### Install EntropyHub from source

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/EHpython.rst.txt

Manual installation method for EntropyHub by running the setup script from the extracted source directory.

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

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

### Install EntropyHub from source

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/EHpython.rst

This command installs EntropyHub from a downloaded source archive. It requires downloading the .tar.gz file, extracting it, and running the setup script. Ensure setuptools is up-to-date.

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

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

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

### Install and Initialize EntropyHub

Source: https://context7.com/mattwillflood/entropyhub/llms.txt

Instructions for installing the Python package via pip and verifying the installation with the built-in greeting function.

```python
pip install EntropyHub
import EntropyHub as EH
EH.greet()
```

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

### Install EntropyHub from Source

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/EHpython.html

An alternative installation method for users who prefer to download the source code and install it manually.

```bash
python -m pip install --upgrade setuptools
python setup.py install
```

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

### Install EntropyHub from GitHub Repository

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/julia/EHjulia.rst.txt

Installing the latest development version of EntropyHub directly from the GitHub repository.

```julia
import Pkg
# Enter package mode with ]
add https://github.com/MattWillFlood/EntropyHub.jl
```

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

### Install EntropyHub via Package REPL

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/julia/EHjulia.rst.txt

Instructions for installing the EntropyHub package using the Julia package REPL interface.

```julia
import Pkg
# Enter package mode with ]
add EntropyHub
```

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

### Load Example Datasets

Source: https://context7.com/mattwillflood/entropyhub/llms.txt

Demonstrates how to load various built-in datasets for testing entropy functions, including random distributions and chaotic attractors.

```python
import EntropyHub as EH
gaussian = EH.ExampleData('gaussian')
uniform = EH.ExampleData('uniform')
randints = EH.ExampleData('randintegers')
chirp = EH.ExampleData('chirp')
lorenz = EH.ExampleData('lorenz')
henon = EH.ExampleData('henon')
mandelbrot = EH.ExampleData('mandelbrot_Mat')
```

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

### Generate Example Data with EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/pyexamples.html

Demonstrates how to generate various types of example data using the EntropyHub.ExampleData() function. An internet connection is required for this function. It supports generating uniform, gaussian, random integers, chirp signals, Lorenz system data, Henon attractor data, and matrix-based data including images.

```python
import EntropyHub as EH

# Example: Generate uniformly distributed random numbers
uniform_data = EH.ExampleData('uniform')

# Example: Generate Lorenz system data
lorenz_data = EH.ExampleData('lorenz')

# Example: Generate a matrix of uniformly distributed random numbers
uniform_matrix = EH.ExampleData('uniform_Mat')

# Example: Generate an image of the entropyhub logo
logo_image = EH.ExampleData('entropyhub_Mat')
```

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

### Install EntropyHub.jl via Package Manager

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/julia/EHjulia.html

Methods to install the EntropyHub package using the Julia package REPL or the Pkg module.

```julia
# Using package REPL
# Press ] to enter pkg mode
add EntropyHub

# Using Pkg module
using Pkg
Pkg.add("EntropyHub")
```

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

### Install EntropyHub via Pkg Module

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/julia/EHjulia.rst.txt

Programmatic installation of the EntropyHub package using the Pkg module within a Julia session.

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

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

### GET /EntropyHub/ExampleData

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/pyexamples.html

Retrieves sample datasets or signal vectors for entropy analysis. Requires an active internet connection.

```APIDOC
## GET /EntropyHub/ExampleData

### Description
Fetches pre-defined sample signals or matrices for testing entropy functions. The function requires an internet connection to download the requested data.

### Method
GET

### Endpoint
EntropyHub.ExampleData(type)

### 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'.

### Request Example
import EntropyHub as EH
data = EH.ExampleData('lorenz')

### Response
#### Success Response (200)
- **data** (array/matrix) - The requested signal vector or matrix.

#### Response Example
[[0.12, 0.45, 0.88], [0.15, 0.48, 0.91], ...]
```

