EntropyHub

https://www.entropyhub.xyz
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EntropyHub is an open-source toolkit for calculating entropy statistics and complexity measures from...
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### Install EntropyHub from source

Source: https://www.entropyhub.xyz/python/EHpython.html

Alternative installation method using the setup script after downloading the source archive.

```bash
python setup.py install
```

--------------------------------

### Verify EntropyHub installation

Source: https://www.entropyhub.xyz/python/EHpython.html

Runs the greet function to confirm the package is installed and imported correctly.

```python
EntropyHub.greet()
```

--------------------------------

### Verify EntropyHub Installation

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Call the greet() function from the EntropyHub package to confirm successful installation and loading.

```julia
julia > EntropyHub.greet()
```

--------------------------------

### GET /ExampleData

Source: https://www.entropyhub.xyz/python/Functions/Other.html

Imports sample data time series or matrices for benchmarking entropy methods.

```APIDOC
## GET /ExampleData

### Description
Imports sample data time series or matrices. Note that this function requires an internet connection to download and import the required datasets.

### Parameters
#### Query Parameters
- **SigName** (string) - Required - The name of the dataset to import (e.g., 'uniform', 'gaussian', 'henon', 'lorenz', 'chirp', 'uniform_Mat', 'gaussian_Mat', 'mandelbrot_Mat', 'entropyhub_Mat').

### Response
#### Success Response (200)
- **Data** (array/matrix) - The sample dataset corresponding to the input SigName.
```

--------------------------------

### Install EntropyHub via pip

Source: https://www.entropyhub.xyz/python/EHpython.html

Recommended method for installing the package using the pip package manager.

```bash
pip install EntropyHub
```

--------------------------------

### Upgrade setuptools

Source: https://www.entropyhub.xyz/python/EHpython.html

Ensures the setuptools package is up-to-date, which is required for proper installation.

```bash
python -m pip install --upgrade setuptools
```

--------------------------------

### GET EntropyHub.ExampleData()

Source: https://www.entropyhub.xyz/matlab/matexamples.html

Retrieves sample datasets or signals for use in entropy analysis. Requires an active internet connection.

```APIDOC
## GET EntropyHub.ExampleData()

### Description
Retrieves specific sample data signals or matrices for testing entropy functions. The function accepts a string identifier to determine the type of data returned.

### Parameters
#### Query Parameters
- **type** (string) - Required - The identifier for the dataset. Supported values: 'uniform', 'gaussian', 'randintegers', 'chirp', 'lorenz', 'henon', 'uniform2', 'gaussian2', 'randintegers2', 'uniform_Mat', 'gaussian_Mat', 'randintegers_Mat', 'mandelbrot_Mat', 'entropyhub_Mat'.

### Response
#### Success Response (200)
- **data** (vector/matrix) - The requested signal vector or matrix based on the provided identifier.
```

--------------------------------

### Verify EntropyHub Installation

Source: https://www.entropyhub.xyz/matlab/EHmatlab.html

Run this command in the MatLab command window to display the EntropyHub logo and confirm successful installation.

```matlab
>> EntropyHub

      ___  _   _  _____  _____  ____  ____  _     _
   |  _|| \ | ||_   _||     \|    ||    || \   / |
   | \_ |  \| |  | |  |   __/|    ||  __|
   |  _|| \ \ |  | |  |   \  |    || |
   | \_ | |\  |  | |  | |\ \ |    || |
   |___||_| \_|  |_|  |_| \_||____||_|
   _   _  _   _  ____                           
   | | | || | | ||    \     An open-source      
   | |_| || | | ||    |     toolkit for         
   |  _  || | | ||    \     entropic time-      
   | | | || |_| ||     \    series analysis     
   |_| |_|\_____/|_____/                         


```

--------------------------------

### Import EntropyHub

Source: https://www.entropyhub.xyz/python/pyexamples.html

Import the EntropyHub library for use in subsequent examples. Assumes the alias EH.

```python
import EntropyHub as EH
EH.SampEn(...)
```

--------------------------------

### Load EntropyHub Package

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Use the 'using' keyword to load the EntropyHub package after installation.

```julia
julia > using EntropyHub
```

