy-alert-title,.rst-content .wy-alert-neutral.error .admonition-title,.rst-content .wy-alert-neutral.error .wy-alert-title,.rst-content .wy-alert-neutral.hint .admonition-title,.rst-content .wy-alert-neutral.hint .wy-alert-title,.rst-content .wy-alert-neutral.important .admonition-title,.rst-content .wy-alert-neutral.important .wy-alert-title,.rst-content .wy-alert-neutral.note .admonition-title,.rst-content .wy-alert-neutral.note .wy-alert-title,.rst-content .wy-alert-neutral.seealso .admonition-title,.rst-content .wy-alert-neutral.seealso .wy-alert-title,.rst-content .wy-alert-neutral.tip .admonition-title,.rst-content .wy-alert-neutral.tip .wy-alert-title,.rst-content .wy-alert-neutral.warning .admonition-title,.rst-content .wy-alert-neutral.warning .wy-alert-title,.rst-content .wy-alert.wy-alert-neutral .admonition-title,.wy-alert.wy-alert-neutral .rst-content .admonition-title,.wy-alert.wy-alert-neutral .wy-alert-title{color:#404040;background:#e1e4e5}.rst-content .wy-alert-neutral.admonition-todo a,.rst-content .wy-alert-neutral.admonition a,.rst-content .wy-alert-neutral.attention a,.rst-content .wy-alert-neutral.caution a,.rst-content .wy-alert-neutral.danger a,.rst-content .wy-alert-neutral.error a,.rst-content .wy-alert-neutral.hint a,.rst-content .wy-alert-neutral.important a,.rst-content .wy-alert-neutral.note a,.rst-content .wy-alert-neutral.seealso a,.rst-content .wy-alert-neutral.tip a,.rst-content .wy-alert-neutral.warning a,.wy-alert.wy-alert-neutral a{color:#2980b9}.rst-content .admonition-todo p:last-child,.rst-content .admonition p:last-child,.rst-content .attention p:last-child,.rst-content .caution p:last-child,.rst-content .danger p:last-child,.rst-content .error p:last-child,.rst-content .hint p:last-child,.rst-content .important p:last-child,.rst-content .note p:last-child,.rst-content .seealso p:last-child,.rst-content .tip p:last-child,.rst-content .warning p:last-child,.wy-alert p:last-child{margin-bottom:0}.wy-tray-container{position:fixed;bottom:0;left:0;z-index:600}.wy-tray-container li{display:block;width:300px;background:transparent;color:#fff;text-align:center;box-shadow:0 5px 5px 0 rgba(0,0,0,.1);padding:0 24px;min-width:20%;opacity:0;height:0;line-height:56px;overflow:hidden;-webkit-transition:all .3s ease-in;-moz-transition:all .3s ease-in;transition:all .3s ease-in}.wy-tray-container li.wy-tray-item-success{background:#27ae60}.wy-tray-container li.wy-tray-item-info{background:#2980b9}.wy-tray-container li.wy-tray-item-warning{background:#e67e22}.wy-tray-container li.wy-tray-item-danger{background:#e74c3c}.wy-tray-container li.on{opacity:1;height:56px}@media screen and (max-width:768px){.wy-tray-container{bottom:auto;top:0;width:100%}.wy-tray-container li{width:100%}}button{font-size:100%;margin:0;vertical-align:baseline;*vertical-align:middle;cursor:pointer;line-height:normal;-webkit-appearance:button;*overflow:visible}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}button[disabled]{cursor:default}.btn{display:inline-block;border-radius:2px;line-height:normal;white-space:nowrap;text-align:center;cursor:pointer;font-size:100%;padding:6px 12px 8px;color:#fff;border:1px solid rgba(0,0,0,.1);background-color:#27ae60;text-decoration:none;font-weight:400;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;box-shadow:inset 0 1px 2px -1px hsla(0,0%,100%,.5),inset 0 -2px 0 0 rgba(0,0,0,.1);outline-none:false;vertical-align:middle;*display:inline;zoom:1;-webkit-user-drag:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;-webkit-transition:all .1s linear;-moz-transition:all .1s linear;transition:all .1s linear}.btn-hover{background:#2e8ece;color:#fff}.btn:hover{background:#2cc36b;color:#fff}.btn:focus{background:#2cc36b;outline:0}.btn:active{box-shadow:inset 0 -1px 0 0 rgba(0,0,0,.05),inset 0 2px 0 0 rgba(0,0,0,.1);padding:8px 12px 6px}.btn:visited{color:#fff}.btn-disabled,.btn-disabled:active,.btn-disabled:focus,.btn-disabled:hover,.btn:disabled{background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);filter:alpha(opacity=40);opacity:.4;cursor:not-allowed;box-shadow:none}.btn::-moz-focus-inner{padding:0;border:0}.btn-small{font-size:80%}.btn-info{background-color:#2980b9!important}.btn-info:hover{background-color:#2e8ece!important}.btn-neutral{background-color:#f3f6f6!important;color:#404040!important}.btn-neutral:hover{background-color:#e5ebeb!important;color:#404040}.btn-neutral:visited{color:#404040!important}.btn-success{background-color:#27ae60!important}.btn-success:hover{background-color:#295!important}.btn-danger{background-color:#e74c3c!important}.btn-danger:hover{background-color:#ea6153!important}.btn-warning{background-color:#e67e22!important}.btn-warning:hover{background-color:#e98b39!important}.btn-invert{background-color:#222}.btn-invert:hover{background-color:#2f2f2f!important}.btn-link{background-color:transparent!important;color:#2980b9;box-shadow:none;border-color:transparent!important}.btn-link:active,.btn-link:hover{background-color:transparent!important;color:#409ad5!important;box-shadow:none}.btn-link:visited{color:#9b59b6}.wy-btn-group .btn,.wy-control .btn{vertical-align:middle}.wy-btn-group{margin-bottom:24px;*zoom:1}.wy-btn-group:after,.wy-btn-group:before{display:table;content:""}.wy-btn-group:after{clear:both}.wy-dropdown{position:relative;display:inline-block}.wy-dropdown-active .wy-dropdown-menu{display:block}.wy-dropdown-menu{position:absolute;left:0;display:none;float:left;top:100%;min-width:100%;background:#fcfcfc;z-index:100;border:1px solid #cfd7dd;box-shadow:0 2px 2px 0 rgba(0,0,0,.1);padding:12px}.wy-dropdown-menu>dd>a{display:block;clear:both;color:#404040;white-space:nowrap;font-size:90%;padding:0 12px;cursor:pointer}.wy-dropdown-menu>dd>a:hover{background:#2980b9;color:#fff}.wy-dropdown-menu>dd.divider{border-top:1px solid #cfd7dd;margin:6px 0}.wy-dropdown-menu>dd.search{padding-bottom:12px}.wy-dropdown-menu>dd.search input[type=search]{width:100%}.wy-dropdown-menu>dd.call-to-action{background:#e3e3e3;text-transform:uppercase;font-weight:500;font-size:80%}.wy-dropdown-menu>dd.call-to-action:hover{background:#e3e3e3}.wy-dropdown-menu>dd.call-to-action .btn{color:#fff}.wy-dropdown.wy-dropdown-up .wy-dropdown-menu{bottom:100%;top:auto;left:auto;right:0}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu{background:#fcfcfc;margin-top:2px}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a{padding:6px 12px}.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a:hover{background:#2980b9;color:#fff}.wy-dropdown.wy-dropdown-left .wy-dropdown-menu{right:0;left:auto;text-align:right}.wy-dropdown-arrow:before{content:" ";border-bottom:5px solid #f5f5f5;border-left:5px solid transparent;border-right:5px solid transparent;position:absolute;display:block;top:-4px;left:50%;margin-left:-3px}.wy-dropdown-arrow.wy-dropdown-arrow-left:before{left:11px}.wy-form-stacked select{display:block}.wy-form-aligned .wy-help-inline,.wy-form-aligned input,.wy-form-aligned label,.wy-form-aligned select,.wy-form-aligned textarea{display:inline-block;*display:inline;*zoom:1;vertical-align:middle}.wy-form-aligned .wy-control-group>label{display:inline-block;vertical-align:middle;width:10em;margin:6px 12px 0 0;float:left}.wy-form-aligned .wy-control{float:left}.wy-form-aligned .wy-control label{display:block}.wy-form-aligned .wy-control select{margin-top:6px}fieldset{margin:0}fieldset,legend{border:0;padding:0}legend{width:100%;white-space:normal;margin-bottom:24px;font-size:150%;*margin-left:-7px}label,legend{display:block}label{margin:0 0 .3125em;color:#333;font-size:90%}input,select,textarea{font-size:100%;margin:0;vertical-align:baseline;*vertical-align:middle}.wy-control-group{margin-bottom:24px;max-width:1200px;margin-left:auto;margin-right:auto;*zoom:1}.wy-control-group:after,.wy-control-group:before{display:table;content:""}.wy-control-group:after{clear:both}.wy-control-group.wy-control-group-required>label:after{content:" *";color:#e74c3c}.wy-control-group .wy-form-full,.wy-control-group .wy-form-halves,.wy-control-group .wy-form-thirds{padding-bottom:12px}.wy-control-group .wy-form-full input[type=color],.wy-control-group .wy-form-full input[type=date],.wy-control-group .wy-form-full input[type=datetime-local],.wy-control-group .wy-form-full input[type=datetime],.wy-control-group .wy-form-full input[type=email],.wy-control-group .wy-form-full input[type=month],.wy-control-group .wy-form-full input[type=number],.wy-control-group .wy-form-full input[type=password],.wy-control-group .wy-form-full input[type=search],.wy-control-group .wy-form-full input[type=tel],.wy-control-group .wy-form-full input[type=text],.wy-control-group .wy-form-full input[type=time],.wy-control-group .wy-form-full input[type=url],.wy-control-group .wy-form-full input[type=week],.wy-control-group .wy-form-full select,.wy-control-group .wy-form-halves input[type=color],.wy-control-group .wy-form-halves input[type=date],.wy-control-group .wy-form-halves input[type=datetime-local],.wy-control-group .wy-form-halves input[type=datetime],.wy-control-group .wy-form-halves input[type=email],.wy-control-group .wy-form-halves input[type=month],.wy-control-group .wy-form-halves input[type=number],.wy-control-group .wy-form-halves input[type=password],.wy-control-group .wy-form-halves input[type=search],.wy-control-group .wy-form-halves input[type=tel],.wy-control-group .wy-form-halves input[type=text],.wy-control-group .wy-form-halves input[type=time],.wy-control-group .wy-form-halves input[type=url],.wy-control-group .wy-form-halves input[type=week],.wy-control-group .wy-form-halves select,.wy-control-group .wy-form-thirds input[type=color],.wy-control-group .wy-form-thirds input[type=date],.wy-control-group .wy-form-thirds input[type=datetime-local],.wy-control-group .wy-form-thirds input[type=datetime],.wy-control-group .wy-form-thirds input[type=email],.wy-control-group .wy-form-thirds input[type=month],.wy-control-group .wy-form-thirds input[type=number],.wy-control-group .wy-form-thirds input[type=password],.wy-control-group .wy-form-thirds input[type=search],.wy-control-group .wy-form-thirds input[type=tel],.wy-control-group .wy-form-thirds input[type=text],.wy-control-group .wy-form-thirds input[type=time],.wy-control-group .wy-form-thirds input[type=url],.wy-control-group .wy-form-thirds input[type=week],.wy-control-group .wy-form-thirds select{width:100%}.wy-control-group .wy-form-full{float:left;display:block;width:100%;margin-right:0}.wy-control-group .wy-form-full:last-child{margin-right:0}.wy-control-group .wy-form-halves{float:left;display:block;margin-right:2.35765%;width:48.82117%}.wy-control-group .wy-form-halves:last-child,.wy-control-group .wy-form-halves:nth-of-type(2n){margin-right:0}.wy-control-group .wy-form-halves:nth-of-type(odd){clear:left}.wy-control-group .wy-form-thirds{float:left;display:block;margin-right:2.35765%;width:31.76157%}.wy-control-group .wy-form-thirds:last-child,.wy-control-group .wy-form-thirds:nth-of-type(3n){margin-right:0}.wy-control-group .wy-form-thirds:nth-of-type(3n+1){clear:left}.wy-control-group.wy-control-group-no-input .wy-control,.wy-control-no-input{margin:6px 0 0;font-size:90%}.wy-control-no-input{display:inline-block}.wy-control-group.fluid-input input[type=color],.wy-control-group.fluid-input input[type=date],.wy-control-group.fluid-input input[type=datetime-local],.wy-control-group.fluid-input input[type=datetime],.wy-control-group.fluid-input input[type=email],.wy-control-group.fluid-input input[type=month],.wy-control-group.fluid-input input[type=number],.wy-control-group.fluid-input input[type=password],.wy-control-group.fluid-input input[type=search],.wy-control-group.fluid-input input[type=tel],.wy-control-group.fluid-input input[type=text],.wy-control-group.fluid-input input[type=time],.wy-control-group.fluid-input input[type=url],.wy-control-group.fluid-input input[type=week]{width:100%}.wy-form-message-inline{padding-left:.3em;color:#666;font-size:90%}.wy-form-message{display:block;color:#999;font-size:70%;margin-top:.3125em;font-style:italic}.wy-form-message p{font-size:inherit;font-style:italic;margin-bottom:6px}.wy-form-message p:last-child{margin-bottom:0}input{line-height:normal}input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;*overflow:visible}input[type=color],input[type=date],input[type=datetime-local],input[type=datetime],input[type=email],input[type=month],input[type=number],input[type=password],input[type=search],input[type=tel],input[type=text],input[type=time],input[type=url],input[type=week]{-webkit-appearance:none;padding:6px;display:inline-block;border:1px solid #ccc;font-size:80%;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;box-shadow:inset 0 1px 3px #ddd;border-radius:0;-webkit-transition:border .3s linear;-moz-transition:border .3s linear;transition:border .3s linear}input[type=datetime-local]{padding:.34375em .625em}input[disabled]{cursor:default}input[type=checkbox],input[type=radio]{padding:0;margin-right:.3125em;*height:13px;*width:13px}input[type=checkbox],input[type=radio],input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}input[type=color]:focus,input[type=date]:focus,input[type=datetime-local]:focus,input[type=datetime]:focus,input[type=email]:focus,input[type=month]:focus,input[type=number]:focus,input[type=password]:focus,input[type=search]:focus,input[type=tel]:focus,input[type=text]:focus,input[type=time]:focus,input[type=url]:focus,input[type=week]:focus{outline:0;outline:thin dotted\9;border-color:#333}input.no-focus:focus{border-color:#ccc!important}input[type=checkbox]:focus,input[type=file]:focus,input[type=radio]:focus{outline:thin dotted #333;outline:1px auto #129fea}input[type=color][disabled],input[type=date][disabled],input[type=datetime-local][disabled],input[type=datetime][disabled],input[type=email][disabled],input[type=month][disabled],input[type=number][disabled],input[type=password][disabled],input[type=search][disabled],input[type=tel][disabled],input[type=text][disabled],input[type=time][disabled],input[type=url][disabled],input[type=week][disabled]{cursor:not-allowed;background-color:#fafafa}input:focus:invalid,select:focus:invalid,textarea:focus:invalid{color:#e74c3c;border:1px solid #e74c3c}input:focus:invalid:focus,select:focus:invalid:focus,textarea:focus:invalid:focus{border-color:#e74c3c}input[type=checkbox]:focus:invalid:focus,input[type=file]:focus:invalid:focus,input[type=radio]:focus:invalid:focus{outline-color:#e74c3c}input.wy-input-large{padding:12px;font-size:100%}textarea{overflow:auto;vertical-align:top;width:100%;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif}select,textarea{padding:.5em .625em;display:inline-block;border:1px solid #ccc;font-size:80%;box-shadow:inset 0 1px 3px #ddd;-webkit-transition:border .3s linear;-moz-transition:border .3s linear;transition:border .3s linear}select{border:1px solid #ccc;background-color:#fff}select[multiple]{height:auto}select:focus,textarea:focus{outline:0}input[readonly],select[disabled],select[readonly],textarea[disabled],textarea[readonly]{cursor:not-allowed;background-color:#fafafa}input[type=checkbox][disabled],input[type=radio][disabled]{cursor:not-allowed}.wy-checkbox,.wy-radio{margin:6px 0;color:#404040;display:block}.wy-checkbox input,.wy-radio input{vertical-align:baseline}.wy-form-message-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle}.wy-input-prefix,.wy-input-suffix{white-space:nowrap;padding:6px}.wy-input-prefix .wy-input-context,.wy-input-suffix .wy-input-context{line-height:27px;padding:0 8px;display:inline-block;font-size:80%;background-color:#f3f6f6;border:1px solid #ccc;color:#999}.wy-input-suffix .wy-input-context{border-left:0}.wy-input-prefix .wy-input-context{border-right:0}.wy-switch{position:relative;display:block;height:24px;margin-top:12px;cursor:pointer}.wy-switch:before{left:0;top:0;width:36px;height:12px;background:#ccc}.wy-switch:after,.wy-switch:before{position:absolute;content:"";display:block;border-radius:4px;-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.wy-switch:after{width:18px;height:18px;background:#999;left:-3px;top:-3px}.wy-switch span{position:absolute;left:48px;display:block;font-size:12px;color:#ccc;line-height:1}.wy-switch.active:before{background:#1e8449}.wy-switch.active:after{left:24px;background:#27ae60}.wy-switch.disabled{cursor:not-allowed;opacity:.8}.wy-control-group.wy-control-group-error .wy-form-message,.wy-control-group.wy-control-group-error>label{color:#e74c3c}.wy-control-group.wy-control-group-error input[type=color],.wy-control-group.wy-control-group-error input[type=date],.wy-control-group.wy-control-group-error input[type=datetime-local],.wy-control-group.wy-control-group-error input[type=datetime],.wy-control-group.wy-control-group-error input[type=email],.wy-control-group.wy-control-group-error input[type=month],.wy-control-group.wy-control-group-error input[type=number],.wy-control-group.wy-control-group-error input[type=password],.wy-control-group.wy-control-group-error input[type=search],.wy-control-group.wy-control-group-error input[type=tel],.wy-control-group.wy-control-group-error input[type=text],.wy-control-group.wy-control-group-error input[type=time],.wy-control-group.wy-control-group-error input[type=url],.wy-control-group.wy-control-group-error input[type=week],.wy-control-group.wy-control-group-error textarea{border:1px solid #e74c3c}.wy-inline-validate{white-space:nowrap}.wy-inline-validate .wy-input-context{padding:.5em .625em;display:inline-block;font-size:80%}.wy-inline-validate.wy-inline-validate-success .wy-input-context{color:#27ae60}.wy-inline-validate.wy-inline-validate-danger .wy-input-context{color:#e74c3c}.wy-inline-validate.wy-inline-validate-warning .wy-input-context{color:#e67e22}.wy-inline-validate.wy-inline-validate-info .wy-input-context{color:#2980b9}.rotate-90{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg)}.rotate-180{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg)}.rotate-270{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg)}.mirror{-webkit-transform:scaleX(-1);-moz-transform:scaleX(-1);-ms-transform:scaleX(-1);-o-transform:scaleX(-1);transform:scaleX(-1)}.mirror.rotate-90{-webkit-transform:scaleX(-1) rotate(90deg);-moz-transform:scaleX(-1) rotate(90deg);-ms-transform:scaleX(-1) rotate(90deg);-o-transform:scaleX(-1) rotate(90deg);transform:scaleX(-1) rotate(90deg)}.mirror.rotate-180{-webkit-transform:scaleX(-1) rotate(180deg);-moz-transform:scaleX(-1) rotate(180deg);-ms-transform:scaleX(-1) rotate(180deg);-o-transform:scaleX(-1) rotate(180deg);transform:scaleX(-1) rotate(180deg)}.mirror.rotate-270{-webkit-transform:scaleX(-1) rotate(270deg);-moz-transform:scaleX(-1) rotate(270deg);-ms-transform:scaleX(-1) rotate(270deg);-o-transform:scaleX(-1) rotate(270deg);transform:scaleX(-1) rotate(270deg)}@media only screen and (max-width:480px){.wy-form button[type=submit]{margin:.7em 0 0}.wy-form input[type=color],.wy-form input[type=date],.wy-form input[type=datetime-local],.wy-form input[type=datetime],.wy-form input[type=email],.wy-form input[type=month],.wy-form input[type=number],.wy-form input[type=password],.wy-form input[type=search],.wy-form input[type=tel],.wy-form input[type=text],.wy-form input[type=time],.wy-form input[type=url],.wy-form input[type=week],.wy-form label{margin-bottom:.3em;display:block}.wy-form input[type=color],.wy-form input[type=date],.wy-form input[type=datetime-local],.wy-form input[type=datetime],.wy-form input[type=email],.wy-form input[type=month],.wy-form input[type=number],.wy-form input[type=password],.wy-form input[type=search],.wy-form input[type=tel],.wy-form input[type=time],.wy-form input[type=url],.wy-form input[type=week]{margin-bottom:0}.wy-form-aligned .wy-control-group label{margin-bottom:.3em;text-align:left;display:block;width:100%}.wy-form-aligned .wy-control{margin:1.5em 0 0}.wy-form-message,.wy-form-message-inline,.wy-form .wy-help-inline{display:block;font-size:80%;padding:6px 0}}@media screen and (max-width:768px){.tablet-hide{display:none}}@media screen and (max-width:480px){.mobile-hide{display:none}}.float-left{float:left}.float-right{float:right}.full-width{width:100%}.rst-content table.docutils,.rst-content table.field-list,.wy-table{border-collapse:collapse;border-spacing:0;empty-cells:show;margin-bottom:24px}.rst-content table.docutils caption,.rst-content table.field-list caption,.wy-table caption{color:#000;font:italic 85%/1 arial,sans-serif;padding:1em 0;text-align:center}.rst-content table.docutils td,.rst-content table.docutils th,.rst-content table.field-list td,.rst-content table.field-list th,.wy-table td,.wy-table th{font-size:90%;margin:0;overflow:visible;padding:8px 16px}.rst-content table.docutils td:first-child,.rst-content table.docutils th:first-child,.rst-content table.field-list td:first-child,.rst-content table.field-list th:first-child,.wy-table td:first-child,.wy-table th:first-child{border-left-width:0}.rst-content table.docutils thead,.rst-content table.field-list thead,.wy-table thead{color:#000;text-align:left;vertical-align:bottom;white-space:nowrap}.rst-content table.docutils thead th,.rst-content table.field-list thead th,.wy-table thead th{font-weight:700;border-bottom:2px solid #e1e4e5}.rst-content table.docutils td,.rst-content table.field-list td,.wy-table td{background-color:transparent;vertical-align:middle}.rst-content table.docutils td p,.rst-content table.field-list td p,.wy-table td p{line-height:18px}.rst-content table.docutils td p:last-child,.rst-content table.field-list td p:last-child,.wy-table td p:last-child{margin-bottom:0}.rst-content table.docutils .wy-table-cell-min,.rst-content table.field-list .wy-table-cell-min,.wy-table .wy-table-cell-min{width:1%;padding-right:0}.rst-content table.docutils .wy-table-cell-min input[type=checkbox],.rst-content table.field-list .wy-table-cell-min input[type=checkbox],.wy-table .wy-table-cell-min input[type=checkbox]{margin:0}.wy-table-secondary{color:grey;font-size:90%}.wy-table-tertiary{color:grey;font-size:80%}.rst-content table.docutils:not(.field-list) tr:nth-child(2n-1) td,.wy-table-backed,.wy-table-odd td,.wy-table-striped tr:nth-child(2n-1) td{background-color:#f3f6f6}.rst-content table.docutils,.wy-table-bordered-all{border:1px solid #e1e4e5}.rst-content table.docutils td,.wy-table-bordered-all td{border-bottom:1px solid #e1e4e5;border-left:1px solid #e1e4e5}.rst-content table.docutils tbody>tr:last-child td,.wy-table-bordered-all tbody>tr:last-child td{border-bottom-width:0}.wy-table-bordered{border:1px solid #e1e4e5}.wy-table-bordered-rows td{border-bottom:1px solid #e1e4e5}.wy-table-bordered-rows tbody>tr:last-child td{border-bottom-width:0}.wy-table-horizontal td,.wy-table-horizontal th{border-width:0 0 1px;border-bottom:1px solid #e1e4e5}.wy-table-horizontal tbody>tr:last-child td{border-bottom-width:0}.wy-table-responsive{margin-bottom:24px;max-width:100%;overflow:auto}.wy-table-responsive table{margin-bottom:0!important}.wy-table-responsive table td,.wy-table-responsive table th{white-space:nowrap}a{color:#2980b9;text-decoration:none;cursor:pointer}a:hover{color:#3091d1}a:visited{color:#9b59b6}html{height:100%}body,html{overflow-x:hidden}body{font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;font-weight:400;color:#404040;min-height:100%;background:#edf0f2}.wy-text-left{text-align:left}.wy-text-center{text-align:center}.wy-text-right{text-align:right}.wy-text-large{font-size:120%}.wy-text-normal{font-size:100%}.wy-text-small,small{font-size:80%}.wy-text-strike{text-decoration:line-through}.wy-text-warning{color:#e67e22!important}a.wy-text-warning:hover{color:#eb9950!important}.wy-text-info{color:#2980b9!important}a.wy-text-info:hover{color:#409ad5!important}.wy-text-success{color:#27ae60!important}a.wy-text-success:hover{color:#36d278!important}.wy-text-danger{color:#e74c3c!important}a.wy-text-danger:hover{color:#ed7669!important}.wy-text-neutral{color:#404040!important}a.wy-text-neutral:hover{color:#595959!important}.rst-content .toctree-wrapper>p.caption,h1,h2,h3,h4,h5,h6,legend{margin-top:0;font-weight:700;font-family:Roboto Slab,ff-tisa-web-pro,Georgia,Arial,sans-serif}p{line-height:24px;font-size:16px;margin:0 0 24px}h1{font-size:175%}.rst-content .toctree-wrapper>p.caption,h2{font-size:150%}h3{font-size:125%}h4{font-size:115%}h5{font-size:110%}h6{font-size:100%}hr{display:block;height:1px;border:0;border-top:1px solid #e1e4e5;margin:24px 0;padding:0}.rst-content code,.rst-content tt,code{white-space:nowrap;max-width:100%;background:#fff;border:1px solid #e1e4e5;font-size:75%;padding:0 5px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#e74c3c;overflow-x:auto}.rst-content tt.code-large,code.code-large{font-size:90%}.rst-content .section ul,.rst-content .toctree-wrapper ul,.rst-content section ul,.wy-plain-list-disc,article ul{list-style:disc;line-height:24px;margin-bottom:24px}.rst-content .section ul li,.rst-content .toctree-wrapper ul li,.rst-content section ul li,.wy-plain-list-disc li,article ul li{list-style:disc;margin-left:24px}.rst-content .section ul li p:last-child,.rst-content .section ul li ul,.rst-content .toctree-wrapper ul li p:last-child,.rst-content .toctree-wrapper ul li ul,.rst-content section ul li p:last-child,.rst-content section ul li ul,.wy-plain-list-disc li p:last-child,.wy-plain-list-disc li ul,article ul li p:last-child,article ul li ul{margin-bottom:0}.rst-content .section ul li li,.rst-content .toctree-wrapper ul li li,.rst-content section ul li li,.wy-plain-list-disc li li,article ul li li{list-style:circle}.rst-content .section ul li li li,.rst-content .toctree-wrapper ul li li li,.rst-content section ul li li li,.wy-plain-list-disc li li li,article ul li li li{list-style:square}.rst-content .section ul li ol li,.rst-content .toctree-wrapper ul li ol li,.rst-content section ul li ol li,.wy-plain-list-disc li ol li,article ul li ol li{list-style:decimal}.rst-content .section ol,.rst-content .section ol.arabic,.rst-content .toctree-wrapper ol,.rst-content .toctree-wrapper ol.arabic,.rst-content section ol,.rst-content section ol.arabic,.wy-plain-list-decimal,article ol{list-style:decimal;line-height:24px;margin-bottom:24px}.rst-content .section ol.arabic li,.rst-content .section ol li,.rst-content .toctree-wrapper ol.arabic li,.rst-content .toctree-wrapper ol li,.rst-content section ol.arabic li,.rst-content section ol li,.wy-plain-list-decimal li,article ol li{list-style:decimal;margin-left:24px}.rst-content .section ol.arabic li ul,.rst-content .section ol li p:last-child,.rst-content .section ol li ul,.rst-content .toctree-wrapper ol.arabic li ul,.rst-content .toctree-wrapper ol li p:last-child,.rst-content .toctree-wrapper ol li ul,.rst-content section ol.arabic li ul,.rst-content section ol li p:last-child,.rst-content section ol li ul,.wy-plain-list-decimal li p:last-child,.wy-plain-list-decimal li ul,article ol li p:last-child,article ol li ul{margin-bottom:0}.rst-content .section ol.arabic li ul li,.rst-content .section ol li ul li,.rst-content .toctree-wrapper ol.arabic li ul li,.rst-content .toctree-wrapper ol li ul li,.rst-content section ol.arabic li ul li,.rst-content section ol li ul li,.wy-plain-list-decimal li ul li,article ol li ul li{list-style:disc}.wy-breadcrumbs{*zoom:1}.wy-breadcrumbs:after,.wy-breadcrumbs:before{display:table;content:""}.wy-breadcrumbs:after{clear:both}.wy-breadcrumbs>li{display:inline-block;padding-top:5px}.wy-breadcrumbs>li.wy-breadcrumbs-aside{float:right}.rst-content .wy-breadcrumbs>li code,.rst-content .wy-breadcrumbs>li tt,.wy-breadcrumbs>li .rst-content tt,.wy-breadcrumbs>li code{all:inherit;color:inherit}.breadcrumb-item:before{content:"/";color:#bbb;font-size:13px;padding:0 6px 0 3px}.wy-breadcrumbs-extra{margin-bottom:0;color:#b3b3b3;font-size:80%;display:inline-block}@media screen and (max-width:480px){.wy-breadcrumbs-extra,.wy-breadcrumbs li.wy-breadcrumbs-aside{display:none}}@media print{.wy-breadcrumbs li.wy-breadcrumbs-aside{display:none}}html{font-size:16px}.wy-affix{position:fixed;top:1.618em}.wy-menu a:hover{text-decoration:none}.wy-menu-horiz{*zoom:1}.wy-menu-horiz:after,.wy-menu-horiz:before{display:table;content:""}.wy-menu-horiz:after{clear:both}.wy-menu-horiz li,.wy-menu-horiz ul{display:inline-block}.wy-menu-horiz li:hover{background:hsla(0,0%,100%,.1)}.wy-menu-horiz li.divide-left{border-left:1px solid #404040}.wy-menu-horiz li.divide-right{border-right:1px solid #404040}.wy-menu-horiz a{height:32px;display:inline-block;line-height:32px;padding:0 16px}.wy-menu-vertical{width:300px}.wy-menu-vertical header,.wy-menu-vertical p.caption{color:#55a5d9;height:32px;line-height:32px;padding:0 1.618em;margin:12px 0 0;display:block;font-weight:700;text-transform:uppercase;font-size:85%;white-space:nowrap}.wy-menu-vertical ul{margin-bottom:0}.wy-menu-vertical li.divide-top{border-top:1px solid #404040}.wy-menu-vertical li.divide-bottom{border-bottom:1px solid #404040}.wy-menu-vertical li.current{background:#e3e3e3}.wy-menu-vertical li.current a{color:grey;border-right:1px solid #c9c9c9;padding:.4045em 2.427em}.wy-menu-vertical li.current a:hover{background:#d6d6d6}.rst-content .wy-menu-vertical li tt,.wy-menu-vertical li .rst-content tt,.wy-menu-vertical li code{border:none;background:inherit;color:inherit;padding-left:0;padding-right:0}.wy-menu-vertical li button.toctree-expand{display:block;float:left;margin-left:-1.2em;line-height:18px;color:#4d4d4d;border:none;background:none;padding:0}.wy-menu-vertical li.current>a,.wy-menu-vertical li.on a{color:#404040;font-weight:700;position:relative;background:#fcfcfc;border:none;padding:.4045em 1.618em}.wy-menu-vertical li.current>a:hover,.wy-menu-vertical li.on a:hover{background:#fcfcfc}.wy-menu-vertical li.current>a:hover button.toctree-expand,.wy-menu-vertical li.on a:hover button.toctree-expand{color:grey}.wy-menu-vertical li.current>a button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand{display:block;line-height:18px;color:#333}.wy-menu-vertical li.toctree-l1.current>a{border-bottom:1px solid #c9c9c9;border-top:1px solid #c9c9c9}.wy-menu-vertical .toctree-l1.current .toctree-l2>ul,.wy-menu-vertical .toctree-l2.current .toctree-l3>ul,.wy-menu-vertical .toctree-l3.current .toctree-l4>ul,.wy-menu-vertical .toctree-l4.current .toctree-l5>ul,.wy-menu-vertical .toctree-l5.current .toctree-l6>ul,.wy-menu-vertical .toctree-l6.current .toctree-l7>ul,.wy-menu-vertical .toctree-l7.current .toctree-l8>ul,.wy-menu-vertical .toctree-l8.current .toctree-l9>ul,.wy-menu-vertical .toctree-l9.current .toctree-l10>ul,.wy-menu-vertical .toctree-l10.current .toctree-l11>ul{display:none}.wy-menu-vertical .toctree-l1.current .current.toctree-l2>ul,.wy-menu-vertical .toctree-l2.current .current.toctree-l3>ul,.wy-menu-vertical .toctree-l3.current .current.toctree-l4>ul,.wy-menu-vertical .toctree-l4.current .current.toctree-l5>ul,.wy-menu-vertical .toctree-l5.current .current.toctree-l6>ul,.wy-menu-vertical .toctree-l6.current .current.toctree-l7>ul,.wy-menu-vertical .toctree-l7.current .current.toctree-l8>ul,.wy-menu-vertical .toctree-l8.current .current.toctree-l9>ul,.wy-menu-vertical .toctree-l9.current .current.toctree-l10>ul,.wy-menu-vertical .toctree-l10.current .current.toctree-l11>ul{display:block}.wy-menu-vertical li.toctree-l3,.wy-menu-vertical li.toctree-l4{font-size:.9em}.wy-menu-vertical li.toctree-l2 a,.wy-menu-vertical li.toctree-l3 a,.wy-menu-vertical li.toctree-l4 a,.wy-menu-vertical li.toctree-l5 a,.wy-menu-vertical li.toctree-l6 a,.wy-menu-vertical li.toctree-l7 a,.wy-menu-vertical li.toctree-l8 a,.wy-menu-vertical li.toctree-l9 a,.wy-menu-vertical li.toctree-l10 a{color:#404040}.wy-menu-vertical li.toctree-l2 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l3 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l4 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l5 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l6 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l7 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l8 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l9 a:hover button.toctree-expand,.wy-menu-vertical li.toctree-l10 a:hover button.toctree-expand{color:grey}.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a,.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a,.wy-menu-vertical li.toctree-l4.current li.toctree-l5>a,.wy-menu-vertical li.toctree-l5.current li.toctree-l6>a,.wy-menu-vertical li.toctree-l6.current li.toctree-l7>a,.wy-menu-vertical li.toctree-l7.current li.toctree-l8>a,.wy-menu-vertical li.toctree-l8.current li.toctree-l9>a,.wy-menu-vertical li.toctree-l9.current li.toctree-l10>a,.wy-menu-vertical li.toctree-l10.current li.toctree-l11>a{display:block}.wy-menu-vertical li.toctree-l2.current>a{padding:.4045em 2.427em}.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a{padding:.4045em 1.618em .4045em 4.045em}.wy-menu-vertical li.toctree-l3.current>a{padding:.4045em 4.045em}.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{padding:.4045em 1.618em .4045em 5.663em}.wy-menu-vertical li.toctree-l4.current>a{padding:.4045em 5.663em}.wy-menu-vertical li.toctree-l4.current li.toctree-l5>a{padding:.4045em 1.618em .4045em 7.281em}.wy-menu-vertical li.toctree-l5.current>a{padding:.4045em 7.281em}.wy-menu-vertical li.toctree-l5.current li.toctree-l6>a{padding:.4045em 1.618em .4045em 8.899em}.wy-menu-vertical li.toctree-l6.current>a{padding:.4045em 8.899em}.wy-menu-vertical li.toctree-l6.current li.toctree-l7>a{padding:.4045em 1.618em .4045em 10.517em}.wy-menu-vertical li.toctree-l7.current>a{padding:.4045em 10.517em}.wy-menu-vertical li.toctree-l7.current li.toctree-l8>a{padding:.4045em 1.618em .4045em 12.135em}.wy-menu-vertical li.toctree-l8.current>a{padding:.4045em 12.135em}.wy-menu-vertical li.toctree-l8.current li.toctree-l9>a{padding:.4045em 1.618em .4045em 13.753em}.wy-menu-vertical li.toctree-l9.current>a{padding:.4045em 13.753em}.wy-menu-vertical li.toctree-l9.current li.toctree-l10>a{padding:.4045em 1.618em .4045em 15.371em}.wy-menu-vertical li.toctree-l10.current>a{padding:.4045em 15.371em}.wy-menu-vertical li.toctree-l10.current li.toctree-l11>a{padding:.4045em 1.618em .4045em 16.989em}.wy-menu-vertical li.toctree-l2.current>a,.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a{background:#c9c9c9}.wy-menu-vertical li.toctree-l2 button.toctree-expand{color:#a3a3a3}.wy-menu-vertical li.toctree-l3.current>a,.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{background:#bdbdbd}.wy-menu-vertical li.toctree-l3 button.toctree-expand{color:#969696}.wy-menu-vertical li.current ul{display:block}.wy-menu-vertical li ul{margin-bottom:0;display:none}.wy-menu-vertical li ul li a{margin-bottom:0;color:#d9d9d9;font-weight:400}.wy-menu-vertical a{line-height:18px;padding:.4045em 1.618em;display:block;position:relative;font-size:90%;color:#d9d9d9}.wy-menu-vertical a:hover{background-color:#4e4a4a;cursor:pointer}.wy-menu-vertical a:hover button.toctree-expand{color:#d9d9d9}.wy-menu-vertical a:active{background-color:#2980b9;cursor:pointer;color:#fff}.wy-menu-vertical a:active button.toctree-expand{color:#fff}.wy-side-nav-search{display:block;width:300px;padding:.809em;margin-bottom:.809em;z-index:200;background-color:#2980b9;text-align:center;color:#fcfcfc}.wy-side-nav-search input[type=text]{width:100%;border-radius:50px;padding:6px 12px;border-color:#2472a4}.wy-side-nav-search img{display:block;margin:auto auto .809em;height:45px;width:45px;background-color:#2980b9;padding:5px;border-radius:100%}.wy-side-nav-search .wy-dropdown>a,.wy-side-nav-search>a{color:#fcfcfc;font-size:100%;font-weight:700;display:inline-block;padding:4px 6px;margin-bottom:.809em;max-width:100%}.wy-side-nav-search .wy-dropdown>a:hover,.wy-side-nav-search>a:hover{background:hsla(0,0%,100%,.1)}.wy-side-nav-search .wy-dropdown>a img.logo,.wy-side-nav-search>a img.logo{display:block;margin:0 auto;height:auto;width:auto;border-radius:0;max-width:100%;background:transparent}.wy-side-nav-search .wy-dropdown>a.icon img.logo,.wy-side-nav-search>a.icon img.logo{margin-top:.85em}.wy-side-nav-search>div.version{margin-top:-.4045em;margin-bottom:.809em;font-weight:400;color:hsla(0,0%,100%,.3)}.wy-nav .wy-menu-vertical header{color:#2980b9}.wy-nav .wy-menu-vertical a{color:#b3b3b3}.wy-nav .wy-menu-vertical a:hover{background-color:#2980b9;color:#fff}[data-menu-wrap]{-webkit-transition:all .2s ease-in;-moz-transition:all .2s ease-in;transition:all .2s ease-in;position:absolute;opacity:1;width:100%;opacity:0}[data-menu-wrap].move-center{left:0;right:auto;opacity:1}[data-menu-wrap].move-left{right:auto;left:-100%;opacity:0}[data-menu-wrap].move-right{right:-100%;left:auto;opacity:0}.wy-body-for-nav{background:#fcfcfc}.wy-grid-for-nav{position:absolute;width:100%;height:100%}.wy-nav-side{position:fixed;top:0;bottom:0;left:0;padding-bottom:2em;width:300px;overflow-x:hidden;overflow-y:hidden;min-height:100%;color:#9b9b9b;background:#343131;z-index:200}.wy-side-scroll{width:320px;position:relative;overflow-x:hidden;overflow-y:scroll;height:100%}.wy-nav-top{display:none;background:#2980b9;color:#fff;padding:.4045em .809em;position:relative;line-height:50px;text-align:center;font-size:100%;*zoom:1}.wy-nav-top:after,.wy-nav-top:before{display:table;content:""}.wy-nav-top:after{clear:both}.wy-nav-top a{color:#fff;font-weight:700}.wy-nav-top img{margin-right:12px;height:45px;width:45px;background-color:#2980b9;padding:5px;border-radius:100%}.wy-nav-top i{font-size:30px;float:left;cursor:pointer;padding-top:inherit}.wy-nav-content-wrap{margin-left:300px;background:#fcfcfc;min-height:100%}.wy-nav-content{padding:1.618em 3.236em;height:100%;max-width:800px;margin:auto}.wy-body-mask{position:fixed;width:100%;height:100%;background:rgba(0,0,0,.2);display:none;z-index:499}.wy-body-mask.on{display:block}footer{color:grey}footer p{margin-bottom:12px}.rst-content footer span.commit tt,footer span.commit .rst-content tt,footer span.commit code{padding:0;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;font-size:1em;background:none;border:none;color:grey}.rst-footer-buttons{*zoom:1}.rst-footer-buttons:after,.rst-footer-buttons:before{width:100%;display:table;content:""}.rst-footer-buttons:after{clear:both}.rst-breadcrumbs-buttons{margin-top:12px;*zoom:1}.rst-breadcrumbs-buttons:after,.rst-breadcrumbs-buttons:before{display:table;content:""}.rst-breadcrumbs-buttons:after{clear:both}#search-results .search li{margin-bottom:24px;border-bottom:1px solid #e1e4e5;padding-bottom:24px}#search-results .search li:first-child{border-top:1px solid #e1e4e5;padding-top:24px}#search-results .search li a{font-size:120%;margin-bottom:12px;display:inline-block}#search-results .context{color:grey;font-size:90%}.genindextable li>ul{margin-left:24px}@media screen and (max-width:768px){.wy-body-for-nav{background:#fcfcfc}.wy-nav-top{display:block}.wy-nav-side{left:-300px}.wy-nav-side.shift{width:85%;left:0}.wy-menu.wy-menu-vertical,.wy-side-nav-search,.wy-side-scroll{width:auto}.wy-nav-content-wrap{margin-left:0}.wy-nav-content-wrap .wy-nav-content{padding:1.618em}.wy-nav-content-wrap.shift{position:fixed;min-width:100%;left:85%;top:0;height:100%;overflow:hidden}}@media screen and (min-width:1100px){.wy-nav-content-wrap{background:rgba(0,0,0,.05)}.wy-nav-content{margin:0;background:#fcfcfc}}@media print{.rst-versions,.wy-nav-side,footer{display:none}.wy-nav-content-wrap{margin-left:0}}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60;*zoom:1}.rst-versions .rst-current-version:after,.rst-versions .rst-current-version:before{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-content .code-block-caption .rst-versions .rst-current-version .headerlink,.rst-content .eqno .rst-versions .rst-current-version .headerlink,.rst-content .rst-versions .rst-current-version .admonition-title,.rst-content code.download .rst-versions .rst-current-version span:first-child,.rst-content dl dt .rst-versions .rst-current-version .headerlink,.rst-content h1 .rst-versions .rst-current-version .headerlink,.rst-content h2 .rst-versions .rst-current-version .headerlink,.rst-content h3 .rst-versions .rst-current-version .headerlink,.rst-content h4 .rst-versions .rst-current-version .headerlink,.rst-content h5 .rst-versions .rst-current-version .headerlink,.rst-content h6 .rst-versions .rst-current-version .headerlink,.rst-content p .rst-versions .rst-current-version .headerlink,.rst-content table>caption .rst-versions .rst-current-version .headerlink,.rst-content tt.download .rst-versions .rst-current-version span:first-child,.rst-versions .rst-current-version .fa,.rst-versions .rst-current-version .icon,.rst-versions .rst-current-version .rst-content .admonition-title,.rst-versions .rst-current-version .rst-content .code-block-caption .headerlink,.rst-versions .rst-current-version .rst-content .eqno .headerlink,.rst-versions .rst-current-version .rst-content code.download span:first-child,.rst-versions .rst-current-version .rst-content dl dt .headerlink,.rst-versions .rst-current-version .rst-content h1 .headerlink,.rst-versions .rst-current-version .rst-content h2 .headerlink,.rst-versions .rst-current-version .rst-content h3 .headerlink,.rst-versions .rst-current-version .rst-content h4 .headerlink,.rst-versions .rst-current-version .rst-content h5 .headerlink,.rst-versions .rst-current-version .rst-content h6 .headerlink,.rst-versions .rst-current-version .rst-content p .headerlink,.rst-versions .rst-current-version .rst-content table>caption .headerlink,.rst-versions .rst-current-version .rst-content tt.download span:first-child,.rst-versions .rst-current-version .wy-menu-vertical li button.toctree-expand,.wy-menu-vertical li .rst-versions .rst-current-version button.toctree-expand{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}.rst-content .toctree-wrapper>p.caption,.rst-content h1,.rst-content h2,.rst-content h3,.rst-content h4,.rst-content h5,.rst-content h6{margin-bottom:24px}.rst-content img{max-width:100%;height:auto}.rst-content div.figure,.rst-content figure{margin-bottom:24px}.rst-content div.figure .caption-text,.rst-content figure .caption-text{font-style:italic}.rst-content div.figure p:last-child.caption,.rst-content figure p:last-child.caption{margin-bottom:0}.rst-content div.figure.align-center,.rst-content figure.align-center{text-align:center}.rst-content .section>a>img,.rst-content .section>img,.rst-content section>a>img,.rst-content section>img{margin-bottom:24px}.rst-content abbr[title]{text-decoration:none}.rst-content.style-external-links a.reference.external:after{font-family:FontAwesome;content:"\f08e";color:#b3b3b3;vertical-align:super;font-size:60%;margin:0 .2em}.rst-content blockquote{margin-left:24px;line-height:24px;margin-bottom:24px}.rst-content pre.literal-block{white-space:pre;margin:0;padding:12px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;display:block;overflow:auto}.rst-content div[class^=highlight],.rst-content pre.literal-block{border:1px solid #e1e4e5;overflow-x:auto;margin:1px 0 24px}.rst-content div[class^=highlight] div[class^=highlight],.rst-content pre.literal-block div[class^=highlight]{padding:0;border:none;margin:0}.rst-content div[class^=highlight] td.code{width:100%}.rst-content .linenodiv pre{border-right:1px solid #e6e9ea;margin:0;padding:12px;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;user-select:none;pointer-events:none}.rst-content div[class^=highlight] pre{white-space:pre;margin:0;padding:12px;display:block;overflow:auto}.rst-content div[class^=highlight] pre .hll{display:block;margin:0 -12px;padding:0 12px}.rst-content .linenodiv pre,.rst-content div[class^=highlight] pre,.rst-content pre.literal-block{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;font-size:12px;line-height:1.4}.rst-content div.highlight .gp,.rst-content div.highlight span.linenos{user-select:none;pointer-events:none}.rst-content div.highlight span.linenos{display:inline-block;padding-left:0;padding-right:12px;margin-right:12px;border-right:1px solid #e6e9ea}.rst-content .code-block-caption{font-style:italic;font-size:85%;line-height:1;padding:1em 0;text-align:center}@media print{.rst-content .codeblock,.rst-content div[class^=highlight],.rst-content div[class^=highlight] pre{white-space:pre-wrap}}.rst-content .admonition,.rst-content .admonition-todo,.rst-content .attention,.rst-content .caution,.rst-content .danger,.rst-content .error,.rst-content .hint,.rst-content .important,.rst-content .note,.rst-content .seealso,.rst-content .tip,.rst-content .warning{clear:both}.rst-content .admonition-todo .last,.rst-content .admonition-todo>:last-child,.rst-content .admonition .last,.rst-content .admonition>:last-child,.rst-content .attention .last,.rst-content .attention>:last-child,.rst-content .caution .last,.rst-content .caution>:last-child,.rst-content .danger .last,.rst-content .danger>:last-child,.rst-content .error .last,.rst-content .error>:last-child,.rst-content .hint .last,.rst-content .hint>:last-child,.rst-content .important .last,.rst-content .important>:last-child,.rst-content .note .last,.rst-content .note>:last-child,.rst-content .seealso .last,.rst-content .seealso>:last-child,.rst-content .tip .last,.rst-content .tip>:last-child,.rst-content .warning .last,.rst-content .warning>:last-child{margin-bottom:0}.rst-content .admonition-title:before{margin-right:4px}.rst-content .admonition table{border-color:rgba(0,0,0,.1)}.rst-content .admonition table td,.rst-content .admonition table th{background:transparent!important;border-color:rgba(0,0,0,.1)!important}.rst-content .section ol.loweralpha,.rst-content .section ol.loweralpha>li,.rst-content .toctree-wrapper ol.loweralpha,.rst-content .toctree-wrapper ol.loweralpha>li,.rst-content section ol.loweralpha,.rst-content section ol.loweralpha>li{list-style:lower-alpha}.rst-content .section ol.upperalpha,.rst-content .section ol.upperalpha>li,.rst-content .toctree-wrapper ol.upperalpha,.rst-content .toctree-wrapper ol.upperalpha>li,.rst-content section ol.upperalpha,.rst-content section ol.upperalpha>li{list-style:upper-alpha}.rst-content .section ol li>*,.rst-content .section ul li>*,.rst-content .toctree-wrapper ol li>*,.rst-content .toctree-wrapper ul li>*,.rst-content section ol li>*,.rst-content section ul li>*{margin-top:12px;margin-bottom:12px}.rst-content .section ol li>:first-child,.rst-content .section ul li>:first-child,.rst-content .toctree-wrapper ol li>:first-child,.rst-content .toctree-wrapper ul li>:first-child,.rst-content section ol li>:first-child,.rst-content section ul li>:first-child{margin-top:0}.rst-content .section ol li>p,.rst-content .section ol li>p:last-child,.rst-content .section ul li>p,.rst-content .section ul li>p:last-child,.rst-content .toctree-wrapper ol li>p,.rst-content .toctree-wrapper ol li>p:last-child,.rst-content .toctree-wrapper ul li>p,.rst-content .toctree-wrapper ul li>p:last-child,.rst-content section ol li>p,.rst-content section ol li>p:last-child,.rst-content section ul li>p,.rst-content section ul li>p:last-child{margin-bottom:12px}.rst-content .section ol li>p:only-child,.rst-content .section ol li>p:only-child:last-child,.rst-content .section ul li>p:only-child,.rst-content .section ul li>p:only-child:last-child,.rst-content .toctree-wrapper ol li>p:only-child,.rst-content .toctree-wrapper ol li>p:only-child:last-child,.rst-content .toctree-wrapper ul li>p:only-child,.rst-content .toctree-wrapper ul li>p:only-child:last-child,.rst-content section ol li>p:only-child,.rst-content section ol li>p:only-child:last-child,.rst-content section ul li>p:only-child,.rst-content section ul li>p:only-child:last-child{margin-bottom:0}.rst-content .section ol li>ol,.rst-content .section ol li>ul,.rst-content .section ul li>ol,.rst-content .section ul li>ul,.rst-content .toctree-wrapper ol li>ol,.rst-content .toctree-wrapper ol li>ul,.rst-content .toctree-wrapper ul li>ol,.rst-content .toctree-wrapper ul li>ul,.rst-content section ol li>ol,.rst-content section ol li>ul,.rst-content section ul li>ol,.rst-content section ul li>ul{margin-bottom:12px}.rst-content .section ol.simple li>*,.rst-content .section ol.simple li ol,.rst-content .section ol.simple li ul,.rst-content .section ul.simple li>*,.rst-content .section ul.simple li ol,.rst-content .section ul.simple li ul,.rst-content .toctree-wrapper ol.simple li>*,.rst-content .toctree-wrapper ol.simple li ol,.rst-content .toctree-wrapper ol.simple li ul,.rst-content .toctree-wrapper ul.simple li>*,.rst-content .toctree-wrapper ul.simple li ol,.rst-content .toctree-wrapper ul.simple li ul,.rst-content section ol.simple li>*,.rst-content section ol.simple li ol,.rst-content section ol.simple li ul,.rst-content section ul.simple li>*,.rst-content section ul.simple li ol,.rst-content section ul.simple li ul{margin-top:0;margin-bottom:0}.rst-content .line-block{margin-left:0;margin-bottom:24px;line-height:24px}.rst-content .line-block .line-block{margin-left:24px;margin-bottom:0}.rst-content .topic-title{font-weight:700;margin-bottom:12px}.rst-content .toc-backref{color:#404040}.rst-content .align-right{float:right;margin:0 0 24px 24px}.rst-content .align-left{float:left;margin:0 24px 24px 0}.rst-content .align-center{margin:auto}.rst-content .align-center:not(table){display:block}.rst-content .code-block-caption .headerlink,.rst-content .eqno .headerlink,.rst-content .toctree-wrapper>p.caption .headerlink,.rst-content dl dt .headerlink,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content p.caption .headerlink,.rst-content p .headerlink,.rst-content table>caption .headerlink{opacity:0;font-size:14px;font-family:FontAwesome;margin-left:.5em}.rst-content .code-block-caption .headerlink:focus,.rst-content .code-block-caption:hover .headerlink,.rst-content .eqno .headerlink:focus,.rst-content .eqno:hover .headerlink,.rst-content .toctree-wrapper>p.caption .headerlink:focus,.rst-content .toctree-wrapper>p.caption:hover .headerlink,.rst-content dl dt .headerlink:focus,.rst-content dl dt:hover .headerlink,.rst-content h1 .headerlink:focus,.rst-content h1:hover .headerlink,.rst-content h2 .headerlink:focus,.rst-content h2:hover .headerlink,.rst-content h3 .headerlink:focus,.rst-content h3:hover .headerlink,.rst-content h4 .headerlink:focus,.rst-content h4:hover .headerlink,.rst-content h5 .headerlink:focus,.rst-content h5:hover .headerlink,.rst-content h6 .headerlink:focus,.rst-content h6:hover .headerlink,.rst-content p.caption .headerlink:focus,.rst-content p.caption:hover .headerlink,.rst-content p .headerlink:focus,.rst-content p:hover .headerlink,.rst-content table>caption .headerlink:focus,.rst-content table>caption:hover .headerlink{opacity:1}.rst-content p a{overflow-wrap:anywhere}.rst-content .wy-table td p,.rst-content .wy-table td ul,.rst-content .wy-table th p,.rst-content .wy-table th ul,.rst-content table.docutils td p,.rst-content table.docutils td ul,.rst-content table.docutils th p,.rst-content table.docutils th ul,.rst-content table.field-list td p,.rst-content table.field-list td ul,.rst-content table.field-list th p,.rst-content table.field-list th ul{font-size:inherit}.rst-content .btn:focus{outline:2px solid}.rst-content table>caption .headerlink:after{font-size:12px}.rst-content .centered{text-align:center}.rst-content .sidebar{float:right;width:40%;display:block;margin:0 0 24px 24px;padding:24px;background:#f3f6f6;border:1px solid #e1e4e5}.rst-content .sidebar dl,.rst-content .sidebar p,.rst-content .sidebar ul{font-size:90%}.rst-content .sidebar .last,.rst-content .sidebar>:last-child{margin-bottom:0}.rst-content .sidebar .sidebar-title{display:block;font-family:Roboto Slab,ff-tisa-web-pro,Georgia,Arial,sans-serif;font-weight:700;background:#e1e4e5;padding:6px 12px;margin:-24px -24px 24px;font-size:100%}.rst-content .highlighted{background:#f1c40f;box-shadow:0 0 0 2px #f1c40f;display:inline;font-weight:700}.rst-content .citation-reference,.rst-content .footnote-reference{vertical-align:baseline;position:relative;top:-.4em;line-height:0;font-size:90%}.rst-content .citation-reference>span.fn-bracket,.rst-content .footnote-reference>span.fn-bracket{display:none}.rst-content .hlist{width:100%}.rst-content dl dt span.classifier:before{content:" : "}.rst-content dl dt span.classifier-delimiter{display:none!important}html.writer-html4 .rst-content table.docutils.citation,html.writer-html4 .rst-content table.docutils.footnote{background:none;border:none}html.writer-html4 .rst-content table.docutils.citation td,html.writer-html4 .rst-content table.docutils.citation tr,html.writer-html4 .rst-content table.docutils.footnote td,html.writer-html4 .rst-content table.docutils.footnote tr{border:none;background-color:transparent!important;white-space:normal}html.writer-html4 .rst-content table.docutils.citation td.label,html.writer-html4 .rst-content table.docutils.footnote td.label{padding-left:0;padding-right:0;vertical-align:top}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.field-list,html.writer-html5 .rst-content dl.footnote{display:grid;grid-template-columns:auto minmax(80%,95%)}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dt{display:inline-grid;grid-template-columns:max-content auto}html.writer-html5 .rst-content aside.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content div.citation{display:grid;grid-template-columns:auto auto minmax(.65rem,auto) minmax(40%,95%)}html.writer-html5 .rst-content aside.citation>span.label,html.writer-html5 .rst-content aside.footnote>span.label,html.writer-html5 .rst-content div.citation>span.label{grid-column-start:1;grid-column-end:2}html.writer-html5 .rst-content aside.citation>span.backrefs,html.writer-html5 .rst-content aside.footnote>span.backrefs,html.writer-html5 .rst-content div.citation>span.backrefs{grid-column-start:2;grid-column-end:3;grid-row-start:1;grid-row-end:3}html.writer-html5 .rst-content aside.citation>p,html.writer-html5 .rst-content aside.footnote>p,html.writer-html5 .rst-content div.citation>p{grid-column-start:4;grid-column-end:5}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.field-list,html.writer-html5 .rst-content dl.footnote{margin-bottom:24px}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dt{padding-left:1rem}html.writer-html5 .rst-content dl.citation>dd,html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.field-list>dd,html.writer-html5 .rst-content dl.field-list>dt,html.writer-html5 .rst-content dl.footnote>dd,html.writer-html5 .rst-content dl.footnote>dt{margin-bottom:0}html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.footnote{font-size:.9rem}html.writer-html5 .rst-content dl.citation>dt,html.writer-html5 .rst-content dl.footnote>dt{margin:0 .5rem .5rem 0;line-height:1.2rem;word-break:break-all;font-weight:400}html.writer-html5 .rst-content dl.citation>dt>span.brackets:before,html.writer-html5 .rst-content dl.footnote>dt>span.brackets:before{content:"["}html.writer-html5 .rst-content dl.citation>dt>span.brackets:after,html.writer-html5 .rst-content dl.footnote>dt>span.brackets:after{content:"]"}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref{text-align:left;font-style:italic;margin-left:.65rem;word-break:break-word;word-spacing:-.1rem;max-width:5rem}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref>a,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref>a{word-break:keep-all}html.writer-html5 .rst-content dl.citation>dt>span.fn-backref>a:not(:first-child):before,html.writer-html5 .rst-content dl.footnote>dt>span.fn-backref>a:not(:first-child):before{content:" "}html.writer-html5 .rst-content dl.citation>dd,html.writer-html5 .rst-content dl.footnote>dd{margin:0 0 .5rem;line-height:1.2rem}html.writer-html5 .rst-content dl.citation>dd p,html.writer-html5 .rst-content dl.footnote>dd p{font-size:.9rem}html.writer-html5 .rst-content aside.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content div.citation{padding-left:1rem;padding-right:1rem;font-size:.9rem;line-height:1.2rem}html.writer-html5 .rst-content aside.citation p,html.writer-html5 .rst-content aside.footnote p,html.writer-html5 .rst-content div.citation p{font-size:.9rem;line-height:1.2rem;margin-bottom:12px}html.writer-html5 .rst-content aside.citation span.backrefs,html.writer-html5 .rst-content aside.footnote span.backrefs,html.writer-html5 .rst-content div.citation span.backrefs{text-align:left;font-style:italic;margin-left:.65rem;word-break:break-word;word-spacing:-.1rem;max-width:5rem}html.writer-html5 .rst-content aside.citation span.backrefs>a,html.writer-html5 .rst-content aside.footnote span.backrefs>a,html.writer-html5 .rst-content div.citation span.backrefs>a{word-break:keep-all}html.writer-html5 .rst-content aside.citation span.backrefs>a:not(:first-child):before,html.writer-html5 .rst-content aside.footnote span.backrefs>a:not(:first-child):before,html.writer-html5 .rst-content div.citation span.backrefs>a:not(:first-child):before{content:" "}html.writer-html5 .rst-content aside.citation span.label,html.writer-html5 .rst-content aside.footnote span.label,html.writer-html5 .rst-content div.citation span.label{line-height:1.2rem}html.writer-html5 .rst-content aside.citation-list,html.writer-html5 .rst-content aside.footnote-list,html.writer-html5 .rst-content div.citation-list{margin-bottom:24px}html.writer-html5 .rst-content dl.option-list kbd{font-size:.9rem}.rst-content table.docutils.footnote,html.writer-html4 .rst-content table.docutils.citation,html.writer-html5 .rst-content aside.footnote,html.writer-html5 .rst-content aside.footnote-list aside.footnote,html.writer-html5 .rst-content div.citation-list>div.citation,html.writer-html5 .rst-content dl.citation,html.writer-html5 .rst-content dl.footnote{color:grey}.rst-content table.docutils.footnote code,.rst-content table.docutils.footnote tt,html.writer-html4 .rst-content table.docutils.citation code,html.writer-html4 .rst-content table.docutils.citation tt,html.writer-html5 .rst-content aside.footnote-list aside.footnote code,html.writer-html5 .rst-content aside.footnote-list aside.footnote tt,html.writer-html5 .rst-content aside.footnote code,html.writer-html5 .rst-content aside.footnote tt,html.writer-html5 .rst-content div.citation-list>div.citation code,html.writer-html5 .rst-content div.citation-list>div.citation tt,html.writer-html5 .rst-content dl.citation code,html.writer-html5 .rst-content dl.citation tt,html.writer-html5 .rst-content dl.footnote code,html.writer-html5 .rst-content dl.footnote tt{color:#555}.rst-content .wy-table-responsive.citation,.rst-content .wy-table-responsive.footnote{margin-bottom:0}.rst-content .wy-table-responsive.citation+:not(.citation),.rst-content .wy-table-responsive.footnote+:not(.footnote){margin-top:24px}.rst-content .wy-table-responsive.citation:last-child,.rst-content .wy-table-responsive.footnote:last-child{margin-bottom:24px}.rst-content table.docutils th{border-color:#e1e4e5}html.writer-html5 .rst-content table.docutils th{border:1px solid #e1e4e5}html.writer-html5 .rst-content table.docutils td>p,html.writer-html5 .rst-content table.docutils th>p{line-height:1rem;margin-bottom:0;font-size:.9rem}.rst-content table.docutils td .last,.rst-content table.docutils td .last>:last-child{margin-bottom:0}.rst-content table.field-list,.rst-content table.field-list td{border:none}.rst-content table.field-list td p{line-height:inherit}.rst-content table.field-list td>strong{display:inline-block}.rst-content table.field-list .field-name{padding-right:10px;text-align:left;white-space:nowrap}.rst-content table.field-list .field-body{text-align:left}.rst-content code,.rst-content tt{color:#000;font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;padding:2px 5px}.rst-content code big,.rst-content code em,.rst-content tt big,.rst-content tt em{font-size:100%!important;line-height:normal}.rst-content code.literal,.rst-content tt.literal{color:#e74c3c;white-space:normal}.rst-content code.xref,.rst-content tt.xref,a .rst-content code,a .rst-content tt{font-weight:700;color:#404040;overflow-wrap:normal}.rst-content kbd,.rst-content pre,.rst-content samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace}.rst-content a code,.rst-content a tt{color:#2980b9}.rst-content dl{margin-bottom:24px}.rst-content dl dt{font-weight:700;margin-bottom:12px}.rst-content dl ol,.rst-content dl p,.rst-content dl table,.rst-content dl ul{margin-bottom:12px}.rst-content dl dd{margin:0 0 12px 24px;line-height:24px}.rst-content dl dd>ol:last-child,.rst-content dl dd>p:last-child,.rst-content dl dd>table:last-child,.rst-content dl dd>ul:last-child{margin-bottom:0}html.writer-html4 .rst-content dl:not(.docutils),html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple){margin-bottom:24px}html.writer-html4 .rst-content dl:not(.docutils)>dt,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{display:table;margin:6px 0;font-size:90%;line-height:normal;background:#e7f2fa;color:#2980b9;border-top:3px solid #6ab0de;padding:6px;position:relative}html.writer-html4 .rst-content dl:not(.docutils)>dt:before,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:before{color:#6ab0de}html.writer-html4 .rst-content dl:not(.docutils)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt{margin-bottom:6px;border:none;border-left:3px solid #ccc;background:#f0f0f0;color:#555}html.writer-html4 .rst-content dl:not(.docutils) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) dl:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt .headerlink{color:#404040;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils)>dt:first-child,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple)>dt:first-child{margin-top:0}html.writer-html4 .rst-content dl:not(.docutils) code.descclassname,html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descclassname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{background-color:transparent;border:none;padding:0;font-size:100%!important}html.writer-html4 .rst-content dl:not(.docutils) code.descname,html.writer-html4 .rst-content dl:not(.docutils) tt.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) code.descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) tt.descname{font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .optional,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .optional{display:inline-block;padding:0 4px;color:#000;font-weight:700}html.writer-html4 .rst-content dl:not(.docutils) .property,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .property{display:inline-block;padding-right:8px;max-width:100%}html.writer-html4 .rst-content dl:not(.docutils) .k,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .k{font-style:italic}html.writer-html4 .rst-content dl:not(.docutils) .descclassname,html.writer-html4 .rst-content dl:not(.docutils) .descname,html.writer-html4 .rst-content dl:not(.docutils) .sig-name,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name{font-family:SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,Courier,monospace;color:#000}.rst-content .viewcode-back,.rst-content .viewcode-link{display:inline-block;color:#27ae60;font-size:80%;padding-left:24px}.rst-content .viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel,.rst-content .menuselection{font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .guilabel,.rst-content .menuselection{border:1px solid #7fbbe3;background:#e7f2fa}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block} \ No newline at end of file diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js index c3db08d..4d67807 100644 --- a/docs/_build/html/_static/doctools.js +++ b/docs/_build/html/_static/doctools.js @@ -4,12 +4,19 @@ * * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ "use strict"; +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + const _ready = (callback) => { if (document.readyState !== "loading") { callback(); @@ -18,73 +25,11 @@ const _ready = (callback) => { } }; -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - /** * Small JavaScript module for the documentation. */ const Documentation = { init: () => { - Documentation.highlightSearchWords(); Documentation.initDomainIndexTable(); Documentation.initOnKeyListeners(); }, @@ -126,51 +71,6 @@ const Documentation = { Documentation.LOCALE = catalog.locale; }, - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

