Table of Contents

Module: analog analog.py

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

  1. 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!


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