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TISEAN package

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== Porting TISEAN ==
This section will focus which focuses on demonstrating how the capabilities of the TISEAN package. The previous information about is to be ported and what is the porting procedure has been moved current state of that process is located in [[TISEAN_package:Procedure|here]]. Current ideas and future plans are available on a board loacated [https://trello.com/b/hJS1Q8wN here]
== Tutorials ==
[[File:tisean_nl_noisereduction_2.png|400px|center]]
=== Lyapunov Exponents ===Here I will demonstrate how to use the function {{Codeline|lyap_k}}. It estimates the maximal Lyapunov exponent from a time series (more information available from the TISEAN documentation located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node27.html here]). In this tutorial we will estimate the maximal Lyapunov exponent for various embedding dimensions and then plot them. {{Code|Creating Lyapunov exponents|<syntaxhighlight lang="octave" style="font-size:13px"># Create time seriesin = sin((1:2500).'./360) + cos((1:2500).'./180);# Estimate Lyapunov exponentsmmax_val = 20lyap_exp = lyap_k (in, 'mmin',2,'mmax',mmax_val,'d',6,'s',400,'t',500);</syntaxhighlight>}}In this function the output ({{Codeline|lyap_exp}} is a {{Codeline|5 x 20}} struct array. We will only use one row for the plot.{{Code|Plotting Lyapunov exponents|<syntaxhighlight lang="octave" style="font-size:13px">cla resethold onfor j=2:mmax_val plot (lyap_exp(1,j-1).exp(:,1),lyap_exp(1,j-1).exp(:,2),'r');endforxlabel ("t [flow samples]");ylabel ("S(eps, embed, t)");hold off</syntaxhighlight>}}[Category[File:lyap_k.png|400px|center]] === Dimensions and Entropies ===This section is discussed on the [http:Octave//www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node29.html#SECTION00080000000000000000 TISEAN documentation page]. One of the functions discussed is {{Codeline|d2}}. It is used to estimate the correlation sum, correlation dimension and correlation entropy of a time series. The time series used here will be the Henon map.{{Code|Calculation correlation sum, dimension and entropy|<syntaxhighlight lang="octave" style="font-Forgesize:13px"># Create mapshen = henon (10000);# Calculate the correlation sum, dimension and entropyvals = d2 (hen, 'd', 1, 'm', 5, 't',50);# Plot correlation sumsubplot (2,3,1)do_plot_corr = @(x) loglog (x{1}(:,1),x{1}(:,2),'b');hold onarrayfun (do_plot_corr, {vals.c2});hold offxlabel ("Epsilon")ylabel ("Correlation sums")title ("c2");# Plot correlation entropysubplot (2,3,4)do_plot_entrop = @(x) semilogx (x{1}(:,1),x{1}(:,2),'g');hold onarrayfun (do_plot_entrop, {vals.h2});hold offxlabel ("Epsilon")ylabel ("Correlation entropies");title ("h2")# Plot correlation dimensionsubplot (2,3,[2 3 5 6])do_plot_slope = @(x) semilogx (x{1}(:,1),x{1}(:,2),'r');hold onarrayfun (do_plot_slope, {vals.d2});hold offxlabel ("Epsilon")ylabel ("Local slopes")title ("d2");</syntaxhighlight>}}[[File:d2_out.png|400px|center]]The output of {{Codeline|d2}} can be further processed using the following functions: {{Codeline|av_d2}}, {{Codeline|c2t}}, {{Codeline|c2g}}. This tutorial will show how to use {{Codeline|av_d2}} which smooths the output of {{Codeline|d2}} (usually used to smooth the "{{Codeline|d2}}" field of the output).{{Code|Smooth output of d2|<syntaxhighlight lang="octave" style="font-size:13px"># Smooth d2 outputfigure 2smooth = av_d2 (vals,'a',2);# Plot the smoothed outputdo_plot_slope = @(x) semilogx (x{1}(:,1),x{1}(:,2),'b');hold onarrayfun (do_plot_slope, {smooth.d2});hold offxlabel ("Epsilon")ylabel ("Local slopes")title ("Smooth");</syntaxhighlight>}}[[File:tisean_av_d2_out.png|400px|center]]Optionally the line "{{Codeline|figure 2}}" can be omitted, which will cause the smoothed version to be superimposed on the "raw" version that came straight from {{Codeline|d2}}. === Testing for Nonlinearity ===This section is discussed on the [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node35.html#SECTION00090000000000000000 TISEAN documentation page]. The focus of this section will be the function {{Codeline|surrogates}}. It uses surrogate data to determine weather data is nonlinear. Let us first create the input data which will be a stationary Gaussian linear stochastic process. It is measured by {{Codeline|s(xn) &#61; xn^3}}. We then run it through {{Codeline|surrogates}} and plot the data.{{Code|Creating data from Gaussian process|<syntaxhighlight lang="octave" style="font-size:13px"># Create Gaussian process datag = zeros (2000,1);for i = 2:2000 g(i) = 0.7 * g(i-1) + (-6 + sum (rand ([size(1), 12]), 3));endfor# Create a measurement of itspike = g.^3;# Create the surrogatesur = surrogates (spike);# Plot the datasubplot (2,1,1)plot (spike,'g');title ("spike")subplot (2,1,2)plot (sur,'b');title ("surrogate")</syntaxhighlight>}} [[File:surrogate_tutorial.png|400px|center]]It is crucial that the length of the input to surrogates is factorizable by only 2,3 and 5. Therefore, if it is not the excess of data is truncated accordingly. Padding with zeros is not allowed. To solve this problem one can use {{Codeline|endtoend}}, and choose the best subset of the input data to be used to generate a surrogate.
== External links ==
* [https://bitbucket.org/josiah425/tisean Bitbucket repository ] where the porting is taking place.
* [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/ TISEAN package website] where the package is described along with references to literature, tutorials and manuals.
 
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