      ' + - '' + - Documentation.gettext("Hide Search Matches") + - "

      " - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - /** * helper function to focus on search bar */ @@ -210,15 +110,11 @@ const Documentation = { ) return; - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; if (!event.shiftKey) { switch (event.key) { @@ -240,10 +136,6 @@ const Documentation = { event.preventDefault(); } break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); } } diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js index 4336358..bca7572 100644 --- a/docs/_build/html/_static/documentation_options.js +++ b/docs/_build/html/_static/documentation_options.js @@ -1,5 +1,4 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), +const DOCUMENTATION_OPTIONS = { VERSION: '2.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, @@ -10,5 +9,5 @@ var DOCUMENTATION_OPTIONS = { SOURCELINK_SUFFIX: '.txt', NAVIGATION_WITH_KEYS: false, SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: false, + ENABLE_SEARCH_SHORTCUTS: true, }; \ No newline at end of file diff --git a/docs/_build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js index 2e22b06..367b8ed 100644 --- a/docs/_build/html/_static/language_data.js +++ b/docs/_build/html/_static/language_data.js @@ -5,7 +5,7 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -13,7 +13,7 @@ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, is available */ +/* Non-minified version is copied as a separate JS file, if available */ /** * Porter Stemmer diff --git a/docs/_build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js index ac4d586..92da3f8 100644 --- a/docs/_build/html/_static/searchtools.js +++ b/docs/_build/html/_static/searchtools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for the full-text search. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -57,14 +57,14 @@ const _removeChildren = (element) => { const _escapeRegExp = (string) => string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -const _displayItem = (item, highlightTerms, searchTerms) => { +const _displayItem = (item, searchTerms, highlightTerms) => { const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; - const [docName, title, anchor, descr] = item; + const [docName, title, anchor, descr, score, _filename] = item; let listItem = document.createElement("li"); let requestUrl; @@ -75,29 +75,35 @@ const _displayItem = (item, highlightTerms, searchTerms) => { if (dirname.match(/\/index\/$/)) dirname = dirname.substring(0, dirname.length - 6); else if (dirname === "index/") dirname = ""; - requestUrl = docUrlRoot + dirname; + requestUrl = contentRoot + dirname; linkUrl = requestUrl; } else { // normal html builders - requestUrl = docUrlRoot + docName + docFileSuffix; + requestUrl = contentRoot + docName + docFileSuffix; linkUrl = docName + docLinkSuffix; } - const params = new URLSearchParams(); - params.set("highlight", [...highlightTerms].join(" ")); let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + "?" + params.toString() + anchor; + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; linkEl.innerHTML = title; - if (descr) - listItem.appendChild(document.createElement("span")).innerText = + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } else if (showSearchSummary) fetch(requestUrl) .then((responseData) => responseData.text()) .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, highlightTerms) + Search.makeSearchSummary(data, searchTerms, anchor) ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); }); Search.output.appendChild(listItem); }; @@ -110,27 +116,43 @@ const _finishSearch = (resultCount) => { ); else Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( results, resultCount, + searchTerms, highlightTerms, - searchTerms ) => { // results left, load the summary and display it // this is intended to be dynamic (don't sub resultsCount) if (results.length) { - _displayItem(results.pop(), highlightTerms, searchTerms); + _displayItem(results.pop(), searchTerms, highlightTerms); setTimeout( - () => _displayNextItem(results, resultCount, highlightTerms, searchTerms), + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), 5 ); } // search finished, update title and status message else _finishSearch(resultCount); }; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; /** * Default splitQuery function. Can be overridden in ``sphinx.search`` with a @@ -154,15 +176,26 @@ const Search = { _queued_query: null, _pulse_status: -1, - htmlToText: (htmlString) => { - const htmlElement = document - .createRange() - .createContextualFragment(htmlString); - _removeChildren(htmlElement.querySelectorAll(".headerlink")); + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; + if (docContent) return docContent.textContent; + console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." ); return ""; }, @@ -235,10 +268,7 @@ const Search = { else Search.deferQuery(query); }, - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { + _parseQuery: (query) => { // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -266,40 +296,95 @@ const Search = { } }); + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + // console.debug("SEARCH: searching for:"); // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); - // array of [docname, title, anchor, descr, score, filename] - let results = []; + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + _removeChildren(document.getElementById("search-progress")); + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + // lookup as object objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) + normalResults.push(...Search.performObjectSearch(term, objectTerms)) ); // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; // remove duplicate search results // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept @@ -313,14 +398,19 @@ const Search = { return acc; }, []); - results = results.reverse(); + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); // for debugging //Search.lastresults = results.slice(); // a copy // console.info("search results:", Search.lastresults); // print the results - _displayNextItem(results, results.length, highlightTerms, searchTerms); + _displayNextItem(results, results.length, searchTerms, highlightTerms); }, /** @@ -401,8 +491,8 @@ const Search = { // prepare search const terms = Search._index.terms; const titleTerms = Search._index.titleterms; - const docNames = Search._index.docnames; const filenames = Search._index.filenames; + const docNames = Search._index.docnames; const titles = Search._index.titles; const scoreMap = new Map(); @@ -418,14 +508,18 @@ const Search = { // add support for partial matches if (word.length > 2) { const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } } // no match but word was a required one @@ -448,9 +542,8 @@ const Search = { // create the mapping files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); }); }); @@ -499,16 +592,15 @@ const Search = { /** * helper function to return a node containing the * search summary for a given text. keywords is a list - * of stemmed words, highlightWords is the list of normal, unstemmed - * words. the first one is used to find the occurrence, the - * latter for highlighting it. + * of stemmed words. */ - makeSearchSummary: (htmlText, keywords, highlightWords) => { - const text = Search.htmlToText(htmlText).toLowerCase(); + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); if (text === "") return null; + const textLower = text.toLowerCase(); const actualStartPosition = [...keywords] - .map((k) => text.indexOf(k.toLowerCase())) + .map((k) => textLower.indexOf(k.toLowerCase())) .filter((i) => i > -1) .slice(-1)[0]; const startWithContext = Math.max(actualStartPosition - 120, 0); @@ -516,13 +608,9 @@ const Search = { const top = startWithContext === 0 ? "" : "..."; const tail = startWithContext + 240 < text.length ? "..." : ""; - let summary = document.createElement("div"); + let summary = document.createElement("p"); summary.classList.add("context"); - summary.innerText = top + text.substr(startWithContext, 240).trim() + tail; - - highlightWords.forEach((highlightWord) => - _highlightText(summary, highlightWord, "highlighted") - ); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; return summary; }, diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js index aae669d..8a96c69 100644 --- a/docs/_build/html/_static/sphinx_highlight.js +++ b/docs/_build/html/_static/sphinx_highlight.js @@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => { } span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); parent.insertBefore( span, parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), + rest, node.nextSibling ) ); node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); if (isInSVG) { const rect = document.createElementNS( @@ -140,5 +145,10 @@ const SphinxHighlight = { }, }; -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/_build/html/_static/togglebutton.css b/docs/_build/html/_static/togglebutton.css new file mode 100644 index 0000000..54a6787 --- /dev/null +++ b/docs/_build/html/_static/togglebutton.css @@ -0,0 +1,160 @@ +/** + * Admonition-based toggles + */ + +/* Visibility of the target */ +.admonition.toggle .admonition-title ~ * { + transition: opacity .3s, height .3s; +} + +/* Toggle buttons inside admonitions so we see the title */ +.admonition.toggle { + position: relative; +} + +/* Titles should cut off earlier to avoid overlapping w/ button */ +.admonition.toggle .admonition-title { + padding-right: 25%; + cursor: pointer; +} + +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:hover { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%); +} + +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:active { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%); +} + +/* Remove extra whitespace below the admonition title when hidden */ +.admonition.toggle-hidden { + padding-bottom: 0; +} + +.admonition.toggle-hidden .admonition-title { + margin-bottom: 0; +} + +/* hides all the content of a page until de-toggled */ +.admonition.toggle-hidden .admonition-title ~ * { + height: 0; + margin: 0; + opacity: 0; + visibility: hidden; +} + +/* General button style and position*/ +button.toggle-button { + /** + * Background and shape. By default there's no background + * but users can style as they wish + */ + background: none; + border: none; + outline: none; + + /* Positioning just inside the admonition title */ + position: absolute; + right: 0.5em; + padding: 0px; + border: none; + outline: none; +} + +/* Display the toggle hint on wide screens */ +@media (min-width: 768px) { + button.toggle-button.toggle-button-hidden:before { + content: attr(data-toggle-hint); /* This will be filled in by JS */ + font-size: .8em; + align-self: center; + } +} + +/* Icon behavior */ +.tb-icon { + transition: transform .2s ease-out; + height: 1.5em; + width: 1.5em; + stroke: currentColor; /* So that we inherit the color of other text */ +} + +/* The icon should point right when closed, down when open. */ +/* Open */ +.admonition.toggle button .tb-icon { + transform: rotate(90deg); +} + +/* Closed */ +.admonition.toggle button.toggle-button-hidden .tb-icon { + transform: rotate(0deg); +} + +/* With details toggles, we don't rotate the icon so it points right */ +details.toggle-details .tb-icon { + height: 1.4em; + width: 1.4em; + margin-top: 0.1em; /* To center the button vertically */ +} + + +/** + * Details-based toggles. + * In this case, we wrap elements with `.toggle` in a details block. + */ + +/* Details blocks */ +details.toggle-details { + margin: 1em 0; +} + + +details.toggle-details summary { + display: flex; + align-items: center; + cursor: pointer; + list-style: none; + border-radius: .2em; + border-left: 3px solid #1976d2; + background-color: rgb(204 204 204 / 10%); + padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */ + font-size: 0.9em; +} + +details.toggle-details summary:hover { + background-color: rgb(204 204 204 / 20%); +} + +details.toggle-details summary:active { + background: rgb(204 204 204 / 28%); +} + +.toggle-details__summary-text { + margin-left: 0.2em; +} + +details.toggle-details[open] summary { + margin-bottom: .5em; +} + +details.toggle-details[open] summary .tb-icon { + transform: rotate(90deg); +} + +details.toggle-details[open] summary ~ * { + animation: toggle-fade-in .3s ease-out; +} + +@keyframes toggle-fade-in { + from {opacity: 0%;} + to {opacity: 100%;} +} + +/* Print rules - we hide all toggle button elements at print */ +@media print { + /* Always hide the summary so the button doesn't show up */ + details.toggle-details summary { + display: none; + } +} \ No newline at end of file diff --git a/docs/_build/html/_static/togglebutton.js b/docs/_build/html/_static/togglebutton.js new file mode 100644 index 0000000..215a7ee --- /dev/null +++ b/docs/_build/html/_static/togglebutton.js @@ -0,0 +1,187 @@ +/** + * Add Toggle Buttons to elements + */ + +let toggleChevron = ` + + + +`; + +var initToggleItems = () => { + var itemsToToggle = document.querySelectorAll(togglebuttonSelector); + console.log(`[togglebutton]: Adding toggle buttons to ${itemsToToggle.length} items`) + // Add the button to each admonition and hook up a callback to toggle visibility + itemsToToggle.forEach((item, index) => { + if (item.classList.contains("admonition")) { + // If it's an admonition block, then we'll add a button inside + // Generate unique IDs for this item + var toggleID = `toggle-${index}`; + var buttonID = `button-${toggleID}`; + + item.setAttribute('id', toggleID); + if (!item.classList.contains("toggle")){ + item.classList.add("toggle"); + } + // This is the button that will be added to each item to trigger the toggle + var collapseButton = ` + `; + + title = item.querySelector(".admonition-title") + title.insertAdjacentHTML("beforeend", collapseButton); + thisButton = document.getElementById(buttonID); + + // Add click handlers for the button + admonition title (if admonition) + admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`) + if (admonitionTitle) { + // If an admonition, then make the whole title block clickable + admonitionTitle.addEventListener('click', toggleClickHandler); + admonitionTitle.dataset.target = toggleID + admonitionTitle.dataset.button = buttonID + } else { + // If not an admonition then we'll listen for the button click + thisButton.addEventListener('click', toggleClickHandler); + } + + // Now hide the item for this toggle button unless explicitly noted to show + if (!item.classList.contains("toggle-shown")) { + toggleHidden(thisButton); + } + } else { + // If not an admonition, wrap the block in a
      block + // Define the structure of the details block and insert it as a sibling + var detailsBlock = ` +
      + + ${toggleChevron} + ${toggleHintShow} + +
      `; + item.insertAdjacentHTML("beforebegin", detailsBlock); + + // Now move the toggle-able content inside of the details block + details = item.previousElementSibling + details.appendChild(item) + item.classList.add("toggle-details__container") + + // Set up a click trigger to change the text as needed + details.addEventListener('click', (click) => { + let parent = click.target.parentElement; + if (parent.tagName.toLowerCase() == "details") { + summary = parent.querySelector("summary"); + details = parent; + } else { + summary = parent; + details = parent.parentElement; + } + // Update the inner text for the proper hint + if (details.open) { + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow; + } else { + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide; + } + + }); + + // If we have a toggle-shown class, open details block should be open + if (item.classList.contains("toggle-shown")) { + details.click(); + } + } + }) +}; + +// This should simply add / remove the collapsed class and change the button text +var toggleHidden = (button) => { + target = button.dataset['target'] + var itemToToggle = document.getElementById(target); + if (itemToToggle.classList.contains("toggle-hidden")) { + itemToToggle.classList.remove("toggle-hidden"); + button.classList.remove("toggle-button-hidden"); + } else { + itemToToggle.classList.add("toggle-hidden"); + button.classList.add("toggle-button-hidden"); + } +} + +var toggleClickHandler = (click) => { + // Be cause the admonition title is clickable and extends to the whole admonition + // We only look for a click event on this title to trigger the toggle. + + if (click.target.classList.contains("admonition-title")) { + button = click.target.querySelector(".toggle-button"); + } else if (click.target.classList.contains("tb-icon")) { + // We've clicked the icon and need to search up one parent for the button + button = click.target.parentElement; + } else if (click.target.tagName == "polyline") { + // We've clicked the SVG elements inside the button, need to up 2 layers + button = click.target.parentElement.parentElement; + } else if (click.target.classList.contains("toggle-button")) { + // We've clicked the button itself and so don't need to do anything + button = click.target; + } else { + console.log(`[togglebutton]: Couldn't find button for ${click.target}`) + } + target = document.getElementById(button.dataset['button']); + toggleHidden(target); +} + +// If we want to blanket-add toggle classes to certain cells +var addToggleToSelector = () => { + const selector = ""; + if (selector.length > 0) { + document.querySelectorAll(selector).forEach((item) => { + item.classList.add("toggle"); + }) + } +} + +// Helper function to run when the DOM is finished +const sphinxToggleRunWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} +sphinxToggleRunWhenDOMLoaded(addToggleToSelector) +sphinxToggleRunWhenDOMLoaded(initToggleItems) + +/** Toggle details blocks to be open when printing */ +if (toggleOpenOnPrint == "true") { + window.addEventListener("beforeprint", () => { + // Open the details + document.querySelectorAll("details.toggle-details").forEach((el) => { + el.dataset["togglestatus"] = el.open; + el.open = true; + }); + + // Open the admonitions + document.querySelectorAll(".admonition.toggle.toggle-hidden").forEach((el) => { + console.log(el); + el.querySelector("button.toggle-button").click(); + el.dataset["toggle_after_print"] = "true"; + }); + }); + window.addEventListener("afterprint", () => { + // Re-close the details that were closed + document.querySelectorAll("details.toggle-details").forEach((el) => { + el.open = el.dataset["togglestatus"] == "true"; + delete el.dataset["togglestatus"]; + }); + + // Re-close the admonition toggle buttons + document.querySelectorAll(".admonition.toggle").forEach((el) => { + if (el.dataset["toggle_after_print"] == "true") { + el.querySelector("button.toggle-button").click(); + delete el.dataset["toggle_after_print"]; + } + }); + }); +} diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html index 0b60022..7345e16 100644 --- a/docs/_build/html/genindex.html +++ b/docs/_build/html/genindex.html @@ -1,22 +1,31 @@ - + Index — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index 2a47b14..62c71e7 100644 --- a/docs/_build/html/index.html +++ b/docs/_build/html/index.html @@ -1,23 +1,33 @@ - + EntropyHub — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + + @@ -182,9 +192,9 @@
      -

      EntropyHub

      +

      EntropyHub

      -

      An open-source toolkit for entropic data analysis

      +

      An open-source toolkit for entropic data analysis

      _images/EntropyHub_Profiler.png

      Available in:

      @@ -196,7 +206,7 @@

      An open-source toolkit for entropic data analysis

      -

      Welcome!

      +

      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.
      @@ -205,7 +215,7 @@

      Welcome!
      -

      About

      +

      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 dynamical systems and information theory, Entropy quantifies that uncertainty.

      @@ -226,7 +236,7 @@

      About

      -

      Documentation & Help

      +

      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.
      @@ -235,7 +245,7 @@

      Documentation & Help
      -

      Citation and Licensing

      +

      Citation and Licensing

      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:

      @@ -269,7 +279,7 @@

      Citation and Licensing
      -

      Contact

      +

      Contact

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

      diff --git a/docs/_build/html/julia/EHjulia.html b/docs/_build/html/julia/EHjulia.html index 4691c10..13961de 100644 --- a/docs/_build/html/julia/EHjulia.html +++ b/docs/_build/html/julia/EHjulia.html @@ -1,23 +1,32 @@ - + EntropyHub: Julia — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -178,7 +187,7 @@
      -

      EntropyHub: Julia

      +

      EntropyHub: Julia

      Links to installation files: GitHub || Julia Registry

      EntropyHub.jl is the EntropyHub package for Julia. The complete documentation for EntropyHub.jl can be found here

      @@ -186,7 +195,7 @@

      EntropyHub: Julia

      -

      Requirements & Installation:

      +

      Requirements & Installation:

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

    @@ -98,13 +119,19 @@
  • Multivariate Entropies
    • +
  • Bidimensional (2D) Entropies
      +
    +
  • Multiscale Entropies
  • Multiscale Cross-Entropies
    • -
  • Bidimensional (2D) Entropies @@ -116,10 +143,13 @@
  • Ex. 4 - Cross-Distribution Entropy
  • Ex. 5 - Multiscale Entropy Object
  • Ex. 6 - Multiscale [Increment] Entropy
    • -
  • Ex. 7 - Refined Multisclae [Sample] Entropy
    • +
  • Ex. 7 - Refined Multiscale [Sample] Entropy
  • Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
  • Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
  • Ex. 10 - Bidimensional Fuzzy Entropy
    • +
  • Ex. 11 - Multivariate Dispersion Entropy
    • +
  • Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • +
  • Ex. 13 - Windowing data with WindowData()
    • @@ -156,7 +186,7 @@
    -

    EntropyHub: Julia

    +

    EntropyHub: Julia

    EntropyHub.jl is the EntropyHub package for Julia.

    Examples in the Julia language can be found `here <https://mattwillflood.github.io/EntropyHub.jl/stable/Examples/Examples/> >`_

    diff --git a/docs/_build/html/mat-modindex.html b/docs/_build/html/mat-modindex.html index 24f010c..29d7617 100644 --- a/docs/_build/html/mat-modindex.html +++ b/docs/_build/html/mat-modindex.html @@ -1,22 +1,31 @@ - + MATLAB Module Index — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + diff --git a/docs/_build/html/matlab/EHmatlab.html b/docs/_build/html/matlab/EHmatlab.html index 4452a2e..0d08b71 100644 --- a/docs/_build/html/matlab/EHmatlab.html +++ b/docs/_build/html/matlab/EHmatlab.html @@ -1,23 +1,33 @@ - + EntropyHub: MatLab — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + + @@ -179,13 +189,13 @@
    -

    EntropyHub: MatLab

    +

    EntropyHub: MatLab

    Links to installation files: GitHub || MatLab File Exchange

    -

    Requirements & Installation:

    +

    Requirements & Installation:

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

    @@ -100,13 +121,19 @@
  • Multivariate Entropies
    • +
  • Bidimensional (2D) Entropies
      +
    +
  • Multiscale Entropies
  • Multiscale Cross-Entropies
    • -
  • Bidimensional (2D) Entropies @@ -118,10 +145,13 @@
  • Ex. 4 - Cross-Distribution Entropy
  • Ex. 5 - Multiscale Entropy Object
  • Ex. 6 - Multiscale [Increment] Entropy
    • -
  • Ex. 7 - Refined Multisclae [Sample] Entropy
    • +
  • Ex. 7 - Refined Multiscale [Sample] Entropy
  • Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
  • Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
  • Ex. 10 - Bidimensional Fuzzy Entropy
    • +
  • Ex. 11 - Multivariate Dispersion Entropy
    • +
  • Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • +
  • Ex. 13 - Windowing data with WindowData()
    • @@ -160,7 +190,7 @@
    -

    Example 1: Sample Entropy

    +

    Example 1: Sample Entropy

    Import a signal of normally distributed random numbers [mean = 0; SD = 1], and calculate the sample entropy for each embedding dimension (m) from 0 to 4.

    X = ExampleData("gaussian");
diff --git a/docs/_build/html/matlab/Examples/Ex10.html b/docs/_build/html/matlab/Examples/Ex10.html
index 1db1308..6f421ea 100644
--- a/docs/_build/html/matlab/Examples/Ex10.html
+++ b/docs/_build/html/matlab/Examples/Ex10.html
@@ -1,28 +1,37 @@
 
-
+
 
   
 
   
   Example 10: Bidimensional Fuzzy Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
         
     
     
     
-    
+    
      
 
 
@@ -64,13 +73,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -82,10 +100,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -101,13 +122,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -119,10 +146,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -161,7 +191,7 @@
            
    
              
   
    
-
    Example 10: Bidimensional Fuzzy Entropy
    
+
    Example 10: Bidimensional Fuzzy Entropy
    
 
    Import an image of a Mandelbrot fractal as a matrix.
    
 
    X = ExampleData('mandelbrot_Mat');
 
@@ -189,7 +219,7 @@ 
    Example 10: Bidimensional Fuzzy Entropy
         
-        
+        
     
    
 
   
    
diff --git a/docs/_build/html/matlab/Examples/Ex11.html b/docs/_build/html/matlab/Examples/Ex11.html
index de8c9c7..e78f2a6 100644
--- a/docs/_build/html/matlab/Examples/Ex11.html
+++ b/docs/_build/html/matlab/Examples/Ex11.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 11: Multivariate Dispersion Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -95,7 +104,7 @@
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
 
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
-
  •  Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
 
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 11: Multivariate Dispersion Entropy
    
+
    Example 11: Multivariate Dispersion Entropy
    
 
    Import a vector of 4096 uniformly distributed random integers in range [1 8] and convert it to a multivariate set of 4 sequences with 1024 samples each.
    
 
    X = ExampleData('randintegers')
 Data = reshape(X, 1024, 4)
diff --git a/docs/_build/html/matlab/Examples/Ex12.html b/docs/_build/html/matlab/Examples/Ex12.html
index ffa2b65..9b551c3 100644
--- a/docs/_build/html/matlab/Examples/Ex12.html
+++ b/docs/_build/html/matlab/Examples/Ex12.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    
+
    Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    
 
    Import the x, y, and z components of the Lorenz system of equations.
    
 
    Data = ExampleData('lorenz');
 
diff --git a/docs/_build/html/matlab/Examples/Ex13.html b/docs/_build/html/matlab/Examples/Ex13.html
index 6df085f..edfed84 100644
--- a/docs/_build/html/matlab/Examples/Ex13.html
+++ b/docs/_build/html/matlab/Examples/Ex13.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 13: Windowing Data with WindowData() — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -95,7 +104,7 @@
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
 
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
-
  •  Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
 
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 13: Windowing Data with WindowData()
    
+
    Example 13: Windowing Data with WindowData()
    
 
    Create a sequence of integers from 1 - 1000 and segment the values into windows of length 75, with no overlap.
    
 
    X = 1:1000
 [WinData, Log] = WindowData(X, WinLen = 75)
diff --git a/docs/_build/html/matlab/Examples/Ex2.html b/docs/_build/html/matlab/Examples/Ex2.html
index 719bffe..dab5704 100644
--- a/docs/_build/html/matlab/Examples/Ex2.html
+++ b/docs/_build/html/matlab/Examples/Ex2.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -63,13 +72,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -81,10 +99,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -100,13 +121,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -118,10 +145,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -160,7 +190,7 @@
            
    
              
   
    
-
    Example 2: (Fine-Grained) Permutation Entropy
    
+
    Example 2: (Fine-Grained) Permutation Entropy
    
 
    Import the x, y, and z components of the Lorenz system of equations.
    
 
    Data = ExampleData('lorenz');
 
diff --git a/docs/_build/html/matlab/Examples/Ex3.html b/docs/_build/html/matlab/Examples/Ex3.html
index 405abc4..9b55536 100644
--- a/docs/_build/html/matlab/Examples/Ex3.html
+++ b/docs/_build/html/matlab/Examples/Ex3.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -63,13 +72,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -81,10 +99,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -100,13 +121,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -118,10 +145,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -160,7 +190,7 @@
            
    
              
   
    
-
    Example 3: Phase Entropy w/ Pioncare Plot
    
+
    Example 3: Phase Entropy w/ Pioncare Plot
    
 
    Import the x and y components of the Henon system of equations.
    
 
    Data = ExampleData('henon');
 
diff --git a/docs/_build/html/matlab/Examples/Ex4.html b/docs/_build/html/matlab/Examples/Ex4.html
index c939fd8..bf05f0c 100644
--- a/docs/_build/html/matlab/Examples/Ex4.html
+++ b/docs/_build/html/matlab/Examples/Ex4.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -95,7 +104,7 @@
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
 
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
-
  •  Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
 
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 4: Cross-Distribution Entropy w/ Different Binning Methods
    
+
    Example 4: Cross-Distribution Entropy w/ Different Binning Methods
    
 
    Import a signal of pseudorandom integers in the range [1, 8] and calculate the cross-
 distribution entropy with an embedding dimension (m) of 5, a time delay (tau) of 3,
 and Sturges’ bin selection method.
    
diff --git a/docs/_build/html/matlab/Examples/Ex5.html b/docs/_build/html/matlab/Examples/Ex5.html
index ced3f22..819a2b4 100644
--- a/docs/_build/html/matlab/Examples/Ex5.html
+++ b/docs/_build/html/matlab/Examples/Ex5.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -63,13 +72,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -81,10 +99,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -100,13 +121,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -118,10 +145,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -160,7 +190,7 @@
            
    
              
   
    
-
    Example 5: Multiscale Entropy Object [MSobject()]
    
+
    Example 5: Multiscale Entropy Object [MSobject()]
    
 
    Create a multiscale entropy object (Mobj) for multiscale fuzzy entropy,
 calculated with an embedding dimension (m) of 5, a time delay (tau) of 2,
 using a sigmoidal fuzzy function with the r scaling parameters (3, 1.2).
    
diff --git a/docs/_build/html/matlab/Examples/Ex6.html b/docs/_build/html/matlab/Examples/Ex6.html
index 56bb7f7..638a367 100644
--- a/docs/_build/html/matlab/Examples/Ex6.html
+++ b/docs/_build/html/matlab/Examples/Ex6.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 6: Multiscale [Increment] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
         
     
     
@@ -64,13 +73,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -82,10 +100,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -101,13 +122,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -119,10 +146,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -161,7 +191,7 @@
            
    
              
   
    
-
    Example 6: Multiscale [Increment] Entropy
    
+
    Example 6: Multiscale [Increment] Entropy
    
 
    Import a signal of uniformly distributed pseudorandom integers in the range [1,8] and
 create a multiscale entropy object with the following parameters:
    
 
    EnType = IncrEn(), embedding dimension = 3, a quantifying resolution = 6, normalization = true.
    
diff --git a/docs/_build/html/matlab/Examples/Ex7.html b/docs/_build/html/matlab/Examples/Ex7.html
index 4457df9..7b96b81 100644
--- a/docs/_build/html/matlab/Examples/Ex7.html
+++ b/docs/_build/html/matlab/Examples/Ex7.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -63,13 +72,22 @@
 
  •  Cross-Entropies
      
 
    
 
    • 
+
  •  Multivariate Entropies
      
+
    
+
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -81,10 +99,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -100,13 +121,19 @@
 
  •  Multivariate Entropies
      
 
    
 
    • 
+
  •  Bidimensional (2D) Entropies
      
+
    
+
    • 
 
  •  Multiscale Entropies
      
 
    
 
    • 
 
  •  Multiscale Cross-Entropies
      
 
    
 
    • 
-
  •  Bidimensional (2D) Entropies
@@ -118,10 +145,13 @@
 
  •  Ex. 4 - Cross-Distribution Entropy
    • 
 
  •  Ex. 5 - Multiscale Entropy Object
    • 
 
  •  Ex. 6 - Multiscale [Increment] Entropy
    • 
-
  •  Ex. 7 - Refined Multisclae [Sample] Entropy
    • 
+
  •  Ex. 7 - Refined Multiscale [Sample] Entropy
    • 
 
  •  Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
    • 
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
+
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
 
@@ -160,7 +190,7 @@
            
    
              
   
    
-
    Example 7: Refined Multiscale [Sample] Entropy
    
+
    Example 7: Refined Multiscale [Sample] Entropy
    
 
    Import a signal of uniformly distributed pseudorandom integers in the range [1, 8] and
 create a multiscale entropy object with the following parameters:
    
 
    EnType = SampEn(), embedding dimension = 4, radius threshold = 1.25
    
diff --git a/docs/_build/html/matlab/Examples/Ex8.html b/docs/_build/html/matlab/Examples/Ex8.html
index 1f5dd4d..87cc5ab 100644
--- a/docs/_build/html/matlab/Examples/Ex8.html
+++ b/docs/_build/html/matlab/Examples/Ex8.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -95,7 +104,7 @@
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
 
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
-
  •  Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
 
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 8: Composite Multiscale Cross-[Approximate] Entropy
    
+
    Example 8: Composite Multiscale Cross-[Approximate] Entropy
    
 
    Import two signals of uniformly distributed pseudorandom integers in the range [1 8] and
 create a multiscale entropy object with the following parameters:
    
 
    EnType = XApEn(), embedding dimension = 2, time delay = 2, radius distance threshold = 0.5.
    
diff --git a/docs/_build/html/matlab/Examples/Ex9.html b/docs/_build/html/matlab/Examples/Ex9.html
index 2aa02f6..535c551 100644
--- a/docs/_build/html/matlab/Examples/Ex9.html
+++ b/docs/_build/html/matlab/Examples/Ex9.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -95,7 +104,7 @@
 
  •  Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    • 
 
  •  Ex. 10 - Bidimensional Fuzzy Entropy
    • 
 
  •  Ex. 11 - Multivariate Dispersion Entropy
    • 
-
  •  Ex. 12 - Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
+
  •  Ex. 12 - [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
    • 
 
  •  Ex. 13 - Windowing data with WindowData()
    • 
 
 
@@ -181,7 +190,7 @@
            
    
              
   
    
-
    Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    
+
    Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
    
 
    Import the x and y components of the Henon system of equations and create a multiscale entropy object
 with the following parameters:
    
 
    EnType = XCondEn(), embedding dimension = 2, time delay = 2, number of symbols = 12, logarithm base = 2,
diff --git a/docs/_build/html/matlab/Functions/matBase.html b/docs/_build/html/matlab/Functions/matBase.html
index 6d29e2d..28a19b7 100644
--- a/docs/_build/html/matlab/Functions/matBase.html
+++ b/docs/_build/html/matlab/Functions/matBase.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Base Entropies — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,17 +190,18 @@
            
    
              
   
    
-
    Base Entropies
    
+
    Base Entropies
    
 
    
 
    
 
    
-
    Functions for estimating the entropy of a single univariate time series.
    
+
    Functions for estimating the entropy of a single univariate time series.
    
 
    The following functions also form the base entropy method used by multiscale entropy functions.
    
 
    
-
    
+
    
 
    
-ApEn(Sig, varargin)
    
+ApEn(Sig, varargin)
 
    ApEn  estimates the approximate entropy of a univariate data sequence.
    
+
    
 
    [Ap, Phi] = ApEn(Sig)
    
 
    Returns the approximate entropy estimates (Ap) and the log-average number of 
 matched vectors (Phi) for m = [0, 1, 2], estimated from the data 
@@ -220,12 +230,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-AttnEn(Sig, varargin)
    
+AttnEn(Sig, varargin)
 
    AttnEn  estimates the attention entropy of a univariate data sequence.
    
+
    
 
    [Attn] = AttnEn(Sig)
    
 
    Returns the attention entropy (Attn) calculated as the average of the
 sub-entropies (Hxx, Hxn, Hnn, Hnx) estimated from the data
@@ -255,12 +267,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-BubbEn(Sig, varargin)
    
+BubbEn(Sig, varargin)
 
    BubbEn  estimates the bubble entropy of a univariate data sequence.
    
+
    
 
    [Bubb, H] = BubbEn(Sig)
    
 
    Returns the bubble entropy (Bubb) and the conditional Renyi entropy (H)
 estimates from the data sequence (Sig) using the default parameters:
@@ -287,12 +301,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-CondEn(Sig, varargin)
    
+CondEn(Sig, varargin)
 
    CondEn  estimates the corrected conditional entropy of a univariate data sequence.
    
+
    
 
    [Cond, SEw, SEz] = CondEn(Sig)
    
 
    Returns the corrected conditional entropy estimates (Cond) and the
 corresponding Shannon entropies (m: SEw, m+1: SEz) for m = [1,2] 
@@ -335,12 +351,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-CoSiEn(Sig, varargin)
    
+CoSiEn(Sig, varargin)
 
    CoSiEn  estimates the cosine similarity entropy of a univariate data sequence.
    
+
    
 
    [CoSi, Bm] = CoSiEn(Sig)
    
 
    Returns the cosine similarity entropy (CoSi) and the corresponding
 global probabilities (Bm) estimated from the data sequence (Sig) 
@@ -381,12 +399,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-DistEn(Sig, varargin)
    
+DistEn(Sig, varargin)
 
    DistEn  estimates the distribution entropy of a univariate data sequence.
    
+
    
 
    [Dist, Ppi] = DistEn(Sig)
    
 
    Returns the distribution entropy estimate (Dist) and the
 corresponding distribution probabilities (Ppi) estimated from the data 
@@ -426,12 +446,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-DispEn(Sig, varargin)
    
+DispEn(Sig, varargin)
 
    DispEn  estimates the dispersion entropy of a univariate data sequence.
    
+
    
 
    [Dispx, RDE] = DispEn(Sig)
    
 
    Returns the dispersion entropy (Dispx) and the reverse dispersion entropy
 (RDE) estimated from the data sequence (Sig) using the default parameters:
@@ -491,12 +513,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-DivEn(Sig, varargin)
    
+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)
@@ -541,12 +565,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-EnofEn(Sig, varargin)
    
+EnofEn(Sig, varargin)
 
    EnofEn  estimates the entropy of entropy from a univariate data sequence.
    
+
    
 
    [EoE, AvEn, S2] = EnofEn(Sig)
    
 
    Returns the entropy of entropy (EoE), the average Shannon entropy 
 (AvEn), and the number of levels (S2) across all windows 
@@ -580,12 +606,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-FuzzEn(Sig, varargin)
    
+FuzzEn(Sig, varargin)
 
    FuzzEn  estimates the fuzzy entropy of a univariate data sequence.
    
+
    
 
    [Fuzz, Ps1, Ps2] = FuzzEn(Sig)
    
 
    Returns the fuzzy entropy estimates (Fuzz) and the average fuzzy distances 
 (m: Ps1, m+1: Ps2) for m = [1,2] estimated from the data sequence 
@@ -704,12 +732,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-GridEn(Sig, varargin)
    
+GridEn(Sig, varargin)
 
    GridEn  estimates the gridded distribution entropy of a univariate data sequence.
    
+
    
 
    [GDE, GDR] = GridEn(Sig)
    
 
    Returns the gridded distribution entropy (GDE) and the gridded 
 distribution rate (GDR) estimated from the data sequence (Sig) using 
@@ -764,12 +794,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-IncrEn(Sig, varargin)
    
+IncrEn(Sig, varargin)
 
    IncrEn  estimates the increment entropy of a univariate data sequence.
    
+
    
 
    [Incr] = IncrEn(Sig)
    
 
    Returns the increment entropy (Incr) estimated from the data sequence 
 (Sig) using the default parameters: 
@@ -815,12 +847,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-K2En(Sig, varargin)
    
+K2En(Sig, varargin)
 
    K2En  estimates the Kolmogorov (K2) entropy of a univariate data sequence.
    
+
    
 
    [K2, Ci] = K2En(Sig)
    
 
    Returns the Kolmogorov entropy estimates (K2) and the correlation
 integrals (Ci) for m = [1,2] estimated from the data sequence (Sig)
@@ -852,12 +886,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-PermEn(Sig, varargin)
    
+PermEn(Sig, varargin)
 
    PermEn  estimates the permutation entropy of a univariate data sequence.
    
+
    
 
    [Perm, Pnorm, cPE] = PermEn(Sig)
    
 
    Returns the permuation entropy estimates (Perm), the normalised
 permutation entropy (Pnorm) and the conditional permutation entropy
@@ -964,12 +1000,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-PhasEn(Sig, varargin)
    
+PhasEn(Sig, varargin)
 
    PhasEn  estimates the phase entropy of a univariate data sequence.
    
+
    
 
    [Phas] = PhasEn(Sig)
    
 
    Returns the phase entropy (Phas) estimate of the data sequence (Sig)
 using the default parameters: 
@@ -1018,12 +1056,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-RangEn(Sig, varargin)
    
+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)
@@ -1057,12 +1097,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-SampEn(Sig, varargin)
    
+SampEn(Sig, varargin)
 
    SampEn  estimates the sample entropy of a univariate data sequence.
    
+
    
 
    [Samp, A, B] = SampEn(Sig)
    
 
    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 
@@ -1105,12 +1147,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-SlopEn(Sig, varargin)
    
+SlopEn(Sig, varargin)
 
    SlopEn  estimates the slope entropy of a univariate data sequence.
    
+
    
 
    [Slop] = SlopEn(Sig)
    
 
    Returns the slope entropy (Slop) estimates for embedding dimensions
 [2, …, m] of the data sequence (Sig) using the default parameters: 
@@ -1149,12 +1193,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-SpecEn(Sig, varargin)
    
+SpecEn(Sig, varargin)
 
    SpecEn  estimates the spectral entropy of a univariate data sequence.
    
+
    
 
    [Spec, BandEn] = SpecEn(Sig)
    
 
    Returns the spectral entropy estimate of the full spectrum (Spec)
 and the within-band entropy (BandEn) estimated from the data sequence 
@@ -1198,12 +1244,14 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
 
    
-SyDyEn(Sig, varargin)
    
+SyDyEn(Sig, varargin)
 
    SyDyEn  estimates the symbolic dynamic entropy of a univariate data sequence.
    
+
    
 
    [SyDy, Zt] = SyDyEn(Sig)
    
 
    Returns the symbolic dynamic entropy (SyDy) and the symbolic sequence
 (Zt) of the data sequence (Sig) using the default parameters: 
@@ -1258,6 +1306,7 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
    
 
    
+
    
 
 
 
    
diff --git a/docs/_build/html/matlab/Functions/matBidimensional.html b/docs/_build/html/matlab/Functions/matBidimensional.html
index 32711ff..e116538 100644
--- a/docs/_build/html/matlab/Functions/matBidimensional.html
+++ b/docs/_build/html/matlab/Functions/matBidimensional.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Bidimensional Entropies — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,11 +190,11 @@
            
    
              
   
    
-
    Bidimensional Entropies
    
+
    Bidimensional Entropies
    
 
    
 
    
 
    
-
    Functions for estimating the entropy of a two-dimensional univariate matrix.
    
+
    Functions for estimating the entropy of a two-dimensional univariate matrix.
    