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

### GET /ExampleData

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/matlab/matexamples.rst

Retrieves specific sample data based on the provided identifier string.

```APIDOC
## GET /ExampleData

### Description
Retrieves a pre-defined dataset or signal for use in entropy calculations. Requires an active internet connection.

### Method
GET

### Endpoint
/ExampleData

### Parameters
#### Query Parameters
- **type** (string) - Required - The identifier for the dataset (e.g., 'uniform', 'gaussian', 'lorenz', 'henon', 'mandelbrot_Mat').

### Request Example
{
  "type": "lorenz"
}

### Response
#### Success Response (200)
- **data** (array/matrix) - The requested signal or matrix data.

#### Response Example
{
  "data": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
}
```

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

### Load Example Data with EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/pyexamples.rst.txt

Explains how to use the ExampleData function to retrieve various signal types for testing. Note that this function requires an active internet connection to fetch the requested datasets.

```python
import EntropyHub as EH
# Example of loading gaussian noise
data = EH.ExampleData('gaussian')
```

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

### Upgrade setuptools

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/EHpython.rst.txt

Ensures that the setuptools package is up to date, which is a prerequisite for successful package installation.

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

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

### Verify EntropyHub Installation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/matlab/EHmatlab.rst.txt

This command verifies that the EntropyHub toolkit is correctly installed and configured in the MatLab environment. Executing it displays the toolkit logo in the command window.

```matlab
EntropyHub
```

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

### Install EntropyHub via pip

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/EHpython.rst.txt

The recommended method to install the EntropyHub package using the Python package manager. This command fetches the latest version from PyPI.

```python
pip install EntropyHub
```

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

### Load and Greet EntropyHub in Julia

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/julia/EHjulia.rst

Demonstrates how to load the EntropyHub package into a Julia session and verify the installation by calling the greet() function, which displays the package's ASCII art logo.

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

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

### Import and verify EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/EHpython.rst.txt

Commands to import the EntropyHub module and verify the installation by triggering the greeting function.

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

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

### Install EntropyHub using Julia Package Manager

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/julia/EHjulia.rst

Instructions for installing the EntropyHub package in Julia using the built-in package manager (Pkg). This method adds the package from the Julia registry.

```julia
julia > ]

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

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

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

### Generating Example Data with EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

Shows how to use the EntropyHub.ExampleData() function to generate various types of synthetic data for testing and demonstration purposes. An internet connection is required for this function.

```python
EH.ExampleData('uniform')
EH.ExampleData('gaussian')
EH.ExampleData('randintegers')
EH.ExampleData('chirp')
EH.ExampleData('lorenz')
EH.ExampleData('henon')
EH.ExampleData('uniform2')
EH.ExampleData('gaussian2')
EH.ExampleData('randintegers2')
EH.ExampleData('uniform_Mat')
EH.ExampleData('gaussian_Mat')
EH.ExampleData('randintegers_Mat')
EH.ExampleData('mandelbrot_Mat')
EH.ExampleData('entropyhub_Mat')
```

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

### Load and Verify EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/julia/EHjulia.rst.txt

Commands to load the EntropyHub package and run the greeting function to confirm successful installation.

```julia
using EntropyHub
EntropyHub.greet()
```

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

### EntropyHub Module Import

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/pyexamples.rst.txt

This snippet shows the recommended way to import the EntropyHub module for use in examples.

```APIDOC
## EntropyHub Module Import

### Description
This code demonstrates the standard import statement for the EntropyHub library, aliasing it as EH for brevity in subsequent examples.

### Method
Import Statement

### Endpoint
N/A

### Parameters
N/A

### Request Example
```python
import EntropyHub as EH
```

### Response
N/A
```

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

### Fine-Grained Permutation Entropy Example (MatLab)

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/matlab/Examples/Ex2.html

This example demonstrates the usage of Fine-Grained Permutation Entropy calculation within the EntropyHub library for MatLab. It likely involves inputting time-series data and parameters to compute the entropy.

