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annotate m-toolbox/test/LTPDA_training/topic5/TrainigSession_T5_Ex03.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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % Training session Topic 5 exercise 03
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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 % Generation of noise with given psd
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 %
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5 % 1) Load fsdata object from file
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % 2) Fit psd of test data with zDomainFit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % 3) Genarate noise from fitted model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % 4) Fit psd of test data with curvefit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % 5) Genarate noise from fitted model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % 6) Compare results
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11 %
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12 % L FERRAIOLI 22-02-09
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13 %
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14 % $Id: TrainigSession_T5_Ex03.m,v 1.3 2009/02/25 18:18:45 luigi Exp $
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15 %
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16 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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17
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18 %% 1) load fsdata
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19
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % load test noise AO from file
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21 tn = ao(plist('filename', 'topic5\T5_Ex03_TestNoise.xml'));
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22 tn.setName;
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23
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 %% 2) Fitting psd of test data with zDomainFit
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25
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26 %% 2.1) We try to identify a proper model for the psd of loaded data
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27
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 % making psd ot test data - we need some average otherwise the fitting
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 % algorithm is not able to run correctly
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30 tnxx1 = tn.psd(plist('Nfft',2000));
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31
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32 % Removing first bins
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33 tnxx1r = split(tnxx1,plist('split_type', 'frequencies', 'frequencies', [2e-3 5e-1]));
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34 iplot(tnxx1r)
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35
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 % finding proper weights for noisy data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 stnxx = smoother(tnxx1r);
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38 wgh = 1./(abs(stnxx.data.y));
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39
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40 %% 2.2) Fitting
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41
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42 plfit1 = plist('FS',1,... % Sampling frequency for the model filters
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43 'AutoSearch','on',... % Automatically search for a good model
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44 'StartPolesOpt','c1',... % Define the properties of the starting poles - complex distributed in the unitary circle
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45 'maxiter',50,... % maximum number of iteration per model order
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46 'minorder',10,... % minimum model order
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47 'maxorder',45,... % maximum model order
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48 'weights',wgh,... % assign externally calculated weights
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49 'ResLogDiff',[],... % Residuals log difference (no need to assign this parameter for the moment)
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50 'ResFlat',0.77,... % Rsiduals spectral flatness
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51 'RMSE',5,... % Root Mean Squared Error Variation
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52 'Plot','on',... % set the plot on or off
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53 'ForceStability','off',... % we do not need a stable filter
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54 'CheckProgress','off'); % display fitting progress on the command window
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55
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56 % Do the fit
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57 [param1,fmod1] = zDomainFit(tnxx1r,plfit1);
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58
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59 %% 3) Generating noise from model psd
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60
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61 % noisegen1D is a noise coloring tool. It accept as input a white noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 % time series and output a colored noise time series with one sided psd
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 % matching the model specifiend into the parameters
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64
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65 % Generate white noise
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66 a1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', 1, 'nsecs', 10000,'yunits','m'));
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67
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68 plng = plist(...
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69 'model', abs(fmod1), ... % model for colored noise psd
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70 'MaxIter', 50, ... % maximum number of fit iteration per model order
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71 'PoleType', 2, ... % generates complex poles distributed in the unitary circle
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72 'MinOrder', 20, ... % minimum model order
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73 'MaxOrder', 50, ... % maximum model order
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74 'Weights', 2, ... % weight with 1/abs(model)
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75 'Plot', false,... % on to show the plot
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76 'Disp', false,... % on to display fit progress on the command window
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77 'RMSEVar', 7,... % Root Mean Squared Error Variation
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78 'FitTolerance', 2); % Residuals log difference
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79
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80 ac1 = noisegen1D(a1, plng);
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81
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82 %% 4) Checking results
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83
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84 % Calculating psd of generated data
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85 acxx1 = ac1.psd(plist('Nfft',2000));
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86
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87 % Comparing with starting data
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88 iplot(tnxx1,acxx1)
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89