1 | #include <math.h>
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2 | #include "toimanager.h"
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3 | #include "correl.h"
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4 | #include "wienerdecor.h"
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5 | extern "C" {
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6 | #include "nrutil.h"
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7 | }
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8 | extern "C" void dtoeplz(double r[], double x[], double y[], int n);
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9 |
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10 | WienerDecorrelator::WienerDecorrelator(int n, int l) {
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11 | nsamples = n;
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12 | lcorr = l;
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13 | doNotLookAt();
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14 | }
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15 |
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16 | void WienerDecorrelator::init() {
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17 | declareInput("signal");
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18 | declareInput("probe");
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19 | declareOutput("signal");
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20 | declareOutput("noiseestim");
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21 | name="WienerDecorrelator";
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22 | setNeededHistory(nsamples+lcorr+1);
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23 | lowExtra = lcorr;
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24 | }
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25 |
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26 | void WienerDecorrelator::run() {
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27 | int snb = getMinIn();
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28 | int sne = getMaxIn();
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29 |
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30 | // cout << "Wiener " << snb << " - " << sne << endl;
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31 |
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32 | CorrelEstimator corr(lcorr, nsamples), autocorr(lcorr, nsamples);
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33 |
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34 | double* r = new double[2*lcorr]; // autocorr toeplitz matrix
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35 | double* w = new double[lcorr+1]; // filter
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36 | double* y = new double[lcorr+1]; // corr vector
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37 | double* window = new double[lcorr];
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38 | uint_8* fwind = new uint_8[lcorr];
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39 | double* filter = new double[lcorr];
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40 | for (int i=0; i<lcorr; i++) filter[i]=0;
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41 |
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42 | int sn = snb;
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43 | int snstartcorr = -1;
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44 |
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45 | while (sn <= sne) {
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46 | if (snstartcorr < 0 ||
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47 | (snstartcorr + nsamples < sn && sn+nsamples < sne)) {
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48 | // let's (re)compute the correlation
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49 | snstartcorr = sn;
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50 | corr.reset();
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51 | autocorr.reset();
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52 | cout << "computing correl " << sn << " -> " << sn+nsamples << endl;
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53 | for (int i=sn; i<sn+nsamples; i++) {
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54 | uint_8 flag1, flag2;
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55 | double sig, prb;
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56 | getData(0, i, sig, flag1);
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57 | if (flag1 & flgNotLookAt) continue;
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58 | getData(1, i, prb, flag2);
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59 | if (flag2 & flgNotLookAt) continue;
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60 | if ((i-sn)%100 == 0) {
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61 | //cout << " sig/prb : " << i << " : " << sig << " / " << prb
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62 | // << hex << " " << flag1 << " " << flag2 << dec << endl;
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63 | }
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64 | corr.push(i, sig, prb);
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65 | autocorr.push(i, prb);
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66 | }
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67 | // correlation is recomputed, let's recompute the wiener filter from wiener equations
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68 | {for (int i=0; i<lcorr; i++) {
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69 | r[lcorr+i] = r[lcorr-i] = autocorr.correl(i);
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70 | y[i+1] = corr.correl(i);
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71 | //cout << "r " << lcorr+i << " " << lcorr -i << " = " << r[lcorr+i]
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72 | // << "\n"
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73 | // << "y " << i+1 << " = " << y[i+1] << endl;
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74 | }}
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75 | dtoeplz(r,w,y,lcorr);
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76 | if (!isnan(w[1])) {
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77 | for (int i=0; i<lcorr; i++) {
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78 | filter[i] = w[i+1];
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79 | }
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80 | } else {
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81 | cout << "Bad inversion, keeping previous filter\n";
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82 | }
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83 | cout << "Wiener filter : " << sn << "\n ";
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84 | {for (int i=0; i<lcorr; i++) {
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85 | cout << filter[i] << " ";
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86 | }}
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87 | cout << endl;
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88 | }
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89 |
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90 | if (sn >= snb+lcorr-1) {
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91 | getData(1, sn-lcorr+1, lcorr, window, fwind);
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92 | uint_8 flag = 0;
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93 | double outSig = 0;
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94 | for (int i=0; i<lcorr; i++) {
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95 | outSig += filter[i] * window[lcorr-1 - i];
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96 | flag |= fwind[lcorr-1 -i];
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97 | }
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98 | putData(0, sn, getData(0, sn) - outSig, flag);
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99 | putData(1, sn, outSig, flag);
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100 | }
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101 | sn++;
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102 | }
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103 |
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104 | delete[] y;
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105 | delete[] w;
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106 | delete[] r;
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107 | delete[] filter;
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108 | }
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