```matlab
let toggleHintShow = 'Click to show';
let toggleHintHide = 'Click to hide';
let toggleOpenOnPrint = 'true';
var togglebuttonSelector = '.toggle, .admonition.dropdown';
```

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

### Importing EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

Demonstrates the standard import convention for the EntropyHub library, aliasing it as EH for brevity in subsequent examples. This is a prerequisite for using any EntropyHub functions.

```python3
import EntropyHub as EH
EH.SampEn(...)
```

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

### Visualize Henon System Components

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/Examples/Ex3.html

Imports the Henon system example data and visualizes the x and y components using matplotlib. This provides a baseline for further entropy analysis.

```python
from matplotlib.pyplot import figure, plot, axis
Data = EH.ExampleData('henon')
fig = figure(facecolor='k')
plot(Data[:,0], Data[:,1], 'g.')
axis('off')
```

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

### Access EntropyHub function documentation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/EHpython.rst

Shows how to access the docstrings for any EntropyHub function directly from the command line using the built-in help() function. For example, 'help SampEn' will display the documentation for the SampEn function.

```python
help SampEn
```

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

### Bidimensional Fuzzy Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/Examples/Ex10.rst

This example demonstrates how to load an image, calculate its Bidimensional Fuzzy Entropy using specified parameters, and shows the expected output.

```APIDOC
## Import Image Data

### Description
Import an image as a matrix for entropy calculation.

### Method
N/A (Python script)

### Endpoint
N/A

### Parameters
None

### Request Example
```python3
X = EH.ExampleData('mandelbrot_Mat')
from matplotlib.pyplot import imshow, show
imshow(X, cmap = 'hot'), show()
```

### Response
N/A

---

## Calculate Bidimensional Fuzzy Entropy

### Description
Calculate the bidimensional fuzzy entropy using the imported image data with specific parameters for template size, time delay, fuzzy function, and normalization.

### Method
N/A (Python script)

### Endpoint
N/A

### Parameters
- **X** (numpy.ndarray) - The input image matrix.
- **m** (tuple) - Template matrix size (height, width). Example: (8, 5).
- **tau** (int) - Time delay. Example: 2.
- **Fx** (str) - Fuzzy function type. Example: 'linear'.
- **r** (int) - Normalization parameter (0 for no normalization, 1 for normalization).
- **Logx** (int) - Logarithm base for entropy calculation. Example: 3 (trits).

### Request Example
```python3
FE2D = EH.FuzzEn2D(X, m = (8, 5), tau = 2, Fx = 'linear', r = 0, Logx = 3)
```

### Response
#### Success Response (200)
- **FE2D** (float) - The calculated Bidimensional Fuzzy Entropy value.

#### Response Example
```json
{
  "FE2D": 0.00159093
}
```
```

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

### Phase Entropy with Time Delay and Plot

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/Examples/Ex3.rst.txt

This example calculates the phase entropy of the x-component of the Henon system using 11 angular partitions and a time delay of 2, also returning a second-order difference plot.

```APIDOC
## Phase Entropy Calculation with Time Delay

### Description
Calculates the phase entropy of a time series with a specified time delay and generates a second-order difference plot.

### Method
`EH.PhasEn(X, K, tau, Plotx)`

### Parameters
#### Input Data
- **X** (numpy.ndarray) - The time series data (e.g., x-component).

#### Core Parameters
- **K** (int) - Number of angular partitions. Example: 11.
- **tau** (int) - The time delay for phase space reconstruction. Example: 2.

#### Plotting Parameter
- **Plotx** (bool) - Whether to return a second-order difference plot. `True` to generate the plot.

