[301] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // trimStats.cc: Find estimates of the mean & rms by looking at the |
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| 3 | // histogram of pixel values trimmed of outliers. |
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| 4 | // ----------------------------------------------------------------------- |
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| 5 | // Copyright (C) 2006, Matthew Whiting, ATNF |
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| 6 | // |
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| 7 | // This program is free software; you can redistribute it and/or modify it |
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| 8 | // under the terms of the GNU General Public License as published by the |
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| 9 | // Free Software Foundation; either version 2 of the License, or (at your |
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| 10 | // option) any later version. |
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| 11 | // |
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| 12 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
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| 13 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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| 14 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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| 15 | // for more details. |
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| 16 | // |
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| 17 | // You should have received a copy of the GNU General Public License |
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| 18 | // along with Duchamp; if not, write to the Free Software Foundation, |
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| 19 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
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| 20 | // |
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| 21 | // Correspondence concerning Duchamp may be directed to: |
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| 22 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
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| 23 | // Postal address: Dr. Matthew Whiting |
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| 24 | // Australia Telescope National Facility, CSIRO |
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| 25 | // PO Box 76 |
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| 26 | // Epping NSW 1710 |
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| 27 | // AUSTRALIA |
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| 28 | // ----------------------------------------------------------------------- |
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[222] | 29 | #include <iostream> |
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| 30 | #include <math.h> |
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[393] | 31 | #include <duchamp/Utils/utils.hh> |
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[222] | 32 | |
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| 33 | void findTrimmedHistStatsOLD(float *array, const int size, |
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| 34 | float &tmean, float &tsigma) |
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| 35 | { |
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| 36 | |
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| 37 | const int nbin = 100; |
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| 38 | float *num = new float[nbin]; |
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| 39 | bool *keep = new bool[nbin]; |
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| 40 | |
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| 41 | // HOW MANY VALUES IN EACH BIN? |
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| 42 | float min,max; |
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| 43 | findMinMax(array,size,min,max); |
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| 44 | for(int i=0; i<nbin; i++) num[i]=0; |
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| 45 | for(int i=0; i<size; i++){ |
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| 46 | float fraction = (array[i] - min) / (max - min); |
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| 47 | int bin = (int)( floor(fraction*nbin) ); |
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| 48 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
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| 49 | } |
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| 50 | int binmax = 0; |
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| 51 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
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| 52 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
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| 53 | float *newarray = new float[size]; |
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| 54 | int newsize = 0; |
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| 55 | for(int i=0; i<size; i++){ |
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| 56 | float fraction = (array[i] - min) / (max - min); |
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| 57 | int bin = (int)( floor(fraction*nbin) ); |
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| 58 | if(keep[bin]) newarray[newsize++] = array[i]; |
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| 59 | } |
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| 60 | |
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| 61 | findNormalStats(newarray,newsize,tmean,tsigma); |
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| 62 | delete [] num; |
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| 63 | delete [] keep; |
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| 64 | delete [] newarray; |
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| 65 | |
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| 66 | } |
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| 67 | //-------------------------------------------------------------------- |
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| 68 | |
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| 69 | void findTrimmedHistStats2(float *array, const int size, |
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| 70 | float &tmean, float &tsigma) |
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| 71 | { |
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| 72 | |
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| 73 | const int nbin = 200; |
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| 74 | float *num = new float[nbin]; |
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| 75 | bool *keep = new bool[nbin]; |
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| 76 | |
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| 77 | // HOW MANY VALUES IN EACH BIN? |
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| 78 | float min,max; |
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| 79 | findMinMax(array,size,min,max); |
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| 80 | for(int i=0; i<nbin; i++) num[i]=0; |
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| 81 | for(int i=0; i<size; i++){ |
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| 82 | float fraction = (array[i] - min) / (max - min); |
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| 83 | int bin = (int)( floor(fraction*nbin) ); |
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| 84 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
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| 85 | } |
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| 86 | int binmax = 0; |
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| 87 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
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| 88 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
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| 89 | float *newarray = new float[size]; |
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| 90 | int newsize = 0; |
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| 91 | for(int i=0; i<size; i++){ |
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| 92 | float fraction = (array[i] - min) / (max - min); |
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| 93 | int bin = (int)( floor(fraction*nbin) ); |
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| 94 | if(keep[bin]) newarray[newsize++] = array[i]; |
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| 95 | } |
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| 96 | |
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| 97 | tmean = findMean(newarray,newsize); |
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| 98 | |
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| 99 | tsigma = newsize * (max-min)/float(nbin) / |
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| 100 | (num[binmax] * erf(sqrt(M_LN2)) * sqrt(2.*M_PI)); |
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| 101 | |
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| 102 | } |
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| 103 | //-------------------------------------------------------------------- |
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| 104 | |
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| 105 | void findTrimmedHistStats(float *array, const int size, |
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| 106 | float &tmean, float &tsigma) |
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| 107 | { |
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| 108 | float datamin,datamax; |
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| 109 | findMinMax(array,size,datamin,datamax); |
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| 110 | float *sorted = new float[size]; |
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| 111 | float *cumul = new float[size]; |
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| 112 | float *angle = new float[size]; |
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| 113 | for(int i=0;i<size;i++) sorted[i] = array[i]/(datamax-datamin); |
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| 114 | std::sort(sorted,sorted+size); |
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| 115 | for(int i=0;i<size;i++) cumul[i] = (float)i/(float)size; |
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| 116 | int width =(int)( 20. * log10((float)size)); |
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| 117 | for(int i=0;i<size;i++){ |
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| 118 | int beg,end; |
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| 119 | float slope,eSlope,inter,eInter,r; |
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| 120 | if(i<width/2) beg=0; else beg=i-width/2; |
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| 121 | if(i>=size-width/2) end=size-1; else end=i+width/2; |
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| 122 | if(linear_regression(size,sorted,cumul,beg,end,slope,eSlope,inter,eInter,r)==0) |
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| 123 | angle[i] = atan( fabs(slope) ) * 180. / M_PI; |
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| 124 | else angle[i] = 90.; |
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| 125 | } |
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| 126 | |
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| 127 | // int start = 0; |
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| 128 | // while(angle[start] < 45.) start++; |
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| 129 | // int finish = size-1; |
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| 130 | // while(angle[finish] < 45.) finish--; |
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| 131 | |
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| 132 | int maxpt = 0; |
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| 133 | for(int i = 1; i<size; i++) if(angle[i]>angle[maxpt]) maxpt=i; |
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| 134 | int start = maxpt; |
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| 135 | while((start>0)&&(angle[start]>45.)) start--; |
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| 136 | int finish = maxpt; |
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| 137 | while((finish < size-1)&&(angle[finish]>45.)) finish++; |
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| 138 | |
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| 139 | float *newarray = new float[finish-start+1]; |
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| 140 | for(int i=0;i<finish-start+1;i++) |
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| 141 | newarray[i] = sorted[i+start]*(datamax-datamin); |
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| 142 | |
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| 143 | findNormalStats(newarray,finish-start+1,tmean,tsigma); |
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| 144 | |
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| 145 | } |
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