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annotate m-toolbox/test/test_ao_psd_variance_montecarlo.m @ 33:5e7477b94d94 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % test_ao_psd_variance_montecarlo
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 % Tests that the standard deviation returned by ao.dy in one
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % frequency bin is equivalent to the matlab's std taking
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % considering all realisations
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % M Nofrarias 22-07-09
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8 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % $Id: test_ao_psd_variance_montecarlo.m,v 1.2 2009/08/11 14:20:10 miquel Exp $
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10
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % function test_ao_psd_variance_montecarlo()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 clear
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14
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 nsecs = 500;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 fs = 5;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'randn(size(t))');
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19
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % Window
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 Nfft = 100;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 win = specwin('Hanning', Nfft);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 pl2 = plist('Nfft',Nfft, 'win',win,'Olap',-1,'scale','PSD')
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % loop
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 for i = 1:100
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 a(i) = ao(pl);
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28 b1(i) = psd(a(i),pl2);
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29 % matlab's
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30 [txy, f] = pwelch(a(i).data.y, win.win, Nfft/2, Nfft, a(i).data.fs);
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31 b2(i) = ao(fsdata(f.', txy.'));
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32 end
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33
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 %% mean
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 index = 6;
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36
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % compare mean
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 mn = [mean(b1(:).y(index)) mean(b2(:).y(index))]
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 % error
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 err = std(b1(:).y(index))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % compare standard deviation
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 clear rel
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 for i =1:len(b1(1))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 mn(i) = [mean(b1(:).y(i))]; % both means are equal
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 rel(:,i) = [std(b1(:).y(i)) mean(b1(:).dy(i))]/abs(mn(i));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 end
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47
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48 figure
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 loglog(b1(1).x,rel')
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50 figure
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 loglog(b1(1).x,rel(2,:)-rel(1,:))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 ylabel('difference (%)')