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comparison m-toolbox/test/test_ao_whiten2D.m @ 0:f0afece42f48

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % A test script for ao/whiten2D
2 %
3 % L. Ferraioli 18-11-08
4 %
5 % $Id: test_ao_whiten2D.m,v 1.6 2009/04/20 14:31:14 luigi Exp $
6 %
7
8 %%
9
10 clear all
11
12 %% CSD Noise models
13
14 % Mdc2 noise model
15 % mod = ltpnoise();
16
17 load mpsd.mat % load mpsd.mat first column is f then csd11, csd12 and csd22
18
19 f = mpsd(:,1); % frequency vector
20 csd11 = mpsd(:,2);
21 csd12 = mpsd(:,3);
22 csd22 = mpsd(:,4);
23 fs = 10; % sampling frequency
24
25 % Building AOs
26 ltp_csd11 = ao(fsdata(f,csd11,fs));
27 ltp_csd12 = ao(fsdata(f,csd12,fs));
28 ltp_csd21 = ao(fsdata(f,conj(csd12),fs));
29 ltp_csd22 = ao(fsdata(f,csd22,fs));
30
31 %%
32
33 iplot(ltp_csd11,ltp_csd12,ltp_csd22)
34
35 %% Colored Noise generation
36
37 % Make white noise
38
39 a1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000));
40 a2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000));
41 a = [a1 a2];
42
43 % Generation from white noise
44
45 pl = plist(...
46 'csd11', ltp_csd11,...
47 'csd12', ltp_csd12,...
48 'csd21', ltp_csd21,...
49 'csd22', ltp_csd22,...
50 'MaxIter', 70, ...
51 'PoleType', 2, ...
52 'MinOrder', 20, ...
53 'MaxOrder', 30, ...
54 'Weights', 2, ...
55 'Plot', false,...
56 'MSEVARTOL', 1e-2,...
57 'FITTOL', 1e-3,...
58 'UseSym', 0,...
59 'Disp', false);
60
61 ac = noisegen2D(a, pl);
62
63 %% Checking generated noise
64
65 acxx1 = ac(1).psd;
66 acxx2 = ac(2).psd;
67 acxx12 = ac.cpsd;
68
69 iplot(abs(acxx1),abs(ltp_csd11))
70 iplot(abs(acxx12),abs(ltp_csd12))
71 iplot(abs(acxx2),abs(ltp_csd22))
72
73 %% Noise whitening
74
75 pl = plist(...
76 'csd11', ltp_csd11, ...
77 'csd12', ltp_csd12, ...
78 'csd21', ltp_csd21, ...
79 'csd22', ltp_csd22, ...
80 'MaxIter', 75, ...
81 'PoleType', 3, ...
82 'MinOrder', 20, ...
83 'MaxOrder', 40, ...
84 'Weights', 2, ...
85 'Plot', false,...
86 'MSEVARTOL', 1e-2,...
87 'FITTOL', 1e-3,...
88 'UseSym', 0,...
89 'Disp', false);
90
91 aw = whiten2D(ac, pl);
92
93 %% Checking whitened noise
94
95 awxx1 = aw(1).psd;
96 awxx2 = aw(2).psd;
97
98 %%
99 iplot(awxx1,awxx2)
100
101 %% Checking
102
103 aw1 = aw(1);
104 aw2 = aw(2);
105 awxx1 = awxx(1,1);
106 awxx2 = awxx(2,2);
107
108 mw1 = aw1.mean; % 0 expected
109 vw1 = aw1.var; % 1 expected
110
111 mw2 = aw2.mean; % 0 expected
112 vw2 = aw2.var; % 1 expected
113
114 mwxx1 = awxx1.mean; % 0.2 expected
115 vwxx1 = awxx1.var;
116
117 mwxx2 = awxx2.mean; % 0.2 expected
118 vwxx2 = awxx2.var;
119
120 % correlation coefficients
121 % diagonal terms are aw1 and aw2 variances
122 % cross terms are data series covariances, a value close to 0 means no
123 % correlation between the data series
124 crc = corrcoef([aw1.data.y.' aw2.data.y.']);
125
126
127 % END