Mercurial > hg > ltpda
view m-toolbox/test/test_ao_whiten2D.m @ 47:dd93c9ba6624 database-connection-manager
Fix Java dialog
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Tue, 06 Dec 2011 19:07:27 +0100 |
parents | f0afece42f48 |
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% A test script for ao/whiten2D % % L. Ferraioli 18-11-08 % % $Id: test_ao_whiten2D.m,v 1.6 2009/04/20 14:31:14 luigi Exp $ % %% clear all %% CSD Noise models % Mdc2 noise model % mod = ltpnoise(); load mpsd.mat % load mpsd.mat first column is f then csd11, csd12 and csd22 f = mpsd(:,1); % frequency vector csd11 = mpsd(:,2); csd12 = mpsd(:,3); csd22 = mpsd(:,4); fs = 10; % sampling frequency % Building AOs ltp_csd11 = ao(fsdata(f,csd11,fs)); ltp_csd12 = ao(fsdata(f,csd12,fs)); ltp_csd21 = ao(fsdata(f,conj(csd12),fs)); ltp_csd22 = ao(fsdata(f,csd22,fs)); %% iplot(ltp_csd11,ltp_csd12,ltp_csd22) %% Colored Noise generation % Make white noise a1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000)); a2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000)); a = [a1 a2]; % Generation from white noise pl = plist(... 'csd11', ltp_csd11,... 'csd12', ltp_csd12,... 'csd21', ltp_csd21,... 'csd22', ltp_csd22,... 'MaxIter', 70, ... 'PoleType', 2, ... 'MinOrder', 20, ... 'MaxOrder', 30, ... 'Weights', 2, ... 'Plot', false,... 'MSEVARTOL', 1e-2,... 'FITTOL', 1e-3,... 'UseSym', 0,... 'Disp', false); ac = noisegen2D(a, pl); %% Checking generated noise acxx1 = ac(1).psd; acxx2 = ac(2).psd; acxx12 = ac.cpsd; iplot(abs(acxx1),abs(ltp_csd11)) iplot(abs(acxx12),abs(ltp_csd12)) iplot(abs(acxx2),abs(ltp_csd22)) %% Noise whitening pl = plist(... 'csd11', ltp_csd11, ... 'csd12', ltp_csd12, ... 'csd21', ltp_csd21, ... 'csd22', ltp_csd22, ... 'MaxIter', 75, ... 'PoleType', 3, ... 'MinOrder', 20, ... 'MaxOrder', 40, ... 'Weights', 2, ... 'Plot', false,... 'MSEVARTOL', 1e-2,... 'FITTOL', 1e-3,... 'UseSym', 0,... 'Disp', false); aw = whiten2D(ac, pl); %% Checking whitened noise awxx1 = aw(1).psd; awxx2 = aw(2).psd; %% iplot(awxx1,awxx2) %% Checking aw1 = aw(1); aw2 = aw(2); awxx1 = awxx(1,1); awxx2 = awxx(2,2); mw1 = aw1.mean; % 0 expected vw1 = aw1.var; % 1 expected mw2 = aw2.mean; % 0 expected vw2 = aw2.var; % 1 expected mwxx1 = awxx1.mean; % 0.2 expected vwxx1 = awxx1.var; mwxx2 = awxx2.mean; % 0.2 expected vwxx2 = awxx2.var; % correlation coefficients % diagonal terms are aw1 and aw2 variances % cross terms are data series covariances, a value close to 0 means no % correlation between the data series crc = corrcoef([aw1.data.y.' aw2.data.y.']); % END