 
    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.
    
@@ -205,10 +214,11 @@ 
    Functions for estimating the entropy of a two-dimensional univariate matrix.
 
    CAUTION: unlocking the permitted matrix size may cause your MatLab application to crash.
    
 
    
 
    
-
    
+
    
 
    
-SampEn2D(Mat, varargin)
    
+SampEn2D(Mat, varargin)
 
    SampEn2D  estimates the bidimensional sample entropy of a data matrix.
    
+
    
 
    [SE2D, Phi1, Phi2] = SampEn2D(Mat)
    
 
    Returns the bidimensional sample entropy estimate (SE2D) and the number
 of matched sub-matricess (m: Phi1, m+1: Phi2) estimated for the data 
@@ -252,12 +262,14 @@ 
    Functions for estimating the entropy of a two-dimensional univariate matrix.
 
    
 
 
    
+
    
 
 
 
    
 
    
-FuzzEn2D(Mat, varargin)
    
+FuzzEn2D(Mat, varargin)
 
    FuzzEn2D  estimates the bidimensional fuzzy entropy of a data matrix.
    
+
    
 
    [Fuzz2D] = FuzzEn2D(Mat)
    
 
    Returns the bidimensional fuzzy entropy estimate (Fuzz2D) estimated for 
 the data matrix (Mat) using the default parameters: time delay = 1,
@@ -386,12 +398,14 @@ 
    Functions for estimating the entropy of a two-dimensional univariate matrix.
 
    
 
 
+
    
 
 
 
    
 
    
-DistEn2D(Mat, varargin)
    
+DistEn2D(Mat, varargin)
 
    DistEn2D  estimates the bidimensional distribution entropy of a data matrix.
    
+
    
 
    [Dist2D] = DistEn2D(Mat)
    
 
    Returns the bidimensional distribution entropy estimate (Dist2D)
 estimated for the data matrix (Mat) using the default parameters:
@@ -450,12 +464,14 @@ 
    Functions for estimating the entropy of a two-dimensional univariate matrix.
 
    
 
 
+
    
 
 
 
    
 
    
-DispEn2D(Mat, varargin)
    
+DispEn2D(Mat, varargin)
 
    DispEn2D  estimates the bidimensional dispersion entropy of a data matrix.
    
+
    
 
    [Disp2D, RDE] = DispEn2D(Mat)
    
 
    Returns the bidimensional dispersion entropy estimate (Disp2D) and 
 reverse bidimensional dispersion entropy (RDE) estimated for the data 
@@ -512,15 +528,18 @@ 
    Functions for estimating the entropy of a two-dimensional univariate matrix.
 
    
 
 
+
    -PermEn2D(Mat, varargin)
    -

    m, tau, Logx -PermEn2D estimates the bidimensional permutation entropy of a data matrix.

    -
    -

    [Perm2D] = PermEn2D(Mat)

    +PermEn2D(Mat, varargin) +
    +
    m, tau, Logx

    PermEn2D estimates the bidimensional permutation entropy of a data matrix.

    +
    +
    +
    +

    [Perm2D] = PermEn2D(Mat)

    Returns the bidimensional permutation entropy estimate (Perm2D) estimated for the data matrix (Mat) using the default parameters: time delay = 1, logarithm = natural, template matrix size = [floor(H/10) floor(W/10)] @@ -601,13 +620,14 @@

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

    -
    +
    -EspEn2D(Mat, varargin)
    +EspEn2D(Mat, varargin)

    EspEn2D estimates the bidimensional Espinosa entropy of a data matrix.

    +

    [Esp2D] = EspEn2D(Mat)

    Returns the bidimensional Espinosa entropy estimate (Esp2D) estimated for the data matrix (Mat) using the default parameters: @@ -651,6 +671,7 @@

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

    +
    diff --git a/docs/_build/html/matlab/Functions/matCross.html b/docs/_build/html/matlab/Functions/matCross.html index d29185e..3fd4159 100644 --- a/docs/_build/html/matlab/Functions/matCross.html +++ b/docs/_build/html/matlab/Functions/matCross.html @@ -1,23 +1,32 @@ - + Cross Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,17 +190,18 @@
    -

    Cross Entropies

    +

    Cross Entropies

    -

    Functions for estimating the entropy between two univariate time series.

    +

    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.

    -
    +
    -XApEn(Sig1, Sig2, varargin)
    +XApEn(Sig1, Sig2, varargin)

    XApEn estimates the cross-approximate entropy between two univariate data sequences.

    +

    [XAp, Phi] = XApEn(Sig1, Sig2)

    Returns the cross-approximate entropy estimates (XAp) and the log-average number of matched vectors (Phi) for m = [0,1,2], estimated for the data @@ -228,12 +238,14 @@

    Functions for estimating the entropy between two univariate time series.

    +
    -XCondEn(Sig1, Sig2, varargin)
    +XCondEn(Sig1, Sig2, varargin)

    XCondEn estimates the corrected cross-conditional entropy between two univariate data sequences.

    +

    [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] @@ -278,12 +290,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XDistEn(Sig1, Sig2, varargin)
    +XDistEn(Sig1, Sig2, varargin)

    XDistEn estimates the cross-distribution entropy between two univariate data sequences.

    +

    [XDist, Ppi] = XDistEn(Sig1, Sig2)

    Returns the cross-distribution entropy estimate (XDist) and the corresponding distribution probabilities (Ppi) estimated between the data @@ -325,12 +339,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XFuzzEn(Sig1, Sig2, varargin)
    +XFuzzEn(Sig1, Sig2, varargin)

    XFuzzEn estimates the cross-fuzzy entropy between two univariate data sequences.

    +

    [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 @@ -445,12 +461,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XK2En(Sig1, Sig2, varargin)
    +XK2En(Sig1, Sig2, varargin)

    XK2En estimates the cross-Kolmogorov (K2) entropy between two univariate data sequences.

    +

    [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 @@ -479,12 +497,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XPermEn(Sig1, Sig2, varargin)
    +XPermEn(Sig1, Sig2, varargin)

    XPermEn estimates the cross-permutation entropy between two univariate data sequences.

    +

    [XPerm] = XPermEn(Sig1, Sig2)

    Returns the cross-permuation entropy estimates (XPerm) estimated betweeen the data sequences contained in Sig1 and Sig2 using the default parameters: @@ -513,12 +533,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XSampEn(Sig1, Sig2, varargin)
    +XSampEn(Sig1, Sig2, varargin)

    XSampEn estimates the cross-sample entropy between two univariate data sequences.

    +

    [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 @@ -563,12 +585,14 @@

    Functions for estimating the entropy between two univariate time series.

    +

    -XSpecEn(Sig1, Sig2, varargin)
    +XSpecEn(Sig1, Sig2, varargin)

    XSpecEn estimates the cross-spectral entropy between two univariate data sequences.

    +

    [XSpec, BandEn] = XSpecEn(Sig1, Sig2)

    Returns the cross-spectral entropy estimate (XSpec) of the full cross- spectrum and the within-band entropy (BandEn) estimated between the data @@ -608,6 +632,7 @@

    Functions for estimating the entropy between two univariate time series.

    +
    diff --git a/docs/_build/html/matlab/Functions/matMultiscale.html b/docs/_build/html/matlab/Functions/matMultiscale.html index 8ab6a59..174f416 100644 --- a/docs/_build/html/matlab/Functions/matMultiscale.html +++ b/docs/_build/html/matlab/Functions/matMultiscale.html @@ -1,23 +1,32 @@ - + Multiscale Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multiscale Entropies

    +

    Multiscale Entropies

    -

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

    +

    Functions 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, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, @@ -199,10 +208,11 @@

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

    -
    +
    -MSobject(EnType, varargin)
    +MSobject(EnType, varargin)

    MSobject creates an object to store multiscale entropy parameters.

    +

    [Mobj] = MSobject()

    Returns a multiscale entropy object (Mobj) based on that orignially proposed by Costa et al. (2002) using the following default @@ -281,15 +291,17 @@

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

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -MSEn(Sig, Mobj, varargin)
    +MSEn(Sig, Mobj, varargin)

    MSEn returns the multiscale entropy of a univariate data sequence.

    +

    [MSx,CI] = MSEn(Sig, Mobj)

    Returns a vector of multiscale entropy values (MSx) and the complexity index (CI) of the data sequence Sig using the parameters specified @@ -390,12 +402,14 @@

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

    +
    -cMSEn(Sig, Mobj, varargin)
    +cMSEn(Sig, Mobj, varargin)

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

    +

    [MSx, CI] = cMSEn(Sig, Mobj)

    Returns a vector of composite multiscale entropy values (MSx) for the data sequence (Sig) using the parameters specified by the multiscale object @@ -464,12 +478,14 @@

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

    +
    -rMSEn(Sig, Mobj, varargin)
    +rMSEn(Sig, Mobj, varargin)

    rMSEn returns the refined multiscale entropy of a univariate data sequence.

    +

    [MSx,CI] = rMSEn(Sig, Mobj)

    Returns a vector of refined multiscale entropy values (MSx) and the complexity index (CI) of the data sequence (Sig) using the parameters specified by @@ -542,12 +558,14 @@

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

    +
    -hMSEn(Sig, Mobj, varargin)
    +hMSEn(Sig, Mobj, varargin)

    hMSEn returns the hierarchical entropy of a univariate data sequence.

    +

    [MSx,Sn,CI] = hMSEn(Sig, Mobj)

    Returns a vector of entropy values (MSx) calculated at each node in the hierarchical tree, the average entropy value across all nodes at each @@ -602,6 +620,7 @@

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

    +
    diff --git a/docs/_build/html/matlab/Functions/matMultiscaleCross.html b/docs/_build/html/matlab/Functions/matMultiscaleCross.html index 4cd0f5c..c31b733 100644 --- a/docs/_build/html/matlab/Functions/matMultiscaleCross.html +++ b/docs/_build/html/matlab/Functions/matMultiscaleCross.html @@ -1,23 +1,32 @@ - + Multiscale Cross-Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multiscale Cross-Entropies

    +

    Multiscale Cross-Entropies

    -

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

    +

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

    Just as one can calculate multiscale entropy using any Base entropy, the same functionality is possible with multiscale cross-entropy using any of the Cross Entropies: XApEn, XSampEn, XK2En, XCondEn, XPermEn, XSpecEn, XDistEn, XFuzzEn

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

    @@ -199,10 +208,11 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    -
    +
    -MSobject(EnType, varargin)
    +MSobject(EnType, varargin)

    MSobject creates an object to store multiscale entropy parameters.

    +

    [Mobj] = MSobject()

    Returns a multiscale entropy object (Mobj) based on that orignially proposed by Costa et al. (2002) using the following default @@ -281,15 +291,17 @@

    Functions for estimating the multiscale cross-entropy between two univariate
    See also:

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -XMSEn(Sig1, Sig2, Mobj, varargin)
    +XMSEn(Sig1, Sig2, Mobj, varargin)

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

    +

    [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 Sig1 @@ -364,12 +376,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -cXMSEn(Sig1, Sig2, Mobj, varargin)
    +cXMSEn(Sig1, Sig2, Mobj, varargin)

    cXMSEn returns the 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 Sig1 and Sig2 using the @@ -440,12 +454,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -rXMSEn(Sig1, Sig2, Mobj, varargin)
    +rXMSEn(Sig1, Sig2, Mobj, varargin)

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

    +

    [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 @@ -529,12 +545,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -hXMSEn(Sig1, Sig2, Mobj, varargin)
    +hXMSEn(Sig1, Sig2, Mobj, varargin)

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

    +

    [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 @@ -598,6 +616,7 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    diff --git a/docs/_build/html/matlab/Functions/matMultivariate.html b/docs/_build/html/matlab/Functions/matMultivariate.html index fc0e2f6..04d0dad 100644 --- a/docs/_build/html/matlab/Functions/matMultivariate.html +++ b/docs/_build/html/matlab/Functions/matMultivariate.html @@ -1,23 +1,32 @@ - + Multivariate Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,17 +190,18 @@
    -

    Multivariate Entropies

    +

    Multivariate Entropies

    -

    Functions for estimating the entropy of a multivariate dataset.

    +

    Functions for estimating the entropy of a multivariate dataset.

    The following functions also form the base entropy method used by multivariate multiscale entropy functions.

    -
    +
    -MvCoSiEn(Data, varargin)
    +MvCoSiEn(Data, varargin)

    MvCoSiEn estimates the multivariate cosine similarity entropy of a multivariate dataset.

    +

    [MCoSi, Bm] = MvCoSiEn(Data)

    Returns the multivariate cosine similarity entropy estimate (MCoSi) and the corresponding global probabilities (Bm) estimated for the @@ -282,12 +292,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvDispEn(Data, varargin)
    +MvDispEn(Data, varargin)

    MvDispEn estimates the multivariate dispersion entropy of a multivariate dataset.

    +

    [MDisp, RDE] = MvDispEn(Data)

    Returns the multivariate dispersion entropy estimate (MDisp) and the reverse dispersion entropy (RDE) for the M multivariate sequences @@ -399,12 +411,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvFuzzEn(Data, varargin)
    +MvFuzzEn(Data, varargin)

    MvFuzzEn estimates the multivariate fuzzy entropy of a multivariate dataset.

    +

    [MFuzz, B0, Bt, B1] = MvFuzzEn(Data)

    Returns the multivariate fuzzy entropy estimate (MFuzz) and the average vector distances (m: B0; joint total @@ -567,12 +581,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvPermEn(Data, varargin)
    +MvPermEn(Data, varargin)

    MvPermEn estimates the multivariate permutation entropy of a multivariate dataset.

    +

    [MPerm MPnorm] = MvPermEn(Data)

    Returns the multivariate permutation entropy estimate (MPerm) and the normalized permutation entropy for the M multivariate sequences in @@ -706,12 +722,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvSampEn(Data, varargin)
    +MvSampEn(Data, varargin)

    MvSampEn estimates the multivariate sample entropy of a multivariate dataset.

    +

    [MSamp, B0, Bt, B1] = MvSampEn(Data)

    Returns the multivariate sample entropy estimate (MSamp) and the average number of matched delay vectors (m: B0; joint total @@ -791,6 +809,7 @@

    Functions for estimating the entropy of a multivariate dataset. + Multivariate Multiscale Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multivariate Multiscale Entropies

    +

    Multivariate Multiscale Entropies

    -

    Functions for estimating the multivariate multiscale entropy of a multivariate dataset.

    +

    Functions for estimating the multivariate multiscale entropy of a multivariate dataset.

    Multivariate multiscale entropy can be calculated using any of the Multivariate Entropies: MvCoSiEn, MvDispEn, MvFuzzEn, MvPermEn, MvSampEn.

    @@ -197,10 +206,11 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    -
    +
    -MSobject(EnType, varargin)
    +MSobject(EnType, varargin)

    MSobject creates an object to store multiscale entropy parameters.

    +

    [Mobj] = MSobject()

    Returns a multiscale entropy object (Mobj) based on that orignially proposed by Costa et al. (2002) using the following default @@ -279,15 +289,17 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria
    See also:

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -MvMSEn(Data, Mobj, varargin)
    +MvMSEn(Data, Mobj, varargin)

    MvMSEn returns the multivariate multiscale entropy of a multivariate dataset.

    +

    [MSx, CI] = MvMSEn(Data, Mobj)

    Returns a vector of multivariate multiscale entropy values (MSx) and the complexity index (CI) of the data sequences Data using the parameters specified @@ -362,12 +374,14 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    +
    -cMvMSEn(Data, Mobj, varargin)
    +cMvMSEn(Data, Mobj, varargin)

    cMvMSEn returns the composite and refined-composite multivariate multiscale entropy of a multivariate dataset.

    +

    [MSx, CI] = cMvMSEn(Data, Mobj)

    Returns a vector of composite multivariate multiscale entropy values (MSx) and the complexity index (CI) of the data sequences Data @@ -435,6 +449,7 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    +
    diff --git a/docs/_build/html/matlab/Functions/matOther.html b/docs/_build/html/matlab/Functions/matOther.html index 0a1ca2d..c6fd68f 100644 --- a/docs/_build/html/matlab/Functions/matOther.html +++ b/docs/_build/html/matlab/Functions/matOther.html @@ -1,23 +1,32 @@ - + Other Functions — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,16 +190,17 @@
    -

    Other Functions

    +

    Other Functions

    -

    For further info on these graining procedures see the - <a href=”matlab: web(’https://github.com/MattWillFlood/EntropyHub/blob/main/EntropyHub%20Guide.pdf’)”>EntropyHub guide</a>.

    +
    -WindowData(Data, varargin)
    +WindowData(Data, varargin)

    WindowData restructures a univariate/multivariate dataset into a collection of subsequence windows.

    +

    [WinData, Log] = WindowData(Data)

    Windows the sequence(s) given in Data into a collection of subsequnces of floor(N/5) elements with no overlap, excluding any remainder @@ -274,6 +286,7 @@

    Supplementary functions for various tasks related to EntropyHub and signal p
    See also:

    ExampleData

    +
    diff --git a/docs/_build/html/matlab/matAPI.html b/docs/_build/html/matlab/matAPI.html index 542e4fc..7692993 100644 --- a/docs/_build/html/matlab/matAPI.html +++ b/docs/_build/html/matlab/matAPI.html @@ -1,23 +1,32 @@ - + MatLab Functions: — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -180,11 +189,11 @@
    -

    MatLab Functions:

    +

    MatLab Functions:

    -

    EntropyHub functions fall into 8 categories:

    +

    EntropyHub functions fall into 8 categories:

    Base

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

    @@ -209,7 +218,8 @@

    EntropyHub functions fall into 8 categories:
    -

    Base Entropies:

    +

    Base Entropies:

    +
    @@ -279,9 +289,11 @@

    Base Entropies: -

    Cross Entropies:

    +

    Cross Entropies:

    +

    Entropy Type

    @@ -315,9 +327,11 @@

    Cross Entropies: -

    Multivariate Entropies:

    +

    Multivariate Entropies:

    +

    Entropy Type

    @@ -342,9 +356,11 @@

    Multivariate Entropies:

    Entropy Type

    +
    -

    Bidimensional Entropies:

    +

    Bidimensional Entropies:

    +
    @@ -372,9 +388,11 @@

    Bidimensional Entropies: -

    Multiscale Entropies:

    +

    Multiscale Entropies:

    +

    Entropy Type

    @@ -397,9 +415,11 @@

    Multiscale Entropies:

    Entropy Type

    +
    -

    Multiscale Cross-Entropies:

    +

    Multiscale Cross-Entropies:

    +
    @@ -422,9 +442,11 @@

    Multiscale Cross-Entropies: -

    Multivariate Multiscale Entropies:

    +

    Multivariate Multiscale Entropies:

    +

    Entropy Type

    @@ -441,9 +463,11 @@

    Multivariate Multiscale Entropies: -

    Other Functions:

    +

    Other Functions:

    +

    Entropy Type

    @@ -460,6 +484,7 @@

    Other Functions: + MatLab Examples: — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + + @@ -180,7 +190,7 @@
    -

    MatLab Examples:

    +

    MatLab Examples:

    • Ex. 1 - Sample Entropy
      • diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv index f38b9b7..c8ff5df 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 62799bd..7b9906e 100644 --- a/docs/_build/html/py-modindex.html +++ b/docs/_build/html/py-modindex.html @@ -1,22 +1,31 @@ - + Python Module Index — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + diff --git a/docs/_build/html/python/EHpython.html b/docs/_build/html/python/EHpython.html index 323e627..dfb8dc8 100644 --- a/docs/_build/html/python/EHpython.html +++ b/docs/_build/html/python/EHpython.html @@ -1,23 +1,33 @@ - + EntropyHub: Python — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + + @@ -179,13 +189,13 @@
      -

      EntropyHub: Python

      +

      EntropyHub: Python

      Links to installation files: GitHub || PyPi

      -

      Requirements & Installation:

      +

      Requirements & Installation:

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

      • Numpy

        • @@ -200,7 +210,7 @@

        Requirements & Installation: -

        Method 1:

        +

        Method 1:

        1. Using pip in your python IDE, type:

          @@ -214,9 +224,9 @@

          Method 1: -

          Method 2:

          -
          -
            +

            Method 2:

            +
            +

            1. Download the EntropyHub.x.x.x.tar.gz folder from the EntropyHub PyPI repo (or the EntropyHub GitHub repo) and unzip it.

              ../_images/pyscreen1.png @@ -242,7 +252,7 @@

              Method 2:
              import EntropyHub
@@ -278,7 +288,7 @@ 
              Method 2:
 
              
-
              Documentation & Help:
              
+
              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, e.g. help SampEn in the command line which will print the docstrings.
              
 
              All information on the EntropyHub package is detailed in the EntropyHub Guide,
diff --git a/docs/_build/html/python/Examples/Ex1.html b/docs/_build/html/python/Examples/Ex1.html
index 145f1ee..e6bcd09 100644
--- a/docs/_build/html/python/Examples/Ex1.html
+++ b/docs/_build/html/python/Examples/Ex1.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 1: Sample Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 1: Sample Entropy
              
+
              Example 1: Sample Entropy
              
 
              Import a signal of normally distributed random numbers [mean = 0; SD = 1], and calculate the
 sample entropy for each embedding dimension (m) from 0 to 4.
              
 
              X = EH.ExampleData("gaussian");
diff --git a/docs/_build/html/python/Examples/Ex10.html b/docs/_build/html/python/Examples/Ex10.html
index da4598f..e4702bb 100644
--- a/docs/_build/html/python/Examples/Ex10.html
+++ b/docs/_build/html/python/Examples/Ex10.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 10: Bidimensional Fuzzy Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
         
     
     
@@ -182,7 +191,7 @@
            
              
              
   
              
-
              Example 10: Bidimensional Fuzzy Entropy
              
+
              Example 10: Bidimensional Fuzzy Entropy
              
 
              Import an image of a Mandelbrot fractal as a matrix.
              
 
              X = EH.ExampleData('mandelbrot_Mat');
 
diff --git a/docs/_build/html/python/Examples/Ex11.html b/docs/_build/html/python/Examples/Ex11.html
index 5262f1e..6b135dd 100644
--- a/docs/_build/html/python/Examples/Ex11.html
+++ b/docs/_build/html/python/Examples/Ex11.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 11: Multivariate Dispersion Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 11: Multivariate Dispersion Entropy
              
+
              Example 11: Multivariate Dispersion Entropy
              
 
              Import a vector of 4096 uniformly distributed random integers in range [1 8] and convert it to a multivariate set of 4 sequences with 1024 samples each.
              
 
              X = EH.ExampleData('randintegers')
 Data = np.reshape(X,(4,1024)).T
diff --git a/docs/_build/html/python/Examples/Ex12.html b/docs/_build/html/python/Examples/Ex12.html
index f758b05..9fa9cfe 100644
--- a/docs/_build/html/python/Examples/Ex12.html
+++ b/docs/_build/html/python/Examples/Ex12.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
              
+
              Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy
              
 
              Import the x, y, and z components of the Lorenz system of equations.
              
 
              Data = EH.ExampleData('lorenz')
 
diff --git a/docs/_build/html/python/Examples/Ex13.html b/docs/_build/html/python/Examples/Ex13.html
index cf2832e..6c77607 100644
--- a/docs/_build/html/python/Examples/Ex13.html
+++ b/docs/_build/html/python/Examples/Ex13.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 13: Windowing Data with WindowData() — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 13: Windowing Data with WindowData()
              
+
              Example 13: Windowing Data with WindowData()
              
 
              Create a sequence of integers from 1 - 1000 and segment the values into windows of length 75, with no overlap.
              
 
              X = np.arange(1,1001)
 WinData, Log = EH.WindowData(X, WinLen = 75)
diff --git a/docs/_build/html/python/Examples/Ex2.html b/docs/_build/html/python/Examples/Ex2.html
index 1dba08e..f61f212 100644
--- a/docs/_build/html/python/Examples/Ex2.html
+++ b/docs/_build/html/python/Examples/Ex2.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 2: (Fine-Grained) Permutation Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 2: (Fine-Grained) Permutation Entropy
              
+
              Example 2: (Fine-Grained) Permutation Entropy
              
 
              Import the x, y, and z components of the Lorenz system of equations.
              
 
              Data = EH.ExampleData('lorenz');
 
diff --git a/docs/_build/html/python/Examples/Ex3.html b/docs/_build/html/python/Examples/Ex3.html
index 3f397c7..d8662f0 100644
--- a/docs/_build/html/python/Examples/Ex3.html
+++ b/docs/_build/html/python/Examples/Ex3.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 3: Phase Entropy w/ Pioncare Plot — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 3: Phase Entropy w/ Pioncare Plot
              
+
              Example 3: Phase Entropy w/ Pioncare Plot
              
 
              Import the x and y components of the Henon system of equations.
              
 
              from matplotlib.pyplot import figure, plot, axis
 
diff --git a/docs/_build/html/python/Examples/Ex4.html b/docs/_build/html/python/Examples/Ex4.html
index 0fc36c0..112558c 100644
--- a/docs/_build/html/python/Examples/Ex4.html
+++ b/docs/_build/html/python/Examples/Ex4.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 4: Cross-Distribution Entropy w/ Different Binning Methods — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 4: Cross-Distribution Entropy w/ Different Binning Methods
              
+
              Example 4: Cross-Distribution Entropy w/ Different Binning Methods
              
 
              Import a signal of pseudorandom integers in the range [1, 8] and calculate the cross-
 distribution entropy with an embedding dimension (m) of 5, a time delay (tau) of 3,
 and Sturges’ bin selection method.
              
diff --git a/docs/_build/html/python/Examples/Ex5.html b/docs/_build/html/python/Examples/Ex5.html
index bd1c9e3..563cab6 100644
--- a/docs/_build/html/python/Examples/Ex5.html
+++ b/docs/_build/html/python/Examples/Ex5.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 5: Multiscale Entropy Object [MSobject()] — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 5: Multiscale Entropy Object [MSobject()]
              
+
              Example 5: Multiscale Entropy Object [MSobject()]
              
 
              Create a multiscale entropy object (Mobj) for multiscale fuzzy entropy,
 calculated with an embedding dimension (m) of 5, a time delay (tau) of 2,
 using a sigmoidal fuzzy function with the r scaling parameters (3, 1.2).
              
diff --git a/docs/_build/html/python/Examples/Ex6.html b/docs/_build/html/python/Examples/Ex6.html
index 0de9cf3..c1efc4c 100644
--- a/docs/_build/html/python/Examples/Ex6.html
+++ b/docs/_build/html/python/Examples/Ex6.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 6: Multiscale [Increment] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
         
     
     
@@ -182,7 +191,7 @@
            
              
              
   
              
-
              Example 6: Multiscale [Increment] Entropy
              
+
              Example 6: Multiscale [Increment] Entropy
              
 
              Import a signal of uniformly distributed pseudorandom integers in the range [1,8] and
 create a multiscale entropy object with the following parameters:
              
 
              EnType = IncrEn(), embedding dimension = 3, a quantifying resolution = 6, normalization = true.
              
diff --git a/docs/_build/html/python/Examples/Ex7.html b/docs/_build/html/python/Examples/Ex7.html
index 4886f39..d45423c 100644
--- a/docs/_build/html/python/Examples/Ex7.html
+++ b/docs/_build/html/python/Examples/Ex7.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 7: Refined Multiscale [Sample] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 7: Refined Multiscale [Sample] Entropy
              
+
              Example 7: Refined Multiscale [Sample] Entropy
              
 
              Import a signal of uniformly distributed pseudorandom integers in the range [1, 8] and
 create a multiscale entropy object with the following parameters:
              
 
              EnType = SampEn(), embedding dimension = 4, radius threshold = 1.25
              
diff --git a/docs/_build/html/python/Examples/Ex8.html b/docs/_build/html/python/Examples/Ex8.html
index af9f6d3..d90e3bf 100644
--- a/docs/_build/html/python/Examples/Ex8.html
+++ b/docs/_build/html/python/Examples/Ex8.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 8: Composite Multiscale Cross-[Approximate] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 8: Composite Multiscale Cross-[Approximate] Entropy
              
+
              Example 8: Composite Multiscale Cross-[Approximate] Entropy
              
 
              Import two signals of uniformly distributed pseudorandom integers in the range [1 8] and
 create a multiscale entropy object with the following parameters:
              
 
              EnType = XApEn(), embedding dimension = 2, time delay = 2, radius distance threshold = 0.5.
              
diff --git a/docs/_build/html/python/Examples/Ex9.html b/docs/_build/html/python/Examples/Ex9.html
index 18018bf..283efb4 100644
--- a/docs/_build/html/python/Examples/Ex9.html
+++ b/docs/_build/html/python/Examples/Ex9.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,7 +190,7 @@
            
              
              
   
              
-
              Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
              
+
              Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy
              
 
              Import the x and y components of the Henon system of equations and create a multiscale entropy object
 with the following parameters:
              
 
              EnType = XCondEn(), embedding dimension = 2, time delay = 2, number of symbols = 12, logarithm base = 2,
diff --git a/docs/_build/html/python/Functions/Base.html b/docs/_build/html/python/Functions/Base.html
index 840da06..28b108d 100644
--- a/docs/_build/html/python/Functions/Base.html
+++ b/docs/_build/html/python/Functions/Base.html
@@ -1,23 +1,32 @@
 
-
+
 
   
 
   
   Base Entropies — EntropyHub 2.0 documentation
-      
-      
-      
+      
+      
+      
+      
+
+  
     
   
   
-        
-        
-        
-        
-        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
     
     
     
@@ -181,11 +190,11 @@
            
              
              
   
              
-
              Base Entropies
              
+
              Base Entropies
              
 
              
 
              
 
              
-
              Functions for estimating the entropy of a single univariate time series.
              
+
              Functions for estimating the entropy of a single univariate time series.
              
 
              The following functions also form the base entropy method used by multiscale entropy functions.
              
 
              
 
              These functions are directly available when EntropyHub is imported:
              
@@ -195,10 +204,11 @@ 
              Functions for estimating the entropy of a single univariate time series.
              
 
              
 
              
-
              
+
              
 
              
-ApEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
              
+ApEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
 
              ApEn  estimates the approximate entropy of a univariate data sequence.
              
+
              
 
              Ap, Phi = ApEn(Sig)
 
              
 
              
@@ -248,12 +258,14 @@ 
              Functions for estimating the entropy of a single univariate time series.
 
              
 
              
+

      -AttnEn(Sig, Logx=2)
      +AttnEn(Sig, Logx=2)

      AttnEn estimates the attention entropy of a univariate data sequence.

      +
      Attn, (Hxx, Hnn, Hxn, Hnx) = AttnEn(Sig)
      @@ -307,12 +319,14 @@

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

      +
      -BubbEn(Sig, m=2, tau=1, Logx=numpy.exp)
      +BubbEn(Sig, m=2, tau=1, Logx=numpy.exp)

      BubbEn estimates the bubble entropy of a univariate data sequence.

      +
      Bubb, H = BubbEn(Sig)
      @@ -357,12 +371,14 @@

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

      +
    -CoSiEn(Sig, m=2, tau=1, r=0.1, Logx=2, Norm=0)
    +CoSiEn(Sig, m=2, tau=1, r=0.1, Logx=2, Norm=0)

    CoSiEn estimates the cosine similarity entropy of a univariate data sequence.

    +
    CoSi, Bm = CoSiEn(Sig)
    @@ -425,12 +441,14 @@

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

    +
    -CondEn(Sig, m=2, tau=1, c=6, Logx=numpy.exp, Norm=False)
    +CondEn(Sig, m=2, tau=1, c=6, Logx=numpy.exp, Norm=False)

    CondEn estimates the corrected conditional entropy of a univariate data sequence.

    +
    Cond, SEw, SEz = CondEn(Sig)
    @@ -491,12 +509,14 @@

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

    +
    -DispEn(Sig, m=2, tau=1, c=3, Typex='NCDF', Logx=numpy.exp, Fluct=False, Norm=False, rho=1)
    +DispEn(Sig, m=2, tau=1, c=3, Typex='NCDF', Logx=numpy.exp, Fluct=False, Norm=False, rho=1)

    DispEn estimates the dispersion entropy of a univariate data sequence.

    +
    Dispx, Ppi = DispEn(Sig)
    @@ -548,9 +568,9 @@

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

    • False no normalisation - default

      • -

    • True normalises w.r.t # possible vector permutations -(c^m or (2c -1)^m-1 if Fluct == True).

      • +

    • True normalises w.r.t # possible vector permutations

    +

    (c^m or (2c -1)^m-1 if Fluct == True).

    rho:
    +
    -DistEn(Sig, m=2, tau=1, Bins='Sturges', Logx=2, Norm=True)
    +DistEn(Sig, m=2, tau=1, Bins='Sturges', Logx=2, Norm=True)

    DistEn estimates the distribution entropy of a univariate data sequence.

    +
    Dist, Ppi = DistEn(Sig)
    @@ -656,12 +678,14 @@

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

    +
    -DivEn(Sig, m=2, tau=1, r=5, Logx=numpy.exp)
    +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)
    @@ -721,12 +745,14 @@

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

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

    EnofEn estimates the entropy of entropy of a univariate data sequence.

    +
    EoE, AvEn, S2 = EnofEn(Sig)
    @@ -777,12 +803,14 @@

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

    +
    -FuzzEn(Sig, m=2, tau=1, r=(0.2, 2.0), 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)
    @@ -818,16 +846,15 @@

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

    • 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: [default: (.2 2)]
      -
    • -
      default: [Tuple]
        +

        The r parameters for each fuzzy function are defined as follows: [default: (.2 2)] +- default: [Tuple]

        +
        +

        • r[0] = divisor of the exponential argument

        • r[1] = argument exponent (pre-division)

        -
      -
      -
      • +

    +
    Logx:
    +
    -GridEn(Sig, m=3, tau=1, Logx=numpy.exp, Plotx=False)
    +GridEn(Sig, m=3, tau=1, Logx=numpy.exp, Plotx=False)

    GridEn estimates the gridded distribution entropy of a univariate data sequence.

    +
    GDE, GDR, _ = GridEn(Sig)
    @@ -1012,12 +1039,14 @@

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

    +
    -IncrEn(Sig, m=2, tau=1, R=4, Logx=2, Norm=False)
    +IncrEn(Sig, m=2, tau=1, R=4, Logx=2, Norm=False)

    IncrEn estimates the increment entropy of a univariate data sequence.

    +
    Incr = IncrEn(Sig)
    @@ -1083,12 +1112,14 @@

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

    +
    -K2En(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
    +K2En(Sig, m=2, tau=1, r=None, Logx=numpy.exp)

    K2En estimates the Kolmogorov (K2) entropy of a univariate data sequence.

    +
    K2, Ci = K2En(Sig)
    @@ -1141,14 +1172,15 @@

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

    +
    -PermEn(Sig, m=2, tau=1, Logx=2, Norm=False, Typex='none', tpx=- 1)
    +PermEn(Sig, m=2, tau=1, Logx=2, Norm=False, Typex='none', tpx=-1)

    PermEn estimates the permutation entropy of a univariate data sequence.

    -
    -
    Perm, Pnorm, cPE = PermEn(Sig) 
+
    
+
    Perm, Pnorm, cPE = PermEn(Sig) 
 
    
 
    
 
    Returns the permuation entropy estimates (Perm), the normalised
@@ -1194,22 +1226,16 @@ 
    Functions for estimating the entropy of a single univariate time series.
 
  • False -  normalises w.r.t log(# of permutation symbols [m-1]) - default
    • 
 
  • True  -  normalises w.r.t log(# of all possible permutations [m!])
    • 
-
-
    
-
      
 
    • Note: Normalised permutation entropy is undefined for m = 1.
      • 
 
    
 
    ** Note: When Typex = 'uniquant' and Norm = True, normalisation is calculated w.r.t. log(tpx^m) **
    
-
    Typex:

    • Permutation entropy variation, one of the following:

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

    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.

    tpx:
    -PhasEn(Sig, K=4, tau=1, Logx=numpy.exp, Norm=True, Plotx=False)
    +PhasEn(Sig, K=4, tau=1, Logx=numpy.exp, Norm=True, Plotx=False)

    PhasEn estimates the phase entropy of a univariate data sequence.

    +
    Phas = PhasEn(Sig)
    @@ -1346,12 +1371,14 @@

    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(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)
    @@ -1411,12 +1438,14 @@

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

    +
    -SampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)
    +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)
    @@ -1487,12 +1516,14 @@

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

    +
    -SlopEn(Sig, m=2, tau=1, Lvls=(5, 45), Logx=2, Norm=True)
    +SlopEn(Sig, m=2, tau=1, Lvls=(5, 45), Logx=2, Norm=True)

    SlopEn estimates the slope entropy of a univariate data sequence.

    +
    Slop = SlopEn(Sig)
    @@ -1552,12 +1583,14 @@

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

    +
    -SpecEn(Sig, N=None, Freqs=(0, 1), Logx=numpy.exp, Norm=True)
    +SpecEn(Sig, N=None, Freqs=(0, 1), Logx=numpy.exp, Norm=True)

    SpecEn estimates the spectral entropy of a univariate data sequence.

    +
    Spec, BandEn = SpecEn(Sig)
    @@ -1595,12 +1628,12 @@

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

    • Normalisation of Spec value, a boolean:

    +

    +

    • [False] no normalisation.

    • [True] normalises w.r.t # of spectrum/band frequency values - default.

    - -

    For more info, see the EntropyHub guide.

    @@ -1622,12 +1655,14 @@

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

    +
    -SyDyEn(Sig, m=2, tau=1, c=3, Typex='MEP', Logx=numpy.exp, Norm=True)
    +SyDyEn(Sig, m=2, tau=1, c=3, Typex='MEP', Logx=numpy.exp, Norm=True)

    SyDyEn estimates the symbolic dynamic entropy of a univariate data sequence.

    +
    SyDy, Zt = SyDyEn(Sig)
    @@ -1642,7 +1677,7 @@

    Functions for estimating the entropy of a single univariate time series.Returns the symbolic dynamic entropy (SyDy) and the symbolic sequence (Zt) of the data sequence (Sig) using the specified ‘keyword’ arguments:

    -

    See the EntropyHub guide for more info on these parameters.

    @@ -1704,6 +1733,7 @@

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

    + diff --git a/docs/_build/html/python/Functions/Bidimensional.html b/docs/_build/html/python/Functions/Bidimensional.html index 1dfd913..1817102 100644 --- a/docs/_build/html/python/Functions/Bidimensional.html +++ b/docs/_build/html/python/Functions/Bidimensional.html @@ -1,23 +1,32 @@ - + Bidimensional Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Bidimensional Entropies

    +

    Bidimensional Entropies

    -

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

    +

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

    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.

    @@ -211,10 +220,11 @@

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

    -
    +
    -DispEn2D(Mat, m=None, tau=1, c=3, Typex='NCDF', Logx=numpy.exp, Norm=False, Lock=True)
    +DispEn2D(Mat, m=None, tau=1, c=3, Typex='NCDF', Logx=numpy.exp, Norm=False, Lock=True)

    DispEn2D Estimates the bidimensional dispersion entropy of a data matrix.

    +
    Disp2D, RDE = DispEn2D(Mat)
    @@ -265,10 +275,15 @@

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

    Norm:
      -

    • Normalisation of Disp2D and RDE values, a boolean:

      • +
    • +
      Normalisation of Disp2D and RDE values, a boolean:
        +

      • False no normalisation - default

        • +
      +
      +
      +
      -

    • False no normalisation - default

    • True normalises w.r.t # possible vector permutations (c^m).

    @@ -297,12 +312,14 @@

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

    +
    -DistEn2D(Mat, m=None, tau=1, Bins='Sturges', Logx=2, Norm=2, Lock=True)
    +DistEn2D(Mat, m=None, tau=1, Bins='Sturges', Logx=2, Norm=2, Lock=True)

    DistEn2D Estimates the bidimensional distribution entropy of a data matrix.

    +
    Dist2D = DistEn2D(Mat)
    @@ -379,12 +396,14 @@

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

    +
    -EspEn2D(Mat, m=None, tau=1, r=20, ps=0.7, Logx=numpy.exp, Lock=True)
    +EspEn2D(Mat, m=None, tau=1, r=20, ps=0.7, Logx=numpy.exp, Lock=True)

    EspEn2D Estimates the bidimensional Espinosa entropy of a data matrix.

    +
    Esp2D = EspEn2D(Mat)
    @@ -395,11 +414,9 @@

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

    -
    -
    Esp2D = EspEn2D(Mat, keyword = value, ...)
+
    Esp2D = EspEn2D(Mat, keyword = value, ...)
 
    
 
    
-

    Returns the bidimensional Espinosa entropy (Esp2D) estimates for the data matrix (Mat) using the specified ‘keyword’ arguments:

    @@ -445,21 +462,21 @@

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

    SampEn2D, FuzzEn2D, DistEn2D, DispEn2D

    References:
    -
    -
    [1] Ricardo Espinosa, et al.,

    “Two-dimensional EspEn: A New Approach to Analyze Image Texture +

    [1] Ricardo Espinosa, et al., +“Two-dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity.” Entropy, 23:1261 (2021)

    -
    -

    +
    -FuzzEn2D(Mat, m=None, tau=1, r=None, Fx='default', Logx=numpy.exp, Lock=True)
    +FuzzEn2D(Mat, m=None, tau=1, r=None, Fx='default', Logx=numpy.exp, Lock=True)

    FuzzEn2D estimates the bidimensional fuzzy entropy of a data matrix.

    +
    Fuzz2D = FuzzEn2D(Mat)
    @@ -496,16 +513,15 @@

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

    • 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: [default: (0.2*SD(Mat), 2)]
      -
    • -
      default: [Tuple]
        +

        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

        • @@ -588,8 +604,6 @@

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

      -
      -
    Logx:

    • Logarithm base, a positive scalar (default: natural)

      • @@ -630,12 +644,14 @@

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

    +
    -PermEn2D(Mat, m=None, tau=1, Norm=True, Logx=numpy.exp, Lock=True)
    +PermEn2D(Mat, m=None, tau=1, Norm=True, Logx=numpy.exp, Lock=True)

    PermEn2D Estimates the bidimensional permutation entropy of a data matrix.

    +
    Perm2D = PermEn2D(Mat)
    @@ -686,16 +702,19 @@

    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. -To overcome this, PermEn2D uses the lowest common rank for +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]
    [7.3 1.1 2.1]
    +

    would normally be mapped to an ordinal pattern like so,

    @@ -714,6 +733,7 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix. normalized by the maximum Shannon entropy (Smax = log((mx*my)!) assuming that no equal values are found in the permutation motif matrices, as presented in [1].

    +

    See also:
    @@ -735,12 +755,14 @@

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

    +
    -SampEn2D(Mat, m=None, tau=1, r=None, Logx=numpy.exp, Lock=True)
    +SampEn2D(Mat, m=None, tau=1, r=None, Logx=numpy.exp, Lock=True)

    SampEn2D Estimates the bidimensional sample entropy of a data matrix.

    +
    SE2D, Phi1, Phi2 = SampEn2D(Mat)
    @@ -751,11 +773,9 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix. 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 dimension size of Mat must be > 10.

    -
    -
    SE2D, Phi1, Phi2 = SampEn2D(Mat, keyword = value, ...)
+
    SE2D, Phi1, Phi2 = SampEn2D(Mat, keyword = value, ...)
 
    
 
    
-

    Returns the bidimensional sample entropy (SE2D) estimates for the data matrix (Mat) using the specified ‘keyword’ arguments:

    @@ -805,6 +825,7 @@

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

    + diff --git a/docs/_build/html/python/Functions/Cross.html b/docs/_build/html/python/Functions/Cross.html index dd8ace8..506e5f8 100644 --- a/docs/_build/html/python/Functions/Cross.html +++ b/docs/_build/html/python/Functions/Cross.html @@ -1,23 +1,32 @@ - + Cross Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Cross Entropies

    +

    Cross Entropies

    -

    Functions for estimating the entropy between two univariate time series.

    +

    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.

    These functions are directly available when EntropyHub is imported:

    @@ -195,10 +204,11 @@

    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(Sig1, Sig2)
    @@ -208,11 +218,9 @@

    Functions for estimating the entropy between two univariate time series.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(Sig1, Sig2, keyword = value, ...)
+
    XAp, Phi = XApEn(Sig1, Sig2, keyword = value, ...)
 
    
 
    
-

    Returns the cross-approximate entropy estimates (XAp) between the data sequences contained in Sig1 and Sig2 using the specified ‘keyword’ arguments:

    @@ -256,12 +264,14 @@

    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(Sig1, Sig2)
    @@ -326,12 +336,14 @@

    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(Sig1, Sig2)
    @@ -396,12 +408,14 @@

    Functions for estimating the entropy between two univariate time series.

    +
    -XFuzzEn(*Sig, m=2, tau=1, r=(0.2, 2.0), 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(Sig1,Sig2)
    @@ -437,16 +451,15 @@

    Functions for estimating the entropy between two univariate time series.

    • 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: [default: (.2 2)]
      -
    • -
      default: [Tuple]
        +

        The r parameters for each fuzzy function are defined as follows: [default: (.2 2)] +- default: [Tuple]

        +
        +

        • r(1) = divisor of the exponential argument

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

        -
      -
      -
      • +

    +
    Logx:
    +
    -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(Sig1, Sig2)
    @@ -613,12 +626,14 @@

    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(Sig1, Sig2)
    @@ -664,12 +679,14 @@

    Functions for estimating the entropy between two univariate time series.

    +
    -XSampEn(*Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)
    +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(Sig1, Sig2)
    @@ -736,12 +753,14 @@

    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(Sig1, Sig2)
    @@ -799,6 +818,7 @@

    Functions for estimating the entropy between two univariate time series.

    + diff --git a/docs/_build/html/python/Functions/Multiscale.html b/docs/_build/html/python/Functions/Multiscale.html index afd2f7f..ed21d4c 100644 --- a/docs/_build/html/python/Functions/Multiscale.html +++ b/docs/_build/html/python/Functions/Multiscale.html @@ -1,23 +1,32 @@ - + Multiscale Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multiscale Entropies

    +

    Multiscale Entropies

    -

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

    +

    Functions 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, DivEn, EnofEn, FuzzEn, GridEn, IncrEn, K2En, @@ -199,10 +208,11 @@

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

    -
    +
    -MSobject(EnType='SampEn', **kwargs)
    +MSobject(EnType='SampEn', **kwargs)

    MSobject creates an object to store multiscale entropy parameters.

    +
    [Mobj] = MSobject()
    @@ -407,15 +417,17 @@

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

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -MSEn(Sig, Mbjx, Scales=3, Methodx='coarse', RadNew=0, Plotx=False)
    +MSEn(Sig, Mbjx, Scales=3, Methodx='coarse', RadNew=0, Plotx=False)

    MSEn Returns the multiscale entropy of a univariate data sequence.

    +
    MSx,CI = MSEn(Sig, Mobj)
    @@ -528,12 +540,14 @@

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

    +
    -cMSEn(Sig, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)
    +cMSEn(Sig, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)

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

    +
    MSx, CI = cMSEn(Sig, Mobj)
    @@ -628,12 +642,14 @@

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

    +
    -hMSEn(Sig, Mbjx, Scales=3, RadNew=0, Plotx=False)
    +hMSEn(Sig, Mbjx, Scales=3, RadNew=0, Plotx=False)

    hMSEn returns the hierarchical entropy of a univariate data sequence.

    +
    MSx, Sn, CI = hMSEn(Sig, Mobj)
    @@ -697,12 +713,14 @@

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

    +
    -rMSEn(Sig, Mbjx, Scales=3, F_Order=6, F_Num=0.5, RadNew=0, Plotx=False)
    +rMSEn(Sig, Mbjx, Scales=3, F_Order=6, F_Num=0.5, RadNew=0, Plotx=False)

    rMSEn returns the refined multiscale entropy of a univariate data sequence.

    +
    MSx, CI = rMSEn(Sig, Mobj)
    @@ -794,6 +812,7 @@

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

    + diff --git a/docs/_build/html/python/Functions/MultiscaleCross.html b/docs/_build/html/python/Functions/MultiscaleCross.html index 74e8415..cc65164 100644 --- a/docs/_build/html/python/Functions/MultiscaleCross.html +++ b/docs/_build/html/python/Functions/MultiscaleCross.html @@ -1,23 +1,32 @@ - + Multiscale Cross-Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multiscale Cross-Entropies

    +

    Multiscale Cross-Entropies

    -

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

    +

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

    Just as one can calculate multiscale entropy using any Base entropy, the same functionality is possible with multiscale cross-entropy using any of the Cross Entropies: XApEn, XSampEn, XK2En, XCondEn, XPermEn, XSpecEn, XDistEn, XFuzzEn

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

    @@ -199,10 +208,11 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    -
    +
    -MSobject(EnType='SampEn', **kwargs)
    +MSobject(EnType='SampEn', **kwargs)

    MSobject creates an object to store multiscale entropy parameters.

    +
    [Mobj] = MSobject()
    @@ -407,15 +417,17 @@

    Functions for estimating the multiscale cross-entropy between two univariate

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -XMSEn(Sig1, Sig2, 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(Sig1, Sig2, Mobj)
    @@ -501,12 +513,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -cXMSEn(Sig1, Sig2, Mbjx, Scales=3, RadNew=0, Refined=False, Plotx=False)
    +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)
    @@ -611,12 +625,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -hXMSEn(Sig1, Sig2, 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(Sig1, Sig2, Mobj)
    @@ -689,12 +705,14 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    +
    -rXMSEn(Sig1, Sig2, 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(Sig1, Sig2, Mobj)
    @@ -796,6 +814,7 @@

    Functions for estimating the multiscale cross-entropy between two univariate

    + diff --git a/docs/_build/html/python/Functions/Multivariate.html b/docs/_build/html/python/Functions/Multivariate.html index fdef193..07e0a17 100644 --- a/docs/_build/html/python/Functions/Multivariate.html +++ b/docs/_build/html/python/Functions/Multivariate.html @@ -1,23 +1,32 @@ - + Multivariate Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multivariate Entropies

    +

    Multivariate Entropies

    -

    Functions for estimating the entropy of a multivariate dataset.

    +

    Functions for estimating the entropy of a multivariate dataset.

    The following functions also form the base entropy method used by multivariate multiscale entropy functions.

    These functions are directly available when EntropyHub is imported:

    @@ -195,10 +204,11 @@

    Functions for estimating the entropy of a multivariate dataset. -
    +
    -MvCoSiEn(Data, m=None, tau=None, r=0.1, Norm=0, Logx=2)
    +MvCoSiEn(Data, m=None, tau=None, r=0.1, Norm=0, Logx=2)

    MvCoSiEn estimates the multivariate cosine similarity entropy of a multivariate dataset.

    +
    MCoSi, Bm = MvCoSiEn(Data)
    @@ -296,12 +306,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvDispEn(Data, m=None, tau=None, c=3, Typex='NCDF', Methodx='v1', Norm=False, Logx=numpy.exp)
    +MvDispEn(Data, m=None, tau=None, c=3, Typex='NCDF', Methodx='v1', Norm=False, Logx=numpy.exp)

    MvDispEn estimates the multivariate dispersion entropy of a multivariate dataset.

    +
    MDisp, RDE = MvDispEn(Data)
    @@ -393,7 +405,9 @@

    Functions for estimating the entropy of a multivariate dataset.

    DispEn, DispEn2D, MvSampEn, MvFuzzEn, MvPermEn, MSEn

    References:
    -
    +

    +
    +
    [1] H Azami, A Fernández, J Escudero

    “Multivariate Multiscale Dispersion Entropy of Biomedical Times Series” Entropy 2019, 21, 913.

    @@ -414,14 +428,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvFuzzEn(Data, m=None, tau=None, r=(0.2, 2.0), Fx='default', Norm=False, Logx=numpy.exp)
    +MvFuzzEn(Data, m=None, tau=None, r=(0.2, 2.0), Fx='default', Norm=False, Logx=numpy.exp)

    MvFuzzEn estimates the multivariate fuzzy entropy of a multivariate dataset.

    +
    MFuzz, B0, Bt, B1 = MvFuzzEn(Data)
    @@ -452,7 +466,7 @@

    Functions for estimating the entropy of a multivariate dataset.
    MFuzz, B0, Bt, B1 = MvFuzzEn(Data, name, value, ...)
+
    MFuzz, B0, Bt, B1 = MvFuzzEn(Data, keyword = value, ...)
 
    
 
    
 
    Returns the multivariate fuzzy entropy estimates (MFuzz) estimated 
@@ -506,20 +520,29 @@ 
    Functions for estimating the entropy of a multivariate dataset.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.
      • 
+
    • 
+
      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 positive scalar whose value is the threshold 
-(corner point) of the triangular function.
      • 
+
    • 
+
      r = a positive scalar whose value is the threshold 
      (corner point) of the triangular function.
      
+
      
+
      
+
      • 
 
    
 
    
 
    trapezoidal1:
    
 
      
-
    • r = a positive scalar whose value corresponds
-to the upper (2r) and lower (r) corner points of the trapezoid.
      • 
+
    • 
+
      r = a positive scalar whose value corresponds
      to the upper (2r) and lower (r) corner points of the trapezoid.
      
+
      
+
      
+
      • 
 
    
 
    
 
    trapezoidal2:
    
@@ -530,8 +553,11 @@ 
    Functions for estimating the entropy of a multivariate dataset.z_shaped:
 
      
-
    • r = a scalar whose value corresponds to the
-upper (2r) and lower (r) corner points of the z-shape.
      • 
+
    • 
+
      r = a scalar whose value corresponds to the
      upper (2r) and lower (r) corner points of the z-shape.
      
