Difference between revisions of "TISEAN package"

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(External links: added link to TISEAN home page)
(Noise Reduction: improved ghkss example)
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{{Code|Locally projective nonlinear noise reduction|<syntaxhighlight lang="octave" style="font-size:13px">
 
{{Code|Locally projective nonlinear noise reduction|<syntaxhighlight lang="octave" style="font-size:13px">
 
clean      = ghkss (hen,'m',7,'q',2,'r',0.05,'k',20,'i',2);
 
clean      = ghkss (hen,'m',7,'q',2,'r',0.05,'k',20,'i',2);
# Create delay vectors for both the clean and noisy data
 
delay_clean = delay (clean);
 
delay_noisy = delay (hen_noisy);
 
# Plot both on one chart
 
plot (delay_noisy(:,1), delay_noisy(:,2), 'b.;Noisy Data;','markersize,3,...
 
      delay_clean(:,1), delay_clean(:,2), 'r.;Clean Data;','markersize,3)
 
 
</syntaxhighlight>}}
 
</syntaxhighlight>}}
 
+
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.
 
[[Category:Octave-Forge]]
 
[[Category:Octave-Forge]]
  

Revision as of 10:51, 1 June 2015

Porting TISEAN

This section will focus on demonstrating the capabilities of the TISEAN package. The previous information about the porting procedure has been moved here.


Tutorials

These tutorials are based on examples, tutorials and the articles located on the TISEAN website:
http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/.
This tutorial will utilize the following dataset:

Please download it as the tutorial will reference it.

Noise Reduction

This tutorial show different methods of the 'Noise Reduction' section of the TISEAN documentation (located here). It shows the use of simple nonlinear noise reduction (function lazy) and locally projective nonlinear noise reduction (function ghkss). To start let's create noisy data to work with.

Code: Creating a noisy henon map
hen       = henon (10000);
hen       = hen(:,1); # We only need the first column
hen_noisy = hen + std (hen) * 0.02 .* (-6 + sum (rand ([size(hen), 12]), 3));

This created a Henon map contaminated by 2% Gaussian noise à la TISEAN. In the tutorials and exercises on the TISEAN website this would be equivalent to calling makenoise -%2 on the Henon map.
Next we will reduce the noise using simple nonlinear noise reduction lazy.

Code: Simple nonlinear noise reduction
clean       = lazy (hen_noisy,7,-0.06,3);
# Create delay vectors for both the clean and noisy data
delay_clean = delay (clean);
delay_noisy = delay (hen_noisy);
# Plot both on one chart
plot (delay_noisy(:,1), delay_noisy(:,2), 'b.;Noisy Data;','markersize,3,...
      delay_clean(:,1), delay_clean(:,2), 'r.;Clean Data;','markersize,3)

On the chart created the red dots represent cleaned up data. It is much closer to the original than the noisy blue set.
Now we will do the same with ghkss.

Code: Locally projective nonlinear noise reduction
clean       = ghkss (hen,'m',7,'q',2,'r',0.05,'k',20,'i',2);

The rest of the code is the same as the code used in the lazy example.
Once both results are compared it is quite obvious that for this particular example ghkss is superior to lazy. The TISEAN documentation points out that this is not always the case.

External links