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comparison m-toolbox/test/test_ao_ltfe_variance_montecarlo.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 % test_ao_ltfe_variance_montecarlo
2 %
3 % Tests that the standard deviation returned by ao.dy in one
4 % frequency bin is equivalent to the matlab's std taking
5 % considering all realisations
6 %
7 % M Nofrarias 22-07-09
8 %
9 % $Id: test_ao_ltfe_variance_montecarlo.m,v 1.1 2009/08/11 14:20:10 miquel Exp $
10
11 % function test_ao_ltfe_variance_montecarlo()
12
13 clear
14
15 % data
16 nsecs = 200;
17 fs = 10;
18 pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'sin(2*pi*2*t) + randn(size(t))');
19
20
21 % Make a filter
22 f1 = miir(plist('type', 'highpass', 'fc', 4, 'fs', fs));
23
24 % Window
25 pl2 = plist('Kdes',20,'win','Hanning','Order',-1)
26
27 % loop
28 for i = 1:100
29 a1 = ao(pl);
30 a2 = filter(a1,plist('filter', f1));
31 t1(i) = ltfe(a1,a2,pl2);
32 end
33
34 %% mean
35 index = 6;
36
37 clear rel
38 for i =1:len(t1(1))
39 mn(i) = [mean(t1(:).y(i))]; % both means are equal
40 rel(:,i) = [std(t1(:).y(i)) mean(t1(:).dy(i))]/abs(mn(i));
41 end
42
43 k = find(t1(1).procinfo,'k')
44
45 figure
46 loglog(t1(1).x,rel')
47 figure
48 loglog(t1(1).x,100*abs(rel(2,:)-rel(1,:)))
49 ylabel('difference (%)')
50