+
      
+
      
+
      • 
 
    
 
    
 
    bell:
    
@@ -547,8 +573,11 @@ 
    Functions for estimating the entropy of a multivariate dataset.constgaussian:
 
      
-
    • r = a scalar whose value defines the lower 
-threshod and shape of the Gaussian curve.
      • 
+
    • 
+
      r = a scalar whose value defines the lower 
      threshod and shape of the Gaussian curve.
      
+
      
+
      
+
      •

    @@ -571,7 +600,9 @@

    Functions for estimating the entropy of a multivariate dataset.

    MvSampEn, FuzzEn, XFuzzEn, FuzzEn2D, MSEn, MvPermEn.

    References:
    -
    +

    +
    +
    [1] Ahmed, Mosabber U., et al.

    “A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis.” Entropy 19.1 (2016): 2.

    @@ -584,14 +615,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvPermEn(Data, m=None, tau=None, Typex=None, tpx=- 1, Norm=False, Logx=2)
    +MvPermEn(Data, m=None, tau=None, Typex=None, tpx=-1, Norm=False, Logx=2)

    MvPermEn estimates the multivariate permutation entropy of a multivariate dataset.

    +
    MPerm, MPnorm = MvPermEn(Data)
    @@ -690,7 +721,9 @@

    Functions for estimating the entropy of a multivariate dataset.

    PermEn, PermEn2D, XPermEn, MSEn, MvFuzzEn, MvSampEn.

    References:
    -
    +

    +
    +
    [1] Ahmed Mosabber Uddin, Danilo P. Mandic

    “Multivariate multiscale entropy: A tool for complexity analysis of multichannel data.” Physical Review E 84.6 (2011): 061918.

    @@ -728,14 +761,14 @@

    Functions for estimating the entropy of a multivariate dataset.
    -MvSampEn(Data, m=None, tau=None, r=0.2, Norm=False, Logx=numpy.exp)
    +MvSampEn(Data, m=None, tau=None, r=0.2, Norm=False, Logx=numpy.exp)

    MvSampEn estimates the multivariate sample entropy of a multivariate dataset.

    +
    MSamp, B0, Bt, B1 = MvSampEn(Data)
    @@ -810,7 +843,9 @@

    Functions for estimating the entropy of a multivariate dataset.

    SampEn, XSampEn, SampEn2D, MSEn, MvFuzzEn, MvPermEn.

    References:
    -
    +

    +
    +
    [1] Ahmed Mosabber Uddin, Danilo P. Mandic

    “Multivariate multiscale entropy: A tool for complexity analysis of multichannel data.” Physical Review E 84.6 (2011): 061918.

    @@ -819,8 +854,7 @@

    Functions for estimating the entropy of a multivariate dataset. + Multivariate Multiscale Entropies — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,11 +190,11 @@
    -

    Multivariate Multiscale Entropies

    +

    Multivariate Multiscale Entropies

    -

    Functions for estimating the multivariate multiscale entropy of a multivariate dataset.

    +

    Functions for estimating the multivariate multiscale entropy of a multivariate dataset.

    Multivariate multiscale entropy can be calculated using any of the Multivariate Entropies: MvCoSiEn, MvDispEn, MvFuzzEn, MvPermEn, MvSampEn.

    @@ -197,10 +206,11 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    -
    +
    -MSobject(EnType='SampEn', **kwargs)
    +MSobject(EnType='SampEn', **kwargs)

    MSobject creates an object to store multiscale entropy parameters.

    +
    [Mobj] = MSobject()
    @@ -405,15 +415,17 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

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

    +

    The following functions use the multiscale entropy object shown above.

    -
    +
    -MvMSEn(Data, Mbjx, Scales=3, Methodx='coarse', Plotx=False)
    +MvMSEn(Data, Mbjx, Scales=3, Methodx='coarse', Plotx=False)

    MvMSEn Returns the multivariate multiscale entropy of a multivariate dataset.

    +
    MSx,CI = MvMSEn(Data, Mobj)
    @@ -493,12 +505,14 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    +

    -cMvMSEn(Data, Mbjx, Scales=3, Refined=False, Plotx=False)
    +cMvMSEn(Data, Mbjx, Scales=3, Refined=False, Plotx=False)

    cMvMSEn Returns the composite + refined-composite multivariate multiscale entropy of a multivariate dataset.

    +
    MSx,CI = cMvMSEn(Data, Mobj)
    @@ -579,6 +593,7 @@

    Functions for estimating the multivariate multiscale entropy of a multivaria

    +

    diff --git a/docs/_build/html/python/Functions/Other.html b/docs/_build/html/python/Functions/Other.html index be72cce..f3bf9d2 100644 --- a/docs/_build/html/python/Functions/Other.html +++ b/docs/_build/html/python/Functions/Other.html @@ -1,23 +1,32 @@ - + Other Functions — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -181,16 +190,17 @@
    -

    Other Functions

    +

    Other Functions

    For further info on these graining procedures see the EntropyHub guide.

    +
    -WindowData(Data, WinLen=None, Overlap=0, Mode='exclude')
    +WindowData(Data, WinLen=None, Overlap=0, Mode='exclude')

    WindowData restructures a univariate/multivariate dataset into a collection of subsequence windows.

    -
    -
    WinData, Log = WindowData(Data) 
+
    
+
    WinData, Log = WindowData(Data) 
 
    
 
    
-

    Windows the sequence(s) given in Data into a collection of subsequnces of floor(N/5) elements with no overlap, excluding any remainder elements that do not fill the final window. @@ -304,7 +314,6 @@

    Supplementary functions for various tasks related to EntropyHub and signal p and WinData is a tuple of 5 matrices of size [(floor*N,5), M]. The Log dictionary contains information about the windowing process, including:

    -
    DataType:
      @@ -341,7 +350,6 @@

      Supplementary functions for various tasks related to EntropyHub and signal p 
      WinData, Log = WindowData(Data, keyword = value, ...)
      -

    Windows the sequence(s) given in Data into a collection of subsequnces using the specified keyword arguments:

    @@ -368,6 +376,7 @@

    Supplementary functions for various tasks related to EntropyHub and signal p

    ExampleData

    +
    diff --git a/docs/_build/html/python/pyAPI.html b/docs/_build/html/python/pyAPI.html index a8e8a82..6ebf5a1 100644 --- a/docs/_build/html/python/pyAPI.html +++ b/docs/_build/html/python/pyAPI.html @@ -1,23 +1,32 @@ - + Python Functions: — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + @@ -180,11 +189,11 @@
    -

    Python Functions:

    +

    Python Functions:

    -

    EntropyHub functions fall into 8 categories:

    +

    EntropyHub functions fall into 8 categories:

    Base

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

    @@ -220,7 +229,8 @@

    EntropyHub functions fall into 8 categories:np. prefix refer to numpy functions.

    -

    Base Entropies:

    +

    Base Entropies:

    +

    Function Description

    @@ -290,9 +300,11 @@

    Base Entropies: -

    Cross Entropies:

    +

    Cross Entropies:

    +

    Entropy Type

    @@ -326,9 +338,11 @@

    Cross Entropies: -

    Multivariate Entropies:

    +

    Multivariate Entropies:

    +

    Entropy Type

    @@ -353,9 +367,11 @@

    Multivariate Entropies:

    Entropy Type

    +
    -

    Bidimensional Entropies:

    +

    Bidimensional Entropies:

    +
    @@ -383,9 +399,11 @@

    Bidimensional Entropies: -

    Multiscale Entropies:

    +

    Multiscale Entropies:

    +

    Entropy Type

    @@ -408,9 +426,11 @@

    Multiscale Entropies:

    Entropy Type

    +
    -

    Multiscale Cross-Entropies:

    +

    Multiscale Cross-Entropies:

    +
    @@ -433,9 +453,11 @@

    Multiscale Cross-Entropies: -

    Multivariate Multiscale Entropies:

    +

    Multivariate Multiscale Entropies:

    +

    Entropy Type

    @@ -452,9 +474,11 @@

    Multivariate Multiscale Entropies: -

    Other Functions:

    +

    Other Functions:

    +

    Entropy Type

    @@ -471,6 +495,7 @@

    Other Functions: + Python Examples: — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + + @@ -180,7 +190,7 @@
    -

    Python Examples:

    +

    Python Examples:

