svdcov_sv

Uses singular value decomposition to return the left and right singular vectors associated with the two input datasets.

Prototype

	function svdcov_sv (
		x       [*][*] : float or double,  
		y       [*][*] : float or double,  
		nsvd           : integer,          
		svLeft  [*][*] : float or double,  
		svRight [*][*] : float or double   
	)

	return_val  :  float or double

Arguments

Two-dimensional input array, dimensioned num_stations (or grid points) in x by time.

Two-dimensional input array, dimensioned num_stations (or grid points) in y by time.

Scalar integer that specifies the number of singular vectors to be returned.

Left singular vector (output), dimensioned nsvd x num_stations in x. Space for this must be explicitly allocated by the user.

Right singular vector (output), dimensioned nsvd x num_stations in y. Space for this must be explicitly allocated by the user.

Return value

This function returns the percent variance explained as a 1D float array (double array if input is double) of length nsvd.

svLeft and svRight must be preallocated by the user, and are both outputs. After function call, svLeft will contain the left singular vector dimensioned nsvd x num_stations in x. svRight will contain the right singular vector dimensioned nsvd x num_stations in y.

Description

svdcov_sv uses singular value decomposition (SVD) of x and y and returns the percent variance explained. Note: A similar function, svdstd_sv, standardizes the input arrays x and y.

This function does not allow for missing data (defined by the _FillValue attribute) to be present.

This function returns the following attribute:

sv (1D array, float or double)

singular values

See Also

svdstd_sv, svdcov, svdstd

Examples

Example 1

begin
                                 ; PARAMETERS
  ntime   = 8                      ; # time steps
  ncols   = 3                      ; # columns (stations or grid pts) for S
  ncolz   = 6                      ; # columns (stations or grid pts) for Z
  nsvd    = 3                      ; # svd patterns to calculate 
                                 ;   [nsvd <= min(ncols, ncolz) ]
  froot = "/fs/scd/home1/shea/ncldata_input/"
  s     = asciiread (froot+"svd_ex01_S.asc",(/ntime,ncols/), "float")
  z     = asciiread (froot+"svd_ex01_Z.asc",(/ntime,ncolz/), "float")

  s!0   = "time"                       ; name dimensions for reordering
  s!1   = "col"
  z!0   = "time"
  z!1   = "col"
 
  svLeft  = new((/nsvd,ncols/),float)  ; pre-allocate space
  svRight = new((/nsvd,ncolz/),float)
                                     ; reorder so time varying fastest
  pcVar = svdcov_sv(s(col|:,time|:),z(col|:,time|:),nsvd,svLeft,svRight)

  print("svdcov_sv: percent variance= " + pcVar)
  print("svdcov_sv: singular values = " + pcVar@sv)

end