### Request Example
```python
import EntropyHub as EH

Data = EH.ExampleData('henon');
X = Data[:,0]
Phas = EH.PhasEn(X, K = 11, tau = 2, Plotx = True)

print(Phas)
```

### Response
#### Success Response (Output)
- **Phas** (float) - The calculated phase entropy value.

#### Response Example
```
0.8395
```

#### Visual Output
A second-order difference plot is generated if `Plotx` is `True`.
```

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

### Estimate Symbolic Dynamic Entropy with SyDyEn

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/matlab/Functions/matBase.html

Demonstrates the usage of the SyDyEn function to calculate symbolic dynamic entropy and the symbolic sequence from a signal. The first example uses default parameters, while the second allows for custom name-value pair arguments.

```matlab
[SyDy, Zt] = SyDyEn(Sig);
[SyDy, Zt] = SyDyEn(Sig, 'm', 2, 'tau', 1, 'c', 3, 'Typex', 'MEP', 'Logx', exp(1), 'Norm', true);
```

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

### Import and Initialize EntropyHub

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/Functions/Bidimensional.html

Demonstrates how to import the EntropyHub library and list available functions.

```python
import EntropyHub as EH
dir(EH)
```

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

### Import Sample Data with ExampleData

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/Functions/Other.html

Imports predefined sample datasets for benchmarking entropy methods. Requires an internet connection. Supports various data types including time series and matrices.

```python
Data = ExampleData(SigName)
```

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

### ExampleData() Function

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/pyexamples.rst.txt

Demonstrates how to use the ExampleData() function to generate various types of sample data for entropy calculations. An internet connection is required.

```APIDOC
## EntropyHub.ExampleData()

### Description
The ``EntropyHub.ExampleData()`` function is used to generate various types of sample signals or data for use with EntropyHub functions. An internet connection is required to download the data.

### Method
Function Call

### Endpoint
N/A

### Parameters
#### Query Parameters
- **data_type** (string) - Required - Specifies the type of data to generate. Accepted values include:
    - ``'uniform'``: vector of uniformly distributed random numbers in range [0 1]
    - ``'gaussian'``: vector of normally distributed random numbers with mean = 0; SD = 1
    - ``'randintegers'``: vector of uniformly distributed pseudorandom integers in range [1 8]
    - ``'chirp'``: vector of chirp signal
    - ``'lorenz'``: 3-column matrix: X, Y, Z components of the Lorenz system
    - ``'henon'``: 2-column matrix: X, Y components of the Henon attractor
    - ``'uniform2'``: 2-column matrix: uniformly distributed random numbers in range [0 1]
    - ``'gaussian2'``: 2-column matrix: normally distributed random numbers with mean = 0; SD = 1
    - ``'randintegers2'``: 2-column matrix: uniformly distributed pseudorandom integers in range [1 8]
    - ``'uniform_Mat'``: Matrix of uniformly distributed random numbers in range [0 1]
    - ``'gaussian_Mat'``: Matrix of normally distributed random numbers with mean = 0; SD = 1
    - ``'randintegers_Mat'``: Matrix of uniformly distributed pseudorandom integers in range [1 8]
    - ``'mandelbrot_Mat'``: Matrix of image of fractal generated from the mandelbrot set
    - ``'entropyhub_Mat'``: Matrix of image of the entropyhub logo

### Request Example
```python
import EntropyHub as EH

# Generate a uniform random vector
uniform_data = EH.ExampleData('uniform')

# Generate Lorenz system data
lorenz_data = EH.ExampleData('lorenz')
```

### Response
#### Success Response (200)
- **data** (numpy.ndarray or list) - The generated data based on the specified type.
```

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

### cMvMSEn Calculation Example

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/matlab/Examples/Ex12.rst.txt

This example demonstrates how to calculate the generalized refined-composite multivariate multiscale fuzzy entropy for the Lorenz system data.

```APIDOC
## POST /api/entropy/cmvmse

### Description
Calculates the generalized refined-composite multivariate multiscale fuzzy entropy (cMvMSEn) for given multivariate time series data. This endpoint utilizes a refined graining procedure and supports plotting the output.

