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== Porting TISEAN ==
 
== Porting TISEAN ==
  
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]].
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This section will focus on demonstrating the capabilities of the TISEAN package. The previous information about the porting procedure has been moved [[TISEAN_package:Procedure|here]].
 +
 
  
 
== Tutorials ==
 
== Tutorials ==
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The {{Codeline|dim_*}} variables hold the dimension (so here 1:5), and {{Codeline|frac_*}} contain the fraction of false nearest neighbors. From this chart we can obtain the sufficient embedding dimension for each system. For a Henon Map {{Codeline|m = 2}} is sufficient, but for an Ikeda map it is better to use {{Codeline|m = 3}}.
 
The {{Codeline|dim_*}} variables hold the dimension (so here 1:5), and {{Codeline|frac_*}} contain the fraction of false nearest neighbors. From this chart we can obtain the sufficient embedding dimension for each system. For a Henon Map {{Codeline|m = 2}} is sufficient, but for an Ikeda map it is better to use {{Codeline|m = 3}}.
 
[[File:tisean_false_neigh.png|400px|center]]
 
[[File:tisean_false_neigh.png|400px|center]]
 
=== Finding Unstable Periodic Orbits ===
 
Here I will demonstrate how to find unstable periodic orbits. This section is based on the TISEAN documentation chapter [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node19.html#SECTION00053000000000000000 Finding unstable periodic orbits]. We will start by finding these orbits using function {{Codeline|upo}}.
 
{{Code|Finding unstable periodic orbits|<syntaxhighlight lang="octave" style="font-size:13px">
 
# Create maps
 
hen            = henon (1000);
 
hen            = hen + std (hen) * 0.02 .* (-6 + sum (rand ([size(hen), 12]), 3));
 
# Find orbits
 
[lengths, data] = upo(hen(:,1), 2, 'p',6,'v',0.1, 'n', 100);
 
</syntaxhighlight>}}
 
The vector {{Codeline|lengths}} contains the size of each of the found orbits and {{Codeline|data}} contains their coordinates. To obtain delay coordinates and plot these orbits onto the original data we will use {{Codeline|upoembed}}.
 
{{Code|Plotting unstable periodic orbits|<syntaxhighlight lang="octave" style="font-size:13px">
 
# Create delay coordinates for the orbits and data
 
up        = upoembed (lengths, data, 1);
 
delay_hen = delay (hen(:,1));
 
# Plot all of the data
 
plot (delay_hen(:,1), delay_hen(:,2), 'r.;Noisy Henon;','markersize',2,...
 
      up{4}(:,1), up{4}(:,2),'gx;Fixed Point;','markersize',20,'linewidth',1, ...
 
      up{3}(:,1), up{3}(:,2),'b+;Period 2;','markersize',20,'linewidth',1, ...
 
      up{2}(:,1), up{2}(:,2),'ms;Period 6;','markersize',20,'linewidth',1, ...
 
      up{1}(:,1), up{1}(:,2),'cs;Period 6;','markersize',20,'linewidth',1);
 
</syntaxhighlight>}}
 
[[File:Upo.png|400px|center]]
 
The plotting options are passed to make the orbits more visible.
 
  
 
=== Nonlinear Prediction ===
 
=== Nonlinear Prediction ===
 
In this section we will demonstrate some functions from the 'Nonlinear Prediction' chapter of the TISEAN documentation (located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node16.html#SECTION00050000000000000000 here]). For now this section will only demonstrate functions that are connected to the [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node18.html#SECTION00052000000000000000 Simple Nonlinear Prediction] section. <br/>
 
In this section we will demonstrate some functions from the 'Nonlinear Prediction' chapter of the TISEAN documentation (located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node16.html#SECTION00050000000000000000 here]). For now this section will only demonstrate functions that are connected to the [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node18.html#SECTION00052000000000000000 Simple Nonlinear Prediction] section. <br/>
There are three functions in this section: {{Codeline|lzo_test}}, {{Codeline|lzo_gm}} and {{Codeline|lzo_run}}. The first is used to estimate the forecast error for a set of chosen parameters, the second gives some global information about the fit and the third produces predicted points. Let us start with the first one (before starting this example remember to download 'amplitude.dat' from above and start Octave in the directory that contains it). The pairs of parameters {{Codeline|(m,d)}} where chosen after the TISEAN documentation.
+
There are three functions in this section: {{Codeline|lzo_test}}, {{Codeline|lzo_gm}} and {{Codeline|lzo_run}}. The first is used to estimate the forecast error for a set of chosen parameters, the second gives some global information about the fit and the third produces predicted points. Let us start with the first one (before completing starting this example remember to download 'amplitude.dat' from above and start Octave in the directory that contains it). The pairs of parameters {{Codeline|(m,d)}} where chosen after the TISEAN documentation.
 
