Euclidean analog search
This module looks for analog patterns in a library dataset. Analogs are
selected according to a minimal Euclidean distance in the search space.
Two different search spaces have been implemented to date. There are
classes to search for analogs in a PCA truncated space and in a CCA
truncated one. Further details can be found in
Fernandez and J. Saenz
Analog search in CCA space.
(Submitted to Climate Research)
Contact the authors for a draft copy.
Imported modules


import Numeric
import math
import os
import pyclimate.bpcca
import pyclimate.mvarstatools
import pyclimate.pyclimateexcpt
import pyclimate.svdeofs
import pyclimate.tools

Functions


get_weights


get_weights

get_weights ( distarray, weightexp )
Returns the weight values for each analog
The weights are normalized so the sum of them all is 1. The
weights are inversely proportional to the weightexp power
of the distance.
Arguments:

distarray
 Array with the distances to the
smoothing (see the class
ANALOGSelector ) nearest analogs

weightexp
 The exponent of the weights.

Classes


ANALOGSelector 
Reconstructs a field averaging over several analog patterns

CCAANALOG 
Analog search in the CCA space

EOFANALOG 
Analog search in the PCA space

__ANALOG 
Base class for the analog search. It cannot be instanciated!