    • Ex. 1 - Sample Entropy
      • diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html index 9d863cf..1dbd5b6 100644 --- a/docs/_build/html/search.html +++ b/docs/_build/html/search.html @@ -1,23 +1,32 @@ - + Search — EntropyHub 2.0 documentation - - - + + + + + + - - - - - + + + + + + + + + + + diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js index a7667db..62343ff 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/Ex11", "matlab/Examples/Ex12", "matlab/Examples/Ex13", "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/Functions/matMultivariate", "matlab/Functions/matMultivariateMultiscale", "matlab/Functions/matOther", "matlab/matAPI", "matlab/matexamples", "python/EHpython", "python/Examples/Ex1", "python/Examples/Ex10", "python/Examples/Ex11", "python/Examples/Ex12", "python/Examples/Ex13", "python/Examples/Ex2", "python/Examples/Ex3", "python/Examples/Ex4", "python/Examples/Ex5", "python/Examples/Ex6", "python/Examples/Ex7", "python/Examples/Ex8", "python/Examples/Ex9", "python/Functions/Base", "python/Functions/Bidimensional", "python/Functions/Cross", "python/Functions/Multiscale", "python/Functions/MultiscaleCross", "python/Functions/Multivariate", "python/Functions/MultivariateMultiscale", "python/Functions/Other", "python/pyAPI", "python/pyexamples"], "filenames": ["EHupdates.rst", "Home.rst", "Publications.rst", "index.rst", "julia\\EHjulia.rst", "julia\\jlexamples.rst", "matlab\\EHmatlab.rst", "matlab\\Examples\\Ex1.rst", "matlab\\Examples\\Ex10.rst", "matlab\\Examples\\Ex11.rst", "matlab\\Examples\\Ex12.rst", "matlab\\Examples\\Ex13.rst", "matlab\\Examples\\Ex2.rst", "matlab\\Examples\\Ex3.rst", "matlab\\Examples\\Ex4.rst", "matlab\\Examples\\Ex5.rst", "matlab\\Examples\\Ex6.rst", "matlab\\Examples\\Ex7.rst", "matlab\\Examples\\Ex8.rst", "matlab\\Examples\\Ex9.rst", "matlab\\Functions\\matBase.rst", "matlab\\Functions\\matBidimensional.rst", "matlab\\Functions\\matCross.rst", "matlab\\Functions\\matMultiscale.rst", "matlab\\Functions\\matMultiscaleCross.rst", "matlab\\Functions\\matMultivariate.rst", "matlab\\Functions\\matMultivariateMultiscale.rst", "matlab\\Functions\\matOther.rst", "matlab\\matAPI.rst", "matlab\\matexamples.rst", "python\\EHpython.rst", "python\\Examples\\Ex1.rst", "python\\Examples\\Ex10.rst", "python\\Examples\\Ex11.rst", "python\\Examples\\Ex12.rst", "python\\Examples\\Ex13.rst", "python\\Examples\\Ex2.rst", "python\\Examples\\Ex3.rst", "python\\Examples\\Ex4.rst", "python\\Examples\\Ex5.rst", "python\\Examples\\Ex6.rst", "python\\Examples\\Ex7.rst", "python\\Examples\\Ex8.rst", "python\\Examples\\Ex9.rst", "python\\Functions\\Base.rst", "python\\Functions\\Bidimensional.rst", "python\\Functions\\Cross.rst", "python\\Functions\\Multiscale.rst", "python\\Functions\\MultiscaleCross.rst", "python\\Functions\\Multivariate.rst", "python\\Functions\\MultivariateMultiscale.rst", "python\\Functions\\Other.rst", "python\\pyAPI.rst", "python\\pyexamples.rst"], "titles": ["Latest Updates", "EntropyHub", "Publications", "EntropyHub", "EntropyHub: Julia", "EntropyHub: Julia", "EntropyHub: MatLab", "Example 1: Sample Entropy", "Example 10: Bidimensional Fuzzy Entropy", "Example 11: Multivariate Dispersion Entropy", "Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy", "Example 13: Windowing Data with WindowData()", "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", "Multivariate Entropies", "Multivariate Multiscale Entropies", "Other Functions", "MatLab Functions:", "MatLab Examples:", "EntropyHub: Python", "Example 1: Sample Entropy", "Example 10: Bidimensional Fuzzy Entropy", "Example 11: Multivariate Dispersion Entropy", "Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy", "Example 13: Windowing Data with WindowData()", "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", "Multivariate Entropies", "Multivariate Multiscale Entropies", "Other Functions", "Python Functions:", "Python Examples:"], "terms": {"new": [0, 20, 21, 22, 25, 28, 44, 45, 46, 49, 52], "announc": 0, "come": [], "soon": [], "entropyhub": [0, 20, 21, 22, 23, 24, 25, 26, 29, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "1": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "year": 0, "old": 0, "There": [0, 1, 3, 4, 6, 29, 30], "ha": [0, 1, 3, 4, 6, 21, 29, 30, 45, 53], "been": [0, 1, 3, 4, 6, 53], "great": 0, "interest": 0, "first": [0, 10, 19, 23, 24, 29, 34, 43, 47, 48, 53], "sinc": 0, "its": [0, 2, 20, 22, 23, 25, 26, 44, 46, 47, 49, 50], "origin": [0, 21, 25, 29, 45, 47, 48, 49, 50, 53], "public": 0, "plo": [0, 1, 2, 3, 21, 45], "One": [0, 1, 2, 3, 30, 51], "To": [0, 1, 3, 4, 6, 21, 23, 24, 25, 26, 29, 30, 45, 47, 48, 49, 50, 53], "date": [0, 30], "ar": [0, 4, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "about": [0, 2, 4, 6, 27, 51], "11": [0, 1, 3, 13, 15, 20, 21, 23, 29, 37, 39, 44, 45, 47, 53], "cite": 0, "cover": 0, "broad": 0, "rang": [0, 1, 3, 8, 9, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "scientif": [0, 1, 3, 6, 28, 52], "disciplin": 0, "includ": [0, 1, 3, 11, 20, 27, 35, 44, 51], "neurosci": [0, 23, 47], "meteorolog": 0, "mathemat": [0, 2, 20, 23, 24, 44, 47, 48], "jl": [0, 4, 5], "github": [0, 1, 3, 4, 5, 6, 22, 24, 27, 30, 46, 48], "repositori": [0, 2, 6], "have": [0, 1, 3, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "over": [0, 10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "50": [0, 27, 51], "star": [0, 1, 3], "42": 0, "respect": [0, 20, 21, 22, 44, 45, 46], "matlab": [0, 1, 2, 3, 21, 27], "toolkit": [0, 4, 6, 21, 29, 30, 45], "download": [0, 1, 3, 4, 6, 27, 30, 51], "600": [0, 11, 35], "time": [0, 1, 2, 3, 4, 6, 7, 8, 12, 13, 14, 15, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 32, 36, 37, 38, 39, 42, 43, 45, 49, 50, 51, 52, 53], "from": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 43, 44, 45, 46, 47, 48, 49, 51, 53], "file": [0, 1, 3, 4, 6, 30], "exchang": [0, 1, 3, 6], "we": [0, 1, 3, 24, 29, 48], "would": [0, 21, 45, 52], "like": [0, 6, 10, 21, 26, 30, 34, 45, 50], "extend": 0, "our": [0, 1, 3], "gratitud": 0, "all": [0, 1, 3, 4, 6, 12, 20, 23, 24, 25, 26, 30, 36, 44, 47, 48, 49, 50], "those": [0, 9, 33], "who": 0, "us": [0, 1, 2, 3, 4, 6, 8, 9, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "work": [0, 6], "provid": [0, 1, 3, 23, 24, 25, 27, 29, 47, 48, 49, 51, 53], "help": [0, 23, 24, 26, 47, 48, 50], "feedback": 0, "plan": 0, "expand": 0, "hope": 0, "you": [0, 1, 3, 6, 29, 30], "continu": [0, 2], "support": 0, "thi": [0, 1, 3, 4, 10, 20, 21, 23, 24, 25, 29, 34, 44, 45, 47, 48, 49, 53], "endeavour": 0, "spread": 0, "word": 0, "ieee": [0, 2, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "embc": [0, 21, 24, 45, 48], "symposium": 0, "entropi": [0, 1, 2, 3, 4, 6, 27, 29, 30, 51, 53], "algorithm": [0, 2, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "At": [0, 23, 24, 47], "confer": [0, 2, 20, 21, 24, 25, 44, 45, 48, 49], "glasgow": 0, "entitl": 0, "recent": 0, "advanc": [0, 1, 3], "biomed": [0, 10, 20, 21, 22, 23, 24, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "signal": [0, 2, 6, 7, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 31, 34, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53], "beyond": 0, "univari": [0, 2, 11, 25, 27, 28, 35, 49, 51, 52], "seri": [0, 1, 2, 3, 4, 6, 21, 25, 26, 27, 28, 29, 30, 45, 49, 50, 51, 52, 53], "wa": [0, 4, 30], "organis": 0, "prof": 0, "javier": [0, 2, 20, 21, 23, 25, 26, 44, 45, 47, 49, 50], "escudero": [0, 10, 20, 21, 23, 25, 26, 34, 44, 45, 47, 49, 50], "ann": [0, 2, 21, 23, 24, 45, 47, 48], "humeau": [0, 2, 21, 23, 24, 45, 47, 48], "huertier": 0, "introduc": 0, "method": [0, 1, 2, 3, 9, 10, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 34, 44, 45, 46, 47, 48, 49, 50, 51, 53], "analysi": [0, 2, 4, 6, 10, 20, 21, 22, 23, 24, 25, 26, 30, 34, 44, 45, 46, 47, 48, 49, 50], "engin": [0, 2, 10, 20, 21, 23, 24, 25, 26, 34, 44, 45, 47, 48, 49, 50], "commun": [0, 20, 21, 44, 45], "As": [0, 1, 3, 10, 20, 34, 44], "part": [0, 1, 3], "dr": 0, "matt": [0, 1, 3], "flood": [0, 1, 3, 21, 22, 24, 45, 46, 48], "present": [0, 1, 3, 10, 21, 25, 34, 45, 49], "audienc": 0, "advantag": [0, 4, 6, 30], "benefit": 0, "offer": 0, "The": [0, 1, 2, 3, 4, 6, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "medicin": [0, 20, 21, 44, 45], "biologi": [0, 20, 21, 44, 45], "take": [0, 1, 3, 20, 22, 44, 46], "place": 0, "15": [0, 19, 20, 23, 24, 26, 44, 47, 48, 50], "titl": 0, "quantif": [0, 20, 44], "where": [0, 4, 6, 16, 17, 18, 20, 21, 22, 25, 29, 30, 40, 41, 42, 44, 45, 46, 49, 53], "novel": [0, 2, 20, 24, 44, 48], "appic": 0, "biomedicin": [0, 2, 20, 25, 44, 49], "discuss": 0, "founder": 0, "lead": [0, 21, 45], "develop": [0, 1, 2, 3], "give": 0, "demonstr": 0, "function": [0, 1, 2, 3, 4, 6, 8, 10, 15, 29, 30, 32, 34, 39, 40, 41, 42, 43, 53], "reveal": 0, "futur": [0, 28, 52], "releas": [0, 28, 52], "read": 0, "more": [1, 3, 4, 6, 20, 21, 22, 25, 29, 44, 45, 46, 49], "programm": 0, "here": [1, 3, 4, 5, 6, 21, 25, 29, 30, 45, 49], "meet": 0, "version": [1, 3, 4, 6, 30], "v0": 0, "2": [1, 3, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "two": [0, 1, 2, 3, 4, 6, 18, 20, 23, 26, 27, 28, 42, 44, 47, 50, 51, 52], "bidimension": [0, 6, 29, 30, 53], "permut": [0, 2, 20, 21, 22, 23, 24, 25, 26, 28, 29, 44, 45, 46, 47, 48, 49, 50, 52, 53], "permen2d": [0, 21, 25, 28, 45, 49, 52], "espinosa": [0, 21, 28, 45, 52], "espen2d": [0, 21, 28, 29, 45, 52, 53], "paper": 0, "bring": 0, "attent": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "wider": 0, "happi": 0, "describ": [0, 1, 3, 47, 48, 50], "publish": [0, 28, 52], "user": [0, 1, 3, 4, 6, 30], "requir": [0, 1, 3, 27, 29, 51, 52, 53], "thei": 0, "matthew": [0, 1, 3, 21, 22, 24, 45, 46, 48], "w": [0, 1, 3, 9, 12, 20, 21, 22, 24, 25, 33, 36, 44, 45, 46, 48, 49], "bernd": [0, 20, 21, 25, 44, 45, 49], "grimm": [0, 21, 45], "an": [0, 2, 4, 6, 7, 8, 12, 14, 15, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 36, 38, 39, 44, 45, 46, 47, 48, 49, 50, 51, 53], "open": [0, 2, 4, 6, 21, 30, 45], "sourc": [0, 2, 4, 6, 21, 30, 45], "entrop": [0, 4, 6, 21, 30, 45], "16": [0, 1, 3, 20, 21, 44, 45], "e0259448": [0, 1, 3, 21, 45], "doi": [0, 1, 3, 25, 49], "10": [0, 1, 3, 10, 12, 20, 21, 23, 24, 25, 26, 27, 29, 36, 44, 45, 47, 48, 49, 50, 51, 53], "1371": [0, 1, 3], "journal": [0, 1, 2, 3, 20, 22, 23, 24, 25, 44, 46, 47, 48, 49], "pone": [0, 1, 3], "0259448": [0, 1, 3], "correct": [0, 15, 20, 22, 29, 39, 44, 46, 47, 48, 50, 53], "enofen": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "follow": [0, 1, 3, 6, 10, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 34, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "can": [0, 1, 2, 3, 4, 5, 6, 21, 23, 24, 25, 26, 27, 30, 45, 47, 48, 49, 50, 51], "specifi": [0, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51], "amplitud": [0, 20, 25, 44, 49], "xmin": [0, 20, 44], "xmax": [0, 20, 44], "which": [0, 1, 3, 4, 6, 25, 27, 29, 30, 49, 51, 52], "number": [0, 1, 3, 7, 9, 12, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 33, 36, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "slice": [0, 20, 44], "s1": [0, 23, 24, 47, 48], "partit": [0, 13, 20, 37, 44], "see": [0, 1, 3, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "literatur": [0, 1, 3, 28, 52], "info": [0, 1, 3, 20, 21, 22, 25, 27, 44, 45, 46, 47, 49, 50, 51], "initi": 0, "platform": 0, "pypi": [0, 1, 3, 30], "julia": [0, 1, 3], "packag": [0, 1, 2, 3, 4, 5, 6, 30, 52], "mai": [0, 1, 3, 6, 21, 26, 45, 50], "some": [0, 6, 29, 53], "bug": [0, 1, 3], "typo": 0, "s": [0, 2, 14, 20, 22, 27, 29, 38, 44, 46, 51, 53], "other": [0, 1, 3, 20, 44], "small": [0, 21, 45], "issu": 0, "iron": 0, "out": [], "avail": [0, 1, 3, 4, 6, 30, 44, 45, 46, 49], "python": [0, 1, 2, 3, 29, 45], "latest": [1, 3], "updat": [1, 3, 23, 24, 47, 48], "survei": [1, 3], "wide": [1, 3], "calcul": [0, 1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50], "differ": [0, 1, 3, 13, 37, 44], "statist": [1, 3, 4, 6, 20, 23, 25, 26, 44, 47, 49, 50], "ever": [1, 3], "grow": [0, 1, 3], "inform": [1, 2, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 30, 44, 45, 46, 49, 51], "theoret": [1, 3, 21, 45], "dynam": [0, 1, 2, 3, 20, 22, 23, 24, 25, 26, 28, 44, 46, 47, 48, 49, 50, 52], "system": [0, 1, 2, 3, 4, 10, 12, 13, 19, 20, 25, 29, 34, 36, 37, 43, 44, 49, 53], "measur": [0, 1, 2, 3, 20, 21, 25, 44, 45, 49], "although": [], "mani": [0, 1, 3, 20, 44], "estim": [0, 1, 3, 9, 12, 28, 33, 36, 52], "found": [0, 4, 5, 20, 21, 22, 44, 45, 46], "variou": [1, 3, 28, 52], "corner": [6, 17, 20, 21, 22, 25, 41, 44, 45, 46, 49], "internet": [27, 29, 51, 53], "current": [0, 6], "perform": [0, 1, 2, 3, 4, 9, 20, 21, 27, 33, 44, 45, 51], "extens": 0, "consist": [0, 1, 3, 25, 49], "syntax": [0, 1, 3, 6, 30, 52], "goal": [1, 3], "integr": [1, 2, 3, 20, 22, 44, 46], "establish": [1, 3], "one": [0, 1, 3, 4, 6, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "uncertainti": [1, 3], "regard": [1, 3], "side": [1, 2, 3], "same": [1, 3, 4, 9, 21, 23, 24, 33, 45, 47, 48], "coin": [1, 3], "gain": [1, 3], "remov": [1, 3, 20, 25, 44, 49], "In": [0, 1, 3, 4, 6, 20, 25, 29, 30, 49, 53], "context": [1, 3], "probabl": [1, 3, 14, 20, 22, 25, 38, 44, 46, 49], "theori": [1, 2, 3], "quantifi": [1, 3, 16, 20, 21, 23, 24, 40, 44, 45, 47], "concept": 2, "classic": [1, 3], "physic": [1, 2, 3, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "under": [1, 3, 6, 20, 44], "second": [13, 24, 37, 44, 48], "law": [1, 3, 23, 47], "thermodynam": [1, 3], "consid": [1, 3, 25, 49], "underpin": [], "fundament": [23, 47], "understand": [], "nonlinear": [1, 2, 3, 20, 22, 25, 44, 46, 49], "central": [1, 3], "degre": [0, 1, 3, 20, 22, 44, 46], "therefor": [1, 3, 21, 22, 25, 45, 46, 49], "explain": [1, 2, 3], "complex": [0, 1, 2, 3, 17, 19, 20, 21, 23, 24, 25, 26, 41, 43, 44, 45, 47, 48, 49, 50], "behaviour": [1, 3], "real": [1, 3], "world": [1, 3], "attempt": [1, 3], "analys": [0, 1, 2, 3], "analog": [1, 3], "around": [1, 3], "discret": [0, 1, 3], "step": [1, 3, 11, 35], "do": [0, 1, 2, 3, 4, 6, 11, 21, 24, 25, 27, 35, 45, 48, 49, 51], "so": [0, 1, 3, 4, 6, 21, 24, 26, 45, 48, 50], "compromis": [1, 3], "abil": [1, 3], "accur": [1, 3], "deriv": [1, 3, 25, 49], "each": [0, 1, 3, 6, 7, 9, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "seek": [1, 3], "best": [1, 3], "captur": [1, 2, 3], "examin": [1, 3, 25, 49], "result": [0, 1, 3, 6, 9, 33], "approxim": [0, 1, 2, 3, 20, 22, 23, 24, 26, 28, 29, 44, 46, 47, 48, 50, 52, 53], "sampl": [0, 1, 2, 3, 4, 6, 9, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "multiscal": [0, 1, 2, 3, 4, 6, 20, 21, 22, 25, 29, 30, 44, 45, 46, 49, 53], "refin": [0, 1, 3, 23, 24, 25, 26, 28, 29, 47, 48, 49, 50, 52, 53], "composit": [0, 1, 3, 23, 24, 26, 28, 29, 47, 48, 50, 52, 53], "cross": [0, 1, 2, 3, 4, 6, 15, 23, 25, 26, 29, 30, 39, 47, 49, 50, 53], "statisitc": [1, 3], "becom": [1, 3, 17, 18, 23, 24, 41, 42, 47, 48], "difficult": [1, 3], "identifi": [0, 1, 3], "contrast": [1, 3], "compar": [1, 3, 9, 20, 22, 25, 26, 33, 44, 46, 49, 50], "overcom": [1, 3, 21, 45], "design": [1, 2, 3, 4, 30], "comprehens": [1, 3, 4, 6, 30], "set": [0, 1, 3, 9, 10, 12, 13, 19, 20, 21, 22, 23, 24, 27, 29, 33, 44, 45, 46, 47, 48, 51, 53], "simpl": [1, 3], "allow": [0, 1, 3], "augment": [1, 3], "paramet": [0, 1, 3, 10, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "command": [1, 3, 4, 6, 30, 52, 53], "line": [1, 3, 4, 6, 20, 30], "enabl": [1, 3, 21, 45], "basic": [1, 3, 29, 53], "implement": [0, 1, 3, 25, 49], "eas": [0, 1, 3], "It": [1, 3, 20, 44], "import": [1, 3, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 21, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 49, 51, 52, 53], "clarifi": [1, 3], "herein": [1, 3], "guid": [1, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 30, 44, 45, 46, 47, 49, 50, 51], "pdf": [1, 3, 4, 6, 27, 30], "booklet": [1, 3], "written": [1, 3, 52, 53], "effect": [1, 3], "find": [0, 1, 3, 6], "descript": [1, 3, 28, 52], "exampl": [0, 1, 3, 4, 5, 6, 21, 27, 28, 45, 51, 52], "refer": [1, 3, 4, 6, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50, 52], "section": [1, 2, 3, 6, 29, 52, 53], "access": [1, 3, 6, 20, 21, 22, 30, 44, 45, 46], "through": [1, 3, 21, 22, 25, 45, 46, 49], "browser": [1, 3, 6], "apach": [1, 3], "0": [1, 3, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "free": [1, 3, 20, 44], "condit": [1, 3, 12, 15, 20, 22, 23, 24, 26, 28, 29, 36, 39, 44, 46, 47, 48, 50, 52, 53], "ani": [0, 1, 3, 6, 11, 23, 24, 26, 27, 28, 29, 30, 35, 47, 48, 50, 51, 52, 53], "output": [1, 3, 9, 10, 20, 22, 33, 34, 44, 46], "realiz": [1, 3], "softwar": [1, 3, 6], "2021": [1, 3, 20, 21, 22, 24, 25, 44, 45, 46, 48, 49], "www": [0, 1, 3, 27, 51], "xyz": [0, 1, 3, 27, 51], "copyright": [1, 3], "except": [0, 1, 3], "complianc": [1, 3], "obtain": [0, 1, 3, 9, 25, 33, 49], "copi": [1, 3], "http": [1, 3, 4, 5, 22, 24, 27, 46, 48], "org": [1, 3], "unless": [1, 3], "applic": [1, 2, 3, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "agre": [1, 3], "write": [1, 3], "distribut": [1, 3, 7, 9, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53], "AS": [1, 3], "IS": [1, 3], "basi": [1, 3], "without": [0, 1, 2, 3, 6, 13, 21, 37, 45], "warranti": [1, 3], "OR": [1, 3], "OF": [1, 3], "kind": [1, 3], "either": [1, 3, 20, 25, 27, 44, 49, 51], "express": [1, 3, 21, 45], "impli": [1, 3], "specif": [0, 1, 3, 4, 6, 27, 51], "languag": [1, 3, 5], "govern": [1, 3], "permiss": [1, 3], "limit": [1, 3], "For": [1, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 29, 44, 45, 46, 47, 49, 50, 51, 52, 53], "term": [1, 2, 3, 20, 25, 44, 49], "com": [1, 3, 4, 22, 24, 27, 46, 48], "mattwillflood": [1, 3, 4, 5, 22, 24, 27, 46, 48], "If": [1, 3, 20, 22, 23, 24, 25, 27, 44, 46, 47, 48, 49, 50, 51], "pleas": [1, 3], "gaug": [1, 3], "satisfact": [1, 3], "question": [1, 3], "hesit": [1, 3], "fix": [0, 1, 3, 23, 47], "need": [1, 3], "instal": [1, 3, 29], "thank": [0, 1, 3], "singl": [2, 11, 28, 35, 52], "neuron": 2, "spike": [2, 20, 25, 44, 49], "variabl": [2, 20, 22, 23, 24, 44, 46, 47, 48], "hippocampu": 2, "track": 2, "sensori": 2, "content": 2, "dure": 2, "memori": [0, 2, 21, 45], "format": 2, "human": [2, 23, 24, 47], "leonhard": 2, "waschk": 2, "fabian": 2, "kamp": 2, "evi": 2, "van": 2, "den": 2, "elzen": 2, "suresh": 2, "krishna": 2, "ulman": 2, "lindenberg": 2, "ueli": 2, "rutishaus": 2, "dougla": [2, 20, 22, 44, 46], "d": [2, 20, 23, 25, 44, 47, 49], "garrett": 2, "biorxiv": 2, "physiolog": [2, 20, 22, 23, 24, 26, 44, 46, 47, 48, 50], "cep": 2, "reson": 2, "breath": 2, "rate": [2, 20, 22, 44, 46], "assess": [2, 20, 21, 22, 27, 44, 45, 46, 51], "fractal": [2, 8, 21, 27, 29, 32, 45, 51, 53], "dimens": [0, 2, 7, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], "heart": [2, 20, 22, 23, 24, 44, 46, 47, 48], "asymmetri": [2, 20, 44], "david": [2, 20, 44], "mayor": 2, "toni": 2, "steffert": 2, "georg": [2, 20, 23, 26, 44, 47, 50], "datseri": 2, "andrea": [2, 20, 25, 44, 49], "firth": 2, "deepak": 2, "pandai": 2, "harikala": 2, "kandel": 2, "duncan": 2, "bank": 2, "enhanc": 2, "classif": [2, 20, 44], "preterm": 2, "birth": 2, "wavelet": [2, 20, 44], "transform": [0, 2, 9, 15, 20, 21, 25, 33, 39, 44, 45, 49], "base": [0, 2, 6, 8, 10, 12, 13, 19, 21, 22, 23, 24, 25, 26, 29, 30, 32, 34, 36, 37, 43, 45, 46, 47, 48, 49, 50, 53], "electrohysterogram": 2, "h\u00e9ctor": 2, "romero": 2, "moral": 2, "jenni": 2, "noem\u00ed": 2, "mu\u00f1oz": 2, "mont": 2, "de": [2, 23, 24, 26, 47, 48, 50], "oca": 2, "rodrigo": 2, "mora": 2, "mart\u00ednez": 2, "yecid": 2, "mina": 2, "paz": 2, "jos\u00e9": [2, 23, 24, 47, 48], "rey": 2, "lago": 2, "frontier": [2, 23, 47], "endocrinolog": 2, "fine": [2, 20, 29, 44, 53], "gross": 2, "motor": [2, 20, 44], "task": [2, 28, 52], "fibromyalgia": 2, "patient": 2, "precis": 2, "retrospect": 2, "studi": 2, "nanci": 2, "anacleto": 2, "br\u00edgida": 2, "reposit\u00f3rio": 2, "cient\u00edfico": 2, "instituto": 2, "polit\u00e9cnico": 2, "santar\u00e9m": 2, "cerebr": 2, "oxygen": 2, "satur": 2, "pneumoperitoneum": 2, "trendelenburg": 2, "posit": [2, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "cohort": 2, "xiaoxiao": 2, "wang": [2, 20, 22, 25, 44, 46, 49], "chang": [0, 2, 6, 20, 44], "liu": [2, 20, 44], "kai": 2, "zhang": [2, 20, 24, 25, 44, 48, 49], "yunliang": 2, "yao": 2, "yu": 2, "weidong": 2, "mi": 2, "hao": 2, "li": [2, 20, 25, 44, 49], "ag": 2, "clinic": [2, 20, 44], "experiment": 2, "chao": [2, 21, 45], "strength": [2, 22, 46], "taylor": 2, "sine": 2, "chaotic": [2, 20, 44], "map": [0, 2, 21, 45], "ioanni": 2, "kafetzi": 2, "lazaro": 2, "moysi": 2, "christo": 2, "volo": 2, "util": [0, 2], "intraop": 2, "nir": 2, "oximetri": 2, "data": [0, 2, 9, 10, 12, 13, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 36, 37, 39, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "diagnosi": [2, 20, 44], "postop": 2, "cognit": [2, 23, 47], "improv": [2, 20, 25, 44, 49], "ran": 2, "huo": [2, 20, 25, 44, 49], "wanzhong": 2, "yuan": 2, "huishu": 2, "tao": [2, 24, 48], "nan": [2, 12, 36], "electroencephalogram": 2, "toward": 2, "psychogen": 2, "non": 2, "epilept": 2, "seizur": 2, "chloe": 2, "hinchliff": 2, "mahinda": 2, "yogarajah": 2, "samia": 2, "elkommo": 2, "hongi": 2, "tang": 2, "daniel": 2, "abasolo": 2, "dimension": [2, 28, 52], "detect": [2, 20, 25, 44, 49], "radiograph": 2, "sign": 2, "tooth": 2, "resorpt": 2, "hypercementosi": 2, "equin": 2, "model": 2, "kamil": 2, "g\u00f3rski": 2, "marta": 2, "borowska": 2, "el\u017cbieta": 2, "stefanik": 2, "izabela": 2, "polkowska": 2, "bernard": 2, "turek": 2, "andrzej": 2, "bereznowski": 2, "ma\u0142gorzata": 2, "domino": 2, "infrar": 2, "thermographi": 2, "rider": 2, "hors": 2, "bodyweight": 2, "ratio": [2, 20], "horseback": 2, "ride": 2, "\u0142ukasz": 2, "zdrojkowski": 2, "tomasz": 2, "jasi\u0144ski": 2, "urszula": 2, "sikorska": 2, "micha\u0142": 2, "skibniewski": 2, "ma\u015bko": 2, "sensor": [2, 20, 25, 44, 49], "what": [0, 2, 29], "metric": [2, 20, 21, 22, 44, 45, 46], "tell": 2, "type": [0, 2, 4, 6, 20, 21, 23, 24, 25, 26, 27, 28, 30, 44, 47, 48, 49, 50, 51, 52], "fixat": 2, "video": 2, "view": [2, 10, 12], "k": [2, 8, 10, 12, 13, 19, 20, 23, 24, 26, 34, 36, 37, 43, 44, 47, 48, 50], "melnyk": 2, "l": [2, 20, 23, 24, 26, 44, 47, 48, 50], "friedman": 2, "o": 2, "komogortsev": 2, "texa": 2, "state": [2, 20, 22, 44, 46], "digit": 2, "librari": 2, "respons": 2, "travel": 2, "ionospher": 2, "disturb": 2, "across": [0, 2, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "eastern": 2, "africa": 2, "sector": [2, 20, 44], "geomagnet": 2, "storm": 2, "neural": [2, 20, 23, 44, 47], "network": [2, 47], "i": [0, 2, 16, 20, 22, 23, 24, 25, 26, 29, 40, 44, 46, 47, 48, 49, 50, 52, 53], "A": [0, 2, 4, 6, 20, 21, 22, 23, 24, 25, 26, 30, 44, 45, 46, 47, 48, 49, 50], "oludehinwa": 2, "velichko": 2, "b": [2, 20, 22, 44, 46], "ogunsua": 2, "olusola": 2, "odeyemi": 2, "n": [2, 11, 16, 20, 22, 25, 26, 27, 29, 35, 40, 44, 46, 49, 50, 51, 53], "njah": 2, "t": [2, 9, 12, 20, 21, 22, 23, 24, 25, 33, 35, 36, 44, 45, 46, 47, 48, 49], "ologun": 2, "geophys": 2, "space": [2, 25, 26, 49, 50], "acoust": [2, 25, 49], "cavit": 2, "identif": 2, "propel": 2, "yang": [2, 20, 24, 44, 48], "lilin": 2, "cui": 2, "marin": 2, "scienc": [2, 20, 22, 44, 46], "deep": 2, "learn": [2, 4, 6], "umang": 2, "goenka": 2, "param": 2, "patil": 2, "kush": 2, "gosalia": 2, "aaryan": 2, "jagetia": 2, "arxiv": 2, "pressur": 2, "injuri": 2, "link": [2, 4, 6, 20, 30, 44], "abdomin": 2, "temperatur": 2, "nikhil": 2, "padhy": 2, "denis": 2, "rio": 2, "vaunett": 2, "fai": 2, "sandra": 2, "hanneman": 2, "23rd": 2, "intern": [2, 20, 21, 24, 25, 44, 45, 48, 49], "reus": 2, "iri": 2, "structur": 2, "empir": [2, 23, 47], "comparison": [2, 20, 21, 22, 44, 45, 46], "indic": [2, 20, 21, 22, 44, 45, 46], "neurokit2": 2, "dominiqu": 2, "makowski": 2, "shu": [2, 20, 44], "te": 2, "tam": 2, "pham": [2, 23, 47], "zen": 2, "juen": 2, "lau": 2, "h": [2, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "annabel": 2, "chen": [2, 20, 44], "techniqu": 2, "nneten": 2, "short": [2, 20, 44], "noisi": [2, 20, 44], "hanif": 2, "heidari": 2, "andrei": 2, "registri": 4, "complet": [4, 6], "sever": [0, 4, 6, 29, 30], "depend": [4, 22, 30, 46], "alongsid": [4, 30], "alreadi": [4, 30, 53], "dsp": 4, "fftw": 4, "delimitedfil": 4, "random": [4, 7, 9, 22, 27, 29, 31, 33, 46, 51, 53], "plot": [4, 10, 19, 20, 23, 24, 26, 34, 43, 44, 47, 48, 50], "statsbas": 4, "statsfun": 4, "groupslic": 4, "combinator": 4, "cluster": 4, "linearalgebra": 4, "dierckx": [], "5": [4, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 32, 34, 35, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "intend": [4, 6, 30], "wai": [4, 6, 23, 24, 30, 47, 48], "repl": 4, "should": [4, 6], "appear": 4, "vx": 4, "y": [0, 4, 10, 12, 13, 19, 20, 22, 27, 29, 34, 36, 37, 43, 44, 46, 51, 53], "pkg": 4, "x": [0, 4, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 53], "your": [0, 4, 6, 21, 30, 45], "add": [4, 6], "note": [0, 4, 6, 14, 20, 22, 27, 38, 44, 46, 51], "case": [2, 4, 6, 23, 24, 25, 26, 47, 48, 49, 50], "sensit": [4, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "altern": [4, 6, 25, 49], "modul": [4, 30, 52, 53], "procedur": [0, 4, 10, 16, 23, 26, 27, 34, 40, 47, 50, 51], "after": 4, "check": [0, 4, 6, 30], "correctli": [4, 6, 30], "load": 4, "greet": [4, 30], "___": [4, 6, 30], "_": [4, 5, 6, 30, 31, 38, 40, 42, 44], "_____": [4, 6, 30], "____": [4, 6, 30], "___________": [4, 6, 30], "__": [4, 6, 30], "_______": [4, 6, 30], "______": [4, 6, 30], "kei": [4, 6, 30], "make": [0, 4, 6, 30], "most": [0, 4, 6, 26, 30, 50], "easili": [4, 6], "name": [4, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 44, 45, 46, 47, 48, 49, 50, 52, 53], "sampen": [0, 4, 6, 7, 17, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 41, 44, 45, 46, 47, 48, 49, 50, 52, 53], "xspecen": [4, 6, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "spectral": [4, 6, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "hxmsen": [4, 6, 19, 23, 24, 26, 28, 29, 43, 47, 48, 50, 52, 53], "hierarch": [4, 6, 23, 24, 28, 29, 47, 48, 52, 53], "detail": [4, 6, 29, 30], "api": [4, 6, 30], "io": [4, 5], "addit": [0, 6, 20, 22, 44, 46], "toolbox": [2, 6], "get": [6, 7, 20, 25, 31, 49], "full": [6, 20, 22, 25, 26, 29, 44, 46, 49, 50, 53], "process": [0, 2, 6, 20, 21, 25, 26, 28, 44, 45, 49, 50, 52], "machin": [2, 6], "howev": [0, 6, 21, 45], "2016a": 6, "2015a": 6, "2015b": 6, "recommend": [2, 6, 20, 29, 30, 44], "older": 6, "than": [6, 21, 29, 45], "2016": [6, 20, 21, 24, 25, 44, 45, 48, 49], "onli": [6, 19, 21, 29, 43, 45, 53], "done": 6, "caution": [6, 21, 45], "Ons": 6, "home": 6, "tab": 6, "click": 6, "search": 6, "bar": 6, "top": 6, "right": [6, 16, 40], "instruct": 6, "must": [6, 21, 45, 47, 48, 50], "log": [6, 11, 20, 21, 22, 25, 27, 35, 44, 45, 46, 49, 51], "mathwork": 6, "account": [6, 21, 45], "go": 6, "folder": [6, 30], "mltbx": 6, "directori": [6, 30], "save": [6, 30], "doubl": 6, "enter": [6, 20, 22, 25, 30, 44, 45, 46, 49], "logo": [6, 27, 29, 51, 53], "displai": 6, "supplement": 6, "custom": [6, 29], "relev": 6, "doc": [0, 6], "functionnam": [6, 30], "simpli": [6, 30], "docstr": [6, 30], "window": [0, 6, 20, 27, 28, 29, 30, 44, 51, 52, 53], "e": [0, 2, 6, 20, 21, 22, 23, 24, 25, 26, 29, 30, 44, 45, 46, 47, 48, 49, 50, 52, 53], "g": [2, 6, 10, 12, 13, 19, 20, 21, 23, 24, 25, 26, 30, 34, 36, 37, 43, 44, 45, 47, 48, 49, 50], "also": [0, 6, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "subsect": [6, 30], "outlin": [0, 6, 25, 29, 30, 49, 53], "normal": [2, 7, 9, 10, 13, 16, 19, 21, 25, 27, 29, 31, 33, 34, 37, 40, 43, 45, 49, 51, 53], "mean": [7, 20, 23, 24, 25, 27, 29, 31, 44, 47, 48, 49, 51, 53], "sd": [7, 20, 21, 23, 24, 26, 27, 29, 31, 44, 45, 47, 48, 50, 51, 53], "embed": [0, 7, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], "m": [0, 2, 7, 8, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 32, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51], "4": [7, 9, 11, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 33, 35, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "exampledata": [7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 27, 28, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 51, 52], "gaussian": [0, 7, 20, 21, 22, 25, 27, 29, 31, 44, 45, 46, 49, 51, 53], "samp": [7, 20, 31, 44], "1x5": 7, "1789": [7, 31], "1757": [7, 31], "1819": [7, 31], "2209": [7, 31], "1756": [7, 31], "select": [2, 7, 14, 20, 21, 22, 23, 24, 25, 31, 38, 44, 45, 46, 47, 48, 49], "last": [7, 19, 31, 43], "valu": [0, 7, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 31, 33, 34, 35, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51], "end": 7, "delai": [7, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 32, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 49, 50], "tau": [7, 8, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 25, 31, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49], "phi1": [7, 21, 31, 45], "phi2": [7, 21, 31, 45], "1833": [7, 31], "1880": [7, 31], "1892": [7, 31], "1441": [7, 31], "imag": [2, 8, 21, 27, 29, 32, 45, 51, 53], "mandelbrot": [8, 27, 29, 32, 51, 53], "matrix": [8, 11, 25, 26, 27, 28, 29, 32, 35, 49, 50, 51, 52, 53], "mandelbrot_mat": [8, 27, 29, 32, 51, 53], "figur": [8, 10, 12, 13, 19, 34, 36, 37, 43], "color": [8, 10, 12, 13, 19], "imshow": [8, 32], "initialmagnif": 8, "500": 8, "colormap": 8, "hot": [8, 32], "trit": [8, 32], "logarithm": [8, 12, 13, 19, 20, 21, 22, 23, 24, 25, 26, 32, 36, 37, 43, 44, 45, 46, 47, 48, 49, 50], "3": [8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "templat": [8, 21, 22, 32, 45, 46], "size": [8, 21, 27, 29, 32, 45, 51, 53], "8": [8, 9, 11, 14, 16, 17, 20, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 38, 40, 41, 44, 45, 47, 48, 49, 50, 51, 53], "linear": [2, 8, 20, 21, 22, 25, 32, 44, 45, 46, 49], "distanc": [0, 8, 18, 20, 21, 22, 25, 32, 42, 44, 45, 46, 49], "linearli": [8, 32], "normalis": [8, 12, 17, 20, 21, 22, 25, 32, 36, 41, 44, 45, 46, 49], "f": [2, 8, 20, 32, 44], "exp": [8, 32, 44, 45, 46, 49], "frac": [8, 16, 32, 40], "x_": [8, 32], "min": [8, 20, 32, 44], "max": [8, 9, 20, 22, 25, 32, 33, 44, 46, 49], "fe2d": [8, 32], "fuzzen2d": [0, 8, 20, 21, 22, 25, 28, 29, 32, 44, 45, 46, 49, 52, 53], "fx": [8, 10, 15, 20, 21, 22, 25, 32, 34, 39, 44, 45, 46, 49], "r": [2, 8, 9, 10, 12, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 32, 33, 34, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49], "logx": [8, 12, 13, 19, 20, 21, 22, 25, 32, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 49], "0016": 8, "z": [10, 12, 20, 21, 22, 25, 27, 29, 34, 36, 44, 45, 46, 49, 51, 53], "compon": [0, 10, 12, 13, 19, 27, 29, 34, 36, 37, 43, 51, 53], "lorenz": [10, 12, 27, 29, 34, 36, 51, 53], "equat": [10, 12, 13, 19, 34, 36, 37, 43], "plot3": [10, 12], "xlabel": [10, 12, 13, 19], "ylabel": [10, 12, 13, 19], "zlabel": [10, 12], "gca": [10, 12, 13, 19], "axi": [10, 12, 13, 19, 20, 34, 36, 37, 43, 44], "squar": [10, 12, 13, 19], "dit": [12, 36], "alpha": [12, 20, 27, 29, 36, 44, 51, 53], "234": [12, 36], "return": [9, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 33, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51], "pnorm": [12, 20, 36, 44], "possibl": [0, 12, 20, 21, 24, 25, 26, 36, 44, 45, 48, 49, 50], "cpe": [12, 20, 36, 44], "perm": [12, 20, 36, 44, 46], "permen": [0, 12, 20, 21, 22, 23, 24, 25, 26, 28, 36, 44, 46, 47, 48, 49, 50, 52], "typex": [12, 20, 21, 25, 36, 44, 45, 49], "finegrain": [12, 20, 36, 44], "tpx": [12, 20, 25, 36, 44, 49], "norm": [9, 10, 12, 13, 16, 19, 20, 21, 22, 25, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 49], "fals": [12, 13, 20, 21, 22, 23, 24, 25, 26, 36, 37, 39, 40, 43, 44, 45, 46, 47, 48, 49, 50], "8687": [12, 36], "9468": [12, 36], "4734": [12, 36], "0781": [12, 36], "henon": [13, 19, 27, 29, 37, 43, 51, 53], "bit": [13, 37], "7": [9, 13, 20, 21, 22, 23, 24, 25, 26, 29, 33, 37, 44, 45, 46, 47, 48, 49, 50, 53], "angular": [13, 20, 25, 37, 44, 49], "order": [13, 17, 22, 23, 24, 37, 41, 44, 46, 47, 48], "pha": [13, 20, 37, 44], "phasen": [13, 20, 23, 24, 26, 28, 37, 44, 47, 48, 50, 52], "plotx": [10, 13, 19, 20, 23, 24, 26, 34, 37, 43, 44, 47, 48, 50], "true": [0, 9, 10, 13, 16, 19, 20, 21, 22, 23, 24, 25, 26, 29, 33, 34, 37, 40, 43, 44, 45, 46, 47, 48, 49, 50, 53], "0193": 13, "8395": [13, 37], "pseudorandom": [14, 16, 17, 18, 27, 29, 38, 40, 41, 42, 51, 53], "integ": [9, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "sturg": [14, 20, 21, 22, 38, 44, 45, 46], "randintegers2": [14, 18, 27, 29, 38, 42, 51, 53], "xdist": [14, 22, 38, 46], "xdisten": [14, 20, 21, 22, 23, 24, 26, 28, 38, 44, 45, 46, 47, 48, 50, 52], "17": [14, 20, 23, 24, 25, 38, 44, 47, 49], "25": [11, 14, 17, 35, 38, 41], "were": [0, 14, 19, 38, 43], "empti": [14, 38], "5248": [14, 38], "rice": [14, 20, 21, 22, 38, 44, 45, 46], "determin": [14, 38], "histogram": [14, 20, 21, 22, 38, 44, 45, 46], "ppi": [14, 20, 22, 38, 44, 46], "407": [14, 38], "415": [14, 38], "2802": [14, 38], "0000": [14, 38], "0047": [14, 38], "0368": [14, 38], "1096": [14, 38], "1978": [14, 38], "2558": [14, 38], "2421": [14, 38], "1531": [14, 38], "creat": [10, 11, 15, 16, 17, 18, 19, 21, 23, 24, 26, 34, 35, 39, 40, 41, 42, 43, 45, 47, 48, 50], "mobj": [10, 15, 16, 17, 18, 19, 23, 24, 26, 34, 39, 40, 41, 42, 43, 47, 48, 50], "fuzzi": [0, 15, 20, 21, 22, 23, 24, 25, 26, 28, 29, 39, 44, 45, 46, 47, 48, 49, 50, 52, 53], "sigmoid": [15, 20, 21, 22, 25, 39, 44, 45, 46, 49], "scale": [10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 34, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "fuzzen": [0, 15, 20, 21, 22, 23, 24, 25, 26, 28, 39, 44, 45, 46, 47, 48, 49, 50, 52], "struct": [11, 15, 16, 17, 18], "field": [0, 11, 15, 16, 17, 18], "func": [15, 16, 17, 18, 39, 40, 41, 42, 43], "2000": [11, 15, 20, 22, 35, 44, 46], "6": [9, 15, 17, 20, 21, 22, 23, 24, 25, 26, 29, 30, 33, 39, 41, 43, 44, 45, 46, 47, 48, 49, 50, 53], "symbol": [0, 9, 15, 19, 20, 21, 22, 23, 24, 25, 26, 28, 33, 39, 43, 44, 45, 46, 47, 48, 49, 50, 52], "xconden": [0, 15, 19, 20, 22, 23, 24, 26, 28, 39, 43, 44, 46, 47, 48, 50, 52], "c": [2, 9, 15, 19, 20, 21, 22, 23, 24, 25, 26, 33, 34, 36, 39, 43, 44, 45, 46, 47, 48, 49, 50], "uniformli": [9, 16, 17, 18, 27, 29, 33, 40, 41, 42, 51, 53], "object": [10, 16, 17, 18, 19, 23, 24, 26, 29, 34, 40, 41, 42, 43, 47, 48, 50, 53], "entyp": [10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "incren": [16, 20, 23, 24, 26, 28, 40, 44, 47, 48, 50, 52], "resolut": [0, 16, 20, 22, 40, 44, 46], "randinteg": [9, 16, 17, 27, 29, 33, 40, 41, 51, 53], "msobject": [10, 16, 17, 18, 19, 23, 24, 26, 29, 34, 40, 41, 42, 43, 47, 48, 50, 53], "tempor": [16, 17, 18, 19, 20, 23, 24, 26, 40, 41, 42, 43, 44, 47, 48, 50], "modifi": [0, 16, 20, 23, 24, 25, 26, 40, 44, 47, 48, 49, 50], "grain": [0, 10, 16, 20, 23, 24, 26, 27, 29, 34, 40, 44, 47, 48, 50, 51, 53], "y_j": [16, 40], "sum_": [16, 40], "left": [16, 40], "j": [2, 16, 20, 22, 25, 40, 44, 46, 49], "_i": [16, 40], "msx": [10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "msen": [0, 16, 20, 21, 22, 23, 24, 25, 26, 28, 40, 44, 45, 46, 47, 48, 49, 50, 52], "methodx": [9, 16, 20, 23, 24, 25, 26, 33, 40, 44, 47, 48, 49, 50], "2719": [16, 40], "3059": [16, 40], "2863": [16, 40], "2494": [16, 40], "2773": [11, 16, 35, 40], "radiu": [0, 17, 18, 20, 21, 22, 23, 24, 25, 26, 41, 42, 44, 45, 46, 47, 48, 49, 50], "threshold": [0, 17, 18, 20, 21, 22, 23, 24, 25, 41, 42, 44, 45, 46, 47, 48, 49], "2500": 17, "index": [17, 19, 23, 24, 26, 41, 43, 47, 48, 50], "ci": [10, 17, 19, 20, 22, 23, 24, 26, 34, 41, 43, 44, 46, 47, 48, 50], "3rd": [17, 41], "butterworth": [17, 23, 24, 41, 47, 48], "filter": [17, 23, 24, 41, 47, 48], "frequenc": [17, 20, 22, 23, 24, 41, 44, 46, 47, 48], "median": [17, 20, 23, 24, 25, 41, 44, 47, 48, 49], "absolut": [17, 23, 24, 41, 47, 48], "deviat": [0, 10, 17, 23, 24, 25, 26, 34, 41, 47, 48, 49, 50], "rmsen": [17, 23, 24, 26, 28, 41, 47, 48, 50, 52], "f_order": [17, 23, 24, 41, 47, 48], "f_num": [17, 23, 24, 41, 47, 48], "radnew": [17, 18, 23, 24, 41, 42, 47, 48], "5280": [17, 41], "5734": [17, 41], "5939": 17, "5908": [17, 41], "5563": 17, "8424": 17, "xapen": [0, 18, 20, 22, 23, 24, 26, 28, 42, 44, 46, 47, 48, 50, 52], "5000": [18, 27, 29, 51, 53], "comsposit": [18, 42], "varianc": [0, 10, 18, 20, 22, 23, 24, 25, 34, 42, 44, 46, 47, 48, 49], "cxmsen": [18, 23, 24, 26, 28, 42, 47, 48, 50, 52], "0893": 18, "4746": [18, 42], "2932": [18, 42], "12": [19, 20, 23, 25, 29, 43, 44, 47, 49, 53], "averag": [19, 20, 22, 23, 24, 25, 43, 44, 46, 47, 48, 49], "sn": [19, 23, 24, 43, 47, 48], "curv": [19, 20, 21, 22, 23, 24, 25, 26, 43, 44, 45, 46, 47, 48, 49, 50], "tree": [19, 23, 24, 43, 47, 48], "node": [19, 23, 24, 43, 47, 48], "4096": [9, 19, 27, 29, 33, 43, 51, 53], "decomposit": [2, 19, 23, 24, 43, 47, 48], "404": [19, 20, 43, 44], "sequenc": [0, 9, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 33, 35, 43, 44, 45, 46, 47, 48, 49, 50, 51], "ignor": [19, 43], "5159": [19, 43], "6245": [19, 43], "5634": [19, 43], "7022": [19, 43], "6533": [19, 43], "5853": [19, 43], "7956": [19, 43], "8447": [19, 43], "7605": [19, 43], "8415": [19, 43], "8115": [19, 43], "5128": [19, 43], "6862": [19, 43], "8679": [19, 43], "8287": [19, 43], "5940": [19, 41, 43], "6841": [19, 43], "7692": [19, 43], "5632": [19, 43], "form": [20, 22, 25, 30, 44, 46, 49], "apen": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "sig": [20, 22, 23, 24, 26, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50], "varargin": [20, 21, 22, 23, 24, 25, 26, 27], "ap": [20, 44], "phi": [0, 20, 22, 44, 46], "match": [0, 20, 21, 22, 25, 44, 45, 46, 49], "vector": [0, 9, 11, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35, 44, 45, 46, 47, 48, 49, 50, 51, 53], "default": [0, 10, 20, 21, 22, 23, 24, 25, 26, 27, 29, 34, 39, 44, 45, 46, 47, 48, 49, 50, 51, 53], "natur": [20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "pair": [20, 21, 22, 23, 24, 25, 26, 27, 44, 46], "argument": [20, 21, 22, 23, 24, 25, 26, 27, 29, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "scalar": [20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "conden": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "dispen": [0, 20, 21, 23, 24, 25, 26, 28, 44, 45, 47, 48, 49, 50, 52], "steven": [20, 22, 44, 46], "pincu": [20, 22, 44, 46], "proceed": [20, 22, 44, 46], "nation": [20, 22, 44, 46], "academi": [2, 20, 22, 44, 46], "88": [20, 25, 27, 44, 49, 51], "1991": [20, 44], "2297": [20, 44], "2301": [20, 44], "attnen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "attn": [20, 44], "sub": [20, 21, 23, 24, 44, 45, 47, 48], "hxx": [20, 44], "hxn": [20, 44], "hnn": [20, 44], "hnx": [20, 44], "local": [20, 44], "maxima": [20, 44], "interv": [20, 44], "minima": [20, 44], "between": [0, 20, 21, 23, 27, 28, 44, 45, 47, 51, 52], "subsequ": [0, 20, 27, 44, 51], "specen": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "jiawei": [20, 44], "et": [0, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "al": [0, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "interbeat": [20, 44], "transact": [2, 20, 23, 24, 44, 47, 48], "affect": [2, 20, 44], "comput": [0, 2, 10, 20, 21, 22, 23, 24, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "2020": [20, 24, 44, 48], "bubben": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "bubbl": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "bubb": [20, 44], "renyi": 20, "aktaruzzaman": [20, 44], "roberto": [20, 44], "sassi": [20, 44], "almost": [20, 44], "64": [20, 23, 44, 47], "2017": [10, 20, 21, 23, 25, 26, 34, 44, 45, 47, 48, 49, 50], "2711": [20, 44], "2718": [20, 44], "cond": [20, 44], "sew": [20, 22, 44, 46], "sez": [20, 22, 44, 46], "correspond": [20, 21, 22, 25, 27, 44, 45, 46, 49, 51], "shannon": [20, 21, 22, 44, 45, 46], "boolean": [20, 21, 22, 25, 44, 45, 46, 49], "disten": [20, 21, 22, 23, 24, 26, 28, 44, 45, 46, 47, 48, 50, 52], "xpermen": [0, 20, 22, 23, 24, 25, 26, 28, 44, 46, 47, 48, 49, 50, 52], "alberto": [2, 20, 22, 23, 25, 26, 44, 46, 49, 50], "porta": [20, 22, 44, 46], "regular": [20, 44], "sympathet": [20, 44], "outflow": [20, 44], "biolog": [10, 20, 22, 23, 24, 25, 26, 34, 44, 46, 47, 48, 49, 50], "cybernet": [20, 22, 44, 46], "78": [11, 20, 35, 44], "1998": [20, 44], "71": [11, 20, 23, 26, 35, 44, 47, 50], "cosien": [20, 23, 24, 25, 26, 28, 44, 47, 48, 49, 50, 52], "cosin": [0, 20, 23, 24, 25, 26, 28, 44, 47, 48, 49, 50, 52], "similar": [0, 20, 21, 23, 24, 25, 26, 28, 44, 45, 47, 48, 49, 50, 52], "cosi": [20, 44], "bm": [20, 25, 44, 49], "global": [20, 25, 44, 49], "zero": [20, 25, 49], "slopen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "griden": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "hmsen": [20, 23, 24, 26, 28, 29, 44, 47, 48, 50, 52, 53], "theerasak": [20, 25, 44, 49], "chanwimalueang": [20, 25, 44, 49], "danilo": [20, 25, 26, 44, 49, 50], "mandic": [20, 25, 26, 44, 49, 50], "self": [20, 25, 44, 49], "correl": [20, 22, 25, 44, 46, 49], "19": [20, 23, 25, 26, 44, 47, 49, 50], "652": [20, 25, 44, 49], "dist": [20, 44], "bin": [20, 21, 22, 44, 45, 46], "string": [20, 21, 22, 25, 27, 29, 44, 45, 46, 47, 48, 49, 50, 51, 53], "sqrt": [20, 21, 22, 44, 45, 46], "doan": [20, 21, 22, 44, 45, 46], "disten2d": [20, 21, 22, 28, 29, 44, 45, 46, 52, 53], "k2en": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "peng": [20, 23, 24, 26, 44, 47, 48, 50], "heartbeat": [20, 23, 24, 25, 44, 47, 49], "medic": [10, 20, 23, 25, 26, 34, 44, 49, 50], "53": [20, 44], "2015": [20, 22, 23, 24, 44, 46, 47, 48], "77": [11, 20, 35, 44], "87": [20, 25, 44, 49], "dispers": [0, 20, 21, 23, 24, 25, 26, 28, 29, 44, 45, 47, 48, 49, 50, 52, 53], "dispx": [20, 44], "rde": [9, 10, 20, 21, 25, 33, 34, 44, 45, 49], "revers": [9, 20, 21, 25, 33, 44, 45, 49], "cumul": [20, 21, 25, 44, 45, 49], "densiti": [20, 21, 25, 44, 45, 49], "ncdf": [20, 21, 25, 44, 45, 49], "kmean": [20, 21, 25, 44, 45, 49], "finesort": [20, 44], "equal": [0, 20, 21, 25, 44, 45, 49], "fluct": [20, 44], "when": [10, 20, 21, 22, 23, 24, 26, 28, 34, 44, 45, 46, 47, 48, 49, 50, 52], "fluctuat": [20, 25, 44, 49], "pattern": [2, 9, 20, 21, 22, 25, 33, 44, 45, 46, 49], "2c": [20, 44], "rho": [20, 27, 29, 44, 51, 53], "tune": [20, 25, 44, 49], "sydyen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "mostafa": [2, 20, 25, 44, 49], "rostaghi": [2, 20, 25, 44, 49], "hame": [2, 20, 21, 22, 23, 25, 44, 45, 46, 47, 48, 49], "azami": [2, 10, 20, 21, 22, 23, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "letter": [20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "23": [20, 21, 25, 44, 45, 49], "610": [20, 25, 44, 49], "614": [20, 25, 44, 49], "20": [11, 20, 21, 23, 25, 26, 27, 29, 35, 44, 45, 47, 48, 49, 50, 51, 53], "2018": [20, 21, 22, 23, 25, 44, 45, 46, 47, 49], "210": [20, 25, 44, 49], "yux": [20, 25, 44, 49], "xiang": [20, 25, 44, 49], "gao": [20, 23, 25, 44, 47, 49], "long": [20, 25, 44, 49], "2019": [20, 21, 22, 24, 25, 44, 45, 46, 48, 49], "5203": [20, 25, 44, 49], "wenlong": [20, 44], "fu": [20, 44], "fault": [2, 20, 44], "roll": [20, 44], "bear": [20, 44], "sort": [20, 44], "svm": [2, 20, 44], "optim": [2, 20, 23, 24, 44, 47, 48], "mutat": [20, 44], "sca": [20, 44], "pso": [20, 44], "21": [20, 25, 44, 49], "eoe": [20, 44], "aven": [20, 44], "s2": [20, 23, 24, 44, 47, 48], "level": [20, 44], "length": [0, 11, 20, 22, 25, 29, 35, 44, 46, 49, 53], "xrang": [20, 44], "element": [0, 20, 21, 22, 23, 24, 25, 27, 44, 45, 46, 47, 48, 49, 51], "franci": [20, 44], "hsu": [2, 20, 44], "550": [20, 44], "fuzz": [20, 44], "ps1": [20, 22, 44, 46], "ps2": [20, 22, 44, 46], "modsampen": [20, 21, 22, 25, 44, 45, 46, 49], "gudermannian": [20, 21, 22, 25, 44, 45, 46, 49], "defin": [20, 21, 22, 25, 44, 45, 46, 49], "divisor": [20, 21, 22, 25, 44, 45, 46, 49], "exponenti": [20, 21, 22, 25, 44, 45, 46, 49], "subtract": [20, 21, 22, 25, 44, 45, 46, 49], "pre": [2, 20, 21, 22, 25, 44, 45, 46, 49], "divis": [20, 21, 22, 25, 44, 45, 46, 49], "expon": [20, 21, 22, 25, 44, 45, 46, 49], "whose": [20, 21, 22, 25, 44, 45, 46, 49], "numer": [20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "gd": [20, 21, 22, 25, 44, 45, 46, 49], "atan": [20, 21, 22, 25, 44, 45, 46, 49], "tanh": [20, 21, 22, 25, 44, 45, 46, 49], "maximum": [20, 21, 22, 25, 29, 44, 45, 46, 49, 53], "minimuum": [20, 21, 22, 44, 45, 46], "further": [0, 20, 22, 23, 25, 26, 27, 44, 46, 47, 49, 50, 51], "xfuzzen": [0, 20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "weit": [20, 44], "character": [2, 20, 44], "surfac": [2, 20, 44], "emg": [20, 25, 44, 49], "rehabilit": [2, 20, 44], "2007": [20, 44], "266": [20, 44], "272": [20, 44], "hong": [20, 22, 44, 46], "bo": [20, 22, 44, 46], "xie": [20, 22, 44, 46], "wei": [2, 20, 44], "xing": [20, 44], "he": [20, 44], "hui": [20, 44], "372": [11, 20, 35, 44], "48": [20, 44], "2008": [20, 44], "7140": [20, 44], "7146": [20, 44], "grid": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "gde": [20, 44], "gdr": [20, 44], "coars": [0, 20, 23, 24, 26, 44, 47, 48, 50], "pix": [20, 44], "gix": [20, 44], "six": [20, 44], "aix": [20, 44], "percentag": [20, 21, 45], "point": [20, 21, 22, 25, 29, 44, 45, 46, 49, 53], "below": [20, 25, 29, 49, 53], "ident": 20, "proport": 20, "abov": [9, 20, 23, 24, 26, 33, 44, 47, 48, 50], "phase": [0, 20, 23, 24, 25, 26, 28, 29, 44, 47, 48, 49, 50, 52, 53], "angl": [20, 25, 49], "area": [20, 44], "poicar": 20, "bivari": [20, 22, 44, 46], "yan": [20, 24, 44, 48], "descriptor": [20, 44], "poincar": 20, "analyz": [20, 21, 24, 44, 45, 48], "109": [20, 44], "280": [20, 44], "289": [20, 24, 44, 48], "onlin": [20, 44], "14": [11, 20, 35, 44], "period": [20, 23, 24, 44, 47, 48], "autonom": [2, 20, 44], "regul": [20, 44], "american": [20, 22, 44, 46], "regulatori": [20, 22, 44, 46], "295": [20, 44], "r550": [20, 44], "r557": [20, 44], "karmakar": [20, 44], "khandok": [20, 44], "palaniswami": [20, 44], "36": [20, 44], "303": [11, 20, 35, 44], "increment": [20, 23, 24, 26, 28, 29, 44, 47, 48, 50, 52, 53], "incr": [20, 44], "xiaofeng": [20, 25, 44, 49], "18": [20, 44], "22": [20, 24, 44, 48], "jiang": [20, 23, 24, 44, 47, 48], "xu": [20, 23, 24, 44, 47, 48], "xue": [20, 44], "133": [20, 44], "appropri": [20, 44], "electrophysiolog": [20, 44], "95": [20, 27, 44, 51], "13": [20, 29, 44, 53], "kolmogorov": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "k2": [20, 22, 44], "xk2en": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "peter": [2, 20, 44], "grassberg": [20, 44], "itamar": [20, 44], "procaccia": [20, 44], "review": [2, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "28": [20, 27, 29, 44, 51, 53], "1983": [20, 44], "2591": [20, 44], "lin": [20, 22, 44, 46], "jue": [20, 44], "longwei": [20, 44], "event": [20, 44], "relat": [0, 2, 20, 28, 44, 52], "desynchron": [20, 44], "synchron": [20, 44], "eeg": [2, 20, 44], "eng": [20, 44, 47, 48], "2013": [20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "jun": [20, 44], "03602": [20, 44], "permuat": [20, 22, 44, 46], "standard": [0, 10, 20, 23, 24, 25, 26, 34, 44, 47, 48, 49, 50], "recommened": [20, 44], "5m": [20, 44], "amigo": [20, 44], "europhi": [20, 44], "lett": [20, 44], "83": [20, 44], "60005": [20, 44], "list": [0, 20, 44], "oper": [20, 25, 49], "undefin": [20, 22, 44, 46], "uniqu": [9, 20, 33, 44], "variat": [0, 20, 25, 44, 49], "ampawar": [20, 25, 44, 49], "weight": [0, 20, 25, 44, 49], "edg": [0, 20, 22, 25, 44, 46, 49], "associ": [20, 25, 44, 49], "variant": [0, 20, 23, 25, 44, 47, 49], "xmsen": [0, 20, 21, 22, 23, 24, 26, 28, 44, 45, 46, 47, 48, 50, 52], "christoph": [2, 20, 25, 44, 49], "bandt": [20, 25, 44, 49], "pomp": [20, 25, 44, 49], "2002": [11, 20, 22, 23, 24, 25, 26, 35, 44, 46, 47, 48, 49, 50], "174102": [20, 25, 44, 49], "xiao": [20, 25, 44, 49], "feng": [20, 24, 44, 48], "yue": [20, 44], "chines": [20, 44], "2009": [20, 23, 24, 44, 47, 48], "2690": [20, 44], "chunhua": [20, 25, 44, 49], "bian": [20, 25, 44, 49], "85": [20, 25, 44, 49], "2012": [20, 21, 25, 44, 45, 49], "021906": [20, 23, 25, 26, 44, 47, 49, 50], "bilal": [2, 20, 25, 44, 49], "fadlallah": [20, 25, 44, 49], "incorpor": [0, 20, 25, 28, 44, 49, 52], "022911": [20, 25, 44, 49], "awar": [0, 20, 25, 44, 49], "illustr": [20, 25, 44, 49], "segment": [0, 11, 20, 25, 28, 35, 44, 49, 52], "program": [2, 20, 25, 44, 49], "128": [11, 20, 21, 25, 29, 35, 44, 45, 49, 53], "40": [20, 25, 44, 49], "51": [20, 25, 44, 49], "zhiqiang": [20, 25, 44, 49], "45th": [20, 25, 44, 49], "annual": [20, 21, 24, 25, 44, 45, 48, 49], "industri": [20, 25, 44, 49], "electron": [20, 25, 44, 49], "soc": [20, 25, 44, 49], "5998": [20, 25, 44, 49], "6003": [20, 25, 44, 49], "zhe": [20, 44], "1403829": [20, 44], "maik": [20, 25, 44, 49], "riedl": [20, 25, 44, 49], "m\u00fcller": [20, 25, 44, 49], "niel": [20, 25, 44, 49], "wessel": [20, 25, 44, 49], "practic": [20, 25, 44, 49], "consider": [20, 25, 44, 49], "european": [20, 25, 44, 49], "special": [20, 25, 44, 49], "topic": [20, 25, 44, 49], "222": [11, 20, 25, 35, 44, 49], "249": [20, 25, 44, 49], "262": [20, 25, 44, 49], "begin": [20, 44], "along": [20, 44], "somewhat": [20, 44], "arbitrari": [20, 44], "even": [20, 25, 26, 44, 49, 50], "prefer": [20, 44], "multipl": [0, 2, 20, 44], "sake": [20, 44], "symmetri": [20, 44], "poincar\u00e9": [20, 44], "ashish": [20, 44], "rohila": [20, 44], "ambalika": [20, 44], "sharma": [20, 44], "105006": [20, 44], "xsampen": [0, 20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "sampen2d": [20, 21, 22, 25, 28, 29, 44, 45, 46, 49, 52, 53], "joshua": [20, 22, 44, 46], "richman": [20, 22, 44, 46], "randal": [20, 22, 44, 