{{Code|Analyzing forecast errors for various parameters|<syntaxhighlight lang="octave" style="font-size:13px">
 
{{Code|Analyzing forecast errors for various parameters|<syntaxhighlight lang="octave" style="font-size:13px">
 
# Load data
 
# Load data
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       delay_clean(:,1), delay_clean(:,2), 'r.;Clean Data;','markersize',3)
 
       delay_clean(:,1), delay_clean(:,2), 'r.;Clean Data;','markersize',3)
 
</syntaxhighlight>}}
 
</syntaxhighlight>}}
[[File:tisean_nl_noisereduction.png|400px|center]]
 
 
 
On the chart created the red dots represent cleaned up data. It is much closer to the original than the noisy blue set.<br/>
 
On the chart created the red dots represent cleaned up data. It is much closer to the original than the noisy blue set.<br/>
 
Now we will do the same with {{Codeline|ghkss}}.
 
Now we will do the same with {{Codeline|ghkss}}.
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The rest of the code is the same as the code used in the {{Codeline|lazy}} example. <br/>
 
The rest of the code is the same as the code used in the {{Codeline|lazy}} example. <br/>
 
Once both results are compared it is quite obvious that for this particular example {{Codeline|ghkss}} is superior to {{Codeline|lazy}}. The TISEAN documentation points out that this is not always the case.
 
Once both results are compared it is quite obvious that for this particular example {{Codeline|ghkss}} is superior to {{Codeline|lazy}}. The TISEAN documentation points out that this is not always the case.
[[File:tisean_nl_noisereduction_2.png|400px|center]]
+
[[Category:Octave-Forge]]
 
 
=== 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 series
 
in      = sin((1:2500).'./360) + cos((1:2500).'./180);
 
# Estimate Lyapunov exponents
 
mmax_val = 20
 
lyap_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 reset
 
hold on
 
for j=2:mmax_val
 
  plot (lyap_exp(1,j-1).exp(:,1),lyap_exp(1,j-1).exp(:,2),'r');
 
endfor
 
xlabel ("t [flow samples]");
 
ylabel ("S(eps, embed, t)");
 
hold off
 
</syntaxhighlight>}}
 
[[File:lyap_k.png|400px|center]]
 
 
 
=== Dimensions and Entropies ===
 
This section is discussed on the [http://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-size:13px">
 
# Create maps
 
hen      = henon (10000);
 
# Calculate the correlation sum, dimension and entropy
 
vals = d2 (hen, 'd', 1, 'm', 5, 't',50);
 
# Plot correlation sum
 
subplot (2,3,1)
 
do_plot_corr  = @(x) loglog (x{1}(:,1),x{1}(:,2),'b');
 
hold on
 
arrayfun (do_plot_corr, {vals.c2});
 
hold off
 
xlabel ("Epsilon")
 
ylabel ("Correlation sums")
 
title ("c2");
 
# Plot correlation entropy
 
subplot (2,3,4)
 
do_plot_entrop  = @(x) semilogx (x{1}(:,1),x{1}(:,2),'g');
 
hold on
 
arrayfun (do_plot_entrop, {vals.h2});
 
hold off
 
xlabel ("Epsilon")
 
ylabel ("Correlation entropies");
 
title ("h2")
 
# Plot correlation dimension
 
subplot (2,3,[2 3 5 6])
 
do_plot_slope = @(x) semilogx (x{1}(:,1),x{1}(:,2),'r');
 
hold on
 
arrayfun (do_plot_slope, {vals.d2});
 
hold off
 
xlabel ("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 output
 
figure 2
 
smooth = av_d2 (vals,'a',2);
 
# Plot the smoothed output
 
do_plot_slope = @(x) semilogx (x{1}(:,1),x{1}(:,2),'b');
 
hold on
 
arrayfun (do_plot_slope, {smooth.d2});
 
hold off
 
xlabel ("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 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.
 
  
 
== 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.
 
[[Category:Octave Forge]]
 

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