[301] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // zscale.cc: Get a suitable pixel value scaling for displaying a 2D |
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| 3 | // image in greyscale. |
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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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[3] | 29 | #include <iostream> |
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[212] | 30 | #include <algorithm> |
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[393] | 31 | #include <duchamp/Utils/utils.hh> |
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[3] | 32 | const int nsample=1000; |
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| 33 | // const int width=300; |
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| 34 | const int width=150; |
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| 35 | const float contrast=0.25; |
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| 36 | |
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| 37 | void zscale(long imagesize, float *image, float &z1, float &z2) |
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| 38 | { |
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| 39 | |
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| 40 | float *smallarray = new float[nsample]; |
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| 41 | float *ct = new float[nsample]; |
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| 42 | long size=0; |
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| 43 | |
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| 44 | float mean=0.,sd=0.; |
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| 45 | for(int i=0;i<imagesize;i++) mean+=image[i]; |
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| 46 | mean /= float(imagesize); |
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| 47 | for(int i=0;i<imagesize;i++) sd+=(image[i]-mean)*(image[i]-mean); |
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| 48 | sd /= float(imagesize); |
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| 49 | |
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| 50 | |
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| 51 | if(imagesize>nsample){ |
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| 52 | int step = (imagesize / nsample) + 1; |
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| 53 | for(int i=0;i<imagesize;i++){ |
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| 54 | if((i%step)==0){ |
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| 55 | smallarray[size] = image[i]; |
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| 56 | ct[size]=(float)size+1.; |
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| 57 | size++; |
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| 58 | } |
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| 59 | } |
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| 60 | } |
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| 61 | else{ |
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| 62 | for(int i=0;i<imagesize;i++){ |
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| 63 | smallarray[i] = image[i]; |
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| 64 | ct[i] = float(i+1); |
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| 65 | } |
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| 66 | size=imagesize; |
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| 67 | } |
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| 68 | |
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[212] | 69 | std::sort(smallarray,smallarray+size); |
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[3] | 70 | |
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| 71 | /* fit a linear slope to the centre of the cumulative distribution */ |
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| 72 | long midpt = size/2; |
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| 73 | long imin = midpt - width; |
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| 74 | float slope,intercept,errSlope,errIntercept,r; |
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| 75 | if(size<2*width) |
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| 76 | linear_regression(size,ct,smallarray,0,size-1,slope,errSlope, |
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| 77 | intercept,errIntercept,r); |
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| 78 | else linear_regression(size,ct,smallarray,imin,imin+2*width,slope,errSlope, |
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| 79 | intercept,errIntercept,r); |
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| 80 | |
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| 81 | z1 = smallarray[midpt] + (slope/contrast)*(float)(1-midpt); |
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| 82 | z2 = smallarray[midpt] + (slope/contrast)*(float)(nsample-midpt); |
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| 83 | |
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| 84 | if(z1==z2){ |
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| 85 | |
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| 86 | if(z1!=0) z2 = z1 * 1.05; |
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| 87 | else z2 = z1+0.01; |
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| 88 | |
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| 89 | } |
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| 90 | |
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| 91 | delete [] smallarray; |
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| 92 | delete [] ct; |
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| 93 | |
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| 94 | } |
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| 95 | |
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| 96 | |
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| 97 | void zscale(long imagesize, float *image, float &z1, float &z2, float blankVal) |
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| 98 | { |
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| 99 | float *newimage = new float[imagesize]; |
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| 100 | int newsize=0; |
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| 101 | for(int i=0;i<imagesize;i++) if(image[i]!=blankVal) newimage[newsize++] = image[i]; |
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| 102 | |
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| 103 | float *smallarray = new float[nsample]; |
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| 104 | float *ct = new float[nsample]; |
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| 105 | long size=0; |
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| 106 | |
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| 107 | float mean=0.,sd=0.; |
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| 108 | for(int i=0;i<newsize;i++) mean+=newimage[i]; |
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| 109 | mean /= float(newsize); |
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| 110 | for(int i=0;i<newsize;i++) sd+=(newimage[i]-mean)*(newimage[i]-mean); |
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| 111 | sd /= float(newsize); |
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| 112 | |
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| 113 | |
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| 114 | if(newsize>nsample){ |
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| 115 | int step = (newsize / nsample) + 1; |
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| 116 | for(int i=0;i<newsize;i++){ |
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| 117 | if((i%step)==0){ |
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| 118 | smallarray[size] = newimage[i]; |
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| 119 | ct[size]=(float)size+1.; |
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| 120 | size++; |
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| 121 | } |
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| 122 | } |
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| 123 | } |
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| 124 | else{ |
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| 125 | for(int i=0;i<newsize;i++){ |
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| 126 | smallarray[i] = newimage[i]; |
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| 127 | ct[i] = float(i+1); |
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| 128 | } |
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| 129 | size=newsize; |
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| 130 | } |
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| 131 | |
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[212] | 132 | std::sort(smallarray,smallarray+size); |
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[3] | 133 | |
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| 134 | /* fit a linear slope to the centre of the cumulative distribution */ |
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| 135 | long midpt = size/2; |
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| 136 | long imin = midpt - width; |
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| 137 | float slope,intercept,errSlope,errIntercept,r; |
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| 138 | if(size<2*width) |
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| 139 | linear_regression(size,ct,smallarray,0,size-1,slope,errSlope, |
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| 140 | intercept,errIntercept,r); |
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| 141 | else linear_regression(size,ct,smallarray,imin,imin+2*width,slope,errSlope, |
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| 142 | intercept,errIntercept,r); |
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| 143 | |
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| 144 | z1 = smallarray[midpt] + (slope/contrast)*(float)(1-midpt); |
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| 145 | z2 = smallarray[midpt] + (slope/contrast)*(float)(nsample-midpt); |
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| 146 | |
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| 147 | if(z1==z2){ |
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| 148 | |
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| 149 | if(z1!=0) z2 = z1 * 1.05; |
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| 150 | else z2 = z1+0.01; |
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| 151 | |
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| 152 | } |
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| 153 | |
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| 154 | delete [] newimage; |
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| 155 | delete [] smallarray; |
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| 156 | delete [] ct; |
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| 157 | |
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| 158 | |
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| 159 | } |
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| 160 | |
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| 161 | |
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