46], "moorman": [20, 22, 44, 46], "circulatori": [20, 22, 44, 46], "slope": [20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "slop": [20, 44], "45": [20, 24, 44, 48], "lvl": [20, 44], "thresold": [20, 44], "monoton": [20, 44], "increas": [0, 20, 35, 44], "90": [20, 44], "cuesta": [20, 44], "frau": [20, 44], "both": [0, 20, 44], "1167": [20, 44], "spec": [20, 44], "banden": [20, 22, 44, 46], "spectrum": [20, 22, 44, 46], "within": [20, 22, 44, 46], "band": [20, 22, 44, 46], "fft": [20, 22, 44, 46], "freq": [20, 22, 44, 46], "nyquist": [20, 22, 44, 46], "fs": [20, 22, 44, 46], "periodogram": [20, 22, 46], "powel": [20, 44], "perciv": [20, 44], "distinguish": [20, 44], "irregular": [20, 21, 22, 44, 45, 46], "motion": [20, 44], "hamiltonian": [20, 44], "gener": [0, 20, 21, 22, 23, 24, 25, 26, 29, 44, 45, 46, 47, 48, 49, 50, 53], "1979": [20, 44], "2053": [20, 44], "tsuyoshi": [20, 44], "inouy": [20, 44], "power": [0, 20, 44], "electroencephalographi": [2, 20, 44], "neurophysiolog": [20, 44], "79": [11, 20, 22, 35, 44, 46], "204": [20, 44], "sydi": [20, 44], "zt": [20, 44], "mep": [20, 44], "uniform": [20, 27, 29, 44, 51, 53], "yongbo": [20, 44], "scheme": [20, 44], "planetari": [20, 44], "gearbox": [20, 44], "multi": [20, 44], "mrmr": [20, 44], "featur": [0, 2, 20, 44], "mechan": [20, 23, 25, 26, 44, 47, 49, 50], "91": [20, 21, 25, 26, 44, 45, 49, 50], "312": [20, 44], "jian": [20, 44], "extract": [2, 20, 27, 30, 44, 51], "electr": [20, 44], "aviat": [20, 44], "electro": [20, 44], "actuat": [20, 44], "icspcc": [20, 44], "venkatesh": [20, 44], "rajagopalan": [20, 44], "asok": [20, 44], "rai": [20, 44], "via": [0, 2, 20, 25, 44, 49], "86": [20, 44], "2006": [20, 23, 26, 44, 47, 50], "3309": [20, 44], "3320": [20, 44], "while": [21, 45], "primarili": [21, 45, 52], "appli": [0, 2, 21, 23, 24, 25, 26, 45, 47, 48, 49, 50], "2d": [21, 45], "matric": [21, 27, 29, 45, 51, 53], "henc": [21, 45], "textur": [21, 45], "lock": [21, 29, 45, 53], "dispen2d": [0, 20, 21, 25, 28, 29, 45, 49, 52, 53], "keyword": [21, 29, 44, 45, 46, 47, 48, 49, 50, 51, 53], "permit": [21, 29, 45, 53], "reason": [21, 45], "becaus": [21, 25, 45, 49], "hundr": [21, 45], "million": [21, 45], "pairwis": [21, 45], "error": [21, 45], "often": [21, 45], "occur": [21, 45], "store": [21, 23, 24, 26, 45, 47, 48, 50], "ram": [21, 45], "200": [21, 45], "753": [11, 21, 35, 45], "049": [21, 45], "836": [21, 45], "777": [21, 45], "448": [11, 21, 35, 45], "524": [11, 21, 35, 45], "pass": [21, 23, 24, 27, 29, 45, 47, 48, 51, 53], "greater": [0, 21, 45], "unlock": [21, 45], "caus": [21, 45], "crash": [21, 45], "mat": [21, 45], "se2d": [21, 45], "matricess": [21, 45], "floor": [21, 27, 45, 51], "repres": [20, 21, 27, 44, 45, 51], "height": [21, 45], "row": [21, 25, 45, 49], "width": [21, 45], "column": [21, 27, 29, 45, 51, 53], "minimum": [21, 45], "submatrix": [21, 45], "By": [21, 25, 26, 45, 49, 50], "prevent": [21, 45], "overload": [21, 45], "753049836": [21, 45], "warn": [21, 43], "could": [21, 45], "luiz": [21, 45], "eduardo": [21, 45], "virgili": [21, 45], "silva": [21, 45], "045002": [21, 45], "fuzz2d": [21, 45], "fernando": [21, 23, 24, 45, 47, 48], "segato": [21, 45], "santo": [2, 21, 45], "multidimension": [21, 45], "sampenmf": [21, 45], "histolog": [21, 45], "colorect": [21, 45], "cancer": [21, 45], "103": [21, 45], "148": [11, 21, 35, 45], "160": [21, 45], "mirvana": [21, 45], "hilal": [21, 45], "heurtier": [2, 21, 23, 24, 45, 47, 48], "principl": [21, 45], "41st": [21, 24, 45, 48], "societi": [21, 45], "dist2d": [21, 45], "24": [21, 23, 24, 25, 45, 47, 48, 49], "9": [20, 21, 22, 23, 24, 29, 44, 45, 46, 47, 48, 53], "1338": [21, 45], "1342": [21, 45], "disp2d": [21, 45], "75": [10, 11, 21, 34, 35, 45], "178": [21, 45], "187": [21, 45], "perm2d": [21, 45], "smax": [21, 45], "mx": [21, 45], "my": [21, 45], "lowest": [21, 45], "common": [21, 45], "rank": [21, 45], "instanc": [21, 45], "given": [21, 23, 24, 27, 29, 45, 47, 48, 51, 53], "ordin": [21, 45], "instead": [21, 45], "longer": [21, 45], "assum": [21, 45, 53], "motif": [21, 45], "haroldo": [21, 45], "ribeiro": [21, 45], "causal": [21, 45], "plane": [21, 45], "ONE": [21, 45], "e40689": [21, 45], "luciano": [21, 45], "zunino": [21, 45], "discrimin": [21, 45], "soliton": [21, 45], "679": [11, 21, 35, 45], "688": [21, 45], "esp2d": [21, 45], "toler": [21, 23, 45, 47], "ps": [21, 45], "ricardo": [21, 45], "espen": [21, 45], "approach": [2, 21, 22, 23, 25, 45, 46, 47, 49], "1261": [21, 45], "separ": 0, "xap": [22, 46], "contain": [20, 22, 24, 27, 44, 46, 48, 51], "direct": [22, 46], "thu": [22, 25, 29, 30, 46, 49, 53], "burton": [22, 46], "singer": [22, 46], "93": [22, 46], "1996": [22, 46], "2083": [22, 46], "2088": [22, 46], "serial": [22, 46], "implic": [22, 46], "health": [2, 22, 46], "annal": [22, 46], "york": [22, 46], "954": [22, 46], "2001": [11, 22, 35, 46], "245": [22, 46], "267": [22, 46], "xcond": [22, 46], "matter": [22, 46], "u": [2, 22, 25, 46, 49], "amount": [22, 46], "carri": [22, 46], "evalu": [22, 23, 46, 47], "coupl": [22, 46], "81": [22, 46], "1999": [22, 46], "119": [22, 46], "129": [11, 22, 35, 46], "pi": 22, "beween": 22, "yuanyuan": [22, 46], "pengjian": [22, 24, 46, 48], "shang": [22, 24, 46, 48], "financi": [22, 46], "stock": [22, 46], "market": [22, 46], "tsalli": [22, 46], "94": [22, 25, 26, 46, 49, 50], "1361": [22, 46], "1376": [22, 46], "xfuzz": [22, 46], "paramt": 22, "test": [22, 46], "synchroni": [22, 46], "180": [22, 46], "2010": [22, 46], "1715": [22, 46], "1724": [22, 46], "xk2": [22, 46], "project": [22, 24, 34, 36, 46, 48], "xperm": [22, 46], "betweeen": [22, 46], "wenbin": [22, 46], "shi": [22, 46], "aij": [22, 46], "2439": [22, 46], "2447": [22, 46], "xsamp": [22, 46], "xspec": [22, 46], "origni": [23, 24, 26], "propos": [23, 24, 26, 47, 48, 50], "costa": [23, 24, 26, 47, 48, 50], "particular": [23, 24, 26, 47, 48, 50], "msen2d": [], "cmsen": [23, 24, 26, 28, 47, 48, 50, 52], "rxmsen": [23, 24, 26, 28, 47, 48, 50, 52], "shown": [23, 24, 26, 29, 47, 48, 50, 52], "imf": [23, 24, 47, 48], "timeshift": [23, 24, 47, 48], "rescal": [23, 24, 47, 48], "xt": [23, 24, 47], "coeffici": [23, 24, 47, 48], "otherwis": [23, 24, 47, 48, 52], "std": [23, 24, 47, 48], "var": [23, 24, 47, 48], "mad": [23, 24, 47, 48], "madalena": [23, 24, 26, 47, 48, 50], "ari": [23, 24, 26, 47, 48, 50], "goldberg": [23, 24, 26, 47, 48, 50], "89": [23, 24, 26, 47, 48, 50], "068102": [23, 24, 26, 47, 48, 50], "vadim": [2, 23, 24, 26, 47, 48, 50], "v": [2, 23, 24, 26, 47, 48, 50], "nikulin": [23, 24, 26, 47, 48, 50], "tom": [23, 24, 26, 47, 48, 50], "brismar": [23, 24, 26, 47, 48, 50], "comment": [23, 24, 26, 47, 48, 50], "92": [23, 24, 26, 27, 47, 48, 50, 51], "2004": [23, 24, 26, 47, 48, 50], "089803": [23, 24, 26, 47, 48, 50], "repli": [23, 24, 26, 47, 48, 50], "089804": [23, 24, 26, 47, 48, 50], "2005": [23, 26, 47, 50], "ranjit": [23, 26, 47, 50], "thuraisingham": [23, 26, 47, 50], "gottwald": [23, 26, 47, 50], "On": [23, 26, 47, 50], "physica": [20, 23, 25, 26, 44, 47, 49, 50], "366": [23, 26, 47, 50], "323": [23, 26, 47, 50], "332": [23, 26, 47, 50], "meng": [23, 47], "hu": [23, 47], "hualou": [23, 47], "liang": [23, 47], "intrins": [23, 47], "mode": [11, 23, 27, 35, 47, 51], "multivari": [0, 2, 11, 23, 24, 27, 29, 35, 47, 48, 51, 53], "neurodynam": [23, 47], "2011": [23, 24, 25, 26, 47, 48, 49, 50], "277": [23, 47], "284": [23, 47], "3110": [23, 47], "3123": [23, 47], "jianbo": [23, 47], "bi": [23, 47], "paolo": [23, 47], "castiglioni": [23, 47], "cardiovascular": [23, 24, 47, 48], "doe": [23, 25, 47, 49], "choic": [2, 23, 47], "vari": [23, 47], "among": [0, 23, 47], "influenc": [2, 23, 47], "interpret": [2, 23, 47], "590": [23, 47], "tuan": [2, 23, 47], "shift": [23, 47], "257": [23, 47], "138": [23, 47], "magdalena": [23, 24], "volatil": [23, 24, 47], "1197": [23, 24, 47], "1203": [23, 24, 47], "cmse": [23, 24, 47, 48], "rcmsen": [23, 26, 47, 50], "shuen": [23, 24, 26, 47, 48, 50], "wu": [2, 23, 24, 26, 47, 48, 50], "1069": [23, 24, 26, 47, 48, 50], "1084": [23, 24, 26, 47, 48, 50], "378": [11, 23, 26, 35, 47, 48, 50], "2014": [23, 26, 47, 48, 50], "1369": [23, 26, 47, 48, 50], "1374": [23, 26, 47, 48, 50], "lpf": [23, 24, 47, 48], "cutoff": [23, 24, 47, 48], "fc": [23, 24, 47, 48], "low": [23, 24, 47, 48], "valencia": [23, 24, 47, 48], "holter": [23, 24, 47, 48], "record": [2, 23, 24, 47, 48], "healthi": [23, 24, 47, 48], "aortic": [23, 24, 47, 48], "stenosi": [23, 24, 47, 48], "subject": [23, 24, 47, 48], "56": [23, 24, 47, 48], "2202": [23, 24, 47, 48], "2213": [23, 24, 47, 48], "puneeta": [23, 24, 47, 48], "marwaha": [23, 24, 47, 48], "ramesh": [23, 24, 47, 48], "kumar": [2, 23, 24, 47, 48], "sunkaria": [23, 24, 47, 48], "technolog": [23, 24, 47, 48], "557": [23, 24, 47, 48], "576": [23, 24, 47, 48], "sum": [23, 24, 25, 47, 48, 49], "root": [23, 24, 47, 48], "s_00": [23, 24, 47, 48], "nth": [23, 24, 47, 48], "subnod": [23, 24, 47, 48], "s_tn": [23, 24, 47, 48], "s_10": [23, 24, 47, 48], "s_11": [23, 24, 47, 48], "s_20": [23, 24, 47, 48], "s_21": [23, 24, 47, 48], "s_22": [23, 24, 47, 48], "s_23": [23, 24, 47, 48], "s_30": [23, 24, 47, 48], "s_31": [23, 24, 47, 48], "s_32": [23, 24, 47, 48], "s_33": [23, 24, 47, 48], "s_34": [23, 24, 47, 48], "s_35": [23, 24, 47, 48], "s_36": [23, 24, 47, 48], "s_37": [23, 24, 47, 48], "s_40": [23, 24, 47, 48], "s0": [23, 24, 47, 48], "st": [23, 24, 47, 48], "entir": [23, 24, 47, 48], "graph": [2, 23, 24, 47, 48], "show": [23, 24, 32, 47, 48], "ying": [23, 24, 47, 48], "yuesheng": [23, 24, 47, 48], "236": [23, 24, 47, 48], "728": [23, 24, 47, 48], "742": [23, 24, 47, 48], "just": [0, 24, 48], "again": [24, 48], "nx2": [], "rui": [24, 48], "zhuo": [24, 48], "5th": [24, 48], "vol": [20, 24, 44, 48], "pp": [20, 24, 25, 44, 48, 49], "411": [24, 48], "413": [24, 48], "antoin": [24, 48], "jamin": [24, 48], "navig": [24, 30, 48], "acquir": [24, 48], "bike": [24, 48], "simul": [2, 24, 48], "rcxmsen": [24, 48], "yi": [24, 48], "yin": [24, 48], "guochen": [24, 48], "98": [24, 48], "110": [24, 48], "effort": [0, 28, 52], "made": [28, 52], "them": [28, 52], "bidimensioanl": [28, 52], "ex": [29, 53], "multiscla": [], "These": [0, 26, 29, 44, 45, 46, 49, 50, 53], "mere": [29, 53], "snippet": [29, 53], "connect": [2, 27, 29, 51, 53], "everi": 29, "consult": 29, "depth": 29, "accept": [29, 53], "chirp": [27, 29, 51, 53], "f0": [27, 29, 51, 53], "01": [27, 29, 51, 53], "t1": [27, 29, 51, 53], "4000": [11, 27, 29, 35, 51, 53], "f1": [27, 29, 51, 53], "025": [27, 29, 51, 53], "beta": [27, 29, 51, 53], "xo": [27, 29, 51, 53], "yo": [27, 29, 51, 53], "zo": [27, 29, 51, 53], "attractor": [27, 29, 51, 53], "uniform2": [27, 29, 51, 53], "gaussian2": [27, 29, 51, 53], "uniform_mat": [27, 29, 51, 53], "gaussian_mat": [27, 29, 51, 53], "randintegers_mat": [27, 29, 51, 53], "entropyhub_mat": [27, 29, 51, 53], "declar": [29, 53], "halv": [29, 53], "1500": [29, 53], "1024": [9, 29, 33, 53], "numpi": [30, 44, 45, 46, 49, 52], "scipi": 30, "matplotlib": [30, 32, 34, 36, 37, 43], "pyemd": 30, "request": [0, 30], "python3": 30, "python2": 30, "strongli": 30, "pip": 30, "id": [30, 45], "tar": 30, "gz": 30, "repo": 30, "unzip": 30, "termin": 30, "cmd": 30, "mac": 30, "anaconda": 30, "prompt": 30, "setup": 30, "py": 30, "ensur": [25, 30, 49], "up": 30, "setuptool": 30, "upgrad": 30, "abbrevi": 30, "eh": [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 53], "print": 30, "arrai": [11, 20, 31, 33, 35, 36, 38, 40, 41, 42, 43, 44], "pyplot": [32, 34, 36, 37, 43], "cmap": 32, "00159093": 32, "fig": [34, 36, 37, 43], "scatter": [34, 36], "facecolor": [34, 36, 37, 43], "ax": [34, 36], "add_subplot": [34, 36], "111": [11, 34, 35, 36], "3d": [34, 36], "set_facecolor": [34, 36], "off": [34, 36, 37, 43], "0192821496913216": 37, "_fuzzen": 39, "71828": [39, 41, 42, 43], "kwarg": [39, 40, 41, 42, 43, 47, 48, 50], "_xconden": [39, 43], "_incren": 40, "_sampen": 41, "none": [25, 41, 42, 44, 45, 46, 49, 51], "5564": 41, "842518179468045": 41, "_xapen": 42, "089": 42, "directli": [44, 45, 46, 49], "dir": [44, 45, 46, 49], "four": 44, "tupl": [44, 45, 46, 51], "r\u00e9nyi": 44, "poicar\u00e9": 44, "len": 44, "fftpack": 44, "funtion": [], "mbjx": [47, 48, 50], "ykj": [47, 48], "keywrod": [], "concis": [52, 53], "exclud": [27, 35, 51, 52], "prefix": 52, "np": [33, 35, 49, 52], "48550": [], "00627": [], "stress": 2, "automat": 2, "respiratori": 2, "talha": 2, "iqbal": 2, "adnan": 2, "elahi": 2, "william": 2, "wijn": 2, "amin": 2, "atif": 2, "shahzad": 2, "privaci": 2, "preserv": 2, "mobil": 2, "activ": 2, "yute": 2, "zhan": 2, "imperi": 2, "colleg": 2, "london": 2, "fast": 2, "prolong": 2, "food": 2, "restrict": 2, "differenti": 2, "gh": 2, "secret": 2, "independ": 2, "receptor": 2, "agrp": 2, "maria": 2, "sousa": 2, "daniela": 2, "gusmao": 2, "willian": 2, "henriqu": 2, "moriya": 2, "felip": 2, "lima": 2, "edward": 2, "john": 2, "kopchick": 2, "jose": 2, "donato": 2, "jr": 2, "neuroendocrinolog": 2, "invas": 2, "waveform": 2, "emerg": 2, "triag": 2, "hemorrhag": 2, "lower": [2, 20, 21, 22, 25, 26, 44, 45, 46, 49, 50], "bodi": 2, "neg": [2, 25, 26, 49, 50], "naimahm": 2, "nesaragi": 2, "lar": 2, "\u00f8ivind": 2, "h\u00f8iseth": 2, "hemin": 2, "ali": 2, "qadir": 2, "leiv": 2, "arn": 2, "rosseland": 2, "per": 2, "steinar": 2, "halvorsen": 2, "ilangko": 2, "balasingham": 2, "earli": 2, "reinforc": 2, "vibrat": 2, "monitor": 2, "wenqin": 2, "zhao": 2, "yaqiong": 2, "lv": 2, "jialun": 2, "carman": 2, "lee": 2, "lei": 2, "tu": 2, "qualiti": 2, "fair": 2, "haddadi": 2, "afra": 2, "mashhadi": 2, "maksim": 2, "belyaev": 2, "yurii": 2, "izotov": 2, "murugappan": 2, "electromyographi": 2, "characterist": 2, "foot": 2, "tap": 2, "forc": 2, "spastic": 2, "sclerosi": 2, "brian": 2, "pribbl": 2, "univers": 2, "oklahoma": 2, "nois": 2, "music": 2, "stimul": 2, "nervou": 2, "mariana": 2, "jacob": 2, "rodrigu": 2, "octavian": 2, "postolach": 2, "francisco": 2, "cerca": 2, "instrument": 2, "brain": 2, "interact": 2, "movement": 2, "challeng": 2, "derek": 2, "monro": 2, "nathaniel": 2, "berri": 2, "fino": 2, "rhea": 2, "impact": [0, 2], "hardwar": 2, "mous": 2, "rest": 2, "magnet": 2, "fmri": 2, "mila": 2, "urosev": 2, "mcgill": 2, "escholarship": 2, "beam": 2, "yongjun": 2, "topolog": 2, "bound": [2, 25, 26, 49, 50], "c\u00e1nova": 2, "adazd": 2, "net": 2, "autom": 2, "adapt": 2, "alzheim": 2, "diseas": 2, "smith": 2, "khare": 2, "rajendra": 2, "acharya": 2, "knowledg": 2, "raw": [], "html": [], "blob": 27, "7ca475740f6578bec6db122512051b47e24c7dd4": [], "svg": [], "plumx": [], "plum": [], "visual": [], "research": 0, "metricscitationscit": [], "23polici": [], "citat": [], "5usageclick": [], "8abstract": [], "46054download": [], "18412capturesread": [], "237export": [], "4mentionsnew": [], "mention": [], "2refer": [], "1social": [], "mediashar": [], "amp": [], "38tweet": [], "2see": [], "page": [], "super": [], "block": [], "extrahead": [], "script": [], "href": 27, "plu": [], "2fjournal": [], "0286707": [], "popup": [], "larg": [], "class": [], "site": [], "hide": [], "waist": [], "gait": 2, "speed": [], "reflect": 0, "adult": [], "endblock": [], "bio": 2, "inspir": 2, "perceptron": 2, "neuro": 2, "petr": 2, "boriskov": 2, "putrolaynen": 2, "ensembl": 2, "mental": 2, "varun": 2, "bajaj": 2, "gaikwad": 2, "sinha": 2, "parkinson": 2, "smart": [], "iot": [], "environ": 0, "dmitri": 2, "korzun": 2, "bell": [20, 21, 22, 25, 44, 45, 46, 49], "triangular": [0, 20, 21, 22, 25, 44, 45, 46, 49], "trapezoidal1": [20, 21, 22, 25, 44, 45, 46, 49], "trapezoidal2": [20, 21, 22, 25, 44, 45, 46, 49], "z_shape": [20, 21, 22, 25, 44, 45, 46, 49], "constgaussian": [10, 20, 21, 22, 25, 34, 44, 45, 46, 49], "depric": [20, 21, 22, 44, 45, 46], "104833": [20, 21, 22, 44, 45, 46], "104847": [20, 21, 22, 44, 45, 46], "upper": [20, 21, 22, 25, 44, 45, 46, 49], "2r": [20, 21, 22, 25, 44, 45, 46, 49], "trapezoid": [0, 20, 21, 22, 25, 44, 45, 46, 49], "shape": [20, 21, 22, 25, 44, 45, 46, 49], "threshod": [20, 21, 22, 25, 44, 45, 46, 49], "vcp": [0, 20, 22, 44, 46], "ka": [20, 22, 44, 46], "kb": [20, 22, 44, 46], "lake": [0, 20, 22, 44, 46], "p": [20, 22, 25, 26, 44, 46, 49, 50], "griffin": [20, 22, 44, 46], "neonat": [20, 22, 44, 46], "283": [20, 22, 44, 46], "r789": [20, 22, 44, 46], "r797": [20, 22, 44, 46], "overlap": [0, 11, 20, 22, 27, 35, 44, 46, 51], "respecitv": [], "_overlapping_": [], "cp": [20, 22, 44, 46], "underfin": [], "zeroth": [0, 20, 22, 44, 46], "due": [20, 22, 44, 46], "demand": [20, 22, 44, 46], "option": [0, 20, 22, 25, 44, 49], "substanti": [20, 22, 44, 46], "appendix": [20, 22, 44, 46], "region": 2, "schizophrenia": 2, "shunliang": 2, "haifeng": 2, "8th": 2, "icsip": 2, "diven": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "divers": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "fetal": 2, "matern": 2, "simultan": 2, "ana": 2, "filipa": 2, "soeiro": 2, "gome": 2, "faculdad": 2, "engenharia": 2, "da": 2, "universidad": 2, "porto": 2, "kang": [20, 25, 44, 49], "huan": [20, 25, 44, 49], "guangbin": [20, 25, 44, 49], "568": [20, 25, 44, 49], "125686": [20, 25, 44, 49], "radiolog": 2, "nuclear": 2, "facil": 2, "workplac": 2, "abinash": 2, "chakraborti": 2, "neeraj": 2, "parashar": 2, "dk": [], "pandei": 2, "pankaj": 2, "uv": 2, "deokar": 2, "jpn": 2, "ssrn": [], "social": [], "shervin": 2, "skaria": 2, "sreelatha": 2, "ks": 2, "korean": 2, "pyschiatr": 2, "nurs": 2, "nldyn": 2, "sadegh": 2, "tarek": 2, "rajji": 2, "spectroscopi": 2, "group": 2, "martti": 2, "ilvesm\u00e4ki": 2, "hani": 2, "ferdinando": 2, "noponen": 2, "tapio": 2, "sepp\u00e4nen": 2, "vesa": 2, "korhonen": 2, "kiviniemi": 2, "teemu": 2, "myllyl\u00e4": 2, "authorea": 2, "acceler": 2, "profil": 2, "adolesc": 2, "soccer": 2, "player": 2, "season": 2, "jake": 2, "melaro": 2, "tennesse": 2, "knoxvil": 2, "classifi": 2, "wael": 2, "suliman": 2, "vinayakumar": 2, "ravi": 2, "tencon": 2, "trunk": 2, "swai": 2, "offic": 2, "worker": 2, "With": [0, 2], "chronic": 2, "spinal": 2, "pain": 2, "afonso": 2, "manuel": 2, "caetano": 2, "julio": 2, "repositorio": 2, "nova": 2, "lisboa": 2, "ocular": 2, "trajectori": 2, "kateryna": 2, "oleg": 2, "rangen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "robert": 2, "rainer": 2, "recognit": 2, "ey": 2, "tong": 2, "qin": 2, "wim": 2, "fia": 2, "nico": 2, "wegh": 2, "haosheng": 2, "huang": 2, "geograph": 2, "cot": 2, "michael": 2, "beacom": 2, "martin": 2, "frasch": 2, "lear": 2, "alistair": 2, "gunn": 2, "pediatr": 2, "sig1": [0, 22, 24, 46, 48], "sig2": [0, 22, 24, 46, 48], "sdpool": [0, 22, 24, 46, 48], "pool": [0, 24, 48], "total": [24, 25, 35, 49], "xa": 24, "xb": 24, "rcmse": [47, 48, 50], "emploi": [0, 10, 25, 34, 47, 48, 49, 50], "med": [47, 48], "biol": [47, 48], "55": [10, 23, 25, 26, 34, 47, 48, 49, 50], "2037": [10, 23, 25, 26, 34, 47, 48, 49, 50], "2052": [10, 23, 25, 26, 34, 47, 48, 49, 50], "2024": [1, 3, 48], "_supplementari": [], "software_": [], "supplementari": [1, 3, 28, 52], "lastest": 0, "major": 0, "v1": [0, 9, 25, 33, 49], "versatil": 0, "easi": 0, "summar": 0, "main": [0, 25, 27, 49], "ad": 0, "membership": [0, 10, 25, 34, 49], "founud": [], "hilbert": [0, 20, 25, 49], "now": [], "input": [0, 27, 51], "earlier": 0, "multical": 0, "genera": 0, "choos": 0, "achiev": 0, "lot": 0, "littl": 0, "want": 0, "alert": 0, "portal": 0, "hint": 0, "compatbl": 0, "resolv": 0, "inconsist": 0, "led": 0, "incorrect": 0, "unlik": 0, "previous": 0, "consum": 0, "keep": 0, "far": 0, "div": [20, 44], "cden": [20, 44], "rightmost": [20, 44], "si": [20, 44], "rotat": [20, 44], "machineri": [20, 44], "informat": [20, 44], "5419": [20, 44], "5429": [20, 44], "aug": [20, 44], "guo": [20, 44], "high": [20, 44], "nonstationari": [20, 44], "9642": [20, 44], "9653": [20, 44], "sept": [20, 44], "2023": [20, 44], "rangx": [20, 44], "omidvarnia": [20, 44], "amir": [20, 44], "bridg": [20, 44], "962": [20, 44], "three": [24, 48], "datainterpol": 4, "univarit": 45, "predict": 2, "skill": 2, "pilot": 2, "catela": 2, "cristiana": 2, "merc\u00ea": 2, "marco": 2, "branco": 2, "sport": 2, "mdpi": 2, "explor": 2, "visibl": 2, "conejero": 2, "\u00f2scar": 2, "garibo": 2, "ort": 2, "viet": 2, "thanh": 2, "stabl": [4, 5], "unwrap": [20, 25, 49], "fern\u00e1ndez": [10, 23, 25, 26, 34, 49, 50], "mvdispen": [9, 20, 25, 26, 28, 33, 47, 48, 49, 50, 52], "mvsampen2d": [23, 24, 26], "mvfuzzen2d": [23, 24, 26], "mvdispen2d": [23, 24, 26], "mvcosien2d": [23, 24, 26], "mvpermen2d": [23, 24, 26], "mvmsen": [10, 23, 24, 25, 26, 28, 34, 47, 48, 50, 52], "exercis": 2, "andrzejewska": 2, "wr\u00f3blewski": 2, "szymon": 2, "cygan": 2, "mateusz": 2, "ozimek": 2, "monika": 2, "petelczyc": 2, "dataset": [27, 28, 51, 52], "mvsampen": [25, 26, 28, 47, 48, 49, 50, 52], "mvfuzzen": [10, 25, 26, 28, 34, 47, 48, 49, 50, 52], "mvpermen": [25, 26, 28, 47, 48, 49, 50, 52], "mvcosien": [25, 26, 28, 47, 48, 49, 50, 52], "cmvmsen": [10, 25, 26, 28, 34, 47, 48, 50, 52], "mcosi": [25, 49], "ones": [25, 49], "________________________________________________________________________": [], "maxim": [25, 49], "msamp": [25, 49], "nxm": [25, 27, 49, 51], "observ": [25, 49], "col": [25, 49], "unit": [10, 25, 34, 49], "speech": [25, 49], "icassp": [25, 49], "5997": [25, 49], "6001": [25, 49], "1109": [25, 49], "icassp43922": [25, 49], "2022": [25, 49], "9747282": [25, 49], "Its": [25, 49], "circular": [25, 49], "properti": [25, 27, 49, 51], "algebra": [25, 49], "1287": [25, 49], "ahm": [25, 26, 49, 50], "mosabb": [25, 26, 49, 50], "uddin": [25, 26, 49, 50], "tool": [0, 25, 26, 28, 49, 50, 52], "multichannel": [25, 26, 49, 50], "84": [25, 26, 49, 50], "061918": [25, 26, 49, 50], "mdisp": [9, 25, 33, 49], "mvdeii": [25, 49], "singular": [10, 25, 34, 49], "v2": [0, 9, 25, 33, 49], "mvde": [9, 25, 33, 49], "combin": [25, 49], "accordingli": [25, 49], "913": [25, 49], "mfuzz": [25, 49], "b0": [25, 49], "bt": [25, 49], "b1": [25, 49], "joint": [25, 49], "subspac": [25, 49], "expans": [25, 26, 49, 50], "contrari": [25, 49], "convent": [10, 25, 34, 49], "definit": [25, 49], "stochast": [25, 26, 49, 50], "naiv": [25, 49], "individu": [25, 49], "uterin": [25, 49], "mperm": [25, 49], "mpnorm": [25, 49], "taken": [25, 49], "theorem": [25, 49], "share": [25, 49], "spatial": [25, 49], "reconstruct": [25, 49], "NOT": [25, 49], "morabito": [25, 49], "channel": [25, 49], "20guid": 27, "defualt": [25, 49], "signam": [27, 51], "commonli": [27, 51], "benchmark": [27, 51], "document": [0, 27, 51], "elsewher": [27, 51], "randomli": [27, 51], "4500": [27, 51], "sigma": [27, 51], "5917": [27, 51], "3000": [11, 27, 35, 51], "60": [27, 51], "120": [27, 51], "255": [27, 51], "115": [27, 51], "127": [27, 51], "windowdata": [0, 27, 28, 29, 51, 52, 53], "winlen": [11, 27, 35, 51], "restructur": [27, 51], "collect": [27, 51], "windata": [11, 27, 35, 51], "subsequnc": [27, 51], "remaind": [27, 51], "fill": [11, 27, 35, 51], "final": [11, 27, 35, 51], "treat": [27, 51], "dictionari": 51, "datatyp": [11, 27, 35, 51], "datalength": [11, 27, 35, 51], "windowlength": [11, 27, 35, 51], "windowoverlap": [11, 27, 35, 51], "totalwindow": [11, 27, 35, 51], "decis": [27, 51], "remain": [11, 27, 35, 51], "web": 27, "cell": 27, "nmultiscal": 26, "introduct": 0, "decad": 0, "ago": 0, "seen": 0, "notabl": 0, "compris": 0, "ubiquit": 0, "robust": 0, "known": [], "five": 0, "_amplitud": [], "modified_": [], "aware_": [], "_edge_": [], "_phase_": [], "_weighted_": [], "_modified_": [], "extra": 0, "acclaim": 0, "mulitvari": [29, 53], "convert": [9, 33], "code": [], "reshap": [9, 33], "9227345": [9, 33], "0009856": [9, 33], "508226": [9, 33], "95439595": [9, 33], "94074854": [9, 33], "93012334": [9, 33], "02675949": [9, 33], "00805324": [9, 33], "00201614": [9, 33], "00796833": [10, 34], "00926765": [10, 34], "01193731": [10, 34], "01686631": [10, 34], "04603960": [10, 34], "alwai": [10, 34], "1000": [11, 35], "arang": 35, "1001": [11, 35], "72": [11, 35], "73": [11, 35], "74": [11, 35], "76": [11, 35], "80": [11, 35], "146": [11, 35], "147": [11, 35], "149": [11, 35], "150": [11, 35], "151": [11, 35], "152": [11, 35], "153": [11, 35], "154": [11, 35], "155": [11, 35], "221": [11, 35], "223": [11, 35], "224": [11, 35], "225": [11, 35], "226": [11, 35], "227": [11, 35], "228": [11, 35], "229": [11, 35], "230": [11, 35], "296": [11, 35], "297": [11, 35], "298": [11, 35], "299": [11, 35], "300": [11, 35], "301": [11, 35], "302": [11, 35], "304": [11, 35], "305": [11, 35], "371": [11, 35], "373": [11, 35], "374": [11, 35], "375": [11, 35], "376": [11, 35], "377": [11, 35], "379": [11, 35], "380": [11, 35], "446": [11, 35], "447": [11, 35], "449": [11, 35], "450": [11, 35], "451": [11, 35], "452": [11, 35], "453": [11, 35], "454": [11, 35], "455": [11, 35], "521": [11, 35], "522": [11, 35], "523": [11, 35], "525": [11, 35], "526": [11, 35], "527": [11, 35], "528": [11, 35], "529": [11, 35], "530": [11, 35], "596": [11, 35], "597": [11, 35], "598": [11, 35], "599": [11, 35], "601": [11, 35], "602": [11, 35], "603": [11, 35], "604": [11, 35], "605": [11, 35], "671": [11, 35], "672": [11, 35], "673": [11, 35], "674": [11, 35], "675": [11, 35], "676": [11, 35], "677": [11, 35], "678": [11, 35], "680": [11, 35], "746": [11, 35], "747": [11, 35], "748": [11, 35], "749": [11, 35], "750": [11, 35], "751": [11, 35], "752": [11, 35], "754": [11, 35], "755": [11, 35], "821": [11, 35], "822": [11, 35], "823": [11, 35], "824": [11, 35], "825": [11, 35], "826": [11, 35], "827": [11, 35], "828": [11, 35], "829": [11, 35], "830": [11, 35], "896": [11, 35], "897": [11, 35], "898": [11, 35], "899": [11, 35], "900": [11, 35], "901": [11, 35], "902": [11, 35], "903": [11, 35], "904": [11, 35], "905": [11, 35], "971": [11, 35], "972": [11, 35], "973": [11, 35], "974": [11, 35], "975": [11, 35], "repeat": [11, 35], "previou": [11, 35], "976": [11, 35], "977": [11, 35], "978": [11, 35], "979": [11, 35], "980": [11, 35], "996": [11, 35], "997": [11, 35], "998": [11, 35], "999": [11, 35], "3001": [11, 35], "130x4": [11, 35], "vstack": 35, "4001": 35, "130": [11, 35], "1002": [11, 35], "3002": [11, 35], "1003": [11, 35], "2003": [11, 35], "3003": [11, 35], "1128": [11, 35], "2128": [11, 35], "3128": [11, 35], "1129": [11, 35], "2129": [11, 35], "3129": [11, 35], "1130": [11, 35], "2130": [11, 35], "3130": [11, 35], "1111": [11, 35], "2111": [11, 35], "3111": [11, 35], "112": [11, 35], "1112": [11, 35], "2112": [11, 35], "3112": [11, 35], "113": [11, 35], "1113": [11, 35], "2113": [11, 35], "3113": [11, 35], "238": [11, 35], "1238": [11, 35], "2238": [11, 35], "3238": [11, 35], "239": [11, 35], "1239": [11, 35], "2239": [11, 35], "3239": [11, 35], "240": [11, 35], "1240": [11, 35], "2240": [11, 35], "3240": [11, 35], "771": [11, 35], "1771": [11, 35], "2771": [11, 35], "3771": [11, 35], "772": [11, 35], "1772": [11, 35], "2772": [11, 35], "3772": [11, 35], "773": [11, 35], "1773": [11, 35], "3773": [11, 35], "1898": [11, 35], "2898": [11, 35], "3898": [11, 35], "1899": [11, 35], "2899": [11, 35], "3899": [11, 35], "1900": [11, 35], "2900": [11, 35], "3900": [11, 35], "edit": 0, "thing": [], "TO": [], "rememb": [], "exlcud": 11, "dhananjai": 2, "ms": 2, "kulkarni": 2, "protect": 2, "sleep": 2, "forecast": 2, "slow": 2, "wave": 2, "defici": 2, "cheng": 2, "hua": 2, "su": 2, "ko": 2, "tzyi": 2, "ping": 2, "jung": 2, "juli": 2, "onton": 2, "shei": 2, "cherng": 2, "tzou": 2, "jia": 2, "chi": 2, "juang": 2, "chung": 2, "diverg": 2, "timescal": 2, "distinct": 2, "aspect": 2, "psychosi": 2, "hirsch": 2, "\u00e2ngelo": 2, "bumanglag": 2, "yifei": 2, "wohlschlaeg": 2, "medrxiv": 2}, "objects": {"": [[27, 0, 1, "", "EntropyHub"], [27, 0, 0, "-", "EntropyHub"], [51, 2, 0, "-", "EntropyHub"]], "EntropyHub": [[20, 1, 1, "", "ApEn"], [20, 1, 1, "", "AttnEn"], [20, 1, 1, "", "BubbEn"], [20, 1, 1, "", "CoSiEn"], [20, 1, 1, "", "CondEn"], [20, 1, 1, "", "DispEn"], [21, 1, 1, "", "DispEn2D"], [20, 1, 1, "", "DistEn"], [21, 1, 1, "", "DistEn2D"], [20, 1, 1, "", "DivEn"], [20, 1, 1, "", "EnofEn"], [21, 1, 1, "", "EspEn2D"], [27, 1, 1, "", "ExampleData"], [20, 1, 1, "", "FuzzEn"], [21, 1, 1, "", "FuzzEn2D"], [20, 1, 1, "", "GridEn"], [20, 1, 1, "", "IncrEn"], [20, 1, 1, "", "K2En"], [23, 1, 1, "", "MSEn"], [26, 1, 1, "", "MSobject"], [25, 1, 1, "", "MvCoSiEn"], [25, 1, 1, "", "MvDispEn"], [25, 1, 1, "", "MvFuzzEn"], [26, 1, 1, "", "MvMSEn"], [25, 1, 1, "", "MvPermEn"], [25, 1, 1, "", "MvSampEn"], [20, 1, 1, "", "PermEn"], [21, 1, 1, "", "PermEn2D"], [20, 1, 1, "", "PhasEn"], [20, 1, 1, "", "RangEn"], [20, 1, 1, "", "SampEn"], [21, 1, 1, "", "SampEn2D"], [20, 1, 1, "", "SlopEn"], [20, 1, 1, "", "SpecEn"], [20, 1, 1, "", "SyDyEn"], [27, 1, 1, "", "WindowData"], [22, 1, 1, "", "XApEn"], [22, 1, 1, "", "XCondEn"], [22, 1, 1, "", "XDistEn"], [22, 1, 1, "", "XFuzzEn"], [22, 1, 1, "", "XK2En"], [24, 1, 1, "", "XMSEn"], [22, 1, 1, "", "XPermEn"], [22, 1, 1, "", "XSampEn"], [22, 1, 1, "", "XSpecEn"], [23, 1, 1, "", "cMSEn"], [26, 1, 1, "", "cMvMSEn"], [24, 1, 1, "", "cXMSEn"], [23, 1, 1, "", "hMSEn"], [24, 1, 1, "", "hXMSEn"], [23, 1, 1, "", "rMSEn"], [24, 1, 1, "", "rXMSEn"], [44, 3, 1, "", "ApEn"], [44, 3, 1, "", "AttnEn"], [44, 3, 1, "", "BubbEn"], [44, 3, 1, "", "CoSiEn"], [44, 3, 1, "", "CondEn"], [44, 3, 1, "", "DispEn"], [45, 3, 1, "", "DispEn2D"], [44, 3, 1, "", "DistEn"], [45, 3, 1, "", "DistEn2D"], [44, 3, 1, "", "DivEn"], [44, 3, 1, "", "EnofEn"], [45, 3, 1, "", "EspEn2D"], [51, 3, 1, "", "ExampleData"], [44, 3, 1, "", "FuzzEn"], [45, 3, 1, "", "FuzzEn2D"], [44, 3, 1, "", "GridEn"], [44, 3, 1, "", "IncrEn"], [44, 3, 1, "", "K2En"], [47, 3, 1, "", "MSEn"], [50, 3, 1, "", "MSobject"], [49, 3, 1, "", "MvCoSiEn"], [49, 3, 1, "", "MvDispEn"], [49, 3, 1, "", "MvFuzzEn"], [50, 3, 1, "", "MvMSEn"], [49, 3, 1, "", "MvPermEn"], [49, 3, 1, "", "MvSampEn"], [44, 3, 1, "", "PermEn"], [45, 3, 1, "", "PermEn2D"], [44, 3, 1, "", "PhasEn"], [44, 3, 1, "", "RangEn"], [44, 3, 1, "", "SampEn"], [45, 3, 1, "", "SampEn2D"], [44, 3, 1, "", "SlopEn"], [44, 3, 1, "", "SpecEn"], [44, 3, 1, "", "SyDyEn"], [51, 3, 1, "", "WindowData"], [46, 3, 1, "", "XApEn"], [46, 3, 1, "", "XCondEn"], [46, 3, 1, "", "XDistEn"], [46, 3, 1, "", "XFuzzEn"], [46, 3, 1, "", "XK2En"], [48, 3, 1, "", "XMSEn"], [46, 3, 1, "", "XPermEn"], [46, 3, 1, "", "XSampEn"], [46, 3, 1, "", "XSpecEn"], [47, 3, 1, "", "cMSEn"], [50, 3, 1, "", "cMvMSEn"], [48, 3, 1, "", "cXMSEn"], [47, 3, 1, "", "hMSEn"], [48, 3, 1, "", "hXMSEn"], [47, 3, 1, "", "rMSEn"], [48, 3, 1, "", "rXMSEn"]]}, "objtypes": {"0": "mat:module", "1": "mat:function", "2": "py:module", "3": "py:function"}, "objnames": {"0": ["mat", "module", "MATLAB module"], "1": ["mat", "function", "MATLAB function"], "2": ["py", "module", "Python module"], "3": ["py", "function", "Python function"]}, "titleterms": {"latest": 0, "updat": 0, "januari": [], "2023": 2, "novemb": 0, "2022": [0, 2], "juli": 0, "februari": 0, "decemb": 0, "2021": 0, "june": 0, "entropyhub": [1, 2, 3, 4, 5, 6, 27, 28, 30, 51, 52], "an": [1, 3], "open": [1, 3], "sourc": [1, 3], "toolkit": [1, 3], "entrop": [1, 3], "time": [20, 22, 23, 24, 44, 46, 47, 48], "seri": [20, 22, 23, 24, 44, 46, 47, 48], "analysi": [1, 3], "welcom": [1, 3], "about": [1, 3], "document": [1, 3, 4, 6, 29, 30], "help": [1, 3, 4, 6, 30], "citat": [1, 3], "licens": [1, 3], "contact": [1, 3], "public": 2, "list": 2, "research": 2, "articl": 2, "cite": 2, "julia": [4, 5], "requir": [4, 6, 30], "instal": [4, 6, 30], "method": [4, 6, 14, 30, 38], "1": [0, 4, 6, 7, 30, 31], "2": [0, 4, 6, 12, 30, 36], "matlab": [6, 28, 29], "exampl": [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 53], "sampl": [7, 17, 31, 41], "entropi": [7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52], "10": [8, 32], "bidimension": [8, 21, 28, 32, 45, 52], "fuzzi": [8, 10, 32, 34], "fine": [12, 36], "grain": [12, 36], "permut": [12, 36], "3": [13, 37], "phase": [13, 37], "w": [13, 14, 37, 38], "pioncar": [13, 37], "plot": [13, 37], "4": [14, 38], "cross": [14, 18, 19, 22, 24, 28, 38, 42, 43, 46, 48, 52], "distribut": [14, 38], "differ": [14, 38], "bin": [14, 38], "5": [15, 39], "multiscal": [10, 15, 16, 17, 18, 19, 23, 24, 26, 28, 34, 39, 40, 41, 42, 43, 47, 48, 50, 52], "object": [15, 39], "msobject": [15, 39], "6": [16, 40], "increment": [16, 40], "7": [17, 41], "refin": [10, 17, 34, 41], "8": [18, 28, 42, 52], "composit": [10, 18, 34, 42], "approxim": [18, 42], "9": [19, 43], "hierarch": [19, 43], "correct": [19, 43], "condit": [19, 43], "base": [20, 28, 44, 52], "function": [20, 21, 22, 23, 24, 25, 26, 27, 28, 44, 45, 46, 47, 48, 49, 50, 51, 52], "estim": [20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "singl": [20, 44], "univari": [20, 21, 22, 23, 24, 44, 45, 46, 47, 48], "two": [21, 22, 24, 45, 46, 48], "dimension": [21, 45], "matrix": [21, 45], "between": [22, 24, 46, 48], "fall": [28, 52], "categori": [28, 52], "exampledata": [29, 53], "browser": 29, "thing": [29, 53], "TO": [29, 53], "rememb": [29, 53], "python": [30, 52, 53], "2024": [0, 2], "march": 0, "data": [1, 3, 11, 35], "new": [], "version": 0, "0": 0, "here": 0, "more": 0, "come": 0, "april": 0, "multivari": [9, 10, 25, 26, 28, 33, 34, 49, 50, 52], "dataset": [25, 26, 49, 50], "other": [27, 28, 51, 52], "supplementari": [27, 51], "variou": [27, 51], "task": [27, 51], "relat": [27, 51], "signal": [27, 51], "process": [27, 51], "out": 0, "now": 0, "11": [9, 33], "dispers": [9, 33], "12": [10, 34], "gener": [10, 34], "13": [11, 35], "window": [11, 35], "windowdata": [11, 35]}, "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 +Search.setIndex({"alltitles": {"2022": [[2, "id4"]], "2023": [[2, "id2"]], "2024": [[2, "id1"]], "About": [[1, "about"], [3, "about"]], "An open-source toolkit for entropic data analysis": [[1, "an-open-source-toolkit-for-entropic-data-analysis"], [3, "an-open-source-toolkit-for-entropic-data-analysis"]], "April 2024": [[0, "april-2024"]], "Base Entropies": [[20, "base-entropies"], [44, "base-entropies"]], "Base Entropies:": [[28, "base-entropies"], [52, "base-entropies"]], "Bidimensional Entropies": [[21, "bidimensional-entropies"], [45, "bidimensional-entropies"]], "Bidimensional Entropies:": [[28, "bidimensional-entropies"], [52, "bidimensional-entropies"]], "Citation and Licensing": [[1, "citation-and-licensing"], [3, "citation-and-licensing"]], "Contact": [[1, "contact"], [3, "contact"]], "Cross Entropies": [[22, "cross-entropies"], [46, "cross-entropies"]], "Cross Entropies:": [[28, "cross-entropies"], [52, "cross-entropies"]], "December 2021": [[0, "december-2021"]], "Documentation & Help": [[1, "documentation-help"], [3, "documentation-help"]], "Documentation & Help:": [[4, "documentation-help"], [6, "documentation-help"], [30, "documentation-help"]], "EntropyHub": [[1, "entropyhub"], [3, "entropyhub"]], "EntropyHub functions fall into 8 categories:": [[28, "entropyhub-functions-fall-into-8-categories"], [52, "entropyhub-functions-fall-into-8-categories"]], "EntropyHub: Julia": [[4, "entropyhub-julia"], [5, "entropyhub-julia"]], "EntropyHub: MatLab": [[6, "entropyhub-matlab"]], "EntropyHub: Python": [[30, "entropyhub-python"]], "Example 10: Bidimensional Fuzzy Entropy": [[8, "example-10-bidimensional-fuzzy-entropy"], [32, "example-10-bidimensional-fuzzy-entropy"]], "Example 11: Multivariate Dispersion Entropy": [[9, "example-11-multivariate-dispersion-entropy"], [33, "example-11-multivariate-dispersion-entropy"]], "Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy": [[10, "example-12-generalized-refined-composite-multivariate-multiscale-fuzzy-entropy"], [34, "example-12-generalized-refined-composite-multivariate-multiscale-fuzzy-entropy"]], "Example 13: Windowing Data with WindowData()": [[11, "example-13-windowing-data-with-windowdata"], [35, "example-13-windowing-data-with-windowdata"]], "Example 1: Sample Entropy": [[7, "example-1-sample-entropy"], [31, "example-1-sample-entropy"]], "Example 2: (Fine-Grained) Permutation Entropy": [[12, "example-2-fine-grained-permutation-entropy"], [36, "example-2-fine-grained-permutation-entropy"]], "Example 3: Phase Entropy w/ Pioncare Plot": [[13, "example-3-phase-entropy-w-pioncare-plot"], [37, "example-3-phase-entropy-w-pioncare-plot"]], "Example 4: Cross-Distribution Entropy w/ Different Binning Methods": [[14, "example-4-cross-distribution-entropy-w-different-binning-methods"], [38, "example-4-cross-distribution-entropy-w-different-binning-methods"]], "Example 5: Multiscale Entropy Object [MSobject()]": [[15, "example-5-multiscale-entropy-object-msobject"], [39, "example-5-multiscale-entropy-object-msobject"]], "Example 6: Multiscale [Increment] Entropy": [[16, "example-6-multiscale-increment-entropy"], [40, "example-6-multiscale-increment-entropy"]], "Example 7: Refined Multiscale [Sample] Entropy": [[17, "example-7-refined-multiscale-sample-entropy"], [41, "example-7-refined-multiscale-sample-entropy"]], "Example 8: Composite Multiscale Cross-[Approximate] Entropy": [[18, "example-8-composite-multiscale-cross-approximate-entropy"], [42, "example-8-composite-multiscale-cross-approximate-entropy"]], "Example 9: Hierarchical Multiscale corrected Cross-[Conditional] Entropy": [[19, "example-9-hierarchical-multiscale-corrected-cross-conditional-entropy"], [43, "example-9-hierarchical-multiscale-corrected-cross-conditional-entropy"]], "ExampleData()": [[29, null], [53, null]], "February 2022": [[0, "february-2022"]], "Functions for estimating the entropy between two univariate time series.": [[22, "functions-for-estimating-the-entropy-between-two-univariate-time-series"], [46, "functions-for-estimating-the-entropy-between-two-univariate-time-series"]], "Functions for estimating the entropy of a multivariate dataset.": [[25, "functions-for-estimating-the-entropy-of-a-multivariate-dataset"], [49, "functions-for-estimating-the-entropy-of-a-multivariate-dataset"]], "Functions for estimating the entropy of a single univariate time series.": [[20, "functions-for-estimating-the-entropy-of-a-single-univariate-time-series"], [44, "functions-for-estimating-the-entropy-of-a-single-univariate-time-series"]], "Functions for estimating the entropy of a two-dimensional univariate matrix.": [[21, "functions-for-estimating-the-entropy-of-a-two-dimensional-univariate-matrix"], [45, "functions-for-estimating-the-entropy-of-a-two-dimensional-univariate-matrix"]], "Functions for estimating the multiscale cross-entropy between two univariate time series.": [[24, "functions-for-estimating-the-multiscale-cross-entropy-between-two-univariate-time-series"], [48, "functions-for-estimating-the-multiscale-cross-entropy-between-two-univariate-time-series"]], "Functions for estimating the multiscale entropy of a univariate time series.": [[23, "functions-for-estimating-the-multiscale-entropy-of-a-univariate-time-series"], [47, "functions-for-estimating-the-multiscale-entropy-of-a-univariate-time-series"]], "Functions for estimating the multivariate multiscale entropy of a multivariate dataset.": [[26, "functions-for-estimating-the-multivariate-multiscale-entropy-of-a-multivariate-dataset"], [50, "functions-for-estimating-the-multivariate-multiscale-entropy-of-a-multivariate-dataset"]], "July 2022": [[0, "july-2022"]], "June 2021": [[0, "june-2021"]], "Latest Updates": [[0, "latest-updates"]], "List of research articles citing EntropyHub": [[2, "list-of-research-articles-citing-entropyhub"]], "March 2024": [[0, "march-2024"]], "MatLab Documentation Browser": [[29, null]], "MatLab Examples:": [[29, "matlab-examples"]], "MatLab Functions:": [[28, "matlab-functions"]], "Method 1:": [[4, "method-1"], [6, "method-1"], [30, "method-1"]], "Method 2:": [[4, "method-2"], [6, "method-2"], [30, "method-2"]], "More to come!": [[0, "more-to-come"]], "Multiscale Entropies:": [[28, "multiscale-entropies"], [52, "multiscale-entropies"]], "Multiscale Cross-Entropies": [[24, "multiscale-cross-entropies"], [48, "multiscale-cross-entropies"]], "Multiscale Cross-Entropies:": [[28, "multiscale-cross-entropies"], [52, "multiscale-cross-entropies"]], "Multiscale Entropies": [[23, "multiscale-entropies"], [47, "multiscale-entropies"]], "Multivariate Entropies": [[25, "multivariate-entropies"], [49, "multivariate-entropies"]], "Multivariate Entropies:": [[28, "multivariate-entropies"], [52, "multivariate-entropies"]], "Multivariate Multiscale Entropies:": [[28, "multivariate-multiscale-entropies"], [52, "multivariate-multiscale-entropies"]], "Multivariate Multiscale Entropies": [[26, "multivariate-multiscale-entropies"], [50, "multivariate-multiscale-entropies"]], "November 2021": [[0, "november-2021"]], "November 2022": [[0, "november-2022"]], "Other Functions": [[27, "other-functions"], [51, "other-functions"]], "Other Functions:": [[28, "other-functions"], [52, "other-functions"]], "Publications": [[2, "publications"]], "Python Examples:": [[53, "python-examples"]], "Python Functions:": [[52, "python-functions"]], "Requirements & Installation:": [[4, "requirements-installation"], [6, "requirements-installation"], [30, "requirements-installation"]], "Supplementary functions for various tasks related to EntropyHub and signal processing.": [[27, "module-EntropyHub"], [51, "supplementary-functions-for-various-tasks-related-to-entropyhub-and-signal-processing"]], "THINGS TO REMEMBER": [[29, null], [53, null]], "Version 1.0 is here!": [[0, "version-1-0-is-here"]], "Version 2.0 is out now!": [[0, "version-2-0-is-out-now"]], "Welcome!": [[1, "welcome"], [3, "welcome"]]}, "docnames": ["EHupdates", "Home", "Publications", "index", "julia/EHjulia", "julia/jlexamples", "matlab/EHmatlab", "matlab/Examples/Ex1", "matlab/Examples/Ex10", "matlab/Examples/Ex11", "matlab/Examples/Ex12", "matlab/Examples/Ex13", "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/Functions/matMultivariate", "matlab/Functions/matMultivariateMultiscale", "matlab/Functions/matOther", "matlab/matAPI", "matlab/matexamples", "python/EHpython", "python/Examples/Ex1", "python/Examples/Ex10", "python/Examples/Ex11", "python/Examples/Ex12", "python/Examples/Ex13", "python/Examples/Ex2", "python/Examples/Ex3", "python/Examples/Ex4", "python/Examples/Ex5", "python/Examples/Ex6", "python/Examples/Ex7", "python/Examples/Ex8", "python/Examples/Ex9", "python/Functions/Base", "python/Functions/Bidimensional", "python/Functions/Cross", "python/Functions/Multiscale", "python/Functions/MultiscaleCross", "python/Functions/Multivariate", "python/Functions/MultivariateMultiscale", "python/Functions/Other", "python/pyAPI", "python/pyexamples"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2}, "filenames": ["EHupdates.rst", "Home.rst", "Publications.rst", "index.rst", "julia/EHjulia.rst", "julia/jlexamples.rst", "matlab/EHmatlab.rst", "matlab/Examples/Ex1.rst", "matlab/Examples/Ex10.rst", "matlab/Examples/Ex11.rst", "matlab/Examples/Ex12.rst", "matlab/Examples/Ex13.rst", "matlab/Examples/Ex2.rst", "matlab/Examples/Ex3.rst", "matlab/Examples/Ex4.rst", "matlab/Examples/Ex5.rst", "matlab/Examples/Ex6.rst", "matlab/Examples/Ex7.rst", "matlab/Examples/Ex8.rst", "matlab/Examples/Ex9.rst", "matlab/Functions/matBase.rst", "matlab/Functions/matBidimensional.rst", "matlab/Functions/matCross.rst", "matlab/Functions/matMultiscale.rst", "matlab/Functions/matMultiscaleCross.rst", "matlab/Functions/matMultivariate.rst", "matlab/Functions/matMultivariateMultiscale.rst", "matlab/Functions/matOther.rst", "matlab/matAPI.rst", "matlab/matexamples.rst", "python/EHpython.rst", "python/Examples/Ex1.rst", "python/Examples/Ex10.rst", "python/Examples/Ex11.rst", "python/Examples/Ex12.rst", "python/Examples/Ex13.rst", "python/Examples/Ex2.rst", "python/Examples/Ex3.rst", "python/Examples/Ex4.rst", "python/Examples/Ex5.rst", "python/Examples/Ex6.rst", "python/Examples/Ex7.rst", "python/Examples/Ex8.rst", "python/Examples/Ex9.rst", "python/Functions/Base.rst", "python/Functions/Bidimensional.rst", "python/Functions/Cross.rst", "python/Functions/Multiscale.rst", "python/Functions/MultiscaleCross.rst", "python/Functions/Multivariate.rst", "python/Functions/MultivariateMultiscale.rst", "python/Functions/Other.rst", "python/pyAPI.rst", "python/pyexamples.rst"], "indexentries": {"apen() (in module entropyhub)": [[20, "EntropyHub.ApEn", false]], "attnen() (in module entropyhub)": [[20, "EntropyHub.AttnEn", false]], "bubben() (in module entropyhub)": [[20, "EntropyHub.BubbEn", false]], "cmsen() (in module entropyhub)": [[23, "EntropyHub.cMSEn", false]], "cmvmsen() (in module entropyhub)": [[26, "EntropyHub.cMvMSEn", false]], "conden() (in module entropyhub)": [[20, "EntropyHub.CondEn", false]], "cosien() (in module entropyhub)": [[20, "EntropyHub.CoSiEn", false]], "cxmsen() (in module entropyhub)": [[24, "EntropyHub.cXMSEn", false]], "dispen() (in module entropyhub)": [[20, "EntropyHub.DispEn", false]], "dispen2d() (in module entropyhub)": [[21, "EntropyHub.DispEn2D", false]], "disten() (in module entropyhub)": [[20, "EntropyHub.DistEn", false]], "disten2d() (in module entropyhub)": [[21, "EntropyHub.DistEn2D", false]], "diven() (in module entropyhub)": [[20, "EntropyHub.DivEn", false]], "enofen() (in module entropyhub)": [[20, "EntropyHub.EnofEn", false]], "entropyhub (module)": [[20, "module-EntropyHub", false], [21, "module-EntropyHub", false], [22, "module-EntropyHub", false], [23, "module-EntropyHub", false], [24, "module-EntropyHub", false], [25, "module-EntropyHub", false], [26, "module-EntropyHub", false], [27, "module-EntropyHub", false]], "espen2d() (in module entropyhub)": [[21, "EntropyHub.EspEn2D", false]], "exampledata() (in module entropyhub)": [[27, "EntropyHub.ExampleData", false]], "fuzzen() (in module entropyhub)": [[20, "EntropyHub.FuzzEn", false]], "fuzzen2d() (in module entropyhub)": [[21, "EntropyHub.FuzzEn2D", false]], "griden() (in module entropyhub)": [[20, "EntropyHub.GridEn", false]], "hmsen() (in module entropyhub)": [[23, "EntropyHub.hMSEn", false]], "hxmsen() (in module entropyhub)": [[24, "EntropyHub.hXMSEn", false]], "incren() (in module entropyhub)": [[20, "EntropyHub.IncrEn", false]], "k2en() (in module entropyhub)": [[20, "EntropyHub.K2En", false]], "msen() (in module entropyhub)": [[23, "EntropyHub.MSEn", false]], "msobject() (in module entropyhub)": [[23, "EntropyHub.MSobject", false], [24, "EntropyHub.MSobject", false], [26, "EntropyHub.MSobject", false]], "mvcosien() (in module entropyhub)": [[25, "EntropyHub.MvCoSiEn", false]], "mvdispen() (in module entropyhub)": [[25, "EntropyHub.MvDispEn", false]], "mvfuzzen() (in module entropyhub)": [[25, "EntropyHub.MvFuzzEn", false]], "mvmsen() (in module entropyhub)": [[26, "EntropyHub.MvMSEn", false]], "mvpermen() (in module entropyhub)": [[25, "EntropyHub.MvPermEn", false]], "mvsampen() (in module entropyhub)": [[25, "EntropyHub.MvSampEn", false]], "permen() (in module entropyhub)": [[20, "EntropyHub.PermEn", false]], "permen2d() (in module entropyhub)": [[21, "EntropyHub.PermEn2D", false]], "phasen() (in module entropyhub)": [[20, "EntropyHub.PhasEn", false]], "rangen() (in module entropyhub)": [[20, "EntropyHub.RangEn", false]], "rmsen() (in module entropyhub)": [[23, "EntropyHub.rMSEn", false]], "rxmsen() (in module entropyhub)": [[24, "EntropyHub.rXMSEn", false]], "sampen() (in module entropyhub)": [[20, "EntropyHub.SampEn", false]], "sampen2d() (in module entropyhub)": [[21, "EntropyHub.SampEn2D", false]], "slopen() (in module entropyhub)": [[20, "EntropyHub.SlopEn", false]], "specen() (in module entropyhub)": [[20, "EntropyHub.SpecEn", false]], "sydyen() (in module entropyhub)": [[20, "EntropyHub.SyDyEn", false]], "windowdata() (in module entropyhub)": [[27, "EntropyHub.WindowData", false]], "xapen() (in module entropyhub)": [[22, "EntropyHub.XApEn", false]], "xconden() (in module entropyhub)": [[22, "EntropyHub.XCondEn", false]], "xdisten() (in module entropyhub)": [[22, "EntropyHub.XDistEn", false]], "xfuzzen() (in module entropyhub)": [[22, "EntropyHub.XFuzzEn", false]], "xk2en() (in module entropyhub)": [[22, "EntropyHub.XK2En", false]], "xmsen() (in module entropyhub)": [[24, "EntropyHub.XMSEn", false]], "xpermen() (in module entropyhub)": [[22, "EntropyHub.XPermEn", false]], "xsampen() (in module entropyhub)": [[22, "EntropyHub.XSampEn", false]], "xspecen() (in module entropyhub)": [[22, "EntropyHub.XSpecEn", false]]}, "objects": {"": [[27, 0, 1, "", "EntropyHub"], [27, 0, 0, "-", "EntropyHub"], [51, 2, 0, "-", "EntropyHub"]], "EntropyHub": [[20, 1, 1, "", "ApEn"], [20, 1, 1, "", "AttnEn"], [20, 1, 1, "", "BubbEn"], [20, 1, 1, "", "CoSiEn"], [20, 1, 1, "", "CondEn"], [20, 1, 1, "", "DispEn"], [21, 1, 1, "", "DispEn2D"], [20, 1, 1, "", "DistEn"], [21, 1, 1, "", "DistEn2D"], [20, 1, 1, "", "DivEn"], [20, 1, 1, "", "EnofEn"], [21, 1, 1, "", "EspEn2D"], [27, 1, 1, "", "ExampleData"], [20, 1, 1, "", "FuzzEn"], [21, 1, 1, "", "FuzzEn2D"], [20, 1, 1, "", "GridEn"], [20, 1, 1, "", "IncrEn"], [20, 1, 1, "", "K2En"], [23, 1, 1, "", "MSEn"], [26, 1, 1, "", "MSobject"], [25, 1, 1, "", "MvCoSiEn"], [25, 1, 1, "", "MvDispEn"], [25, 1, 1, "", "MvFuzzEn"], [26, 1, 1, "", "MvMSEn"], [25, 1, 1, "", "MvPermEn"], [25, 1, 1, "", "MvSampEn"], [20, 1, 1, "", "PermEn"], [21, 1, 1, "", "PermEn2D"], [20, 1, 1, "", "PhasEn"], [20, 1, 1, "", "RangEn"], [20, 1, 1, "", "SampEn"], [21, 1, 1, "", "SampEn2D"], [20, 1, 1, "", "SlopEn"], [20, 1, 1, "", "SpecEn"], [20, 1, 1, "", "SyDyEn"], [27, 1, 1, "", "WindowData"], [22, 1, 1, "", "XApEn"], [22, 1, 1, "", "XCondEn"], [22, 1, 1, "", "XDistEn"], [22, 1, 1, "", "XFuzzEn"], [22, 1, 1, "", "XK2En"], [24, 1, 1, "", "XMSEn"], [22, 1, 1, "", "XPermEn"], [22, 1, 1, "", "XSampEn"], [22, 1, 1, "", "XSpecEn"], [23, 