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Module: svdeofs svdeofs.py

EOF decomposition based on SVD

Given a dataset as read from readdat, that is, a matrix NxM, with N the number of samples and M the number of channels or spatial samples, these functions and classes compute the unrotated EOF decomposition of the field, the principal components and some utility routines.

For better stability the computations are carried out by means of singular value decomposition.

Imported modules   
import LinearAlgebra
import Numeric
import Scientific.Statistics
import pyclimate.mctest
import pyclimate.mvarstatools
import pyclimate.pyclimateexcpt
import pyclimate.pydcdflib
import pyclimate.tools
import sys
Functions   
bartletttest
eofsasexplainedvariance
getchiprob
getgencol
getvariancefraction
mctesteofs
northtest
pcseriescorrelation
svdeofs
  bartletttest 
bartletttest ( lambdas,  samples )

  eofsasexplainedvariance 
eofsasexplainedvariance (
        eofs,
        pcscaling=0,
        lambdas=None,
        )

Exceptions   
pex.ScalingError( pcscaling )
  getchiprob 
getchiprob ( chival,  dof )

  getgencol 
getgencol ( a,  ncol=0 )

  getvariancefraction 
getvariancefraction ( lambdas )

  mctesteofs 
mctesteofs (
        dataset,
        eofs,
        subsamples,
        length,
        )

Monte Carlo test for the stability of the EOFs

def mctesteofs(dataset,eofs,subsamples,length):

Test the leading master EOFs obtained from the complete sample and input in eofs by means of a Monte Carlo test based on making subsamples subsamples with length members in each

  northtest 
northtest ( lambdas,  tsamples )

  pcseriescorrelation 
pcseriescorrelation (
        pcs,
        eofs,
        dataset,
        )

Calculates the correlation between the PCs and time series at each grid point.

Arguments:

pcs
the PCs as returned by svdeofs
eofs
the EOFs as returned by svdeofs
dataset
the dataset

Returns an array which generalized columns are the correlation fields of the original time series with each PC.

  svdeofs 
svdeofs ( dataset,  pcscaling=0 )

Calculates de EOF decomposition of a field.

Arguments:

dataset
NumPy array containing the field to be decomposed. First dimension must be time.
pcscaling
sets the scale factor of the PCs: 0 means eigenvalues are PC variances and the EOFs are orthonormal. 1 means PCs with unit variance and orthogonal EOFs. (Defaults to 0)

Returns a tuple containing: PCs, eigenvalues, EOFs

If the field has more than one spatial dimension it can be processed anyway and each EOF can be recovered as generalized columns: EOFs[..., eofnumber]

Exceptions   
pex.ScalingError( pcscaling )
Classes   

SVDEOFs

Class implementation of the EOF routines


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