# Python Examples:¶

- Ex. 1 - Sample Entropy
- Ex. 2 - [Fine-Grained] Permutation Entropy
- Ex. 3 - Phase Entropy
- Ex. 4 - Cross-Distribution Entropy
- Ex. 5 - Multiscale Entropy Object
- Ex. 6 - Multiscale [Increment] Entropy
- Ex. 7 - Refined Multisclae [Sample] Entropy
- Ex. 8 - Composite Multiscale Cross-[Approximate] Entropy
- Ex. 9 - Hierarchical Multiscale corrected Cross-[Conditional] Entropy
- Ex. 10 - Bidimensional Fuzzy Entropy

Important

For concision, function commands written in the following example sections assume that the EntropyHub module has already been imported as EH.

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

**The following sections provide some basic examples of EntropyHub functions.
These examples are merely a snippet of the full range of EntropyHub functionality.**

`ExampleData()`

In the following examples, signals / data are imported into Python using the `EntropyHub.ExampleData()`

function.
To use this function as outlined in the examples below, **an internet connection is required**.

`EntropyHub.ExampleData()`

accepts any of the following strings:

`'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 with the following parameters, f0 = :01; t1 = 4000; f1 = :025

`'lorenz'`

3-column matrix: X, Y, Z components of the Lorenz system, (alpha = 10; beta = 8/3; rho = 28); [Xo = 10; Yo = 20; Zo = 10]

`'henon'`

2-column matrix: X, Y components of the Henon attractor (alpha = 1.4; beta = 0.3); [Xo = 0; Yo = 0]

`'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

THINGS TO REMEMBER

For *cross-entropy* and *multiscale cross-entropy* functions, the two time series signals are passed as a two-column or two-row matrix.
At present, it is not possible to pass signals of different lengths separately.

Parameters of the *base* or *cross-* entropy methods are passed to *multiscale* and
*multiscale cross-* entropy functions using the multiscale entropy object given by `MSobject()`

.
*Base* and *cross-* entropy methods are declared with `MSobject()`

using a string of the function name.

Each bidimensional entropy function (*SampEn2D*, *FuzzEn2D*, *DistEn2D*, *DispEn2D*) has
an important keyword argument - `Lock`

. *Bidimensional* entropy functions are
“locked” by default (`Lock == True`

) to only permit matrices with a maximum size of 128 x 128.

In *hierarchical multiscale entropy* (`hMSEn()`

) and *hierarchical multiscale cross-
entropy* (`hXMSEn()`

) functions, the length of the time series signal(s) is halved at each scale.
Thus, `hMSEn()`

and `hXMSEn()`

only use the first 2^N data points where 2^N <= the length of the original time series signal.
i.e. For a signal of 5000 points, only the first 4096 are used. For a signal of 1500 points, only the first 1024 are used.