[idvusers] spatial Principal Component Analysis (PCA) on a long time series of various oceanographic variables

• To: "idvusers@xxxxxxxxxxxxxxxx" <idvusers@xxxxxxxxxxxxxxxx>
• Subject: [idvusers] spatial Principal Component Analysis (PCA) on a long time series of various oceanographic variables
• From: Valentijn Venus <venus@xxxxxx>
• Date: Thu, 13 May 2010 11:25:50 +0200

Hi List, 

we intend to compute a Principal Component Analysis (PCA) on a long time series 
of various oceanographic variables (ie. sea-surface temperature anomalies). The 
input would consist of a multiple gridded fields from which the covariance 
matrix will be calculated. The output of the principal component analysis 
operation is: 
- an output matrix denoting the transformation coefficients (calculated from 
the covariance matrix), 
- an output list of computed fields containing the set of transformed input 
fields, or the components.

We noticed there are some matrix functions available already in visad-IDV 
though these JAMA wrappers: 
http://www.ssec.wisc.edu/visad-docs/javadoc/visad/matrix/package-summary.html. 

visad.Matrix works on FlatFieldImpl, see 
http://www.ssec.wisc.edu/visad-docs/javadoc/Jama/class-use/Matrix.html. Can a 
single-banded image/grid be here be passed on as FlatFieldImpl? 

Anyone who ever tried (or has an idea) of how the Spatial PCA could be done? 
Any third party JAVA components one could advise to call IDV's jython library?

Tyn
Faculty of Geo-Information Science and Earth Observation (ITC)
University of Twente

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• References:
  • [idvusers] Formula to compute anomalies
    • From: Joseph Maina