--------------------------------

### Generate Example Data

Source: https://www.entropyhub.xyz/python/pyexamples.html

Generates various types of sample data for entropy calculations. Requires an internet connection.

```python
EH.ExampleData('uniform')
```

```python
EH.ExampleData('gaussian')
```

```python
EH.ExampleData('randintegers')
```

```python
EH.ExampleData('chirp')
```

```python
EH.ExampleData('lorenz')
```

```python
EH.ExampleData('henon')
```

```python
EH.ExampleData('uniform2')
```

```python
EH.ExampleData('gaussian2')
```

```python
EH.ExampleData('randintegers2')
```

```python
EH.ExampleData('uniform_Mat')
```

```python
EH.ExampleData('gaussian_Mat')
```

```python
EH.ExampleData('randintegers_Mat')
```

```python
EH.ExampleData('mandelbrot_Mat')
```

```python
EH.ExampleData('entropyhub_Mat')
```

--------------------------------

### Access Function Documentation

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Use the '?' help system in Julia to get documentation for specific EntropyHub functions like SampEn, XSpecEn, or hXMSEn.

```julia
julia> ?
help?> SampEn     # Documentation on sample entropy function
```

```julia
julia> ?
help?> XSpecEn    # Documentation on cross-spectral entropy function
```

```julia
julia> ?
help?> hXMSEn     # Documentation on hierarchical multiscale cross-entropy function
```

--------------------------------

### Add EntropyHub using Pkg Module

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Install the EntropyHub package programmatically using the Pkg module in Julia.

```julia
julia > using Pkg
julia > Pkg.add("EntropyHub")
```

--------------------------------

### Prepare Multivariate Data

Source: https://www.entropyhub.xyz/python/Examples/Ex11.html

Load example random integer data and reshape it into a multivariate set of 4 sequences.

```python
X = EH.ExampleData('randintegers')
Data = np.reshape(X,(4,1024)).T
```

--------------------------------

### Add EntropyHub via GitHub URL

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Install the EntropyHub package directly from its GitHub repository URL in the Julia package REPL.

```julia
@vX.Y. pkg >  add https://github.com/MattWillFlood/EntropyHub.jl
```

--------------------------------

### Access Function Documentation (MatLab)

Source: https://www.entropyhub.xyz/matlab/EHmatlab.html

Use the 'doc' command followed by the function name to view detailed documentation in the MatLab help browser. For example, 'doc SampEn' provides information on the sample entropy function.

```matlab
doc SampEn
```

```matlab
doc XSpecEn
```

```matlab
doc hXMSEn
```

--------------------------------

### Add EntropyHub via Package Name

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Install the EntropyHub package using its name in the Julia package REPL. This is case-sensitive.

```julia
@vX.Y. pkg > add EntropyHub
```

--------------------------------

### Access Function Docstrings (MatLab)

Source: https://www.entropyhub.xyz/matlab/EHmatlab.html

Use the 'help' command followed by the function name to display the function's docstrings directly in the MatLab command window. For example, 'help SampEn' shows the docstrings for the sample entropy function.

```matlab
help SampEn
```

--------------------------------

### Import Lorenz System Data and Plot

Source: https://www.entropyhub.xyz/python/Examples/Ex12.html

Imports data from the Lorenz system and visualizes it in a 3D scatter plot. Ensure matplotlib is installed for plotting.

```python
Data = EH.ExampleData('lorenz')

from matplotlib.pyplot import fig, scatter, axis

fig = figure(facecolor='k')
ax = fig.add_subplot(111, projection='3d')
ax.set_facecolor('k')
ax.scatter(Data[:,0], Data[:,1], Data[:,2], c='g')
ax.axis('off')
```

--------------------------------

### Import and Display Mandelbrot Image

Source: https://www.entropyhub.xyz/python/Examples/Ex10.html

Imports a Mandelbrot fractal image as a matrix using EntropyHub and displays it using matplotlib. Ensure matplotlib is installed.

```python
X = EH.ExampleData('mandelbrot_Mat');

from matplotlib.pyplot import imshow, show

imshow(X, cmap = 'hot'), show()
```

--------------------------------

### Calculate Normalized MvDispEn and RDE

Source: https://www.entropyhub.xyz/matlab/Examples/Ex11.html

Perform the same calculation as the basic example but with the output entropy estimate normalized with respect to the number of unique dispersion patterns. Use 'Norm', true for normalized results.

```matlab
[MDisp, RDE] = MvDispEn(Data, 'm', [1,1,2,3], 'c', 7, 'Norm', true)
```