### Method
POST

### Endpoint
/api/entropy/cmvmse

### Parameters
#### Request Body
- **Data** (array) - Required - The multivariate time series data. Each row represents a time point, and each column represents a component (e.g., x, y, z).
- **Mobj** (object) - Required - The multiscale entropy object, defining the entropy type, fuzzy membership function, fuzzy function parameter, and normalization.
  - **EnType** (string) - Required - The type of entropy to calculate (e.g., 'MvFuzzEn').
  - **Fx** (string) - Optional - The fuzzy membership function (e.g., 'constgaussian').
  - **r** (float) - Optional - The fuzzy function parameter.
  - **Norm** (boolean) - Optional - Whether to normalize the data to unit variance.
- **Scales** (integer) - Optional - The number of scales to compute the entropy over. Defaults to 5.
- **Refined** (boolean) - Optional - Whether to use the refined graining procedure. Defaults to true.
- **Plotx** (boolean) - Optional - Whether to plot the output. Defaults to true.

### Request Example
```json
{
  "Data": [
    [1.0, 2.0, 3.0],
    [1.1, 2.1, 3.1],
    [1.2, 2.2, 3.2]
  ],
  "Mobj": {
    "EnType": "MvFuzzEn",
    "Fx": "constgaussian",
    "r": 1.75,
    "Norm": true
  },
  "Scales": 5,
  "Refined": true,
  "Plotx": true
}
```

### Response
#### Success Response (200)
- **MSx** (array) - The calculated multiscale entropy values for each scale.
- **CI** (float) - Confidence interval (if applicable).
- **plot_url** (string) - URL to the generated plot if Plotx is true.

#### Response Example
```json
{
  "MSx": [0, 0.00796833, 0.00926765, 0.01193731, 0.01686631],
  "CI": 0.04603960,
  "plot_url": "/path/to/plot.png"
}
```

### Error Handling
- **400 Bad Request**: Invalid input parameters or data format.
- **500 Internal Server Error**: An error occurred during calculation.
```

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

### Initialize EntropyHub Module

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/pyexamples.rst.txt

Demonstrates the standard import procedure for the EntropyHub library and the syntax for calling entropy functions.

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

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

### Refined Multiscale Sample Entropy Example (Python)

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/Examples/Ex7.html

This snippet demonstrates the usage of Refined Multiscale Sample Entropy in Python. It is part of the EntropyHub library's example collection, showcasing advanced entropy calculation techniques.

```python
import EntropyHub

# Placeholder for actual code demonstrating Refined Multiscale Sample Entropy
# Example usage would involve calling a function from the EntropyHub library
# For instance:
# result = EntropyHub.RefinedMultiscaleSampleEntropy(data, order, radius)
print('Example 7: Refined Multiscale Sample Entropy')
```

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

### Refined Multiscale Sample Entropy Example (MATLAB)

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/python/Examples/Ex7.html

This snippet demonstrates the usage of Refined Multiscale Sample Entropy in MATLAB. It is part of the EntropyHub library's example collection for MATLAB, showcasing advanced entropy calculation techniques.

```matlab
% Placeholder for actual code demonstrating Refined Multiscale Sample Entropy in MATLAB
% Example usage would involve calling a function from the EntropyHub library for MATLAB
% For instance:
% result = RefinedMultiscaleSampleEntropy(data, order, radius);
disp('Example 7: Refined Multiscale Sample Entropy');
```

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

### Import and Inspect EntropyHub Functions

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/Functions/Base.rst.txt

Demonstrates how to import the EntropyHub library and list all available entropy estimation functions using the dir() command.

```python
import EntropyHub as EH

dir(EH)
```

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

### Import Benchmark Datasets with ExampleData

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/matlab/Functions/matOther.html

The ExampleData function retrieves standard benchmark datasets (time series or matrices) used for testing entropy methods. It requires an active internet connection to download the requested signal by name.