1, 1, "", "cMSEn"], [26, 1, 1, "", "cMvMSEn"], [24, 1, 1, "", "cXMSEn"], [23, 1, 1, "", "hMSEn"], [24, 1, 1, "", "hXMSEn"], [23, 1, 1, "", "rMSEn"], [24, 1, 1, "", "rXMSEn"], [44, 3, 1, "", "ApEn"], [44, 3, 1, "", "AttnEn"], [44, 3, 1, "", "BubbEn"], [44, 3, 1, "", "CoSiEn"], [44, 3, 1, "", "CondEn"], [44, 3, 1, "", "DispEn"], [45, 3, 1, "", "DispEn2D"], [44, 3, 1, "", "DistEn"], [45, 3, 1, "", "DistEn2D"], [44, 3, 1, "", "DivEn"], [44, 3, 1, "", "EnofEn"], [45, 3, 1, "", "EspEn2D"], [51, 3, 1, "", "ExampleData"], [44, 3, 1, "", "FuzzEn"], [45, 3, 1, "", "FuzzEn2D"], [44, 3, 1, "", "GridEn"], [44, 3, 1, "", "IncrEn"], [44, 3, 1, "", "K2En"], [47, 3, 1, "", "MSEn"], [48, 3, 1, "", "MSobject"], [49, 3, 1, "", "MvCoSiEn"], [49, 3, 1, "", "MvDispEn"], [49, 3, 1, "", "MvFuzzEn"], [50, 3, 1, "", "MvMSEn"], [49, 3, 1, "", "MvPermEn"], [49, 3, 1, "", "MvSampEn"], [44, 3, 1, "", "PermEn"], [45, 3, 1, "", "PermEn2D"], [44, 3, 1, "", "PhasEn"], [44, 3, 1, "", "RangEn"], [44, 3, 1, "", "SampEn"], [45, 3, 1, "", "SampEn2D"], [44, 3, 1, "", "SlopEn"], [44, 3, 1, "", "SpecEn"], [44, 3, 1, "", "SyDyEn"], [51, 3, 1, "", "WindowData"], [46, 3, 1, "", "XApEn"], [46, 3, 1, "", "XCondEn"], [46, 3, 1, "", "XDistEn"], [46, 3, 1, "", "XFuzzEn"], [46, 3, 1, "", "XK2En"], [48, 3, 1, "", "XMSEn"], [46, 3, 1, "", "XPermEn"], [46, 3, 1, "", "XSampEn"], [46, 3, 1, "", "XSpecEn"], [47, 3, 1, "", "cMSEn"], [50, 3, 1, "", "cMvMSEn"], [48, 3, 1, "", "cXMSEn"], [47, 3, 1, "", "hMSEn"], [48, 3, 1, "", "hXMSEn"], [47, 3, 1, "", "rMSEn"], [48, 3, 1, "", "rXMSEn"]]}, "objnames": {"0": ["mat", "module", "MATLAB module"], "1": ["mat", "function", "MATLAB function"], "2": ["py", "module", "Python module"], "3": ["py", "function", "Python function"]}, "objtypes": {"0": "mat:module", "1": "mat:function", "2": "py:module", "3": "py:function"}, "terms": {"": [0, 2, 14, 20, 22, 27, 29, 38, 44, 46, 51, 53], "0": [1, 3, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "0000": [14, 38], "0009856": [9, 33], "00159093": 32, "0016": 8, "00201614": [9, 33], "0047": [14, 38], "00796833": [10, 34], "00805324": [9, 33], "00926765": [10, 34], "01": [27, 29, 51, 53], "01193731": [10, 34], "01686631": [10, 34], "0192821496913216": 37, "0193": 13, "021906": [20, 23, 25, 26, 44, 47, 49, 50], "022911": [20, 25, 44, 49], "025": [27, 29, 51, 53], "0259448": [0, 1, 3], "02675949": [9, 33], "03602": [20, 44], "0368": [14, 38], "045002": [21, 45], "04603960": [10, 34], "049": [21, 45], "061918": [25, 26, 49, 50], "068102": [23, 24, 26, 47, 48, 50], "0781": [12, 36], "089": 42, "0893": 18, "089803": [23, 24, 26, 47, 48, 50], "089804": [23, 24, 26, 47, 48, 50], "1": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "10": [0, 1, 3, 10, 12, 20, 21, 23, 24, 25, 26, 27, 29, 36, 44, 45, 47, 48, 49, 50, 51, 53], "1000": [11, 35], "1001": [11, 35], "1002": [11, 35], "1003": [11, 35], "1024": [9, 29, 33, 53], "103": [21, 45], "104833": [20, 21, 22, 44, 45, 46], "104847": [20, 21, 22, 44, 45, 46], "105006": [20, 44], "1069": [23, 24, 26, 47, 48, 50], "1084": [23, 24, 26, 47, 48, 50], "109": [20, 44], "1096": [14, 38], "11": [0, 1, 3, 13, 15, 20, 21, 23, 29, 37, 39, 44, 45, 47, 53], "110": [24, 48], "1109": [25, 49], "111": [11, 34, 35, 36], "1111": [11, 35], "1112": [11, 35], "1113": [11, 35], "112": [11, 35], "1128": [11, 35], "1129": [11, 35], "113": [11, 35], "1130": [11, 35], "115": [27, 51], "1167": [20, 44], "119": [22, 46], "1197": [23, 24, 47], "12": [19, 20, 23, 25, 29, 43, 44, 47, 49, 53], "120": [27, 51], "1203": [23, 24, 47], "1238": [11, 35], "1239": [11, 35], "1240": [11, 35], "125686": [20, 25, 44, 49], "1261": [21, 45], "127": [27, 51], "128": [11, 20, 21, 25, 29, 35, 44, 45, 49, 53], "1287": [25, 49], "129": [11, 22, 35, 46], "13": [20, 29, 44, 53], "130": [11, 35], "130x4": [11, 35], "133": [20, 44], "1338": [21, 45], "1342": [21, 45], "1361": [22, 46], "1369": [23, 26, 47, 48, 50], "1371": [0, 1, 3], "1374": [23, 26, 47, 48, 50], "1376": [22, 46], "138": [23, 47], "14": [11, 20, 35, 44], "1403829": [20, 44], "1441": [7, 31], "146": [11, 35], "147": [11, 35], "148": [11, 21, 35, 45], "149": [11, 35], "15": [0, 19, 20, 23, 24, 26, 44, 47, 48, 50], "150": [11, 35], "1500": [29, 53], "151": [11, 35], "152": [11, 35], "153": [11, 35], "1531": [14, 38], "154": [11, 35], "155": [11, 35], "16": [0, 1, 3, 20, 21, 44, 45], "160": [21, 45], "17": [14, 20, 23, 24, 25, 38, 44, 47, 49], "1715": [22, 46], "1724": [22, 46], "174102": [20, 25, 44, 49], "1756": [7, 31], "1757": [7, 31], "1771": [11, 35], "1772": [11, 35], "1773": [11, 35], "178": [21, 45], "1789": [7, 31], "18": [20, 44], "180": [22, 46], "1819": [7, 31], "1833": [7, 31], "187": [21, 45], "1880": [7, 31], "1892": [7, 31], "1898": [11, 35], "1899": [11, 35], "19": [20, 23, 25, 26, 44, 47, 49, 50], "1900": [11, 35], "1978": [14, 38], "1979": [20, 44], "1983": [20, 44], "1991": [20, 44], "1996": [22, 46], "1998": [20, 44], "1999": [22, 46], "1x5": 7, "2": [1, 3, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "20": [11, 20, 21, 23, 25, 26, 27, 29, 35, 44, 45, 47, 48, 49, 50, 51, 53], "200": [21, 45], "2000": [11, 15, 20, 22, 35, 44, 46], "2001": [11, 22, 35, 46], "2002": [11, 20, 22, 23, 24, 25, 26, 35, 44, 46, 47, 48, 49, 50], "2003": [11, 35], "2004": [23, 24, 26, 47, 48, 50], "2005": [23, 26, 47, 50], "2006": [20, 23, 26, 44, 47, 50], "2007": [20, 44], "2008": [20, 44], "2009": [20, 23, 24, 44, 47, 48], "2010": [22, 46], "2011": [23, 24, 25, 26, 47, 48, 49, 50], "2012": [20, 21, 25, 44, 45, 49], "2013": [20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "2014": [23, 26, 47, 48, 50], "2015": [20, 22, 23, 24, 44, 46, 47, 48], "2015a": 6, "2015b": 6, "2016": [6, 20, 21, 24, 25, 44, 45, 48, 49], "2016a": 6, "2017": [10, 20, 21, 23, 25, 26, 34, 44, 45, 47, 48, 49, 50], "2018": [20, 21, 22, 23, 25, 44, 45, 46, 47, 49], "2019": [20, 21, 22, 24, 25, 44, 45, 46, 48, 49], "2020": [20, 24, 44, 48], "2021": [1, 3, 20, 21, 22, 24, 25, 44, 45, 46, 48, 49], "2022": [25, 49], "2023": [20, 44], "2024": [1, 3, 48], "2037": [10, 23, 25, 26, 34, 47, 48, 49, 50], "204": [20, 44], "2052": [10, 23, 25, 26, 34, 47, 48, 49, 50], "2053": [20, 44], "2083": [22, 46], "2088": [22, 46], "20guid": 27, "21": [20, 25, 44, 49], "210": [20, 25, 44, 49], "2111": [11, 35], "2112": [11, 35], "2113": [11, 35], "2128": [11, 35], "2129": [11, 35], "2130": [11, 35], "22": [20, 24, 44, 48], "2202": [23, 24, 47, 48], "2209": [7, 31], "221": [11, 35], "2213": [23, 24, 47, 48], "222": [11, 20, 25, 35, 44, 49], "223": [11, 35], "2238": [11, 35], "2239": [11, 35], "224": [11, 35], "2240": [11, 35], "225": [11, 35], "226": [11, 35], "227": [11, 35], "228": [11, 35], "229": [11, 35], "2297": [20, 44], "23": [20, 21, 25, 44, 45, 49], "230": [11, 35], "2301": [20, 44], "234": [12, 36], "236": [23, 24, 47, 48], "238": [11, 35], "239": [11, 35], "23rd": 2, "24": [21, 23, 24, 25, 45, 47, 48, 49], "240": [11, 35], "2421": [14, 38], "2439": [22, 46], "2447": [22, 46], "245": [22, 46], "249": [20, 25, 44, 49], "2494": [16, 40], "25": [11, 14, 17, 35, 38, 41], "2500": 17, "255": [27, 51], "2558": [14, 38], "257": [23, 47], "2591": [20, 44], "262": [20, 25, 44, 49], "266": [20, 44], "267": [22, 46], "2690": [20, 44], "2711": [20, 44], "2718": [20, 44], "2719": [16, 40], "272": [20, 44], "277": [23, 47], "2771": [11, 35], "2772": [11, 35], "2773": [11, 16, 35, 40], "28": [20, 27, 29, 44, 51, 53], "280": [20, 44], "2802": [14, 38], "283": [20, 22, 44, 46], "284": [23, 47], "2863": [16, 40], "289": [20, 24, 44, 48], "2898": [11, 35], "2899": [11, 35], "2900": [11, 35], "2932": [18, 42], "295": [20, 44], "296": [11, 35], "297": [11, 35], "298": [11, 35], "299": [11, 35], "2c": [20, 44], "2d": [21, 45], "2r": [20, 21, 22, 25, 44, 45, 46, 49], "3": [8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "300": [11, 35], "3000": [11, 27, 35, 51], "3001": [11, 35], "3002": [11, 35], "3003": [11, 35], "301": [11, 35], "302": [11, 35], "303": [11, 20, 35, 44], "304": [11, 35], "305": [11, 35], "3059": [16, 40], "3110": [23, 47], "3111": [11, 35], "3112": [11, 35], "3113": [11, 35], "312": [20, 44], "3123": [23, 47], "3128": [11, 35], "3129": [11, 35], "3130": [11, 35], "323": [23, 26, 47, 50], "3238": [11, 35], "3239": [11, 35], "3240": [11, 35], "3309": [20, 44], "332": [23, 26, 47, 50], "3320": [20, 44], "36": [20, 44], "366": [23, 26, 47, 50], "371": [11, 35], "372": [11, 20, 35, 44], "373": [11, 35], "374": [11, 35], "375": [11, 35], "376": [11, 35], "377": [11, 35], "3771": [11, 35], "3772": [11, 35], "3773": [11, 35], "378": [11, 23, 26, 35, 47, 48, 50], "379": [11, 35], "380": [11, 35], "3898": [11, 35], "3899": [11, 35], "3900": [11, 35], "3d": [34, 36], "3rd": [17, 41], "4": [7, 9, 11, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 33, 35, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "40": [20, 25, 44, 49], "4000": [11, 27, 29, 35, 51, 53], "4001": 35, "404": [19, 20, 43, 44], "407": [14, 38], "4096": [9, 19, 27, 29, 33, 43, 51, 53], "411": [24, 48], "413": [24, 48], "415": [14, 38], "41st": [21, 24, 45, 48], "42": 0, "446": [11, 35], "447": [11, 35], "448": [11, 21, 35, 45], "449": [11, 35], "45": [20, 24, 44, 48], "450": [11, 35], "4500": [27, 51], "451": [11, 35], "452": [11, 35], "453": [11, 35], "454": [11, 35], "455": [11, 35], "45th": [20, 25, 44, 49], "4734": [12, 36], "4746": [18, 42], "48": [20, 44], "5": [4, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 32, 34, 35, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "50": [0, 27, 51], "500": 8, "5000": [18, 27, 29, 51, 53], "508226": [9, 33], "51": [20, 25, 44, 49], "5128": [19, 43], "5159": [19, 43], "5203": [20, 25, 44, 49], "521": [11, 35], "522": [11, 35], "523": [11, 35], "524": [11, 21, 35, 45], "5248": [14, 38], "525": [11, 35], "526": [11, 35], "527": [11, 35], "528": [11, 35], "5280": [17, 41], "529": [11, 35], "53": [20, 44], "530": [11, 35], "5419": [20, 44], "5429": [20, 44], "55": [10, 23, 25, 26, 34, 47, 48, 49, 50], "550": [20, 44], "5563": 17, "5564": 41, "557": [23, 24, 47, 48], "56": [23, 24, 47, 48], "5632": [19, 43], "5634": [19, 43], "568": [20, 25, 44, 49], "5734": [17, 41], "576": [23, 24, 47, 48], "5853": [19, 43], "590": [23, 47], "5908": [17, 41], "5917": [27, 51], "5939": 17, "5940": [19, 41, 43], "596": [11, 35], "597": [11, 35], "598": [11, 35], "599": [11, 35], "5997": [25, 49], "5998": [20, 25, 44, 49], "5m": [20, 44], "5th": [24, 48], "6": [9, 15, 17, 20, 21, 22, 23, 24, 25, 26, 29, 30, 33, 39, 41, 43, 44, 45, 46, 47, 48, 49, 50, 53], "60": [27, 51], "600": [0, 11, 35], "60005": [20, 44], "6001": [25, 49], "6003": [20, 25, 44, 49], "601": [11, 35], "602": [11, 35], "603": [11, 35], "604": [11, 35], "605": [11, 35], "610": [20, 25, 44, 49], "614": [20, 25, 44, 49], "6245": [19, 43], "64": [20, 23, 44, 47], "652": [20, 25, 44, 49], "6533": [19, 43], "671": [11, 35], "672": [11, 35], "673": [11, 35], "674": [11, 35], "675": [11, 35], "676": [11, 35], "677": [11, 35], "678": [11, 35], "679": [11, 21, 35, 45], "680": [11, 35], "6841": [19, 43], "6862": [19, 43], "688": [21, 45], "7": [9, 13, 20, 21, 22, 23, 24, 25, 26, 29, 33, 37, 44, 45, 46, 47, 48, 49, 50, 53], "7022": [19, 43], "71": [11, 20, 23, 26, 35, 44, 47, 50], "7140": [20, 44], "7146": [20, 44], "71828": [39, 41, 42, 43], "72": [11, 35], "728": [23, 24, 47, 48], "73": [11, 35], "74": [11, 35], "742": [23, 24, 47, 48], "746": [11, 35], "747": [11, 35], "748": [11, 35], "749": [11, 35], "75": [10, 11, 21, 34, 35, 45], "750": [11, 35], "751": [11, 35], "752": [11, 35], "753": [11, 21, 35, 45], "753049836": [21, 45], "754": [11, 35], "755": [11, 35], "76": [11, 35], "7605": [19, 43], "7692": [19, 43], "77": [11, 20, 35, 44], "771": [11, 35], "772": [11, 35], "773": [11, 35], "777": [21, 45], "78": [11, 20, 35, 44], "79": [11, 20, 22, 35, 44, 46], "7956": [19, 43], "8": [8, 9, 11, 14, 16, 17, 20, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 38, 40, 41, 44, 45, 47, 48, 49, 50, 51, 53], "80": [11, 35], "81": [22, 46], "8115": [19, 43], "821": [11, 35], "822": [11, 35], "823": [11, 35], "824": [11, 35], "825": [11, 35], "826": [11, 35], "827": [11, 35], "828": [11, 35], "8287": [19, 43], "829": [11, 35], "83": [20, 44], "830": [11, 35], "836": [21, 45], "8395": [13, 37], "84": [25, 26, 49, 50], "8415": [19, 43], "8424": 17, "842518179468045": 41, "8447": [19, 43], "85": [20, 25, 44, 49], "86": [20, 44], "8679": [19, 43], "8687": [12, 36], "87": [20, 25, 44, 49], "88": [20, 25, 27, 44, 49, 51], "89": [23, 24, 26, 47, 48, 50], "896": [11, 35], "897": [11, 35], "898": [11, 35], "899": [11, 35], "8th": 2, "9": [20, 21, 22, 23, 24, 29, 44, 45, 46, 47, 48, 53], "90": [20, 44], "900": [11, 35], "901": [11, 35], "902": [11, 35], "903": [11, 35], "904": [11, 35], "905": [11, 35], "91": [20, 21, 25, 26, 44, 45, 49, 50], "913": [25, 49], "92": [23, 24, 26, 27, 47, 48, 50, 51], "9227345": [9, 33], "93": [22, 46], "93012334": [9, 33], "94": [22, 25, 26, 46, 49, 50], "94074854": [9, 33], "9468": [12, 36], "95": [20, 27, 44, 51], "954": [22, 46], "95439595": [9, 33], "962": [20, 44], "9642": [20, 44], "9653": [20, 44], "971": [11, 35], "972": [11, 35], "973": [11, 35], "974": [11, 35], "9747282": [25, 49], "975": [11, 35], "976": [11, 35], "977": [11, 35], "978": [11, 35], "979": [11, 35], "98": [24, 48], "980": [11, 35], "996": [11, 35], "997": [11, 35], "998": [11, 35], "999": [11, 35], "A": [0, 2, 4, 6, 20, 21, 22, 23, 24, 25, 26, 30, 44, 45, 46, 47, 48, 49, 50], "AS": [1, 3], "As": [0, 1, 3, 10, 20, 34, 44], "At": [0, 23, 24, 47], "By": [21, 25, 26, 45, 49, 50], "For": [1, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 29, 44, 45, 46, 47, 49, 50, 51, 52, 53], "If": [1, 3, 20, 22, 23, 24, 25, 27, 44, 46, 47, 48, 49, 50, 51], "In": [0, 1, 3, 4, 6, 20, 25, 29, 30, 49, 53], "It": [1, 3, 20, 44], "Its": [25, 49], "NOT": [25, 49], "OF": [1, 3], "ONE": [21, 45], "OR": [1, 3], "On": [23, 26, 47, 50], "One": [0, 1, 2, 3, 30, 51], "Ons": 6, "The": [0, 1, 2, 3, 4, 6, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "There": [0, 1, 3, 4, 6, 29, 30], "These": [0, 26, 29, 44, 45, 46, 49, 50, 53], "To": [0, 1, 3, 4, 6, 21, 23, 24, 25, 26, 29, 30, 45, 47, 48, 49, 50, 53], "With": [0, 2], "_": [4, 5, 6, 30, 31, 38, 40, 42, 44], "__": [4, 6, 30], "___": [4, 6, 30], "____": [4, 6, 30], "_____": [4, 6, 30], "______": [4, 6, 30], "_______": [4, 6, 30], "___________": [4, 6, 30], "_fuzzen": 39, "_i": [16, 40], "_incren": 40, "_sampen": 41, "_xapen": 42, "_xconden": [39, 43], "aaryan": 2, "abasolo": 2, "abbrevi": 30, "abdomin": 2, "abil": [1, 3], "abinash": 2, "abl": [], "about": [0, 2, 4, 6, 27, 51], "abov": [9, 20, 23, 24, 26, 33, 44, 47, 48, 50], "absolut": [17, 23, 24, 41, 47, 48], "academi": [2, 20, 22, 44, 46], "acceler": 2, "accept": [29, 53], "access": [1, 3, 6, 20, 21, 22, 30, 44, 45, 46], "acclaim": 0, "accordingli": [25, 49], "account": [6, 21, 45], "accur": [1, 3], "acharya": 2, "achiev": 0, "acoust": [2, 25, 49], "acquir": [24, 48], "across": [0, 2, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "activ": 2, "actuat": [20, 44], "ad": 0, "adapt": 2, "adazd": 2, "add": [4, 6], "add_subplot": [34, 36], "addit": [0, 6, 20, 22, 44, 46], "adnan": 2, "adolesc": 2, "advanc": [0, 1, 3], "advantag": [0, 4, 6, 30], "affect": [2, 20, 44], "afonso": 2, "afra": 2, "africa": 2, "after": 4, "ag": 2, "again": [24, 48], "ago": 0, "agre": [1, 3], "agrp": 2, "ahm": [25, 26, 49, 50], "aij": [22, 46], "aix": [20, 44], "aktaruzzaman": [20, 44], "al": [0, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "alberto": [2, 20, 22, 23, 25, 26, 44, 46, 49, 50], "alert": 0, "algebra": [25, 49], "algorithm": [0, 2, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "ali": 2, "alistair": 2, "all": [0, 1, 3, 4, 6, 12, 20, 23, 24, 25, 26, 30, 36, 44, 47, 48, 49, 50], "allow": [0, 1, 3], "almost": [20, 44], "along": [20, 44], "alongsid": [4, 30], "alpha": [12, 20, 27, 29, 36, 44, 51, 53], "alreadi": [4, 30, 53], "also": [0, 6, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "altern": [4, 6, 25, 49], "alwai": [10, 34], "alzheim": 2, "ambalika": [20, 44], "american": [20, 22, 44, 46], "amigo": [20, 44], "amin": 2, "amir": [20, 44], "among": [0, 23, 47], "amount": [22, 46], "ampawar": [20, 25, 44, 49], "amplitud": [0, 20, 25, 44, 49], "an": [0, 2, 4, 6, 7, 8, 12, 14, 15, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 36, 38, 39, 44, 45, 46, 47, 48, 49, 50, 51, 53], "ana": 2, "anacleto": 2, "anaconda": 30, "analog": [1, 3], "analys": [0, 1, 2, 3], "analysi": [0, 2, 4, 6, 10, 20, 21, 22, 23, 24, 25, 26, 30, 34, 44, 45, 46, 47, 48, 49, 50], "analyz": [20, 21, 24, 44, 45, 48], "andrea": [2, 20, 25, 44, 49], "andrei": 2, "andrzej": 2, "andrzejewska": 2, "angl": [20, 25, 49], "angular": [13, 20, 25, 37, 44, 49], "ani": [0, 1, 3, 6, 11, 23, 24, 26, 27, 28, 29, 30, 35, 47, 48, 50, 51, 52, 53], "ann": [0, 2, 21, 23, 24, 45, 47, 48], "annabel": 2, "annal": [22, 46], "announc": 0, "annual": [20, 21, 24, 25, 44, 45, 48, 49], "antoin": [24, 48], "aortic": [23, 24, 47, 48], "ap": [20, 44], "apach": [1, 3], "apen": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "api": [4, 6, 30], "appear": 4, "appendix": [20, 22, 44, 46], "appic": 0, "appli": [0, 2, 21, 23, 24, 25, 26, 45, 47, 48, 49, 50], "applic": [1, 2, 3, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "approach": [2, 21, 22, 23, 25, 45, 46, 47, 49], "appropri": [20, 44], "approxim": [0, 1, 2, 3, 20, 22, 23, 24, 26, 28, 29, 44, 46, 47, 48, 50, 52, 53], "ar": [0, 4, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "arang": 35, "arbitrari": [20, 44], "area": [20, 44], "argument": [20, 21, 22, 23, 24, 25, 26, 27, 29, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "ari": [23, 24, 26, 47, 48, 50], "arn": 2, "around": [1, 3], "arrai": [11, 20, 31, 33, 35, 36, 38, 40, 41, 42, 43, 44], "arxiv": 2, "ashish": [20, 44], "asok": [20, 44], "aspect": 2, "assess": [2, 20, 21, 22, 27, 44, 45, 46, 51], "associ": [20, 25, 44, 49], "assum": [21, 45, 53], "asymmetri": [2, 20, 44], "atan": [20, 21, 22, 25, 44, 45, 46, 49], "atif": 2, "attempt": [1, 3], "attent": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "attn": [20, 44], "attnen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "attractor": [27, 29, 51, 53], "audienc": 0, "aug": [20, 44], "augment": [1, 3], "authorea": 2, "autom": 2, "automat": 2, "autonom": [2, 20, 44], "avail": [0, 1, 3, 4, 6, 30, 44, 45, 46, 49], "aven": [20, 44], "averag": [19, 20, 22, 23, 24, 25, 43, 44, 46, 47, 48, 49], "aviat": [20, 44], "awar": [0, 20, 25, 44, 49], "ax": [34, 36], "axi": [10, 12, 13, 19, 20, 34, 36, 37, 43, 44], "azami": [2, 10, 20, 21, 22, 23, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "b": [2, 20, 22, 44, 46], "b0": [25, 49], "b1": [25, 49], "bajaj": 2, "balasingham": 2, "band": [20, 22, 44, 46], "banden": [20, 22, 44, 46], "bandt": [20, 25, 44, 49], "bank": 2, "bar": 6, "base": [0, 2, 6, 8, 10, 12, 13, 19, 21, 22, 23, 24, 25, 26, 29, 30, 32, 34, 36, 37, 43, 45, 46, 47, 48, 49, 50, 53], "basi": [1, 3], "basic": [1, 3, 29, 53], "beacom": 2, "beam": 2, "bear": [20, 44], "becaus": [21, 25, 45, 49], "becom": [1, 3, 17, 18, 23, 24, 41, 42, 47, 48], "been": [0, 1, 3, 4, 6, 53], "begin": [20, 44], "behaviour": [1, 3], "bell": [20, 21, 22, 25, 44, 45, 46, 49], "below": [20, 25, 29, 49, 53], "belyaev": 2, "benchmark": [27, 51], "benefit": 0, "bereznowski": 2, "bernard": 2, "bernd": [0, 20, 21, 25, 44, 45, 49], "berri": 2, "best": [1, 3], "beta": [27, 29, 51, 53], "betweeen": [22, 46], "between": [0, 20, 21, 23, 27, 28, 44, 45, 47, 51, 52], "beween": 22, "beyond": 0, "bi": [23, 47], "bian": [20, 25, 44, 49], "bidimensioanl": [28, 52], "bidimension": [0, 6, 29, 30, 53], "bike": [24, 48], "bilal": [2, 20, 25, 44, 49], "bin": [20, 21, 22, 44, 45, 46], "bio": 2, "biol": [47, 48], "biolog": [10, 20, 22, 23, 24, 25, 26, 34, 44, 46, 47, 48, 49, 50], "biologi": [0, 20, 21, 44, 45], "biomed": [0, 10, 20, 21, 22, 23, 24, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "biomedicin": [0, 2, 20, 25, 44, 49], "biorxiv": 2, "birth": 2, "bit": [13, 37], "bivari": [20, 22, 44, 46], "blob": 27, "block": [], "bm": [20, 25, 44, 49], "bo": [20, 22, 44, 46], "bodi": 2, "bodyweight": 2, "booklet": [1, 3], "boolean": [20, 21, 22, 25, 44, 45, 46, 49], "boriskov": 2, "borowska": 2, "both": [0, 20, 44], "bound": [2, 25, 26, 49, 50], "brain": 2, "branco": 2, "breath": 2, "brian": 2, "bridg": [20, 44], "bring": 0, "brismar": [23, 24, 26, 47, 48, 50], "broad": 0, "browser": [1, 3, 6], "br\u00edgida": 2, "bt": [25, 49], "bubb": [20, 44], "bubben": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "bubbl": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "bug": [0, 1, 3], "bumanglag": 2, "burton": [22, 46], "butterworth": [17, 23, 24, 41, 47, 48], "c": [2, 9, 15, 19, 20, 21, 22, 23, 24, 25, 26, 33, 34, 36, 39, 43, 44, 45, 46, 47, 48, 49, 50], "caetano": 2, "calcul": [0, 1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50], "can": [0, 1, 2, 3, 4, 5, 6, 21, 23, 24, 25, 26, 27, 30, 45, 47, 48, 49, 50, 51], "cancer": [21, 45], "captur": [1, 2, 3], "cardiovascular": [23, 24, 47, 48], "carman": 2, "carri": [22, 46], "case": [2, 4, 6, 23, 24, 25, 26, 47, 48, 49, 50], "castiglioni": [23, 47], "catela": 2, "caus": [21, 45], "causal": [21, 45], "caution": [6, 21, 45], "cavit": 2, "cden": [20, 44], "cell": 27, "central": [1, 3], "cep": 2, "cerca": 2, "cerebr": 2, "chakraborti": 2, "challeng": 2, "chang": [0, 2, 6, 20, 44], "channel": [25, 49], "chanwimalueang": [20, 25, 44, 49], "chao": [2, 21, 45], "chaotic": [2, 20, 44], "character": [2, 20, 44], "characterist": 2, "check": [0, 4, 6, 30], "chen": [2, 20, 44], "cheng": 2, "cherng": 2, "chi": 2, "chines": [20, 44], "chirp": [27, 29, 51, 53], "chloe": 2, "choic": [2, 23, 47], "choos": 0, "christo": 2, "christoph": [2, 20, 25, 44, 49], "chronic": 2, "chung": 2, "chunhua": [20, 25, 44, 49], "ci": [10, 17, 19, 20, 22, 23, 24, 26, 34, 41, 43, 44, 46, 47, 48, 50], "cient\u00edfico": 2, "circular": [25, 49], "circulatori": [20, 22, 44, 46], "cite": 0, "clarifi": [1, 3], "class": [], "classic": [1, 3], "classif": [2, 20, 44], "classifi": 2, "click": 6, "clinic": [2, 20, 44], "cluster": 4, "cmap": 32, "cmd": 30, "cmse": [23, 24, 47, 48], "cmsen": [23, 24, 26, 28, 47, 48, 50, 52], "cmvmsen": [10, 25, 26, 28, 34, 47, 48, 50, 52], "coars": [0, 20, 23, 24, 26, 44, 47, 48, 50], "code": [], "coeffici": [23, 24, 47, 48], "cognit": [2, 23, 47], "cohort": 2, "coin": [1, 3], "col": [25, 49], "collaps": [], "collect": [27, 51], "colleg": 2, "color": [8, 10, 12, 13, 19], "colorect": [21, 45], "colormap": 8, "column": [21, 27, 29, 45, 51, 53], "com": [1, 3, 4, 22, 24, 27, 46, 48], "combin": [25, 49], "combinator": 4, "command": [1, 3, 4, 6, 30, 52, 53], "comment": [23, 24, 26, 47, 48, 50], "common": [21, 45], "commonli": [27, 51], "commun": [0, 20, 21, 44, 45], "compar": [1, 3, 9, 20, 22, 25, 26, 33, 44, 46, 49, 50], "comparison": [2, 20, 21, 22, 44, 45, 46], "compatbl": 0, "complet": [4, 6], "complex": [0, 1, 2, 3, 17, 19, 20, 21, 23, 24, 25, 26, 41, 43, 44, 45, 47, 48, 49, 50], "complianc": [1, 3], "compon": [0, 10, 12, 13, 19, 27, 29, 34, 36, 37, 43, 51, 53], "composit": [0, 1, 3, 23, 24, 26, 28, 29, 47, 48, 50, 52, 53], "comprehens": [1, 3, 4, 6, 30], "compris": 0, "compromis": [1, 3], "comput": [0, 2, 10, 20, 21, 22, 23, 24, 25, 26, 34, 44, 45, 46, 47, 48, 49, 50], "comsposit": [18, 42], "concept": 2, "concis": [52, 53], "cond": [20, 44], "conden": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "condit": [1, 3, 12, 15, 20, 22, 23, 24, 26, 28, 29, 36, 39, 44, 46, 47, 48, 50, 52, 53], "conejero": 2, "confer": [0, 2, 20, 21, 24, 25, 44, 45, 48, 49], "connect": [2, 27, 29, 51, 53], "consid": [1, 3, 25, 49], "consider": [20, 25, 44, 49], "consist": [0, 1, 3, 25, 49], "constgaussian": [10, 20, 21, 22, 25, 34, 44, 45, 46, 49], "consult": 29, "consum": 0, "contain": [20, 22, 24, 27, 44, 46, 48, 51], "content": 2, "context": [1, 3], "continu": [0, 2], "contrari": [25, 49], "contrast": [1, 3], "convent": [10, 25, 34, 49], "convert": [9, 33], "copi": [1, 3], "copyright": [1, 3], "corner": [6, 17, 20, 21, 22, 25, 41, 44, 45, 46, 49], "correct": [0, 15, 20, 22, 29, 39, 44, 46, 47, 48, 50, 53], "correctli": [4, 6, 30], "correl": [20, 22, 25, 44, 46, 49], "correspond": [20, 21, 22, 25, 27, 44, 45, 46, 49, 51], "cosi": [20, 44], "cosien": [20, 23, 24, 25, 26, 28, 44, 47, 48, 49, 50, 52], "cosin": [0, 20, 23, 24, 25, 26, 28, 44, 47, 48, 49, 50, 52], "costa": [23, 24, 26, 47, 48, 50], "cot": 2, "could": [21, 45], "coupl": [22, 46], "cover": 0, "cp": [20, 22, 44, 46], "cpe": [12, 20, 36, 44], "crash": [21, 45], "creat": [10, 11, 15, 16, 17, 18, 19, 21, 23, 24, 26, 34, 35, 39, 40, 41, 42, 43, 45, 47, 48, 50], "cristiana": 2, "cross": [0, 1, 2, 3, 4, 6, 15, 23, 25, 26, 29, 30, 39, 47, 49, 50, 53], "cuesta": [20, 44], "cui": 2, "cumul": [20, 21, 25, 44, 45, 49], "current": [0, 6], "curv": [19, 20, 21, 22, 23, 24, 25, 26, 43, 44, 45, 46, 47, 48, 49, 50], "custom": [6, 29], "cutoff": [23, 24, 47, 48], "cxmsen": [18, 23, 24, 26, 28, 42, 47, 48, 50, 52], "cybernet": [20, 22, 44, 46], "cygan": 2, "c\u00e1nova": 2, "d": [2, 20, 23, 25, 44, 47, 49], "da": 2, "daniel": 2, "daniela": 2, "danilo": [20, 25, 26, 44, 49, 50], "data": [0, 2, 9, 10, 12, 13, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 36, 37, 39, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "datainterpol": 4, "datalength": [11, 27, 35, 51], "dataset": [27, 28, 51, 52], "datatyp": [11, 27, 35, 51], "date": [0, 30], "datseri": 2, "david": [2, 20, 44], "de": [2, 23, 24, 26, 47, 48, 50], "decad": 0, "decis": [27, 51], "declar": [29, 53], "decomposit": [2, 19, 23, 24, 43, 47, 48], "deep": 2, "deepak": 2, "default": [0, 10, 20, 21, 22, 23, 24, 25, 26, 27, 29, 34, 39, 44, 45, 46, 47, 48, 49, 50, 51, 53], "defici": 2, "defin": [20, 21, 22, 25, 44, 45, 46, 49], "definit": [25, 49], "defualt": [25, 49], "degre": [0, 1, 3, 20, 22, 44, 46], "delai": [7, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 32, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 49, 50], "delimitedfil": 4, "demand": [20, 22, 44, 46], "demonstr": 0, "den": 2, "denis": 2, "densiti": [20, 21, 25, 44, 45, 49], "deokar": 2, "depend": [4, 22, 30, 46], "depric": [20, 21, 22, 44, 45, 46], "depth": 29, "derek": 2, "deriv": [1, 3, 25, 49], "describ": [0, 1, 3, 47, 48, 50], "descript": [1, 3, 28, 52], "descriptor": [20, 44], "design": [1, 2, 3, 4, 30], "desynchron": [20, 44], "detail": [4, 6, 29, 30], "detect": [2, 20, 25, 44, 49], "determin": [14, 38], "develop": [0, 1, 2, 3], "deviat": [0, 10, 17, 23, 24, 25, 26, 34, 41, 47, 48, 49, 50], "dhananjai": 2, "diagnosi": [2, 20, 44], "dictionari": 51, "differ": [0, 1, 3, 13, 37, 44], "differenti": 2, "difficult": [1, 3], "digit": 2, "dimens": [0, 2, 7, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], "dimension": [2, 28, 52], "dir": [44, 45, 46, 49], "direct": [22, 46], "directli": [44, 45, 46, 49], "directori": [6, 30], "disciplin": 0, "discret": [0, 1, 3], "discrimin": [21, 45], "discuss": 0, "diseas": 2, "disp2d": [21, 45], "dispen": [0, 20, 21, 23, 24, 25, 26, 28, 44, 45, 47, 48, 49, 50, 52], "dispen2d": [0, 20, 21, 25, 28, 29, 45, 49, 52, 53], "dispers": [0, 20, 21, 23, 24, 25, 26, 28, 29, 44, 45, 47, 48, 49, 50, 52, 53], "displai": 6, "dispx": [20, 44], "dist": [20, 44], "dist2d": [21, 45], "distanc": [0, 8, 18, 20, 21, 22, 25, 32, 42, 44, 45, 46, 49], "disten": [20, 21, 22, 23, 24, 26, 28, 44, 45, 46, 47, 48, 50, 52], "disten2d": [20, 21, 22, 28, 29, 44, 45, 46, 52, 53], "distinct": 2, "distinguish": [20, 44], "distribut": [1, 3, 7, 9, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53], "disturb": 2, "dit": [12, 36], "div": [20, 44], "diven": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "diverg": 2, "divers": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "divis": [20, 21, 22, 25, 44, 45, 46, 49], "divisor": [20, 21, 22, 25, 44, 45, 46, 49], "dmitri": 2, "do": [0, 1, 2, 3, 4, 6, 11, 21, 24, 25, 27, 35, 45, 48, 49, 51], "doan": [20, 21, 22, 44, 45, 46], "doc": [0, 6], "docstr": [6, 30], "document": [0, 27, 51], "doe": [23, 25, 47, 49], "doi": [0, 1, 3, 25, 49], "dominiqu": 2, "domino": 2, "donato": 2, "done": 6, "doubl": 6, "dougla": [2, 20, 22, 44, 46], "download": [0, 1, 3, 4, 6, 27, 30, 51], "dr": 0, "dsp": 4, "due": [20, 22, 44, 46], "duncan": 2, "dure": 2, "dynam": [0, 1, 2, 3, 20, 22, 23, 24, 25, 26, 28, 44, 46, 47, 48, 49, 50, 52], "e": [0, 2, 6, 20, 21, 22, 23, 24, 25, 26, 29, 30, 44, 45, 46, 47, 48, 49, 50, 52, 53], "e0259448": [0, 1, 3, 21, 45], "e40689": [21, 45], "each": [0, 1, 3, 6, 7, 9, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "earli": 2, "earlier": 0, "eas": [0, 1, 3], "easi": 0, "easili": [4, 6], "eastern": 2, "edg": [0, 20, 22, 25, 44, 46, 49], "edit": 0, "eduardo": [21, 45], "edward": 2, "eeg": [2, 20, 44], "effect": [1, 3], "effort": [0, 28, 52], "eh": [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 53], "either": [1, 3, 20, 25, 27, 44, 49, 51], "elahi": 2, "electr": [20, 44], "electro": [20, 44], "electroencephalogram": 2, "electroencephalographi": [2, 20, 44], "electrohysterogram": 2, "electromyographi": 2, "electron": [20, 25, 44, 49], "electrophysiolog": [20, 44], "element": [0, 20, 21, 22, 23, 24, 25, 27, 44, 45, 46, 47, 48, 49, 51], "elkommo": 2, "elsewher": [27, 51], "elzen": 2, "el\u017cbieta": 2, "embc": [0, 21, 24, 45, 48], "embed": [0, 7, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], "emerg": 2, "emg": [20, 25, 44, 49], "empir": [2, 23, 47], "emploi": [0, 10, 25, 34, 47, 48, 49, 50], "empti": [14, 38], "enabl": [1, 3, 21, 45], "end": 7, "endeavour": 0, "endocrinologi": 2, "eng": [20, 44, 47, 48], "engenharia": 2, "engin": [0, 2, 10, 20, 21, 23, 24, 25, 26, 34, 44, 45, 47, 48, 49, 50], "enhanc": 2, "enofen": [0, 20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "ensembl": 2, "ensur": [25, 30, 49], "enter": [6, 20, 22, 25, 30, 44, 45, 46, 49], "entir": [23, 24, 47, 48], "entitl": 0, "entrop": [0, 4, 6, 21, 30, 45], "entropi": [0, 1, 2, 3, 4, 6, 27, 29, 30, 51, 53], "entropyhub": [0, 20, 21, 22, 23, 24, 25, 26, 29, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "entropyhub_mat": [27, 29, 51, 53], "entyp": [10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "environ": 0, "eoe": [20, 44], "epilept": 2, "equal": [0, 20, 21, 25, 44, 45, 49], "equat": [10, 12, 13, 19, 34, 36, 37, 43], "equin": 2, "error": [21, 45], "escholarship": 2, "escudero": [0, 10, 20, 21, 23, 25, 26, 34, 44, 45, 47, 49, 50], "esp2d": [21, 45], "espen": [21, 45], "espen2d": [0, 21, 28, 29, 45, 52, 53], "espinosa": [0, 21, 28, 45, 52], "establish": [1, 3], "estim": [0, 1, 3, 9, 12, 28, 33, 36, 52], "et": [0, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "european": [20, 25, 44, 49], "europhi": [20, 44], "evalu": [22, 23, 46, 47], "even": [20, 25, 26, 44, 49, 50], "event": [20, 44], "ever": [1, 3], "everi": 29, "evi": 2, "ex": [29, 53], "examin": [1, 3, 25, 49], "exampl": [0, 1, 3, 4, 5, 6, 21, 27, 28, 45, 51, 52], "exampledata": [7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 27, 28, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 51, 52], "except": [0, 1, 3], "exchang": [0, 1, 3, 6], "exclud": [27, 35, 51, 52], "exercis": 2, "exlcud": 11, "exp": [8, 32, 44, 45, 46, 49], "expand": 0, "expans": [25, 26, 49, 50], "experiment": 2, "explain": [1, 2, 3], "explor": 2, "expon": [20, 21, 22, 25, 44, 45, 46, 49], "exponenti": [20, 21, 22, 25, 44, 45, 46, 49], "express": [1, 3, 21, 45], "extend": 0, "extens": 0, "extra": 0, "extract": [2, 20, 27, 30, 44, 51], "ey": 2, "f": [2, 8, 20, 22, 32, 44, 46], "f0": [27, 29, 51, 53], "f1": [27, 29, 51, 53], "f_num": [17, 23, 24, 41, 47, 48], "f_order": [17, 23, 24, 41, 47, 48], "fabian": 2, "facecolor": [34, 36, 37, 43], "facil": 2, "faculdad": 2, "fadlallah": [20, 25, 44, 49], "fai": 2, "fair": 2, "fals": [12, 13, 20, 21, 22, 23, 24, 25, 26, 36, 37, 39, 40, 43, 44, 45, 46, 47, 48, 49, 50], "far": 0, "fast": 2, "fault": [2, 20, 44], "fc": [23, 24, 47, 48], "fe2d": [8, 32], "featur": [0, 2, 20, 44], "feedback": 0, "felip": 2, "feng": [20, 24, 44, 48], "ferdinando": 2, "fernando": [21, 23, 24, 45, 47, 48], "fern\u00e1ndez": [10, 23, 25, 26, 34, 49, 50], "fetal": 2, "fft": [20, 22, 44, 46], "fftpack": 44, "fftw": 4, "fia": 2, "fibromyalgia": 2, "field": [0, 11, 15, 16, 17, 18], "fig": [34, 36, 37, 43], "figur": [8, 10, 12, 13, 19, 34, 36, 37, 43], "file": [0, 1, 3, 4, 6, 30], "filipa": 2, "fill": [11, 27, 35, 51], "filter": [17, 23, 24, 41, 47, 48], "final": [11, 27, 35, 51], "financi": [22, 46], "find": [0, 1, 3, 6], "fine": [2, 20, 29, 44, 53], "finegrain": [12, 20, 36, 44], "finesort": [20, 44], "fino": 2, "first": [0, 10, 19, 23, 24, 29, 34, 43, 47, 48, 53], "firth": 2, "five": 0, "fix": [0, 1, 3, 23, 47], "fixat": 2, "flood": [0, 1, 3, 21, 22, 24, 45, 46, 48], "floor": [21, 27, 45, 51], "fluct": [20, 44], "fluctuat": [20, 25, 44, 49], "fmri": 2, "folder": [6, 30], "follow": [0, 1, 3, 6, 10, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 34, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "food": 2, "foot": 2, "forc": 2, "forecast": 2, "form": [20, 22, 25, 30, 44, 46, 49], "format": 2, "found": [0, 4, 5, 20, 21, 22, 44, 45, 46], "founder": 0, "four": 44, "frac": [8, 16, 32, 40], "fractal": [2, 8, 21, 27, 29, 32, 45, 51, 53], "franci": [20, 44], "francisco": 2, "frasch": 2, "frau": [20, 44], "free": [1, 3, 20, 44], "freq": [20, 22, 44, 46], "frequenc": [17, 20, 22, 23, 24, 41, 44, 46, 47, 48], "friedman": 2, "from": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 43, 44, 45, 46, 47, 48, 49, 51, 53], "frontier": [2, 23, 47], "fu": [20, 44], "full": [6, 20, 22, 25, 26, 29, 44, 46, 49, 50, 53], "func": [15, 16, 17, 18, 39, 40, 41, 42, 43], "function": [0, 1, 2, 3, 4, 6, 8, 10, 15, 29, 30, 32, 34, 39, 40, 41, 42, 43, 53], "functionnam": [6, 30], "fundament": [23, 47], "further": [0, 20, 22, 23, 25, 26, 27, 44, 46, 47, 49, 50, 51], "futur": [0, 28, 52], "fuzz": [20, 44], "fuzz2d": [21, 45], "fuzzen": [0, 15, 20, 21, 22, 23, 24, 25, 26, 28, 39, 44, 45, 46, 47, 48, 49, 50, 52], "fuzzen2d": [0, 8, 20, 21, 22, 25, 28, 29, 32, 44, 45, 46, 49, 52, 53], "fuzzi": [0, 15, 20, 21, 22, 23, 24, 25, 26, 28, 29, 39, 44, 45, 46, 47, 48, 49, 50, 52, 53], "fx": [8, 10, 15, 20, 21, 22, 25, 32, 34, 39, 44, 45, 46, 49], "g": [2, 6, 10, 12, 13, 19, 20, 21, 23, 24, 25, 26, 30, 34, 36, 37, 43, 44, 45, 47, 48, 49, 50], "gaikwad": 2, "gain": [1, 3], "gait": 2, "gao": [20, 23, 25, 44, 47, 49], "garibo": 2, "garrett": 2, "gaug": [1, 3], "gaussian": [0, 7, 20, 21, 22, 25, 27, 29, 31, 44, 45, 46, 49, 51, 53], "gaussian2": [27, 29, 51, 53], "gaussian_mat": [27, 29, 51, 53], "gca": [10, 12, 13, 19], "gd": [20, 21, 22, 25, 44, 45, 46, 49], "gde": [20, 44], "gdr": [20, 44], "gearbox": [20, 44], "gener": [0, 20, 21, 22, 23, 24, 25, 26, 29, 44, 45, 46, 47, 48, 49, 50, 53], "genera": 0, "geograph": 2, "geomagnet": 2, "geophys": 2, "georg": [2, 20, 23, 26, 44, 47, 50], "get": [6, 7, 20, 25, 31, 49], "gh": 2, "github": [0, 1, 3, 4, 5, 6, 22, 24, 27, 30, 46, 48], "give": 0, "given": [21, 23, 24, 27, 29, 45, 47, 48, 51, 53], "gix": [20, 44], "glasgow": 0, "global": [20, 25, 44, 49], "go": 6, "goal": [1, 3], "goenka": 2, "goldberg": [23, 24, 26, 47, 48, 50], "gome": 2, "gosalia": 2, "gottwald": [23, 26, 47, 50], "govern": [1, 3], "grain": [0, 10, 16, 20, 23, 24, 26, 27, 29, 34, 40, 44, 47, 48, 50, 51, 53], "graph": [2, 23, 24, 47, 48], "grassberg": [20, 44], "gratitud": 0, "great": 0, "greater": [0, 21, 45], "greet": [4, 30], "grid": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "griden": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "griffin": [20, 22, 44, 46], "grimm": [0, 21, 45], "gross": 2, "group": 2, "groupslic": 4, "grow": [0, 1, 3], "guangbin": [20, 25, 44, 49], "gudermannian": [20, 21, 22, 25, 44, 45, 46, 49], "guid": [1, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 30, 44, 45, 46, 47, 49, 50, 51], "gunn": 2, "guo": [20, 44], "guochen": [24, 48], "gusmao": 2, "gz": 30, "g\u00f3rski": 2, "h": [2, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "ha": [0, 1, 3, 4, 6, 21, 29, 30, 45, 53], "haddadi": 2, "haifeng": 2, "halv": [29, 53], "halvorsen": 2, "hame": [2, 20, 21, 22, 23, 25, 44, 45, 46, 47, 48, 49], "hamiltonian": [20, 44], "hani": 2, "hanif": 2, "hanneman": 2, "hao": 2, "haosheng": 2, "happi": 0, "hardwar": 2, "harikala": 2, "haroldo": [21, 45], "have": [0, 1, 3, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "he": [20, 44], "health": [2, 22, 46], "healthi": [23, 24, 47, 48], "heart": [2, 20, 22, 23, 24, 44, 46, 47, 48], "heartbeat": [20, 23, 24, 25, 44, 47, 49], "heidari": 2, "height": [21, 45], "help": [0, 23, 24, 26, 47, 48, 50], "hemin": 2, "hemorrhag": 2, "henc": [21, 45], "henon": [13, 19, 27, 29, 37, 43, 51, 53], "henriqu": 2, "here": [1, 3, 4, 5, 6, 21, 25, 29, 30, 45, 49], "herein": [1, 3], "hesit": [1, 3], "heurtier": [2, 21, 23, 24, 45, 47, 48], "hierarch": [4, 6, 23, 24, 28, 29, 47, 48, 52, 53], "high": [20, 44], "hilal": [21, 45], "hilbert": [0, 20, 25, 49], "hinchliff": 2, "hint": 0, "hippocampu": 2, "hirsch": 2, "histogram": [14, 20, 21, 22, 38, 44, 45, 46], "histolog": [21, 45], "hmsen": [20, 23, 24, 26, 28, 29, 44, 47, 48, 50, 52, 53], "hnn": [20, 44], "hnx": [20, 44], "holter": [23, 24, 47, 48], "home": 6, "hong": [20, 22, 44, 46], "hongi": 2, "hope": 0, "hors": 2, "horseback": 2, "hot": [8, 32], "howev": [0, 6, 21, 45], "href": 27, "hsu": [2, 20, 44], "http": [1, 3, 4, 5, 22, 24, 27, 46, 48], "hu": [23, 47], "hua": 2, "hualou": [23, 47], "huan": [20, 25, 44, 49], "huang": 2, "huertier": 0, "hui": [20, 44], "huishu": 2, "human": [2, 23, 24, 47], "humeau": [0, 2, 21, 23, 24, 45, 47, 48], "hundr": [21, 45], "huo": [2, 20, 25, 44, 49], "hxmsen": [4, 6, 19, 23, 24, 26, 28, 29, 43, 47, 48, 50, 52, 53], "hxn": [20, 44], "hxx": [20, 44], "hypercementosi": 2, "h\u00e9ctor": 2, "h\u00f8iseth": 2, "i": [1, 2, 3, 4, 5, 6, 10, 16, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 34, 40, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "icassp": [25, 49], "icassp43922": [25, 49], "icsip": 2, "icspcc": [20, 44], "id": [30, 45], "ident": 20, "identif": 2, "identifi": [0, 1, 3], "ieee": [0, 2, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "ignor": [19, 43], "ilangko": 2, "illustr": [20, 25, 44, 49], "ilvesm\u00e4ki": 2, "imag": [2, 8, 21, 27, 29, 32, 45, 51, 53], "imf": [23, 24, 47, 48], "impact": [0, 2], "imperi": 2, "implement": [0, 1, 3, 25, 49], "impli": [1, 3], "implic": [22, 46], "import": [1, 3, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 21, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 49, 51, 52, 53], "improv": [2, 20, 25, 44, 49], "imshow": [8, 32], "includ": [0, 1, 3, 11, 20, 27, 35, 44, 51], "inconsist": 0, "incorpor": [0, 20, 25, 28, 44, 49, 52], "incorrect": 0, "incr": [20, 44], "increas": [0, 20, 35, 44], "increment": [20, 23, 24, 26, 28, 29, 44, 47, 48, 50, 52, 53], "incren": [16, 20, 23, 24, 26, 28, 40, 44, 47, 48, 50, 52], "independ": 2, "index": [17, 19, 23, 24, 26, 41, 43, 47, 48, 50], "indic": [2, 20, 21, 22, 44, 45, 46], "individu": [25, 49], "industri": [20, 25, 44, 49], "influenc": [2, 23, 47], "info": [0, 1, 3, 20, 21, 22, 25, 27, 44, 45, 46, 47, 49, 50, 51], "inform": [1, 2, 3, 4, 6, 20, 21, 22, 23, 25, 26, 27, 30, 44, 45, 46, 49, 51], "informat": [20, 44], "infrar": 2, "initi": 0, "initialmagnif": 8, "injuri": 2, "inouy": [20, 44], "input": [0, 27, 51], "inspir": 2, "instal": [1, 3, 29], "instanc": [21, 45], "instead": [21, 45], "instituto": 2, "instruct": 6, "instrument": 2, "integ": [9, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53], "integr": [1, 2, 3, 20, 22, 44, 46], "intend": [4, 6, 30], "interact": 2, "interbeat": [20, 44], "interest": 0, "intern": [2, 20, 21, 24, 25, 44, 45, 48, 49], "internet": [27, 29, 51, 53], "interpret": [2, 23, 47], "interv": [20, 44], "intraop": 2, "intrins": [23, 47], "introduc": 0, "introduct": 0, "invas": 2, "io": [4, 5], "ioanni": 2, "ionospher": 2, "iqbal": 2, "iri": 2, "iron": 0, "irregular": [20, 21, 22, 44, 45, 46], "issu": 0, "itamar": [20, 44], "its": [0, 2, 20, 22, 23, 25, 26, 44, 46, 47, 49, 50], "izabela": 2, "izotov": 2, "j": [2, 16, 20, 22, 25, 40, 44, 46, 49], "jacob": 2, "jagetia": 2, "jake": 2, "jamin": [24, 48], "jasi\u0144ski": 2, "javier": [0, 2, 20, 21, 23, 25, 26, 44, 45, 47, 49, 50], "jenni": 2, "jia": 2, "jialun": 2, "jian": [20, 44], "jianbo": [23, 47], "jiang": [20, 23, 24, 44, 47, 48], "jiawei": [20, 44], "jl": [0, 4, 5], "john": 2, "joint": [25, 49], "jose": 2, "joshua": [20, 22, 44, 46], "jos\u00e9": [2, 23, 24, 47, 48], "journal": [0, 1, 2, 3, 20, 22, 23, 24, 25, 44, 46, 47, 48, 49], "jpn": 2, "jr": 2, "juang": 2, "jue": [20, 44], "juen": 2, "juli": 2, "julia": [0, 1, 3], "julio": 2, "jun": [20, 44], "jung": 2, "just": [0, 24, 48], "k": [2, 8, 10, 12, 13, 19, 20, 23, 24, 26, 34, 36, 37, 43, 44, 47, 48, 50], "k2": [20, 22, 44], "k2en": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "ka": [20, 22, 44, 46], "kafetzi": 2, "kai": 2, "kamil": 2, "kamp": 2, "kandel": 2, "kang": [20, 25, 44, 49], "karmakar": [20, 44], "kateryna": 2, "kb": [20, 22, 44, 46], "keep": 0, "kei": [4, 6, 30], "keyword": [21, 29, 44, 45, 46, 47, 48, 49, 50, 51, 53], "khandok": [20, 44], "khare": 2, "kind": [1, 3], "kiviniemi": 2, "kmean": [20, 21, 25, 44, 45, 49], "knowledg": 2, "knoxvil": 2, "ko": 2, "kolmogorov": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "komogortsev": 2, "kopchick": 2, "korean": 2, "korhonen": 2, "korzun": 2, "krishna": 2, "kulkarni": 2, "kumar": [2, 23, 24, 47, 48], "kush": 2, "kwarg": [39, 40, 41, 42, 43, 47, 48, 50], "l": [2, 20, 23, 24, 26, 44, 47, 48, 50], "lago": 2, "lake": [0, 20, 22, 44, 46], "languag": [1, 3, 5], "lar": 2, "last": [7, 19, 31, 43], "lastest": 0, "latest": [1, 3], "lau": 2, "law": [1, 3, 23, 47], "lazaro": 2, "lead": [0, 21, 45], "lear": 2, "learn": [2, 4, 6], "led": 0, "lee": 2, "left": [16, 40], "lei": 2, "leiv": 2, "len": 44, "length": [0, 11, 20, 22, 25, 29, 35, 44, 46, 49, 53], "leonhard": 2, "lett": [20, 44], "letter": [20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "level": [20, 44], "li": [2, 20, 25, 44, 49], "liang": [23, 47], "librari": 2, "like": [0, 6, 10, 21, 26, 30, 34, 45, 50], "lilin": 2, "lima": 2, "limit": [1, 3], "lin": [20, 22, 44, 46], "lindenberg": 2, "line": [1, 3, 4, 6, 20, 30], "linear": [2, 8, 20, 21, 22, 25, 32, 44, 45, 46, 49], "linearalgebra": 4, "linearli": [8, 32], "link": [2, 4, 6, 20, 30, 44], "lisboa": 2, "list": [0, 20, 44], "literatur": [0, 1, 3, 28, 52], "littl": 0, "liu": [2, 20, 44], "load": 4, "local": [20, 44], "lock": [21, 29, 45, 53], "log": [6, 11, 20, 21, 22, 25, 27, 35, 44, 45, 46, 49, 51], "logarithm": [8, 12, 13, 19, 20, 21, 22, 23, 24, 25, 26, 32, 36, 37, 43, 44, 45, 46, 47, 48, 49, 50], "logo": [6, 27, 29, 51, 53], "logx": [8, 12, 13, 19, 20, 21, 22, 25, 32, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 49], "london": 2, "long": [20, 25, 44, 49], "longer": [21, 45], "longwei": [20, 44], "lorenz": [10, 12, 27, 29, 34, 36, 51, 53], "lot": 0, "low": [23, 24, 47, 48], "lower": [2, 20, 21, 22, 25, 26, 44, 45, 46, 49, 50], "lowest": [21, 45], "lpf": [23, 24, 47, 48], "luciano": [21, 45], "luiz": [21, 45], "lv": 2, "lvl": [20, 44], "m": [0, 2, 7, 8, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 32, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51], "mac": 30, "machin": [2, 6], "machineri": [20, 44], "mad": [23, 24, 47, 48], "madalena": [23, 24, 26, 47, 48, 50], "made": [28, 52], "magdalena": [23, 24], "magnet": 2, "mahinda": 2, "mai": [0, 1, 3, 6, 21, 26, 45, 50], "maik": [20, 25, 44, 49], "main": [0, 25, 27, 49], "major": 0, "make": [0, 4, 6, 30], "makowski": 2, "maksim": 2, "mandelbrot": [8, 27, 29, 32, 51, 53], "mandelbrot_mat": [8, 27, 29, 32, 51, 53], "mandic": [20, 25, 26, 44, 49, 50], "mani": [0, 1, 3, 20, 44], "manuel": 2, "map": [0, 2, 21, 45], "marco": 2, "maria": 2, "mariana": 2, "marin": 2, "market": [22, 46], "marta": 2, "martin": 2, "martti": 2, "mart\u00ednez": 2, "marwaha": [23, 24, 47, 48], "mashhadi": 2, "mat": [21, 45], "match": [0, 20, 21, 22, 25, 44, 45, 46, 49], "matern": 2, "mateusz": 2, "mathemat": [0, 2, 20, 23, 24, 44, 47, 48], "mathwork": 6, "matlab": [0, 1, 2, 3, 21, 27], "matplotlib": [30, 32, 34, 36, 37, 43], "matric": [21, 27, 29, 45, 51, 53], "matricess": [21, 45], "matrix": [8, 11, 25, 26, 27, 28, 29, 32, 35, 49, 50, 51, 52, 53], "matt": [0, 1, 3], "matter": [22, 46], "matthew": [0, 1, 3, 21, 22, 24, 45, 46, 48], "mattwillflood": [1, 3, 4, 5, 22, 24, 27, 46, 48], "max": [8, 9, 20, 22, 25, 32, 33, 44, 46, 49], "maxim": [25, 49], "maxima": [20, 44], "maximum": [20, 21, 22, 25, 29, 44, 45, 46, 49, 53], "mayor": 2, "ma\u0142gorzata": 2, "ma\u015bko": 2, "mbjx": [47, 48, 50], "mcgill": 2, "mcosi": [25, 49], "mdisp": [9, 25, 33, 49], "mdpi": 2, "mean": [7, 20, 23, 24, 25, 27, 29, 31, 44, 47, 48, 49, 51, 53], "measur": [0, 1, 2, 3, 20, 21, 25, 44, 45, 49], "mechan": [20, 23, 25, 26, 44, 47, 49, 50], "med": [47, 48], "median": [17, 20, 23, 24, 25, 41, 44, 47, 48, 49], "medic": [10, 20, 23, 25, 26, 34, 44, 49, 50], "medicin": [0, 20, 21, 44, 45], "medrxiv": 2, "meet": 0, "melaro": 2, "melnyk": 2, "membership": [0, 10, 25, 34, 49], "memori": [0, 2, 21, 45], "meng": [23, 47], "mental": 2, "mep": [20, 44], "merc\u00ea": 2, "mere": [29, 53], "meteorologi": 0, "method": [0, 1, 2, 3, 9, 10, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 34, 44, 45, 46, 47, 48, 49, 50, 51, 53], "methodx": [9, 16, 20, 23, 24, 25, 26, 33, 40, 44, 47, 48, 49, 50], "metric": [2, 20, 21, 22, 44, 45, 46], "mfuzz": [25, 49], "mi": 2, "michael": 2, "micha\u0142": 2, "mila": 2, "million": [21, 45], "min": [8, 20, 32, 44], "mina": 2, "minima": [20, 44], "minimum": [21, 45], "minimuum": [20, 21, 22, 44, 45, 46], "mirvana": [21, 45], "mltbx": 6, "mobil": 2, "mobj": [10, 15, 16, 17, 18, 19, 23, 24, 26, 34, 39, 40, 41, 42, 43, 47, 48, 50], "mode": [11, 23, 27, 35, 47, 51], "model": 2, "modifi": [0, 16, 20, 23, 24, 25, 26, 40, 44, 47, 48, 49, 50], "modsampen": [20, 21, 22, 25, 44, 45, 46, 49], "modul": [4, 30, 52, 53], "monika": 2, "monitor": 2, "monoton": [20, 44], "monro": 2, "mont": 2, "moorman": [20, 22, 44, 46], "mora": 2, "morabito": [25, 49], "moral": 2, "more": [1, 3, 4, 6, 20, 21, 22, 25, 29, 44, 45, 46, 49], "moriya": 2, "mosabb": [25, 26, 49, 50], "most": [0, 4, 6, 26, 30, 50], "mostafa": [2, 20, 25, 44, 49], "motif": [21, 45], "motion": [20, 44], "motor": [2, 20, 44], "mous": 2, "movement": 2, "moysi": 2, "mperm": [25, 49], "mpnorm": [25, 49], "mrmr": [20, 44], "msamp": [25, 49], "msen": [0, 16, 20, 21, 22, 23, 24, 25, 26, 28, 40, 44, 45, 46, 47, 48, 49, 50, 52], "msobject": [10, 16, 17, 18, 19, 23, 24, 26, 29, 34, 40, 41, 42, 43, 47, 48, 50, 53], "msx": [10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "mulitvari": [29, 53], "multi": [20, 44], "multical": 0, "multichannel": [25, 26, 49, 50], "multidimension": [21, 45], "multipl": [0, 2, 20, 44], "multiscal": [0, 1, 2, 3, 4, 6, 20, 21, 22, 25, 29, 30, 44, 45, 46, 49, 53], "multivari": [0, 2, 11, 23, 24, 27, 29, 35, 47, 48, 51, 53], "murugappan": 2, "music": 2, "must": [6, 21, 45, 47, 48, 50], "mutat": [20, 44], "mu\u00f1oz": 2, "mvcosien": [25, 26, 28, 47, 48, 49, 50, 52], "mvcosien2d": [23, 24, 26], "mvde": [9, 25, 33, 49], "mvdeii": [25, 49], "mvdispen": [9, 20, 25, 26, 28, 33, 47, 48, 49, 50, 52], "mvdispen2d": [23, 24, 26], "mvfuzzen": [10, 25, 26, 28, 34, 47, 48, 49, 50, 52], "mvfuzzen2d": [23, 24, 26], "mvmsen": [10, 23, 24, 25, 26, 28, 34, 47, 48, 50, 52], "mvpermen": [25, 26, 28, 47, 48, 49, 50, 52], "mvpermen2d": [23, 24, 26], "mvsampen": [25, 26, 28, 47, 48, 49, 50, 52], "mvsampen2d": [23, 24, 26], "mx": [21, 45], "my": [21, 45], "myllyl\u00e4": 2, "m\u00fcller": [20, 25, 44, 49], "n": [2, 11, 16, 20, 22, 25, 26, 27, 29, 35, 40, 44, 46, 49, 50, 51, 53], "naimahm": 2, "naiv": [25, 49], "name": [4, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 44, 45, 46, 47, 48, 49, 50, 52, 53], "nan": [2, 12, 36], "nanci": 2, "nathaniel": 2, "nation": [20, 22, 44, 46], "natur": [20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "navig": [24, 30, 48], "ncdf": [20, 21, 25, 44, 45, 49], "need": [1, 3], "neeraj": 2, "neg": [2, 25, 26, 49, 50], "neonat": [20, 22, 44, 46], "nervou": 2, "nesaragi": 2, "net": 2, "network": [2, 47], "neural": [2, 20, 23, 44, 47], "neuro": 2, "neurodynam": [23, 47], "neuroendocrinologi": 2, "neurokit2": 2, "neuron": 2, "neurophysiologi": [20, 44], "neurosci": [0, 23, 47], "new": [0, 20, 21, 22, 25, 28, 44, 45, 46, 49, 52], "nico": 2, "niel": [20, 25, 44, 49], "nikhil": 2, "nikulin": [23, 24, 26, 47, 48, 50], "nir": 2, "njah": 2, "nldyn": 2, "nmultiscal": 26, "nneten": 2, "node": [19, 23, 24, 43, 47, 48], "noem\u00ed": 2, "nois": 2, "noisi": [2, 20, 44], "non": 2, "none": [25, 41, 42, 44, 45, 46, 49, 51], "nonlinear": [1, 2, 3, 20, 22, 25, 44, 46, 49], "nonstationari": [20, 44], "noponen": 2, "norm": [9, 10, 12, 13, 16, 19, 20, 21, 22, 25, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 49], "normal": [2, 7, 9, 10, 13, 16, 19, 21, 25, 27, 29, 31, 33, 34, 37, 40, 43, 45, 49, 51, 53], "normalis": [8, 12, 17, 20, 21, 22, 25, 32, 36, 41, 44, 45, 46, 49], "notabl": 0, "note": [0, 4, 6, 14, 20, 22, 27, 38, 44, 46, 51], "nova": 2, "novel": [0, 2, 20, 24, 44, 48], "np": [33, 35, 49, 52], "nth": [23, 24, 47, 48], "nuclear": 2, "number": [0, 1, 3, 7, 9, 12, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 33, 36, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53], "numer": [20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "numpi": [30, 44, 45, 46, 49, 52], "nurs": 2, "nxm": [25, 27, 49, 