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annotate m-toolbox/test/test_ao_whiten2D.m @ 41:6def6533cb16 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 18:04:34 +0100
parents f0afece42f48
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % A test script for ao/whiten2D
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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 % L. Ferraioli 18-11-08
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % $Id: test_ao_whiten2D.m,v 1.6 2009/04/20 14:31:14 luigi Exp $
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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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 %%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 clear all
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 %% CSD Noise models
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % Mdc2 noise model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % mod = ltpnoise();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 load mpsd.mat % load mpsd.mat first column is f then csd11, csd12 and csd22
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 f = mpsd(:,1); % frequency vector
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 csd11 = mpsd(:,2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 csd12 = mpsd(:,3);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 csd22 = mpsd(:,4);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 fs = 10; % sampling frequency
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % Building AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 ltp_csd11 = ao(fsdata(f,csd11,fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 ltp_csd12 = ao(fsdata(f,csd12,fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 ltp_csd21 = ao(fsdata(f,conj(csd12),fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 ltp_csd22 = ao(fsdata(f,csd22,fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 %%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 iplot(ltp_csd11,ltp_csd12,ltp_csd22)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 %% Colored Noise generation
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % Make white noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 a1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 a2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 10, 'nsecs', 10000));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 a = [a1 a2];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 % Generation from white noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 pl = plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 'csd11', ltp_csd11,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 'csd12', ltp_csd12,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 'csd21', ltp_csd21,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 'csd22', ltp_csd22,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 'MaxIter', 70, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 'PoleType', 2, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 'MinOrder', 20, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 'MaxOrder', 30, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 'Weights', 2, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 'Plot', false,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 'MSEVARTOL', 1e-2,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 'FITTOL', 1e-3,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 'UseSym', 0,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 'Disp', false);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 ac = noisegen2D(a, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 %% Checking generated noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 acxx1 = ac(1).psd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 acxx2 = ac(2).psd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 acxx12 = ac.cpsd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 iplot(abs(acxx1),abs(ltp_csd11))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 iplot(abs(acxx12),abs(ltp_csd12))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 iplot(abs(acxx2),abs(ltp_csd22))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 %% Noise whitening
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 pl = plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 'csd11', ltp_csd11, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 'csd12', ltp_csd12, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 'csd21', ltp_csd21, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 'csd22', ltp_csd22, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 'MaxIter', 75, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 'PoleType', 3, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 'MinOrder', 20, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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83 'MaxOrder', 40, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 'Weights', 2, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 'Plot', false,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 'MSEVARTOL', 1e-2,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 'FITTOL', 1e-3,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 'UseSym', 0,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 'Disp', false);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 aw = whiten2D(ac, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 %% Checking whitened noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 awxx1 = aw(1).psd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 awxx2 = aw(2).psd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 %%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 iplot(awxx1,awxx2)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 %% Checking
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 aw1 = aw(1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 aw2 = aw(2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 awxx1 = awxx(1,1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 awxx2 = awxx(2,2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 mw1 = aw1.mean; % 0 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 vw1 = aw1.var; % 1 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 mw2 = aw2.mean; % 0 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 vw2 = aw2.var; % 1 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 mwxx1 = awxx1.mean; % 0.2 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 vwxx1 = awxx1.var;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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116
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 mwxx2 = awxx2.mean; % 0.2 expected
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 vwxx2 = awxx2.var;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 % correlation coefficients
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 % diagonal terms are aw1 and aw2 variances
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 % cross terms are data series covariances, a value close to 0 means no
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 % correlation between the data series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124 crc = corrcoef([aw1.data.y.' aw2.data.y.']);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 % END