--------------------------------

### Import sample data with ExampleData

Source: https://www.entropyhub.xyz/matlab/Functions/matOther.html

Imports benchmark time series or matrix datasets. Requires an active internet connection to download the requested data.

```Matlab
Data = ExampleData(SigName)
```

--------------------------------

### GET /IncrEn

Source: https://www.entropyhub.xyz/matlab/Functions/matBase.html

Estimates the increment entropy of a univariate data sequence.

```APIDOC
## GET /IncrEn

### Description
Estimates the increment entropy of a univariate data sequence.

### Method
GET

### Endpoint
/IncrEn

### Parameters
#### Query Parameters
- **Sig** (array) - Required - The univariate data sequence.
- **m** (integer) - Optional - Embedding Dimension, > 1.
- **tau** (integer) - Optional - Time Delay.
- **R** (integer) - Optional - Quantifying resolution.
- **Logx** (scalar) - Optional - Logarithm base (0 for natural log).
- **Norm** (boolean) - Optional - Normalisation of IncrEn value.

### Response
#### Success Response (200)
- **Incr** (float) - The estimated increment entropy.
```

--------------------------------

### ExampleData Function

Source: https://www.entropyhub.xyz/matlab/Functions/matOther.html

Imports sample data time series and matrices commonly used for benchmarking entropy methods. Requires an internet connection.

```APIDOC
## ExampleData(_SigName_)

### Description
Imports sample data time series and matrices.

### Method
Function Call

### Endpoint
N/A (MatLab Function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
*   **SigName** (string) - Required - Specifies the type of sample dataset to import. Accepted values include:
    *   `uniform`: Uniformly distributed random numbers [0, 1], N = 5000
    *   `randintegers`: Randomly distributed integers [1, 8], N = 4096
    *   `gaussian`: Normally distributed numbers [mean: 0, SD: 1], N = 5000
    *   `henon`: Henon attractor components [alpha: 1.4, beta: .3], N = 4500
    *   `lorenz`: Lorenz attractor components [sigma: 10, beta: 8/3, rho: 28], N = 5917
    *   `chirp`: Chirp signal (f0 = .01, t1 = 4000, f1 = .025), N = 5000
    *   `uniform2`: Two uniformly distributed random number sequences [0,1], N = 4096
    *   `gaussian2`: Two normally distributed number sequences [mean: 0, SD: 1], N = 3000
    *   `randintegers2`: Two uniformly distributed pseudorandom integer sequences [1, 8], N = 3000
    *   `uniform_Mat`: Matrix of uniformly distributed random numbers [0, 1], N = 50 x 50
    *   `gaussian_Mat`: Matrix of normally distributed numbers [mean: 0, SD: 1], N = 60 x 120
    *   `randintegers_Mat`: Matrix of randomly distributed integers [1, 8], N = 88 x 88
    *   `mandelbrot_Mat`: Matrix representing Mandelbrot fractal image [0, 255], N = 92 x 115
    *   `entropyhub_Mat`: Matrix representing EntropyHub logo [0, 255], N = 127 x 95

### Request Example
```matlab
Data = ExampleData('gaussian');
```

### Response
#### Success Response (200)
*   **Data** (numeric array or cell array) - The sample dataset imported corresponding to the `SigName` input.

#### Response Example
```matlab
% For SigName = 'gaussian'
Data = [-0.52, 1.23, 0.87, ...]; % A vector of normally distributed numbers

% For SigName = 'gaussian_Mat'
Data = [
    -0.12,  0.56, -0.98, ...;
     1.05, -0.34,  0.77, ...;
     ... 
]; % A matrix of normally distributed numbers
```
```

--------------------------------

### GET /K2En

Source: https://www.entropyhub.xyz/matlab/Functions/matBase.html

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

```APIDOC
## GET /K2En

### Description
Estimates the Kolmogorov (K2) entropy of a univariate data sequence.

### Method
GET

### Endpoint
/K2En

### Parameters
#### Query Parameters
- **Sig** (array) - Required - The univariate data sequence.

### Response
#### Success Response (200)
- **K2** (float) - The estimated Kolmogorov entropy.
- **Ci** (array) - Correlation integral values.
```

