Other Functions

Supplementary functions for various tasks related to EntropyHub and signal processing.

ExampleData(SigName)

ExampleData imports sample data time series and matrices.

Data = ExampleData(SigName)

Returns sample datasets (time series or matrices) with specific properties that are commonly used as benchmarks for assessing the performance of various entropy methods. The datasets returned by ExampleData() are used in the examples provided in documentation on www.EntropyHub.xyz and elsewhere. *Note* ExampleData() requires an internet connection to download and import the required datasets!

Data is the sample dataset imported corresponding to the string input SigName which can be one of the following,

SigName:

  • uniform - uniformly distributed random number sequence in range [0 1], N = 5000

  • randintegers - randomly distributed integer sequence in range [1 8], N = 4096

  • gaussian - normally distributed number sequence [mean: 0, SD: 1], N = 5000

  • henon - X and Y components of the Henon attractor [alpha: 1.4, beta: .3, Xo = 0, Yo = 0], N = 4500

  • lorenz - X, Y, and Z components of the Lorenz attractor [sigma: 10, beta: 8/3, rho: 28, Xo = 10, Yo = 20, Zo = 10], N = 5917

  • chirp - chirp signal (f0 = .01, t1 = 4000, f1 = .025), N = 5000

  • uniform2 - two uniformly distributed random number sequences in range [0,1], N = 4096

  • gaussian2 - two normally distributed number sequences [mean: 0, SD: 1], N = 3000

  • randintegers2 - two uniformly distributed pseudorandom integer sequences in range [1 8], N = 3000

  • uniform_Mat - matrix of uniformly distributed random numbers in range [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 in range [1 8], N = 88 x 88

  • mandelbrot_Mat - matrix representing a Mandelbrot fractal image with values in range [0 255], N = 92 x 115

  • entropyhub_Mat - matrix representing the EntropyHub logo with values in range [0 255], N = 127 x 95

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 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 elements that do not fill the final window. If Data is a univariate sequence (vector), Windata is a cell of 5 vectors. If Data is a set of multivariate sequences (NxM matrix), each of M columns is treated as a sequence with N elements and WinData is a cell of 5 matrices of size [(floor*N,5), M]. Log contains information about the windowing process, including:

  • DataType - The type of data sequence passed as Data

  • DataLength - The number of sequence elements in Data

  • WindowLength - The number of elements in each window of WinData

  • WindowOverlap - The number of overlapping elements between windows

  • TotalWindows - The number of windows extracted from Data

  • Mode - Decision to include or exclude any remaining sequence elements (< WinLen) that do not fill the window.

[WinData, Log] = WindowData(Data, name, value, …)

Windows the sequence(s) given in Data into a collection of subsequnces using the specified name/value pair arguments:

WinLen:

  • Number of elements in each window, a positive integer (>10)

Overlap:

  • Number of overlapping elements between windows, a positive integer (< WinLen)

Mode:

  • Decision to include or exclude any remaining sequence elements (< WinLen) that do not fill the window, a string - either "include" or "exclude" (default).

See also:

ExampleData