wtcdemo.m

交互小波分析及其一致性分析

%% Example 
% This example illustrates how simple it is to do 
% continuous wavelet transform (CWT), Cross wavelet transform (XWT)
% and Wavelet Coherence (WTC) plots of your own data. 
%
% The time series we will be analyzing are the winter 
% Arctic Oscillation index (AO) and 
% the maximum sea ice extent in the Baltic (BMI).
%
% http://www.pol.ac.uk/home/research/waveletcoherence/


%% Load the data
% First we load the two time series into the matrices d1 and d2.

seriesname={'AO' 'BMI'};
d1=load('jao.txt');
d2=load('jbaltic.txt');

%% Change the pdf.
% The time series of Baltic Sea ice extent is highly bi-modal and we
% therefore transform the timeseries into a series of percentiles. The
% transformed series probably reacts 'more linearly' to climate.

d2(:,2)=boxpdf(d2(:,2));


%% Continuous wavelet transform (CWT)
% The CWT expands the time series into time
% frequency space. 

figure('color',[1 1 1])
tlim=[min(d1(1,1),d2(1,1)) max(d1(end,1),d2(end,1))]; 
subplot(2,1,1);
wt(d1);
title(seriesname{1});
set(gca,'xlim',tlim);
subplot(2,1,2)
wt(d2)
title(seriesname{2})
set(gca,'xlim',tlim)


%% Cross wavelet transform (XWT)
% The XWT finds regions in time frequency space where
% the time series show high common power.

figure('color',[1 1 1])
xwt(d1,d2)
title(['XWT: ' seriesname{1} '-' seriesname{2} ] )

%% Wavelet coherence (WTC)
% The WTC finds regions in time frequency space where the two 
% time series co-vary (but does not necessarily have high power).


figure('color',[1 1 1])
wtc(d1,d2)
title(['WTC: ' seriesname{1} '-' seriesname{2} ] )





%% Copyright notice
%   Copyright (C) 2002-2004, Aslak Grinsted
%
%   This software may be used, copied, or redistributed as long as it is not
%   sold and this copyright notice is reproduced on each copy made.  This
%   routine is provided as is without any express or implied warranties
%   whatsoever.