--------------------------------

### GET /GridEn

Source: https://www.entropyhub.xyz/matlab/Functions/matBase.html

Estimates the gridded distribution entropy of a univariate data sequence.

```APIDOC
## GET /GridEn

### Description
Estimates the gridded distribution entropy (GDE) and gridded distribution rate (GDR) of a univariate data sequence.

### Method
GET

### Endpoint
/GridEn

### Parameters
#### Query Parameters
- **Sig** (array) - Required - The univariate data sequence.
- **m** (integer) - Optional - Grid coarse-grain (m x m sectors), > 1.
- **tau** (integer) - Optional - Time Delay.
- **Logx** (scalar) - Optional - Logarithm base.
- **Plotx** (boolean) - Optional - If true, returns gridded Poincare plot and bivariate histogram.

### Response
#### Success Response (200)
- **GDE** (float) - Gridded distribution entropy.
- **GDR** (float) - Gridded distribution rate.
- **PIx** (float) - Percentage of points below the line of identity.
- **GIx** (float) - Proportion of point distances above the line of identity.
- **SIx** (float) - Ratio of phase angles of the points above the line of identity.
- **AIx** (float) - Ratio of the cumulative area of sectors of points above the line of identity.
```

--------------------------------

### Import EntropyHub Library

Source: https://www.entropyhub.xyz/python/Functions/Cross.html

Import the EntropyHub library for use in your project. This allows access to all its functions.

```python
import EntropyHub as EH

dir(EH)
```

--------------------------------

### Import EntropyHub

Source: https://www.entropyhub.xyz/python/EHpython.html

Commands to import the EntropyHub module into a Python environment.

```python
import EntropyHub
```

```python
import EntropyHub as EH
```

--------------------------------

### Initialize Multiscale Entropy Object

Source: https://www.entropyhub.xyz/matlab/Examples/Ex9.html

Sets up the Henon system data and configures the MSobject with specific parameters for cross-conditional entropy analysis.

```matlab
Data = ExampleData('henon');

figure('Color', 'k')
plot(Data(:,1), Data(:,2), 'g.')
xlabel('x-component','color','g')
ylabel('y-component','color','g')
set(gca,'color','k'), axis square

Mobj = MSobject('XCondEn', 'm', 2, 'tau', 2, 'c', 12, 'Logx', 2, 'Norm', true)
```

--------------------------------

### Initialize Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Examples/Ex8.html

Import signals and initialize a multiscale entropy object with specified parameters for entropy type, embedding dimension, time delay, and radius distance threshold.

```python
X = EH.ExampleData('randintegers2');

Mobj = EH.MSobject('XApEn', m = 2, tau = 2, r = 0.5)
```

--------------------------------

### Extract Sample Entropy for m = 4

Source: https://www.entropyhub.xyz/python/Examples/Ex1.html

Select the last value from the calculated sample entropy array to get the sample entropy for m = 4. This is useful when only a specific embedding dimension's entropy is needed.

```python
Samp[-1]
```

```python
>>> 2.1756
```

--------------------------------

### Open Julia Package REPL

Source: https://www.entropyhub.xyz/julia/EHjulia.html

Access the Julia package manager REPL by typing ']' in the Julia command line.

```julia
julia > ]

@vX.Y. pkg >
```

--------------------------------

### Segmenting Univariate Data with WindowData

Source: https://www.entropyhub.xyz/python/Examples/Ex13.html

Demonstrates basic windowing of a univariate sequence with a fixed length and no overlap, using default exclusion of remaining data.

```python
X = np.arange(1,1001)
WinData, Log = EH.WindowData(X, WinLen = 75)
```

--------------------------------

### Estimate Permutation Entropy with Custom Parameters (PermEn)

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Estimates permutation entropy for specified embedding dimensions using custom keyword arguments for time delay, logarithm base, normalization method, and entropy variation type. Consult the EntropyHub guide for details on 'Typex' variants and their associated 'tpx' parameters.

```python
Perm, Pnorm, cPE = PermEn(Sig, keyword = value, ...)
```

--------------------------------

### Estimate GridEn with default and custom parameters

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Functions for calculating gridded distribution entropy. Use the keyword argument version to customize grid coarse-grain, time delay, logarithm base, and plotting options.

```python
GDE, GDR, _ = GridEn(Sig) 
```

```python
GDE, GDR, PIx, GIx, SIx, AIx = GridEn(Sig, keyword = value)
```