51], "nyquist": [20, 22, 44, 46], "o": 2, "object": [10, 16, 17, 18, 19, 23, 24, 26, 29, 34, 40, 41, 42, 43, 47, 48, 50, 53], "observ": [25, 49], "obtain": [0, 1, 3, 9, 25, 33, 49], "oca": 2, "occur": [21, 45], "octavian": 2, "ocular": 2, "odeyemi": 2, "off": [34, 36, 37, 43], "offer": 0, "offic": 2, "often": [21, 45], "ogunsua": 2, "oklahoma": 2, "old": 0, "older": 6, "oleg": 2, "ologun": 2, "oludehinwa": 2, "olusola": 2, "omidvarnia": [20, 44], "one": [0, 1, 3, 4, 6, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "ones": [25, 49], "onli": [6, 19, 21, 29, 43, 45, 53], "onlin": [20, 44], "onton": 2, "open": [0, 2, 4, 6, 21, 30, 45], "oper": [20, 25, 49], "optim": [2, 20, 23, 24, 44, 47, 48], "option": [0, 20, 22, 25, 44, 49], "order": [13, 17, 22, 23, 24, 37, 41, 44, 46, 47, 48], "ordin": [21, 45], "org": [1, 3], "organis": 0, "origin": [0, 21, 25, 29, 45, 47, 48, 49, 50, 53], "origni": [23, 24, 26], "ort": 2, "other": [0, 1, 3, 20, 44], "otherwis": [23, 24, 47, 48, 52], "our": [0, 1, 3], "outflow": [20, 44], "outlin": [0, 6, 25, 29, 30, 49, 53], "output": [1, 3, 9, 10, 20, 22, 33, 34, 44, 46], "over": [0, 10, 16, 17, 18, 19, 23, 24, 26, 34, 40, 41, 42, 43, 47, 48, 50], "overcom": [1, 3, 21, 45], "overlap": [0, 11, 20, 22, 27, 35, 44, 46, 51], "overload": [21, 45], "oximetri": 2, "oxygen": 2, "ozimek": 2, "p": [20, 21, 22, 25, 26, 44, 45, 46, 49, 50], "packag": [0, 1, 2, 3, 4, 5, 6, 30, 52], "padhy": 2, "pain": 2, "pair": [20, 21, 22, 23, 24, 25, 26, 27, 44, 46], "pairwis": [21, 45], "palaniswami": [20, 44], "pandai": 2, "pandei": 2, "pankaj": 2, "paolo": [23, 47], "paper": 0, "param": 2, "paramet": [0, 1, 3, 10, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "paramt": 22, "parashar": 2, "parkinson": 2, "part": [0, 1, 3], "particular": [23, 24, 26, 47, 48, 50], "partit": [0, 13, 20, 37, 44], "pass": [21, 23, 24, 27, 29, 45, 47, 48, 51, 53], "patient": 2, "patil": 2, "pattern": [2, 9, 20, 21, 22, 25, 33, 44, 45, 46, 49], "paz": 2, "pdf": [1, 3, 4, 6, 27, 30], "pediatr": 2, "peng": [20, 23, 24, 26, 44, 47, 48, 50], "pengjian": [22, 24, 46, 48], "per": 2, "percentag": [20, 21, 45], "perceptron": 2, "perciv": [20, 44], "perform": [0, 1, 2, 3, 4, 9, 20, 21, 27, 33, 44, 45, 51], "period": [20, 23, 24, 44, 47, 48], "periodogram": [20, 22, 46], "perm": [12, 20, 36, 44, 46], "perm2d": [21, 45], "permen": [0, 12, 20, 21, 22, 23, 24, 25, 26, 28, 36, 44, 46, 47, 48, 49, 50, 52], "permen2d": [0, 21, 25, 28, 45, 49, 52], "permiss": [1, 3], "permit": [21, 29, 45, 53], "permuat": [20, 22, 44, 46], "permut": [0, 2, 20, 21, 22, 23, 24, 25, 26, 28, 29, 44, 45, 46, 47, 48, 49, 50, 52, 53], "petelczyc": 2, "peter": [2, 20, 44], "petr": 2, "pha": [13, 20, 37, 44], "pham": [2, 23, 47], "phase": [0, 20, 23, 24, 25, 26, 28, 29, 44, 47, 48, 49, 50, 52, 53], "phasen": [13, 20, 23, 24, 26, 28, 37, 44, 47, 48, 50, 52], "phi": [0, 20, 22, 44, 46], "phi1": [7, 21, 31, 45], "phi2": [7, 21, 31, 45], "physic": [1, 2, 3, 20, 21, 23, 24, 25, 26, 44, 45, 47, 48, 49, 50], "physica": [20, 23, 25, 26, 44, 47, 49, 50], "physiolog": [2, 20, 22, 23, 24, 26, 44, 46, 47, 48, 50], "physiologi": [2, 20, 22, 44, 46], "pi": 22, "pilot": 2, "pincu": [20, 22, 44, 46], "ping": 2, "pip": 30, "pix": [20, 44], "pkg": 4, "place": 0, "plan": 0, "plane": [21, 45], "planetari": [20, 44], "platform": 0, "player": 2, "pleas": [1, 3], "plo": [0, 1, 2, 3, 21, 45], "plot": [4, 10, 19, 20, 23, 24, 26, 34, 43, 44, 47, 48, 50], "plot3": [10, 12], "plotx": [10, 13, 19, 20, 23, 24, 26, 34, 37, 43, 44, 47, 48, 50], "pneumoperitoneum": 2, "pnorm": [12, 20, 36, 44], "poicar": 20, "poicar\u00e9": 44, "poincar": 20, "poincar\u00e9": [20, 44], "point": [20, 21, 22, 25, 29, 44, 45, 46, 49, 53], "polit\u00e9cnico": 2, "polkowska": 2, "pomp": [20, 25, 44, 49], "pone": [0, 1, 3], "pool": [0, 24, 48], "porta": [20, 22, 44, 46], "portal": 0, "porto": 2, "posit": [2, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "possibl": [0, 12, 20, 21, 24, 25, 26, 36, 44, 45, 48, 49, 50], "postolach": 2, "postop": 2, "powel": [20, 44], "power": [0, 20, 44], "pp": [20, 24, 25, 44, 48, 49], "ppi": [14, 20, 22, 38, 44, 46], "practic": [20, 25, 44, 49], "pre": [2, 20, 21, 22, 25, 44, 45, 46, 49], "precis": 2, "predict": 2, "prefer": [20, 44], "prefix": 52, "present": [0, 1, 3, 10, 21, 25, 34, 45, 49], "preserv": 2, "pressur": 2, "preterm": 2, "prevent": [21, 45], "previou": [11, 35], "previous": 0, "pribbl": 2, "primarili": [21, 45, 52], "principl": [21, 45], "print": 30, "privaci": 2, "probabl": [1, 3, 14, 20, 22, 25, 38, 44, 46, 49], "procaccia": [20, 44], "procedur": [0, 4, 10, 16, 23, 26, 27, 34, 40, 47, 50, 51], "proceed": [20, 22, 44, 46], "process": [0, 2, 6, 20, 21, 25, 26, 28, 44, 45, 49, 50, 52], "prof": 0, "profil": 2, "program": [2, 20, 25, 44, 49], "programm": 0, "project": [22, 24, 34, 36, 46, 48], "prolong": 2, "prompt": 30, "propel": 2, "properti": [25, 27, 49, 51], "proport": 20, "propos": [23, 24, 26, 47, 48, 50], "protect": 2, "provid": [0, 1, 3, 23, 24, 25, 27, 29, 47, 48, 49, 51, 53], "ps1": [20, 22, 44, 46], "ps2": [20, 22, 44, 46], "pseudorandom": [14, 16, 17, 18, 27, 29, 38, 40, 41, 42, 51, 53], "pso": [20, 44], "psychogen": 2, "psychosi": 2, "public": 0, "publish": [0, 28, 52], "puneeta": [23, 24, 47, 48], "putrolaynen": 2, "py": 30, "pyemd": 30, "pypi": [0, 1, 3, 30], "pyplot": [32, 34, 36, 37, 43], "pyschiatr": 2, "python": [0, 1, 2, 3, 29, 45], "python2": 30, "python3": 30, "qadir": 2, "qin": 2, "qualiti": 2, "quantif": [0, 20, 44], "quantifi": [1, 3, 16, 20, 21, 23, 24, 40, 44, 45, 47], "question": [1, 3], "r": [2, 8, 9, 10, 12, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 32, 33, 34, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49], "r550": [20, 44], "r557": [20, 44], "r789": [20, 22, 44, 46], "r797": [20, 22, 44, 46], "radiograph": 2, "radiolog": 2, "radiu": [0, 17, 18, 20, 21, 22, 23, 24, 25, 26, 41, 42, 44, 45, 46, 47, 48, 49, 50], "radnew": [17, 18, 23, 24, 41, 42, 47, 48], "rai": [20, 44], "rainer": 2, "rajagopalan": [20, 44], "rajendra": 2, "rajji": 2, "ram": [21, 45], "ramesh": [23, 24, 47, 48], "ran": 2, "randal": [20, 22, 44, 46], "randinteg": [9, 16, 17, 27, 29, 33, 40, 41, 51, 53], "randintegers2": [14, 18, 27, 29, 38, 42, 51, 53], "randintegers_mat": [27, 29, 51, 53], "random": [4, 7, 9, 22, 27, 29, 31, 33, 46, 51, 53], "randomli": [27, 51], "rang": [0, 1, 3, 8, 9, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "rangen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "rangx": [20, 44], "ranjit": [23, 26, 47, 50], "rank": [21, 45], "rate": [2, 20, 22, 44, 46], "ratio": [2, 20], "ravi": 2, "rcmse": [47, 48, 50], "rcmsen": [23, 26, 47, 50], "rcxmsen": [24, 48], "rde": [9, 10, 20, 21, 25, 33, 34, 44, 45, 49], "read": 0, "real": [1, 3], "realiz": [1, 3], "reason": [21, 45], "recent": 0, "receptor": 2, "recognit": 2, "recommend": [2, 6, 20, 29, 30, 44], "recommened": [20, 44], "reconstruct": [25, 49], "record": [2, 23, 24, 47, 48], "refer": [1, 3, 4, 6, 20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50, 52], "refin": [0, 1, 3, 23, 24, 25, 26, 28, 29, 47, 48, 49, 50, 52, 53], "reflect": 0, "regard": [1, 3], "region": 2, "registri": 4, "regul": [20, 44], "regular": [20, 44], "regulatori": [20, 22, 44, 46], "rehabilit": [2, 20, 44], "reinforc": 2, "relat": [0, 2, 20, 28, 44, 52], "releas": [0, 28, 52], "relev": 6, "remain": [11, 27, 35, 51], "remaind": [27, 51], "remov": [1, 3, 20, 25, 44, 49], "renyi": 20, "repeat": [11, 35], "repl": 4, "repli": [23, 24, 26, 47, 48, 50], "repo": 30, "repositori": [0, 2, 6], "repositorio": 2, "reposit\u00f3rio": 2, "repres": [20, 21, 27, 44, 45, 51], "request": [0, 30], "requir": [0, 1, 3, 27, 29, 51, 52, 53], "rescal": [23, 24, 47, 48], "research": 0, "reshap": [9, 33], "resolut": [0, 16, 20, 22, 40, 44, 46], "resolv": 0, "reson": 2, "resorpt": 2, "respect": [0, 20, 21, 22, 44, 45, 46], "respiratori": 2, "respons": 2, "rest": 2, "restrict": 2, "restructur": [27, 51], "result": [0, 1, 3, 6, 9, 33], "retrospect": 2, "return": [9, 12, 13, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 33, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51], "reus": 2, "reveal": 0, "revers": [9, 20, 21, 25, 33, 44, 45, 49], "review": [2, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "rey": 2, "rhea": 2, "rho": [20, 27, 29, 44, 51, 53], "ribeiro": [21, 45], "ricardo": [21, 45], "rice": [14, 20, 21, 22, 38, 44, 45, 46], "richman": [20, 22, 44, 46], "ride": 2, "rider": 2, "riedl": [20, 25, 44, 49], "right": [6, 16, 40], "rightmost": [20, 44], "rio": 2, "rmsen": [17, 23, 24, 26, 28, 41, 47, 48, 50, 52], "robert": 2, "roberto": [20, 44], "robust": 0, "rodrigo": 2, "rodrigu": 2, "rohila": [20, 44], "roll": [20, 44], "romero": 2, "root": [23, 24, 47, 48], "rosseland": 2, "rostaghi": [2, 20, 25, 44, 49], "rotat": [20, 44], "row": [21, 25, 45, 49], "rui": [24, 48], "rutishaus": 2, "rxmsen": [23, 24, 26, 28, 47, 48, 50, 52], "r\u00e9nyi": 44, "s0": [23, 24, 47, 48], "s1": [0, 23, 24, 47, 48], "s2": [20, 23, 24, 44, 47, 48], "s_00": [23, 24, 47, 48], "s_10": [23, 24, 47, 48], "s_11": [23, 24, 47, 48], "s_20": [23, 24, 47, 48], "s_21": [23, 24, 47, 48], "s_22": [23, 24, 47, 48], "s_23": [23, 24, 47, 48], "s_30": [23, 24, 47, 48], "s_31": [23, 24, 47, 48], "s_32": [23, 24, 47, 48], "s_33": [23, 24, 47, 48], "s_34": [23, 24, 47, 48], "s_35": [23, 24, 47, 48], "s_36": [23, 24, 47, 48], "s_37": [23, 24, 47, 48], "s_40": [23, 24, 47, 48], "s_tn": [23, 24, 47, 48], "sadegh": 2, "sake": [20, 44], "same": [1, 3, 4, 9, 21, 23, 24, 33, 45, 47, 48], "samia": 2, "samp": [7, 20, 31, 44], "sampen": [0, 4, 6, 7, 17, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 41, 44, 45, 46, 47, 48, 49, 50, 52, 53], "sampen2d": [20, 21, 22, 25, 28, 29, 44, 45, 46, 49, 52, 53], "sampenmf": [21, 45], "sampl": [0, 1, 2, 3, 4, 6, 9, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "sandra": 2, "santar\u00e9m": 2, "santo": [2, 21, 45], "sassi": [20, 44], "satisfact": [1, 3], "satur": 2, "save": [6, 30], "sca": [20, 44], "scalar": [20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49], "scale": [10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 34, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53], "scatter": [34, 36], "scheme": [20, 44], "schizophrenia": 2, "scienc": [2, 20, 22, 44, 46], "scientif": [0, 1, 3, 6, 28, 52], "scipi": 30, "sclerosi": 2, "sd": [7, 20, 21, 23, 24, 26, 27, 29, 31, 44, 45, 47, 48, 50, 51, 53], "sdpool": [0, 22, 24, 46, 48], "se2d": [21, 45], "search": 6, "season": 2, "second": [13, 24, 37, 44, 48], "secret": 2, "section": [1, 2, 3, 6, 29, 52, 53], "sector": [2, 20, 44], "see": [0, 1, 3, 20, 21, 22, 23, 24, 25, 26, 27, 44, 45, 46, 47, 48, 49, 50, 51], "seek": [1, 3], "seen": 0, "segato": [21, 45], "segment": [0, 11, 20, 25, 28, 35, 44, 49, 52], "seizur": 2, "select": [2, 7, 14, 20, 21, 22, 23, 24, 25, 31, 38, 44, 45, 46, 47, 48, 49], "self": [20, 25, 44, 49], "sensit": [4, 20, 23, 24, 25, 26, 44, 47, 48, 49, 50], "sensor": [2, 20, 25, 44, 49], "sensori": 2, "separ": 0, "sepp\u00e4nen": 2, "sept": [20, 44], "sequenc": [0, 9, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 33, 35, 43, 44, 45, 46, 47, 48, 49, 50, 51], "seri": [0, 1, 2, 3, 4, 6, 21, 25, 26, 27, 28, 29, 30, 45, 49, 50, 51, 52, 53], "serial": [22, 46], "set": [0, 1, 3, 9, 10, 12, 13, 19, 20, 21, 22, 23, 24, 27, 29, 33, 44, 45, 46, 47, 48, 51, 53], "set_facecolor": [34, 36], "setup": 30, "setuptool": 30, "sever": [0, 4, 6, 29, 30], "sew": [20, 22, 44, 46], "sez": [20, 22, 44, 46], "shahzad": 2, "shang": [22, 24, 46, 48], "shannon": [20, 21, 22, 44, 45, 46], "shape": [20, 21, 22, 25, 44, 45, 46, 49], "share": [25, 49], "sharma": [20, 44], "shei": 2, "shervin": 2, "shi": [22, 46], "shift": [23, 47], "short": [2, 20, 44], "should": [4, 6], "show": [23, 24, 32, 47, 48], "shown": [23, 24, 26, 29, 47, 48, 50, 52], "shu": [2, 20, 44], "shuen": [23, 24, 26, 47, 48, 50], "shunliang": 2, "si": [20, 44], "side": [1, 2, 3], "sig": [20, 22, 23, 24, 26, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50], "sig1": [0, 22, 24, 46, 48], "sig2": [0, 22, 24, 46, 48], "sigma": [27, 51], "sigmoid": [15, 20, 21, 22, 25, 39, 44, 45, 46, 49], "sign": 2, "signal": [0, 2, 6, 7, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 31, 34, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53], "signam": [27, 51], "sikorska": 2, "silva": [21, 45], "similar": [0, 20, 21, 23, 24, 25, 26, 28, 44, 45, 47, 48, 49, 50, 52], "simpl": [1, 3], "simpli": [6, 30], "simul": [2, 24, 48], "simultan": 2, "sinc": 0, "sine": 2, "singer": [22, 46], "singl": [2, 11, 28, 35, 52], "singular": [10, 25, 34, 49], "sinha": 2, "six": [20, 44], "size": [8, 21, 27, 29, 32, 45, 51, 53], "skaria": 2, "skibniewski": 2, "skill": 2, "sleep": 2, "slice": [0, 20, 44], "slop": [20, 44], "slope": [20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "slopen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "slow": 2, "small": [0, 21, 45], "smax": [21, 45], "smith": 2, "sn": [19, 23, 24, 43, 47, 48], "snippet": [29, 53], "so": [0, 1, 3, 4, 6, 21, 24, 26, 45, 48, 50], "soc": [20, 25, 44, 49], "soccer": 2, "societi": [21, 45], "soeiro": 2, "softwar": [1, 3, 6], "soliton": [21, 45], "some": [0, 6, 29, 53], "somewhat": [20, 44], "sort": [20, 44], "sourc": [0, 2, 4, 6, 21, 30, 45], "sousa": 2, "space": [2, 25, 26, 49, 50], "spastic": 2, "spatial": [25, 49], "spec": [20, 44], "specen": [20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "special": [20, 25, 44, 49], "specif": [0, 1, 3, 4, 6, 27, 51], "specifi": [0, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51], "spectral": [4, 6, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "spectroscopi": 2, "spectrum": [20, 22, 44, 46], "speech": [25, 49], "spike": [2, 20, 25, 44, 49], "spinal": 2, "sport": 2, "spread": 0, "sqrt": [20, 21, 22, 44, 45, 46], "squar": [10, 12, 13, 19], "sreelatha": 2, "st": [23, 24, 47, 48], "stabl": [4, 5], "standard": [0, 10, 20, 23, 24, 25, 26, 34, 44, 47, 48, 49, 50], "star": [0, 1, 3], "state": [2, 20, 22, 44, 46], "statisitc": [1, 3], "statist": [1, 3, 4, 6, 20, 23, 25, 26, 44, 47, 49, 50], "statsbas": 4, "statsfun": 4, "std": [23, 24, 47, 48], "stefanik": 2, "steffert": 2, "steinar": 2, "stenosi": [23, 24, 47, 48], "step": [1, 3, 11, 35], "steven": [20, 22, 44, 46], "stimul": 2, "stochast": [25, 26, 49, 50], "stock": [22, 46], "store": [21, 23, 24, 26, 45, 47, 48, 50], "storm": 2, "strength": [2, 22, 46], "stress": 2, "string": [20, 21, 22, 25, 27, 29, 44, 45, 46, 47, 48, 49, 50, 51, 53], "strongli": 30, "struct": [11, 15, 16, 17, 18], "structur": 2, "studi": 2, "sturg": [14, 20, 21, 22, 38, 44, 45, 46], "su": 2, "sub": [20, 21, 23, 24, 44, 45, 47, 48], "subject": [23, 24, 47, 48], "submatrix": [21, 45], "subnod": [23, 24, 47, 48], "subsect": [6, 30], "subsequ": [0, 20, 27, 44, 51], "subsequnc": [27, 51], "subspac": [25, 49], "substanti": [20, 22, 44, 46], "subtract": [20, 21, 22, 25, 44, 45, 46, 49], "suliman": 2, "sum": [23, 24, 25, 47, 48, 49], "sum_": [16, 40], "summar": 0, "sunkaria": [23, 24, 47, 48], "supplement": 6, "supplementari": [1, 3, 28, 52], "support": 0, "suresh": 2, "surfac": [2, 20, 44], "survei": [1, 3], "svm": [2, 20, 44], "swai": 2, "sydi": [20, 44], "sydyen": [20, 23, 24, 26, 28, 44, 47, 48, 50, 52], "symbol": [0, 9, 15, 19, 20, 21, 22, 23, 24, 25, 26, 28, 33, 39, 43, 44, 45, 46, 47, 48, 49, 50, 52], "symmetri": [20, 44], "sympathet": [20, 44], "symposium": 0, "synchron": [20, 44], "synchroni": [22, 46], "syntax": [0, 1, 3, 6, 30, 52], "system": [0, 1, 2, 3, 4, 10, 12, 13, 19, 20, 25, 29, 34, 36, 37, 43, 44, 49, 53], "szymon": 2, "t": [2, 9, 12, 20, 21, 22, 23, 24, 25, 33, 35, 36, 44, 45, 46, 47, 48, 49], "t1": [27, 29, 51, 53], "tab": 6, "take": [0, 1, 3, 20, 22, 44, 46], "taken": [25, 49], "talha": 2, "tam": 2, "tang": 2, "tanh": [20, 21, 22, 25, 44, 45, 46, 49], "tao": [2, 24, 48], "tap": 2, "tapio": 2, "tar": 30, "tarek": 2, "task": [2, 28, 52], "tau": [7, 8, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 25, 31, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49], "taylor": 2, "te": 2, "techniqu": 2, "technologi": [23, 24, 47, 48], "teemu": 2, "tell": 2, "temperatur": 2, "templat": [8, 21, 22, 32, 45, 46], "tempor": [16, 17, 18, 19, 20, 23, 24, 26, 40, 41, 42, 43, 44, 47, 48, 50], "tencon": 2, "tennesse": 2, "term": [1, 2, 3, 20, 25, 44, 49], "termin": 30, "test": [22, 46], "texa": 2, "textur": [21, 45], "than": [6, 21, 29, 45], "thanh": 2, "thank": [0, 1, 3], "theerasak": [20, 25, 44, 49], "thei": 0, "them": [28, 52], "theorem": [25, 49], "theoret": [1, 3, 21, 45], "theori": [1, 2, 3], "therefor": [1, 3, 21, 22, 25, 45, 46, 49], "thermodynam": [1, 3], "thermographi": 2, "thi": [0, 1, 3, 4, 10, 20, 21, 23, 24, 25, 29, 34, 44, 45, 47, 48, 49, 53], "those": [0, 9, 33], "three": [24, 48], "threshod": [20, 21, 22, 25, 44, 45, 46, 49], "threshold": [0, 17, 18, 20, 21, 22, 23, 24, 25, 41, 42, 44, 45, 46, 47, 48, 49], "thresold": [20, 44], "through": [1, 3, 21, 22, 25, 45, 46, 49], "thu": [22, 25, 29, 30, 46, 49, 53], "thuraisingham": [23, 26, 47, 50], "time": [0, 1, 2, 3, 4, 6, 7, 8, 12, 13, 14, 15, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 32, 36, 37, 38, 39, 42, 43, 45, 49, 50, 51, 52, 53], "timescal": 2, "timeshift": [23, 24, 47, 48], "titl": 0, "toggl": [], "toler": [21, 23, 45, 47], "tom": [23, 24, 26, 47, 48, 50], "tomasz": 2, "tong": 2, "toni": 2, "tool": [0, 25, 26, 28, 49, 50, 52], "toolbox": [2, 6], "toolkit": [0, 4, 6, 21, 29, 30, 45], "tooth": 2, "top": 6, "topic": [20, 25, 44, 49], "topolog": 2, "topologi": 2, "total": [24, 25, 35, 49], "totalwindow": [11, 27, 35, 51], "toward": 2, "tpx": [12, 20, 25, 36, 44, 49], "track": 2, "trajectori": 2, "transact": [2, 20, 23, 24, 44, 47, 48], "transform": [0, 2, 9, 15, 20, 21, 25, 33, 39, 44, 45, 49], "trapezoid": [0, 20, 21, 22, 25, 44, 45, 46, 49], "trapezoidal1": [20, 21, 22, 25, 44, 45, 46, 49], "trapezoidal2": [20, 21, 22, 25, 44, 45, 46, 49], "travel": 2, "treat": [27, 51], "tree": [19, 23, 24, 43, 47, 48], "trendelenburg": 2, "triag": 2, "triangular": [0, 20, 21, 22, 25, 44, 45, 46, 49], "trit": [8, 32], "true": [0, 9, 10, 13, 16, 19, 20, 21, 22, 23, 24, 25, 26, 29, 33, 34, 37, 40, 43, 44, 45, 46, 47, 48, 49, 50, 53], "trunk": 2, "tsalli": [22, 46], "tsuyoshi": [20, 44], "tu": 2, "tuan": [2, 23, 47], "tune": [20, 25, 44, 49], "tupl": [44, 45, 46, 51], "turek": 2, "two": [0, 1, 2, 3, 4, 6, 18, 20, 23, 26, 27, 28, 42, 44, 47, 50, 51, 52], "type": [0, 2, 4, 6, 20, 21, 23, 24, 25, 26, 27, 28, 30, 44, 47, 48, 49, 50, 51, 52], "typex": [12, 20, 21, 25, 36, 44, 45, 49], "typo": 0, "tzou": 2, "tzyi": 2, "u": [0, 1, 2, 3, 22, 25, 46, 49], "ubiquit": 0, "uddin": [25, 26, 49, 50], "ueli": 2, "ulman": 2, "umang": 2, "uncertainti": [1, 3], "undefin": [20, 22, 44, 46], "under": [1, 3, 6, 20, 44], "uniform": [20, 27, 29, 44, 51, 53], "uniform2": [27, 29, 51, 53], "uniform_mat": [27, 29, 51, 53], "uniformli": [9, 16, 17, 18, 27, 29, 33, 40, 41, 42, 51, 53], "uniqu": [9, 20, 33, 44], "unit": [10, 25, 34, 49], "univari": [0, 2, 11, 25, 27, 28, 35, 49, 51, 52], "univarit": 45, "univers": 2, "universidad": 2, "unless": [1, 3], "unlik": 0, "unlock": [21, 45], "unwrap": [20, 25, 49], "unzip": 30, "up": 30, "updat": [1, 3, 23, 24, 47, 48], "upgrad": 30, "upper": [20, 21, 22, 25, 44, 45, 46, 49], "urosev": 2, "urszula": 2, "us": [0, 1, 2, 3, 4, 6, 8, 9, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53], "user": [0, 1, 3, 4, 6, 30], "uterin": [25, 49], "util": [0, 2], "uv": 2, "v": [2, 23, 24, 26, 47, 48, 50], "v0": 0, "v1": [0, 9, 25, 33, 49], "v2": [0, 9, 25, 33, 49], "vadim": [2, 23, 24, 26, 47, 48, 50], "valencia": [23, 24, 47, 48], "valu": [0, 7, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 31, 33, 34, 35, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51], "van": 2, "var": [23, 24, 47, 48], "varargin": [20, 21, 22, 23, 24, 25, 26, 27], "vari": [23, 47], "variabl": [2, 20, 22, 23, 24, 44, 46, 47, 48], "varianc": [0, 10, 18, 20, 22, 23, 24, 25, 34, 42, 44, 46, 47, 48, 49], "variant": [0, 20, 23, 25, 44, 47, 49], "variat": [0, 20, 25, 44, 49], "variou": [1, 3, 28, 52], "varun": 2, "vaunett": 2, "vcp": [0, 20, 22, 44, 46], "vector": [0, 9, 11, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35, 44, 45, 46, 47, 48, 49, 50, 51, 53], "velichko": 2, "venkatesh": [20, 44], "versatil": 0, "version": [1, 3, 4, 6, 30], "vesa": 2, "via": [0, 2, 20, 25, 44, 49], "vibrat": 2, "video": 2, "viet": 2, "view": [2, 10, 12], "vinayakumar": 2, "virgili": [21, 45], "visibl": 2, "vol": [20, 24, 44, 48], "volatil": [23, 24, 47], "volo": 2, "vstack": 35, "vx": 4, "w": [0, 1, 3, 9, 12, 20, 21, 22, 24, 25, 33, 36, 44, 45, 46, 48, 49], "wa": [0, 4, 30], "wael": 2, "wai": [4, 6, 23, 24, 30, 47, 48], "wang": [2, 20, 22, 25, 44, 46, 49], "want": 0, "wanzhong": 2, "warn": [21, 43], "warranti": [1, 3], "waschk": 2, "wave": 2, "waveform": 2, "wavelet": [2, 20, 44], "we": [0, 1, 3, 24, 29, 48], "web": 27, "wegh": 2, "wei": [2, 20, 44], "weidong": 2, "weight": [0, 20, 25, 44, 49], "weit": [20, 44], "wenbin": [22, 46], "wenlong": [20, 44], "wenqin": 2, "were": [0, 14, 19, 38, 43], "wessel": [20, 25, 44, 49], "what": [0, 2, 29], "when": [10, 20, 21, 22, 23, 24, 26, 28, 34, 44, 45, 46, 47, 48, 49, 50, 52], "where": [0, 4, 6, 16, 17, 18, 20, 21, 22, 25, 29, 30, 40, 41, 42, 44, 45, 46, 49, 53], "which": [0, 1, 3, 4, 6, 25, 27, 29, 30, 49, 51, 52], "while": [21, 45], "who": 0, "whose": [20, 21, 22, 25, 44, 45, 46, 49], "wide": [1, 3], "wider": 0, "width": [21, 45], "wijn": 2, "william": 2, "willian": 2, "wim": 2, "windata": [11, 27, 35, 51], "window": [0, 6, 20, 27, 28, 29, 30, 44, 51, 52, 53], "windowdata": [0, 27, 28, 29, 51, 52, 53], "windowlength": [11, 27, 35, 51], "windowoverlap": [11, 27, 35, 51], "winlen": [11, 27, 35, 51], "within": [20, 22, 44, 46], "without": [0, 1, 2, 3, 6, 13, 21, 37, 45], "wohlschlaeg": 2, "word": 0, "work": [0, 6], "worker": 2, "workplac": 2, "world": [1, 3], "would": [0, 21, 45, 52], "write": [1, 3], "written": [1, 3, 52, 53], "wr\u00f3blewski": 2, "wu": [2, 23, 24, 26, 47, 48, 50], "www": [0, 1, 3, 27, 51], "x": [0, 4, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 53], "x_": [8, 32], "xa": 24, "xap": [22, 46], "xapen": [0, 18, 20, 22, 23, 24, 26, 28, 42, 44, 46, 47, 48, 50, 52], "xb": 24, "xcond": [22, 46], "xconden": [0, 15, 19, 20, 22, 23, 24, 26, 28, 39, 43, 44, 46, 47, 48, 50, 52], "xdist": [14, 22, 38, 46], "xdisten": [14, 20, 21, 22, 23, 24, 26, 28, 38, 44, 45, 46, 47, 48, 50, 52], "xfuzz": [22, 46], "xfuzzen": [0, 20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "xiang": [20, 25, 44, 49], "xiao": [20, 25, 44, 49], "xiaofeng": [20, 25, 44, 49], "xiaoxiao": 2, "xie": [20, 22, 44, 46], "xing": [20, 44], "xk2": [22, 46], "xk2en": [0, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "xlabel": [10, 12, 13, 19], "xmax": [0, 20, 44], "xmin": [0, 20, 44], "xmsen": [0, 20, 21, 22, 23, 24, 26, 28, 44, 45, 46, 47, 48, 50, 52], "xo": [27, 29, 51, 53], "xperm": [22, 46], "xpermen": [0, 20, 22, 23, 24, 25, 26, 28, 44, 46, 47, 48, 49, 50, 52], "xrang": [20, 44], "xsamp": [22, 46], "xsampen": [0, 20, 21, 22, 23, 24, 25, 26, 28, 44, 45, 46, 47, 48, 49, 50, 52], "xspec": [22, 46], "xspecen": [4, 6, 20, 22, 23, 24, 26, 28, 44, 46, 47, 48, 50, 52], "xt": [23, 24, 47], "xu": [20, 23, 24, 44, 47, 48], "xue": [20, 44], "xyz": [0, 1, 3, 27, 51], "y": [0, 4, 10, 12, 13, 19, 20, 22, 27, 29, 34, 36, 37, 43, 44, 46, 51, 53], "y_j": [16, 40], "yan": [20, 24, 44, 48], "yang": [2, 20, 24, 44, 48], "yao": 2, "yaqiong": 2, "year": 0, "yecid": 2, "yi": [24, 48], "yifei": 2, "yin": [24, 48], "ying": [23, 24, 47, 48], "ykj": [47, 48], "ylabel": [10, 12, 13, 19], "yo": [27, 29, 51, 53], "yogarajah": 2, "yongbo": [20, 44], "yongjun": 2, "york": [22, 46], "you": [0, 1, 3, 6, 29, 30], "your": [0, 4, 6, 21, 30, 45], "yu": 2, "yuan": 2, "yuanyuan": [22, 46], "yue": [20, 44], "yuesheng": [23, 24, 47, 48], "yunliang": 2, "yurii": 2, "yute": 2, "yux": [20, 25, 44, 49], "z": [10, 12, 20, 21, 22, 25, 27, 29, 34, 36, 44, 45, 46, 49, 51, 53], "z_shape": [20, 21, 22, 25, 44, 45, 46, 49], "zdrojkowski": 2, "zen": 2, "zero": [20, 25, 49], "zeroth": [0, 20, 22, 44, 46], "zhan": 2, "zhang": [2, 20, 24, 25, 44, 48, 49], "zhao": 2, "zhe": [20, 44], "zhiqiang": [20, 25, 44, 49], "zhuo": [24, 48], "zlabel": [10, 12], "zo": [27, 29, 51, 53], "zt": [20, 44], "zunino": [21, 45], "\u00e2ngelo": 2, "\u00f2scar": 2, "\u00f8ivind": 2, "\u0142ukasz": 2}, "titles": ["Latest Updates", "EntropyHub", "Publications", "EntropyHub", "EntropyHub: Julia", "EntropyHub: Julia", "EntropyHub: MatLab", "Example 1: Sample Entropy", "Example 10: Bidimensional Fuzzy Entropy", "Example 11: Multivariate Dispersion Entropy", "Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy", "Example 13: Windowing Data with WindowData()", "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", "Multivariate Entropies", "Multivariate Multiscale Entropies", "Other Functions", "MatLab Functions:", "MatLab Examples:", "EntropyHub: Python", "Example 1: Sample Entropy", "Example 10: Bidimensional Fuzzy Entropy", "Example 11: Multivariate Dispersion Entropy", "Example 12: [Generalized] Refined-composite Multivariate Multiscale Fuzzy Entropy", "Example 13: Windowing Data with WindowData()", "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", "Multivariate Entropies", "Multivariate Multiscale Entropies", "Other Functions", "Python Functions:", "Python Examples:"], "titleterms": {"0": 0, "1": [0, 4, 6, 7, 30, 31], "10": [8, 32], "11": [9, 33], "12": [10, 34], "13": [11, 35], "2": [0, 4, 6, 12, 30, 36], "2021": 0, "2022": [0, 2], "2023": 2, "2024": [0, 2], "3": [13, 37], "4": [14, 38], "5": [15, 39], "6": [16, 40], "7": [17, 41], "8": [18, 28, 42, 52], "9": [19, 43], "TO": [29, 53], "about": [1, 3], "an": [1, 3], "analysi": [1, 3], "approxim": [18, 42], "april": 0, "articl": 2, "base": [20, 28, 44, 52], "between": [22, 24, 46, 48], "bidimension": [8, 21, 28, 32, 45, 52], "bin": [14, 38], "browser": 29, "categori": [28, 52], "citat": [1, 3], "cite": 2, "come": 0, "composit": [10, 18, 34, 42], "condit": [19, 43], "contact": [1, 3], "correct": [19, 43], "cross": [14, 18, 19, 22, 24, 28, 38, 42, 43, 46, 48, 52], "data": [1, 3, 11, 35], "dataset": [25, 26, 49, 50], "decemb": 0, "differ": [14, 38], "dimension": [21, 45], "dispers": [9, 33], "distribut": [14, 38], "document": [1, 3, 4, 6, 29, 30], "entrop": [1, 3], "entropi": [7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52], "entropyhub": [1, 2, 3, 4, 5, 6, 27, 28, 30, 51, 52], "estim": [20, 21, 22, 23, 24, 25, 26, 44, 45, 46, 47, 48, 49, 50], "exampl": [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 53], "exampledata": [29, 53], "fall": [28, 52], "februari": 0, "fine": [12, 36], "function": [20, 21, 22, 23, 24, 25, 26, 27, 28, 44, 45, 46, 47, 48, 49, 50, 51, 52], "fuzzi": [8, 10, 32, 34], "gener": [10, 34], "grain": [12, 36], "help": [1, 3, 4, 6, 30], "here": 0, "hierarch": [19, 43], "i": 0, "increment": [16, 40], "instal": [4, 6, 30], "juli": 0, "julia": [4, 5], "june": 0, "latest": 0, "licens": [1, 3], "list": 2, "march": 0, "matlab": [6, 28, 29], "matrix": [21, 45], "method": [4, 6, 14, 30, 38], "more": 0, "msobject": [15, 39], "multiscal": [10, 15, 16, 17, 18, 19, 23, 24, 26, 28, 34, 39, 40, 41, 42, 43, 47, 48, 50, 52], "multivari": [9, 10, 25, 26, 28, 33, 34, 49, 50, 52], "novemb": 0, "now": 0, "object": [15, 39], "open": [1, 3], "other": [27, 28, 51, 52], "out": 0, "permut": [12, 36], "phase": [13, 37], "pioncar": [13, 37], "plot": [13, 37], "process": [27, 51], "public": 2, "python": [30, 52, 53], "refin": [10, 17, 34, 41], "relat": [27, 51], "rememb": [29, 53], "requir": [4, 6, 30], "research": 2, "sampl": [7, 17, 31, 41], "seri": [20, 22, 23, 24, 44, 46, 47, 48], "signal": [27, 51], "singl": [20, 44], "sourc": [1, 3], "supplementari": [27, 51], "task": [27, 51], "thing": [29, 53], "time": [20, 22, 23, 24, 44, 46, 47, 48], "toolkit": [1, 3], "two": [21, 22, 24, 45, 46, 48], "univari": [20, 21, 22, 23, 24, 44, 45, 46, 47, 48], "updat": 0, "variou": [27, 51], "version": 0, "w": [13, 14, 37, 38], "welcom": [1, 3], "window": [11, 35], "windowdata": [11, 35]}}) \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 2edf341..f69f438 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -5,6 +5,7 @@ # import os, sys sys.path.insert(0, os.path.abspath('../EntropyHubPy/')) +#sys.path.insert(0, os.path.abspath('.')) import sphinx_rtd_theme def setup(app): @@ -21,8 +22,8 @@ def setup(app): # -- General configuration --------------------------------------------------- extensions = [ 'sphinx_rtd_theme', 'sphinx.ext.autodoc', - 'sphinxcontrib.matlab', 'sphinx.ext.githubpages'] -#extensions = [ 'sphinx_rtd_theme', 'sphinx.ext.autodoc'] + 'sphinxcontrib.matlab', 'sphinx.ext.githubpages', 'sphinx_togglebutton' ] +#extensions = [ 'sphinx_rtd_theme', 'sphinx.ext.autodoc', 'sphinx_toolbox.collapse', 'hidden_code_block'] autodoc_mock_imports = ["numpy", 'scipy','matplotlib','PyEMD','requests'] pygments_style = 'sphinx' diff --git a/docs/matlab/EHmatlab.rst b/docs/matlab/EHmatlab.rst index 93843c9..bfe5331 100644 --- a/docs/matlab/EHmatlab.rst +++ b/docs/matlab/EHmatlab.rst @@ -35,77 +35,81 @@ There are 2 ways to install EntropyHub for MatLab. Method 1: ********* +.. toggle:: + + 1. In MatLab, open the Add-Ons browser under the home tab by clicking 'Get Add-Ons'. -1. In MatLab, open the Add-Ons browser under the home tab by clicking 'Get Add-Ons'. + .. image:: ../_images/MATLAB_README3.png + :width: 800px + :align: center + :height: 400px - .. image:: ../_images/MATLAB_README3.png - :width: 800px - :align: center - :height: 400px + 2. In the search bar, search for *'EntropyHub'*. -2. In the search bar, search for *'EntropyHub'*. + .. image:: ../_images/matscreen2.png + :width: 800px + :align: center + :height: 400px - .. image:: ../_images/matscreen2.png - :width: 800px - :align: center - :height: 400px + .. image:: ../_images/matscreen3.png + :width: 800px + :align: center + :height: 250px - .. image:: ../_images/matscreen3.png - :width: 800px - :align: center - :height: 250px + 3. Open the resulting link, and click '*add*' in the top-right corner. -3. Open the resulting link, and click '*add*' in the top-right corner. + .. image:: ../_images/matscreen4.png + :width: 600px + :align: center + :height: 500px - .. image:: ../_images/matscreen4.png - :width: 600px - :align: center - :height: 500px + 4. Follow the instructions to install the toolbox. **Note: You must be logged in to your MathWorks account**. -4. Follow the instructions to install the toolbox. **Note: You must be logged in to your MathWorks account**. - - .. image:: ../_images/matscreen5.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen5.png + :width: 600px + :align: center + :height: 500px Method 2: ********* -1. Go to the `MatLab folder in the EntropyHub repository `_ on GitHub. +.. toggle:: + + + 1. Go to the `MatLab folder in the EntropyHub repository `_ on GitHub. - .. image:: ../_images/MATLAB_README4.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/MATLAB_README4.png + :width: 600px + :align: center + :height: 500px -2. Open the link to the MatLab toolbox file (**EntropyHub.mltbx**) file. + 2. Open the link to the MatLab toolbox file (**EntropyHub.mltbx**) file. - .. image:: ../_images/matscreen8.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen8.png + :width: 600px + :align: center + :height: 500px -3. Download the toolbox file. + 3. Download the toolbox file. - .. image:: ../_images/matscreen9.png - :width: 600px - :align: center - :height: 500px + .. image:: ../_images/matscreen9.png + :width: 600px + :align: center + :height: 500px -4. Open matlab and change the current folder to the directory where the .mltbx file is saved. + 4. Open matlab and change the current folder to the directory where the .mltbx file is saved. - .. image:: ../_images/MATLAB_README1.png - :width: 600px - :align: center - :height: 600px + .. image:: ../_images/MATLAB_README1.png + :width: 600px + :align: center + :height: 600px -5. Double-click the .mltbx file to open it and click install. + 5. Double-click the .mltbx file to open it and click install. - .. image:: ../_images/MATLAB_README2.png - :align: center + .. image:: ../_images/MATLAB_README2.png + :align: center diff --git a/docs/matlab/matAPI.rst b/docs/matlab/matAPI.rst index e10ef51..0d73879 100644 --- a/docs/matlab/matAPI.rst +++ b/docs/matlab/matAPI.rst @@ -46,166 +46,176 @@ EntropyHub functions fall into 8 categories: 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 | -+------------------------------+----------------+ -|Diversity Entropy | DivEn | -+------------------------------+----------------+ -|Range Entropy | RangEn | -+------------------------------+----------------+ - +.. toggle:: + + +------------------------------+----------------+ + |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 | + +------------------------------+----------------+ + |Diversity Entropy | DivEn | + +------------------------------+----------------+ + |Range Entropy | RangEn | + +------------------------------+----------------+ Cross Entropies: **************** - -+------------------------------------+----------------+ -|Entropy Type | Function Name | -+====================================+================+ -|Cross-Approximate Entropy | XApEn | -+------------------------------------+----------------+ -|Cross-Sample Entropy | XSampEn | -+------------------------------------+----------------+ -|Cross-Fuzzy Entropy | XFuzzEn | -+------------------------------------+----------------+ -|Cross-Kolmogorov Entropy | XK2En | -+------------------------------------+----------------+ -|Cross-Permutation Entropy | XPermEn | -+------------------------------------+----------------+ -|Cross-Conditional Entropy | XCondEn | -+------------------------------------+----------------+ -|Cross-Distribution Entropy | XDistEn | -+------------------------------------+----------------+ -|Cross-Spectral Entropy | XSpecEn | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Cross-Approximate Entropy | XApEn | + +------------------------------------+----------------+ + |Cross-Sample Entropy | XSampEn | + +------------------------------------+----------------+ + |Cross-Fuzzy Entropy | XFuzzEn | + +------------------------------------+----------------+ + |Cross-Kolmogorov Entropy | XK2En | + +------------------------------------+----------------+ + |Cross-Permutation Entropy | XPermEn | + +------------------------------------+----------------+ + |Cross-Conditional Entropy | XCondEn | + +------------------------------------+----------------+ + |Cross-Distribution Entropy | XDistEn | + +------------------------------------+----------------+ + |Cross-Spectral Entropy | XSpecEn | + +------------------------------------+----------------+ Multivariate Entropies: *********************** +.. toggle:: + + +----------------------------------------+----------------+ + | 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 | + +----------------------------------------+----------------+ + -+----------------------------------------+----------------+ -| 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 | -+------------------------------------+----------------+ -|Bidimensioanl Distribution Entropy | DistEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Dispersion Entropy | DispEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Permutation Entropy | PermEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Espinosa Entropy | EspEn2D | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Bidimensional Sample Entropy | SampEn2D | + +------------------------------------+----------------+ + |Bidimensional Fuzzy Entropy | FuzzEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Distribution Entropy | DistEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Dispersion Entropy | DispEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Permutation Entropy | PermEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Espinosa Entropy | EspEn2D | + +------------------------------------+----------------+ Multiscale Entropies: ********************** - -+------------------------------------------+----------------+ -|Entropy Type | Function Name | -+==========================================+================+ -|Multiscale Entropy | MSEn | -+------------------------------------------+----------------+ -|Composite Multiscale Entropy | cMSEn | -|(+ Refined-Composite Multiscale Entropy) | | -+------------------------------------------+----------------+ -|Refined Multiscale Entropy | rMSEn | -+------------------------------------------+----------------+ -|Hierarchical Multiscale Entropy | hMSEn | -+------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------+----------------+ + |Entropy Type | Function Name | + +==========================================+================+ + |Multiscale Entropy | MSEn | + +------------------------------------------+----------------+ + |Composite Multiscale Entropy | cMSEn | + |(+ Refined-Composite Multiscale Entropy) | | + +------------------------------------------+----------------+ + |Refined Multiscale Entropy | rMSEn | + +------------------------------------------+----------------+ + |Hierarchical Multiscale Entropy | hMSEn | + +------------------------------------------+----------------+ Multiscale Cross-Entropies: *************************** +.. toggle:: + + +------------------------------------------------+----------------+ + |Entropy Type | Function Name | + +================================================+================+ + |Multiscale Cross-Entropy | XMSEn | + +------------------------------------------------+----------------+ + |Composite Multiscale Cross-Entropy | cXMSEn | + |(+ Refined-Composite Multiscale Cross-Entropy) | | + +------------------------------------------------+----------------+ + |Refined Multiscale Cross-Entropy | rXMSEn | + +------------------------------------------------+----------------+ + |Hierarchical Multiscale Cross-Entropy | hXMSEn | + +------------------------------------------------+----------------+ -+------------------------------------------------+----------------+ -|Entropy Type | Function Name | -+================================================+================+ -|Multiscale Cross-Entropy | XMSEn | -+------------------------------------------------+----------------+ -|Composite Multiscale Cross-Entropy | cXMSEn | -|(+ Refined-Composite Multiscale Cross-Entropy) | | -+------------------------------------------------+----------------+ -|Refined Multiscale Cross-Entropy | rXMSEn | -+------------------------------------------------+----------------+ -|Hierarchical Multiscale Cross-Entropy | hXMSEn | -+------------------------------------------------+----------------+ Multivariate Multiscale Entropies: *********************************** +.. toggle:: -+------------------------------------------+----------------+ -| Entropy Type | Function Name | -+==========================================+================+ -| Multivariate Multiscale Entropy | MvMSEn | -+------------------------------------------+----------------+ -| Composite (+ Refined-Composite) | cMvMSEn | -| Multivariate Multiscale Entropy | | -+------------------------------------------+----------------+ + +------------------------------------------+----------------+ + | Entropy Type | Function Name | + +==========================================+================+ + | Multivariate Multiscale Entropy | MvMSEn | + +------------------------------------------+----------------+ + | Composite (+ Refined-Composite) | cMvMSEn | + | Multivariate Multiscale Entropy | | + +------------------------------------------+----------------+ Other Functions: **************** - -+------------------------------------------+----------------+ -| Function Description | Function Name | -+==========================================+================+ -| Example dataset import tool | ExampleData | -+------------------------------------------+----------------+ -| Windowing tool | WindowData | -| (for data segmentation) | | -+------------------------------------------+----------------+ \ No newline at end of file +.. toggle:: + + +------------------------------------------+----------------+ + | Function Description | Function Name | + +==========================================+================+ + | Example dataset import tool | ExampleData | + +------------------------------------------+----------------+ + | Windowing tool | WindowData | + | (for data segmentation) | | + +------------------------------------------+----------------+ \ No newline at end of file diff --git a/docs/python/EHpython.rst b/docs/python/EHpython.rst index b952ffa..700762d 100644 --- a/docs/python/EHpython.rst +++ b/docs/python/EHpython.rst @@ -41,6 +41,9 @@ Method 1: Method 2: ********* + +.. toggle:: + 1. Download the ``EntropyHub.x.x.x.tar.gz`` folder from the `EntropyHub PyPI repo `_ (or the `EntropyHub GitHub repo `_) and unzip it. diff --git a/docs/python/pyAPI.rst b/docs/python/pyAPI.rst index e9a7fed..db32a83 100644 --- a/docs/python/pyAPI.rst +++ b/docs/python/pyAPI.rst @@ -61,168 +61,176 @@ EntropyHub functions fall into 8 categories: 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 | -+------------------------------+----------------+ -|Diversity Entropy | DivEn | -+------------------------------+----------------+ -|Range Entropy | RangEn | -+------------------------------+----------------+ +.. toggle:: + + +------------------------------+----------------+ + |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 | + +------------------------------+----------------+ + |Diversity Entropy | DivEn | + +------------------------------+----------------+ + |Range Entropy | RangEn | + +------------------------------+----------------+ Cross Entropies: **************** - -+------------------------------------+----------------+ -|Entropy Type | Function Name | -+====================================+================+ -|Cross-Approximate Entropy | XApEn | -+------------------------------------+----------------+ -|Cross-Sample Entropy | XSampEn | -+------------------------------------+----------------+ -|Cross-Fuzzy Entropy | XFuzzEn | -+------------------------------------+----------------+ -|Cross-Kolmogorov Entropy | XK2En | -+------------------------------------+----------------+ -|Cross-Permutation Entropy | XPermEn | -+------------------------------------+----------------+ -|Cross-Conditional Entropy | XCondEn | -+------------------------------------+----------------+ -|Cross-Distribution Entropy | XDistEn | -+------------------------------------+----------------+ -|Cross-Spectral Entropy | XSpecEn | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Cross-Approximate Entropy | XApEn | + +------------------------------------+----------------+ + |Cross-Sample Entropy | XSampEn | + +------------------------------------+----------------+ + |Cross-Fuzzy Entropy | XFuzzEn | + +------------------------------------+----------------+ + |Cross-Kolmogorov Entropy | XK2En | + +------------------------------------+----------------+ + |Cross-Permutation Entropy | XPermEn | + +------------------------------------+----------------+ + |Cross-Conditional Entropy | XCondEn | + +------------------------------------+----------------+ + |Cross-Distribution Entropy | XDistEn | + +------------------------------------+----------------+ + |Cross-Spectral Entropy | XSpecEn | + +------------------------------------+----------------+ Multivariate Entropies: *********************** +.. toggle:: -+----------------------------------------+----------------+ -| 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 | -+----------------------------------------+----------------+ + +----------------------------------------+----------------+ + | 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 | -+------------------------------------+----------------+ -|Bidimensioanl Distribution Entropy | DistEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Dispersion Entropy | DispEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Permutation Entropy | PermEn2D | -+------------------------------------+----------------+ -|Bidimensioanl Espinosa Entropy | EspEn2D | -+------------------------------------+----------------+ +.. toggle:: + + +------------------------------------+----------------+ + |Entropy Type | Function Name | + +====================================+================+ + |Bidimensional Sample Entropy | SampEn2D | + +------------------------------------+----------------+ + |Bidimensional Fuzzy Entropy | FuzzEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Distribution Entropy | DistEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Dispersion Entropy | DispEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Permutation Entropy | PermEn2D | + +------------------------------------+----------------+ + |Bidimensioanl Espinosa Entropy | EspEn2D | + +------------------------------------+----------------+ Multiscale Entropies: ********************** - -+------------------------------------------+----------------+ -|Entropy Type | Function Name | -+==========================================+================+ -|Multiscale Entropy | MSEn | -+------------------------------------------+----------------+ -|Composite Multiscale Entropy | cMSEn | -|(+ Refined-Composite Multiscale Entropy) | | -+------------------------------------------+----------------+ -|Refined Multiscale Entropy | rMSEn | -+------------------------------------------+----------------+ -|Hierarchical Multiscale Entropy | hMSEn | -+------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------+----------------+ + |Entropy Type | Function Name | + +==========================================+================+ + |Multiscale Entropy | MSEn | + +------------------------------------------+----------------+ + |Composite Multiscale Entropy | cMSEn | + |(+ Refined-Composite Multiscale Entropy) | | + +------------------------------------------+----------------+ + |Refined Multiscale Entropy | rMSEn | + +------------------------------------------+----------------+ + |Hierarchical Multiscale Entropy | hMSEn | + +------------------------------------------+----------------+ Multiscale Cross-Entropies: *************************** - -+------------------------------------------------+----------------+ -|Entropy Type | Function Name | -+================================================+================+ -|Multiscale Cross-Entropy | XMSEn | -+------------------------------------------------+----------------+ -|Composite Multiscale Cross-Entropy | cXMSEn | -|(+ Refined-Composite Multiscale Cross-Entropy) | | -+------------------------------------------------+----------------+ -|Refined Multiscale Cross-Entropy | rXMSEn | -+------------------------------------------------+----------------+ -|Hierarchical Multiscale Cross-Entropy | hXMSEn | -+------------------------------------------------+----------------+ +.. toggle:: + + +------------------------------------------------+----------------+ + |Entropy Type | Function Name | + +================================================+================+ + |Multiscale Cross-Entropy | XMSEn | + +------------------------------------------------+----------------+ + |Composite Multiscale Cross-Entropy | cXMSEn | + |(+ Refined-Composite Multiscale Cross-Entropy) | | + +------------------------------------------------+----------------+ + |Refined Multiscale Cross-Entropy | rXMSEn | + +------------------------------------------------+----------------+ + |Hierarchical Multiscale Cross-Entropy | hXMSEn | + +------------------------------------------------+----------------+ Multivariate Multiscale Entropies: *********************************** +.. toggle:: -+------------------------------------------+----------------+ -| Entropy Type | Function Name | -+==========================================+================+ -| Multivariate Multiscale Entropy | MvMSEn | -+------------------------------------------+----------------+ -| Composite (+ Refined-Composite) | cMvMSEn | -| Multivariate Multiscale Entropy | | -+------------------------------------------+----------------+ + +------------------------------------------+----------------+ + | Entropy Type | Function Name | + +==========================================+================+ + | Multivariate Multiscale Entropy | MvMSEn | + +------------------------------------------+----------------+ + | Composite (+ Refined-Composite) | cMvMSEn | + | Multivariate Multiscale Entropy | | + +------------------------------------------+----------------+ Other Functions: **************** - -+------------------------------------------+----------------+ -| Function Description | Function Name | -+==========================================+================+ -| Example dataset import tool | ExampleData | -+------------------------------------------+----------------+ -| Windowing tool | WindowData | -| (for data segmentation) | | -+------------------------------------------+----------------+ \ No newline at end of file +.. toggle:: + + +------------------------------------------+----------------+ + | Function Description | Function Name | + +==========================================+================+ + | Example dataset import tool | ExampleData | + +------------------------------------------+----------------+ + | Windowing tool | WindowData | + | (for data segmentation) | | + +------------------------------------------+----------------+ \ No newline at end of file

    Function Description