Mercurial > hg > ltpda

annotate m-toolbox/test/test_ao_cohere_variance_montecarlo.m @ 51:9d5c88356247 database-connection-manager

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Make unit tests database connection parameters configurable
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 07 Dec 2011 17:24:37 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % test_ao_cohere_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_cohere_variance_montecarlo.m,v 1.1 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_cohere_variance_montecarlo()
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12
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13 clear
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14
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15 % data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 nsecs = 200;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'sin(2*pi*2*t) + randn(size(t))');
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19
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20
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % Make a filter
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 f1 = miir(plist('type', 'highpass', 'fc', 4, 'fs', fs));
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23
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 % Window
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 Nfft = 100;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 win = specwin('Hanning', Nfft);
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27 pl2 = plist('Nfft',Nfft, 'win',win,'Olap',0)
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28
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29 % loop
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 for i = 1:100
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31 a1 = ao(pl);
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32 a2 = filter(a1,plist('filter', f1));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 c1(i) = cohere(a1,a2,plist('Nfft',100,'type','MS'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 % Do with MATLAB
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 [cxy, f] = mscohere(a1.data.y, a2.data.y, win.win, Nfft/2, Nfft, a1.data.fs);
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36 c2(i) = ao(fsdata(f, cxy));
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37 end
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38
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39 %% mean
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 index = 6;
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41
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42 % compare mean
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43 mn = [mean(c1(:).y(index)) mean(c2(:).y(index))]
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44 % error
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 err = std(c1(:).y(index))
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46 % compare standard deviation
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47 clear rel
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 for i =1:len(c1(1))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 mn(i) = [mean(c1(:).y(i))]; % both means are equal
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50 rel(:,i) = [std(c1(:).y(i)) mean(c1(:).dy(i))]/abs(mn(i));
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51 end
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52
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53 figure
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54 loglog(c1(1).x,rel')
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55 figure
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56 loglog(c1(1).x(:),100*abs(rel(2,:)-rel(1,:)))
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57 ylabel('difference (%)')
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58