--------------------------------

### Create Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Functions/Multiscale.html

Initializes a multiscale entropy object with default parameters. Use this to set up parameters before calculating entropy.

```matlab
[Mobj] = MSobject()
```

--------------------------------

### Create a Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Functions/MultivariateMultiscale.html

Initializes an object to store multiscale entropy parameters. Use different signatures to set default or custom entropy methods and parameters.

```MATLAB
[Mobj] = MSobject() 
```

```MATLAB
[Mobj] = MSobject(EnType)
```

```MATLAB
[Mobj] = MSobject(EnType, keyword = value, ...)
```

--------------------------------

### Load and Reshape Data for MvDispEn

Source: https://www.entropyhub.xyz/matlab/Examples/Ex11.html

Import random integers and reshape them into a multivariate dataset suitable for MvDispEn calculations. Ensure the data is preprocessed before use.

```matlab
X = ExampleData('randintegers')
Data = reshape(X, 1024, 4)
```

--------------------------------

### View Entropy Object Parameters

Source: https://www.entropyhub.xyz/python/Examples/Ex8.html

Inspect the function and keyword arguments associated with the initialized multiscale entropy object.

```python
Mobj.Func
>>> <function EntropyHub._XApEn.XApEn(Sig, m=2, tau=1, r=None, Logx=2.71828)>
```

```python
Mobj.Kwargs
>>> {'m': 2, 'tau': 2, 'r': 0.5}
```

--------------------------------

### Create Multiscale Entropy Object with Custom Parameters

Source: https://www.entropyhub.xyz/python/Functions/Multiscale.html

Creates a multiscale entropy object with a specified entropy method and custom parameters. Use 'help(EnType)' to find available parameters for each method.

```matlab
[Mobj] = MSobject(EnType, keyword = value, ...)
```

--------------------------------

### Initialize Multiscale Entropy Object and Plot Data

Source: https://www.entropyhub.xyz/python/Examples/Ex9.html

Imports data from the Henon system, plots the x and y components, and initializes a multiscale entropy object with specified parameters. Ensure data is loaded before plotting and object initialization.

```python
from matplotlib.pyplot import figure, plot, axis

Data = EH.ExampleData('henon');

fig = figure(facecolor='k')
plot(Data[:,0], Data[:,1], 'g.')
axis('off')

Mobj = EH.MSobject('XCondEn', m = 2, tau = 2, c = 12, Logx = 2, Norm = True)

Mobj.Func
>>> <function EntropyHub._XCondEn.XCondEn(Sig, m=2, tau=1, c=6, Logx=2.71828, Norm=False)>

Mobj.Kwargs
>>> {'m': 2, 'tau': 2, 'c': 12, 'Logx': 2, 'Norm': True}
```

--------------------------------

### Estimate Multivariate Sample Entropy (Custom Parameters)

Source: https://www.entropyhub.xyz/matlab/Functions/matMultivariate.html

Use this syntax to estimate multivariate sample entropy with custom parameters specified via name-value pairs. This allows for fine-tuning of embedding dimension, time delay, radius threshold, normalization, and logarithm base.

```matlab
[MSamp, B0, Bt, B1] = MvSampEn(Data, name, value, ...)
```

--------------------------------

### Estimate Multivariate Sample Entropy (Default Parameters)

Source: https://www.entropyhub.xyz/matlab/Functions/matMultivariate.html

Use this syntax to estimate multivariate sample entropy using default parameters for embedding dimension, time delay, and radius threshold. The 'full' method is used for calculation, which may result in negative entropy values.

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

--------------------------------

### Import and Display Mandelbrot Image in MatLab

Source: https://www.entropyhub.xyz/matlab/Examples/Ex10.html

Imports a Mandelbrot fractal image and displays it with a black background and a 'hot' colormap. Ensure the 'ExampleData' function is available.

```matlab
X = ExampleData('mandelbrot_Mat');

figure('Color','k'),
imshow(X,[],'InitialMagnification',500),
colormap('hot')
```

