1 | #include <iostream> |
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2 | #include <math.h> |
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3 | #include <Utils/utils.hh> |
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4 | |
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5 | void findTrimmedHistStatsOLD(float *array, const int size, |
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6 | float &tmean, float &tsigma) |
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7 | { |
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8 | |
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9 | const int nbin = 100; |
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10 | float *num = new float[nbin]; |
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11 | bool *keep = new bool[nbin]; |
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12 | |
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13 | // HOW MANY VALUES IN EACH BIN? |
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14 | float min,max; |
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15 | findMinMax(array,size,min,max); |
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16 | for(int i=0; i<nbin; i++) num[i]=0; |
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17 | for(int i=0; i<size; i++){ |
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18 | float fraction = (array[i] - min) / (max - min); |
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19 | int bin = (int)( floor(fraction*nbin) ); |
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20 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
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21 | } |
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22 | int binmax = 0; |
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23 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
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24 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
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25 | float *newarray = new float[size]; |
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26 | int newsize = 0; |
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27 | for(int i=0; i<size; i++){ |
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28 | float fraction = (array[i] - min) / (max - min); |
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29 | int bin = (int)( floor(fraction*nbin) ); |
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30 | if(keep[bin]) newarray[newsize++] = array[i]; |
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31 | } |
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32 | |
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33 | findNormalStats(newarray,newsize,tmean,tsigma); |
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34 | delete [] num; |
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35 | delete [] keep; |
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36 | delete [] newarray; |
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37 | |
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38 | } |
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39 | //-------------------------------------------------------------------- |
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40 | |
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41 | void findTrimmedHistStats2(float *array, const int size, |
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42 | float &tmean, float &tsigma) |
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43 | { |
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44 | |
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45 | const int nbin = 200; |
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46 | float *num = new float[nbin]; |
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47 | bool *keep = new bool[nbin]; |
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48 | |
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49 | // HOW MANY VALUES IN EACH BIN? |
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50 | float min,max; |
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51 | findMinMax(array,size,min,max); |
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52 | for(int i=0; i<nbin; i++) num[i]=0; |
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53 | for(int i=0; i<size; i++){ |
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54 | float fraction = (array[i] - min) / (max - min); |
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55 | int bin = (int)( floor(fraction*nbin) ); |
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56 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
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57 | } |
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58 | int binmax = 0; |
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59 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
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60 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
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61 | float *newarray = new float[size]; |
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62 | int newsize = 0; |
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63 | for(int i=0; i<size; i++){ |
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64 | float fraction = (array[i] - min) / (max - min); |
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65 | int bin = (int)( floor(fraction*nbin) ); |
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66 | if(keep[bin]) newarray[newsize++] = array[i]; |
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67 | } |
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68 | |
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69 | tmean = findMean(newarray,newsize); |
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70 | |
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71 | tsigma = newsize * (max-min)/float(nbin) / |
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72 | (num[binmax] * erf(sqrt(M_LN2)) * sqrt(2.*M_PI)); |
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73 | |
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74 | } |
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75 | //-------------------------------------------------------------------- |
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76 | |
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77 | void findTrimmedHistStats(float *array, const int size, |
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78 | float &tmean, float &tsigma) |
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79 | { |
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80 | float datamin,datamax; |
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81 | findMinMax(array,size,datamin,datamax); |
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82 | float *sorted = new float[size]; |
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83 | float *cumul = new float[size]; |
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84 | float *angle = new float[size]; |
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85 | for(int i=0;i<size;i++) sorted[i] = array[i]/(datamax-datamin); |
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86 | std::sort(sorted,sorted+size); |
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87 | for(int i=0;i<size;i++) cumul[i] = (float)i/(float)size; |
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88 | int width =(int)( 20. * log10((float)size)); |
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89 | for(int i=0;i<size;i++){ |
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90 | int beg,end; |
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91 | float slope,eSlope,inter,eInter,r; |
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92 | if(i<width/2) beg=0; else beg=i-width/2; |
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93 | if(i>=size-width/2) end=size-1; else end=i+width/2; |
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94 | if(linear_regression(size,sorted,cumul,beg,end,slope,eSlope,inter,eInter,r)==0) |
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95 | angle[i] = atan( fabs(slope) ) * 180. / M_PI; |
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96 | else angle[i] = 90.; |
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97 | } |
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98 | |
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99 | // int start = 0; |
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100 | // while(angle[start] < 45.) start++; |
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101 | // int finish = size-1; |
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102 | // while(angle[finish] < 45.) finish--; |
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103 | |
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104 | int maxpt = 0; |
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105 | for(int i = 1; i<size; i++) if(angle[i]>angle[maxpt]) maxpt=i; |
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106 | int start = maxpt; |
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107 | while((start>0)&&(angle[start]>45.)) start--; |
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108 | int finish = maxpt; |
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109 | while((finish < size-1)&&(angle[finish]>45.)) finish++; |
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110 | |
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111 | float *newarray = new float[finish-start+1]; |
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112 | for(int i=0;i<finish-start+1;i++) |
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113 | newarray[i] = sorted[i+start]*(datamax-datamin); |
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114 | |
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115 | findNormalStats(newarray,finish-start+1,tmean,tsigma); |
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116 | |
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117 | } |
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