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== Porting TISEAN == | == Porting TISEAN == | ||
This section | This section which focuses on demonstrating how the package is to be ported and what is the current state of that process is located in [[TISEAN_package:Procedure]]. | ||
== Tutorials == | == Tutorials == | ||
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=== Lyapunov Exponents === | === Lyapunov Exponents === | ||
Here I will demonstrate how to use the function {{Codeline|lyap_k}}. It | 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"> | {{Code|Creating Lyapunov exponents|<syntaxhighlight lang="octave" style="font-size:13px"> | ||
# Create time series | # Create time series | ||
in = sin((1:2500).'./360) + cos((1:2500).'./180); | in = sin((1:2500).'./360) + cos((1:2500).'./180); | ||
# | # Estimate Lyapunov exponents | ||
mmax_val = 20 | mmax_val = 20 | ||
lyap_exp = lyap_k (in, 'mmin',2,'mmax',mmax_val,'d',6,'s',400,'t',500); | lyap_exp = lyap_k (in, 'mmin',2,'mmax',mmax_val,'d',6,'s',400,'t',500); | ||
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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}}. | 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}}. | ||
[[Category:Octave | === 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) = 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 data | |||
g = 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 it | |||
spike = g.^3; | |||
# Create the surrogate | |||
sur = surrogates (spike); | |||
# Plot the data | |||
subplot (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. | |||
[[Category:Octave Forge]] | |||
== External links == | == External links == | ||
* [https://bitbucket.org/josiah425/tisean Bitbucket repository ] where the porting is taking place. | * [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. | * [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. |