--------------------------------

### Calculate Sample Entropy with Default Parameters

Source: https://www.entropyhub.xyz/python/Examples/Ex1.html

Import a signal of normally distributed random numbers and calculate the sample entropy for each embedding dimension (m) from 0 to 4. Use this for a quick estimate of sample entropy.

```python
X = EH.ExampleData("gaussian");
Samp, _ = EH.SampEn(X, m = 4)
```

```python
>>> Samp =
    array([2.1789, 2.1757, 2.1819, 2.2209, 2.1756])
```

--------------------------------

### Calculate Phase Entropy with Custom Partitions

Source: https://www.entropyhub.xyz/python/Examples/Ex3.html

Calculates phase entropy for the y-component of the Henon system without normalization, and for the x-component with a specified time delay.

```python
Y = Data[:,1];
Phas = EH.PhasEn(Y, K = 7, Norm = False, Logx = 2, Plotx = True)

>>> Phas
    2.0192821496913216
```

```python
X = Data[:,0]
Phas = EH.PhasEn(X, K = 11, tau = 2, Plotx = True)

>>> Phas
    0.8395
```

--------------------------------

### Visualize Henon System Data

Source: https://www.entropyhub.xyz/python/Examples/Ex3.html

Imports the Henon system dataset and generates a Poincaré plot using matplotlib.

```python
from matplotlib.pyplot import figure, plot, axis

Data = EH.ExampleData('henon');

fig = figure(facecolor='k')
plot(Data[:,0], Data[:,1], 'g.')
axis('off')
```

--------------------------------

### Initialize Multiscale Entropy Object

Source: https://www.entropyhub.xyz/matlab/Examples/Ex8.html

Creates a multiscale entropy object with specified parameters for cross-approximate entropy.

```matlab
X = ExampleData('randintegers2');

Mobj = MSobject('XApEn', 'm', 2, 'tau', 2, 'r', 0.5)
```

--------------------------------

### Estimate DistEn2D with default parameters

Source: https://www.entropyhub.xyz/matlab/Functions/matBidimensional.html

Calculates the bidimensional distribution entropy using default settings for template size, time delay, binning, and logarithm.

```matlab
[Dist2D] = DistEn2D(Mat)
```

--------------------------------

### Configure Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Examples/Ex7.html

Initializes a multiscale entropy object with specific embedding dimension and radius threshold parameters.

```python
X = EH.ExampleData('randintegers');

Mobj = EH.MSobject('SampEn', m = 4, r = 1.25)

Mobj.Func
>>> <function EntropyHub._SampEn.SampEn(Sig, m=2, tau=1, r=None, Logx=2.71828)>

Mobj.Kwargs
>>> {'m': 4, 'r': 1.25}
```

--------------------------------

### Calculate MvDispEn and RDE using 'v2' Method

Source: https://www.entropyhub.xyz/matlab/Examples/Ex11.html

Compare results with the 'v1' method by using the 'v2' method for MvDispEn calculation. This method returns estimates for each value from 1 to max(m).

```matlab
[MDisp, RDE] = MvDispEn(Data, 'm', [1,1,2,3], 'c', 7, 'Norm', true, 'Methodx', 'v2')
```

--------------------------------

### Visualize Lorenz System Data

Source: https://www.entropyhub.xyz/python/Examples/Ex2.html

Imports data for the Lorenz system and visualizes its x, y, and z components using a 3D scatter plot. Requires matplotlib.

```python
Data = EH.ExampleData('lorenz');

from matplotlib.pyplot import fig, scatter, axis

fig = figure(facecolor='k')
ax = fig.add_subplot(111, projection='3d')
ax.set_facecolor('k')
ax.scatter(Data[:,0], Data[:,1], Data[:,2], c='g')
ax.axis('off')
```

--------------------------------

### POST /WindowData

Source: https://www.entropyhub.xyz/python/Functions/Other.html

Restructures a univariate or multivariate dataset into a collection of subsequence windows.

```APIDOC
## POST /WindowData

### Description
Windows the sequence(s) given in Data into a collection of subsequences based on specified length and overlap parameters.

### Parameters
#### Request Body
- **Data** (array/matrix) - Required - The univariate or multivariate dataset to be windowed.
- **WinLen** (integer) - Optional - Number of elements in each window (must be > 10).
- **Overlap** (integer) - Optional - Number of overlapping elements between windows (must be < WinLen).
- **Mode** (string) - Optional - Decision to 'include' or 'exclude' remaining sequence elements that do not fill the final window.

### Response
#### Success Response (200)
- **WinData** (tuple) - A collection of subsequences.
- **Log** (dictionary) - Information about the windowing process including DataType, DataLength, WindowLength, WindowOverlap, TotalWindows, and Mode.
```