```matlab
Data = ExampleData('gaussian');
Data = ExampleData('lorenz');
Data = ExampleData('entropyhub_Mat');
```

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

### Initialize Sphinx Documentation Navigation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/search.html

Initializes the Sphinx ReadTheDocs theme navigation and loads the search index for the documentation portal.

```javascript
jQuery(function () { SphinxRtdTheme.Navigation.enable(true); }); jQuery(function() { Search.loadIndex("searchindex.js"); });
```

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

### Accessing Documentation via Julia Help

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/julia/EHjulia.rst.txt

Using the built-in Julia help system to view documentation for specific EntropyHub functions.

```julia
# Type ? in the REPL followed by the function name
?SampEn
?XSpecEn
?hXMSEn
```

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

### Phase Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

Provides an example for calculating Phase Entropy with EntropyHub. This entropy measure is sensitive to the phase dynamics of a signal.

```python
EH.PhEn(...)
```

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

### Windowing Data with WindowData()

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

An example demonstrating the use of the WindowData() function in EntropyHub for segmenting time series data into overlapping or non-overlapping windows.

```python
EH.WindowData(...)
```

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

### Sample Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

An example of calculating Sample Entropy (SampEn) using the EntropyHub library. This function measures the predictability of a time series.

```python
EH.SampEn(...)
```

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

### Multiscale Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

An example of calculating Multiscale Entropy using EntropyHub. This method analyzes the entropy of a time series at different time scales.

```python
EH.MSEn(...)
```

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

### Initialize Multiscale Entropy Object

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/Examples/Ex7.rst.txt

Demonstrates how to import sample data and create a multiscale entropy object with specific embedding dimensions and radius thresholds.

```python
X = EH.ExampleData('randintegers');
Mobj = EH.MSobject('SampEn', m = 4, r = 1.25)
```

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

### Multiscale Increment Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/python/Examples/Ex6.rst.txt

This snippet demonstrates how to calculate multiscale increment entropy using predefined example data and custom object parameters.

```APIDOC
## Multiscale Increment Entropy Calculation

### Description
This example shows how to compute multiscale increment entropy (IncrEn) for a given signal. It involves initializing an example dataset, creating a multiscale entropy object with specific parameters (embedding dimension, resolution, normalization), and then calculating the entropy over multiple temporal scales using a modified graining procedure.

### Method
Python 3 with EntropyHub library

### Parameters
- **Signal**: `X` - Uniformly distributed pseudorandom integers in the range [1,8].
- **Entropy Type**: `IncrEn()`
- **Embedding Dimension**: `m = 3`
- **Quantifying Resolution**: `R = 6`
- **Normalization**: `Norm = True`
- **Number of Scales**: `Scales = 5`
- **Calculation Method**: `Methodx = 'modified'`

### Request Example
```python
import EntropyHub as EH

X = EH.ExampleData('randintegers')

Mobj = EH.MSobject('IncrEn', m = 3, R = 6, Norm = True)

MSx, _ = EH.MSEn(X, Mobj, Scales = 5, Methodx = 'modified')
```

### Response
#### Success Response (200)
- **MSx** (numpy.array) - An array containing the calculated multiscale increment entropy values for each scale.

#### Response Example
```
MSx = array([4.2719, 4.3059, 4.2863, 4.2494, 4.2773])
```

### Additional Information
- The `Mobj.Func` attribute shows the function signature for `IncrEn`.
- The `Mobj.Kwargs` attribute displays the keyword arguments used to initialize the `MSobject`.
```

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

### Multivariate Dispersion Entropy Calculation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/python/pyexamples.rst

Provides an example for calculating Multivariate Dispersion Entropy with EntropyHub. This function analyzes entropy in multivariate time series data.

```python
EH.DispEn(...)
```

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

### Access Function Documentation

Source: https://github.com/mattwillflood/entropyhub/blob/main/docs/_build/html/_sources/matlab/EHmatlab.rst.txt

Use the built-in MatLab documentation system to view detailed help for specific EntropyHub functions. This command opens the help browser for the specified function name.

```matlab
doc SampEn
```