--------------------------------

### MSobject

Source: https://www.entropyhub.xyz/python/Functions/MultivariateMultiscale.html

Creates an object to store multiscale entropy parameters for use in entropy estimation functions.

```APIDOC
## MSobject

### Description
Creates an object to store multiscale entropy parameters. This object is required for multiscale entropy functions.

### Parameters
#### Request Body
- **EnType** (string) - Optional - The entropy method name (e.g., 'SampEn', 'MvSampEn').
- **kwargs** (keyword arguments) - Optional - Name/value parameters specific to the chosen entropy method.

### Response
#### Success Response (200)
- **Mobj** (object) - A multiscale entropy object containing the specified parameters.
```

--------------------------------

### MSobject

Source: https://www.entropyhub.xyz/matlab/Functions/matMultiscale.html

Creates an object to store multiscale entropy parameters for use in entropy calculations.

```APIDOC
## MSobject

### Description
Creates an object to store multiscale entropy parameters. This object is required for multiscale entropy functions.

### Parameters
#### Request Body
- **EnType** (string) - Optional - The entropy method to use (e.g., 'SampEn', 'ApEn', 'FuzzEn').
- **name, value** (pair) - Optional - Name/value parameters specific to the chosen entropy method.

### Response
#### Success Response (200)
- **Mobj** (object) - A multiscale entropy object configured with the specified parameters.
```

--------------------------------

### Create Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Functions/MultiscaleCross.html

Initializes a multiscale entropy object with default parameters or custom settings for a specified entropy type. Use this to configure parameters before calculating multiscale cross-entropy.

```matlab
[Mobj] = MSobject()
```

```matlab
[Mobj] = MSobject(EnType)
```

```matlab
[Mobj] = MSobject(EnType, keyword = value, ...)
```

--------------------------------

### Visualize Henon System Data with Poincaré Plot

Source: https://www.entropyhub.xyz/matlab/Examples/Ex3.html

Loads Henon system data and generates a scatter plot of the x and y components.

```matlab
Data = ExampleData('henon');

figure('Color', 'k')
plot(Data(:,1), Data(:,2), 'g.')
xlabel('x-component','color','g'),
ylabel('y-component','color','g')
set(gca,'color','k'), axis square
```

--------------------------------

### Create Multiscale Entropy Object in MatLab

Source: https://www.entropyhub.xyz/matlab/Examples/Ex6.html

Import a signal and create a multiscale entropy object with specified parameters for increment entropy, embedding dimension, resolution, and normalization.

```matlab
X = ExampleData('randintegers');

Mobj = MSobject('IncrEn', 'm', 3, 'R', 6, 'Norm', true)
```

```matlab
Mobj = struct with fields:
    Func: @IncrEn
    m: 3
    R: 6
    Norm: 1
```

--------------------------------

### Estimate Multivariate Permutation Entropy (Defaults)

Source: https://www.entropyhub.xyz/python/Functions/Multivariate.html

Estimates the multivariate permutation entropy using default parameters. Returns the entropy estimate and normalized entropy.

```python
MPerm, MPnorm = MvPermEn(Data)
```

--------------------------------

### Estimate Kolmogorov Entropy with K2En

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Estimates the Kolmogorov entropy and correlation integrals of a univariate data sequence using default parameters. Use when default embedding dimension (2), time delay (1), and distance threshold (0.2*SD(Sig)) are appropriate.

```python
K2, Ci = K2En(Sig)
```

--------------------------------

### Estimate Permutation Entropy with PermEn

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Estimates permutation entropy, normalized permutation entropy, and conditional permutation entropy using default parameters. Recommended for signals where length N > 5m. Note: Permutation entropy is 0 when m=1.

```python
Perm, Pnorm, cPE = PermEn(Sig)
```

--------------------------------

### Estimate FuzzEn2D with default parameters

Source: https://www.entropyhub.xyz/python/Functions/Bidimensional.html

Calculates the bidimensional fuzzy entropy using default settings for a given data matrix.

```python
Fuzz2D = FuzzEn2D(Mat)
```

--------------------------------

### Create Multiscale Entropy Object

Source: https://www.entropyhub.xyz/python/Examples/Ex12.html

Initializes a multiscale entropy object for Multivariate Fuzzy Entropy (MvFuzzEn) with specified parameters for fuzzy membership function and normalization.

```python
Mobj = EH.MSobject('MvFuzzEn', Fx = 'constgaussian', r = 1.75, Norm = True)
```

--------------------------------

### Calculate Cross-Distribution Entropy with Sturges' Binning

Source: https://www.entropyhub.xyz/python/Examples/Ex4.html

Calculates cross-distribution entropy using Sturges' method for bin selection. Note that some bins may be empty.

```python
X = EH.ExampleData('randintegers2');
XDist, _ = EH.XDistEn(X[:,0], X[:,1], m = 5, tau = 3)
```

--------------------------------

### Estimate EspEn2D with default parameters

Source: https://www.entropyhub.xyz/python/Functions/Bidimensional.html

Calculates the bidimensional Espinosa entropy using default settings for a given data matrix.

```python
Esp2D = EspEn2D(Mat)
```

--------------------------------

### Estimate Bubble Entropy (BubbEn)

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Estimates the bubble entropy of a univariate data sequence. This method is known for being almost free of parameters. Default parameters are used if not specified.

```python
Bubb, H = BubbEn(Sig)
```

```python
Bubb, H = BubbEn(Sig, keyword = value, ...)
```

--------------------------------

### Estimate Phase Entropy (PhasEn)

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Calculates phase entropy for a univariate data sequence. Use even-numbered partitions for symmetry.

```python
Phas = PhasEn(Sig)
```

```python
Phas = PhasEn(Sig, keyword = value, ...)
```

--------------------------------

### Estimate IncrEn with default and custom parameters

Source: https://www.entropyhub.xyz/python/Functions/Base.html

Functions for calculating increment entropy. Use keyword arguments to adjust embedding dimension, time delay, resolution, logarithm base, and normalization.

```python
Incr = IncrEn(Sig) 
```

```python
Incr = IncrEn(Sig, keyword = value, ...)
```

--------------------------------

### Estimate Multivariate Sample Entropy with Default Parameters

Source: https://www.entropyhub.xyz/python/Functions/Multivariate.html

Use this snippet to calculate the multivariate sample entropy using default parameters. The function returns the entropy estimate and statistics about matched delay vectors.

```python
MSamp, B0, Bt, B1 = MvSampEn(Data)
```

--------------------------------

### POST /MSobject

Source: https://www.entropyhub.xyz/matlab/Functions/matMultivariateMultiscale.html

Creates a multiscale entropy object (Mobj) used to store parameters for entropy calculations.

```APIDOC
## POST /MSobject

### Description
Creates an object to store multiscale entropy parameters. This object is required for subsequent entropy estimation functions.

### Parameters
#### Request Body
- **EnType** (string) - Optional - The type of entropy method to use (e.g., 'SampEn', 'MvSampEn2D', 'ApEn').
- **name/value** (mixed) - Optional - Specific parameters for the chosen entropy method.

### Response
#### Success Response (200)
- **Mobj** (object) - The configured multiscale entropy object.
```

--------------------------------

### Estimate Cross-Approximate Entropy (XApEn)

Source: https://www.entropyhub.xyz/python/Functions/Cross.html

Estimates the cross-approximate entropy between two univariate data sequences. Note that XApEn is direction-dependent.

```python
XAp, Phi = XApEn(Sig1, Sig2)
```

```python
XAp, Phi = XApEn(Sig1, Sig2, keyword = value, ...)
```

--------------------------------

### Compare Multivariate Dispersion Entropy Methods

Source: https://www.entropyhub.xyz/python/Examples/Ex11.html

Calculate entropy estimates using the mvDE method (Methodx = 'v2') to return values for each embedding dimension.

```python
MDisp, RDE = EH.MvDispEn(Data, m = np.array([1,1,2,3]), c = 7, Norm = True, Methodx = 'v2')
```

--------------------------------

### Estimate Permutation Entropy with Default Parameters

Source: https://www.entropyhub.xyz/python/Functions/Bidimensional.html

Use this to estimate bidimensional permutation entropy for a data matrix using default parameters. The minimum number of rows and columns of the data matrix must be greater than 10.

```python
Perm2D = PermEn2D(Mat)
```
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