[299] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // smoothCube: Smooth a Cube's array, and search for objects. |
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| 3 | // ----------------------------------------------------------------------- |
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| 4 | // Copyright (C) 2006, Matthew Whiting, ATNF |
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| 5 | // |
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| 6 | // This program is free software; you can redistribute it and/or modify it |
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| 7 | // under the terms of the GNU General Public License as published by the |
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| 8 | // Free Software Foundation; either version 2 of the License, or (at your |
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| 9 | // option) any later version. |
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| 10 | // |
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| 11 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
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| 12 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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| 13 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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| 14 | // for more details. |
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| 15 | // |
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| 16 | // You should have received a copy of the GNU General Public License |
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| 17 | // along with Duchamp; if not, write to the Free Software Foundation, |
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| 18 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
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| 19 | // |
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| 20 | // Correspondence concerning Duchamp may be directed to: |
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| 21 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
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| 22 | // Postal address: Dr. Matthew Whiting |
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| 23 | // Australia Telescope National Facility, CSIRO |
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| 24 | // PO Box 76 |
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| 25 | // Epping NSW 1710 |
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| 26 | // AUSTRALIA |
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| 27 | // ----------------------------------------------------------------------- |
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[275] | 28 | #include <vector> |
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[393] | 29 | #include <duchamp/duchamp.hh> |
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| 30 | #include <duchamp/Cubes/cubes.hh> |
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| 31 | #include <duchamp/Detection/detection.hh> |
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| 32 | #include <duchamp/PixelMap/Object2D.hh> |
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| 33 | #include <duchamp/Utils/feedback.hh> |
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| 34 | #include <duchamp/Utils/Hanning.hh> |
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| 35 | #include <duchamp/Utils/GaussSmooth.hh> |
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| 36 | #include <duchamp/Utils/Statistics.hh> |
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| 37 | #include <duchamp/Utils/utils.hh> |
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[201] | 38 | |
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[378] | 39 | namespace duchamp |
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| 40 | { |
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| 41 | |
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[258] | 42 | void Cube::SmoothSearch() |
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| 43 | { |
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| 44 | /** |
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| 45 | * The Cube is first smoothed, using Cube::SmoothCube(). |
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[275] | 46 | * It is then searched, using search3DArray() |
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[258] | 47 | * The resulting object list is stored in the Cube, and outputted |
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| 48 | * to the log file if the user so requests. |
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| 49 | */ |
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[275] | 50 | |
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| 51 | if(!this->par.getFlagSmooth()){ |
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| 52 | duchampWarning("SmoothSearch", |
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| 53 | "FlagSmooth not set! Using basic CubicSearch.\n"); |
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| 54 | this->CubicSearch(); |
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| 55 | } |
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| 56 | else{ |
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[258] | 57 | |
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[290] | 58 | this->SmoothCube(); |
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[258] | 59 | |
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[275] | 60 | if(this->par.isVerbose()) std::cout << " "; |
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| 61 | |
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| 62 | this->setCubeStats(); |
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| 63 | |
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[291] | 64 | // this->Stats.scaleNoise(1./gauss.getStddevScale()); |
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| 65 | |
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[275] | 66 | if(this->par.isVerbose()) std::cout << " Searching... " << std::flush; |
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| 67 | |
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[309] | 68 | *(this->objectList) = search3DArraySimple(this->axisDim,this->recon, |
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| 69 | this->par,this->Stats); |
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[275] | 70 | |
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| 71 | if(this->par.isVerbose()) std::cout << " Updating detection map... " |
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| 72 | << std::flush; |
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| 73 | this->updateDetectMap(); |
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[258] | 74 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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[275] | 75 | |
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| 76 | if(this->par.getFlagLog()){ |
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| 77 | if(this->par.isVerbose()) |
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| 78 | std::cout << " Logging intermediate detections... " << std::flush; |
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| 79 | this->logDetectionList(); |
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| 80 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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| 81 | } |
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| 82 | |
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[258] | 83 | } |
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| 84 | |
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| 85 | } |
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[275] | 86 | //----------------------------------------------------------- |
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[258] | 87 | |
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[290] | 88 | void Cube::SmoothCube() |
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| 89 | { |
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| 90 | /** |
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| 91 | * Switching function that chooses the appropriate function with |
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| 92 | * which to smooth the cube, based on the Param::smoothType |
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| 93 | * parameter. |
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| 94 | * |
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| 95 | */ |
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| 96 | if(this->par.getSmoothType()=="spectral"){ |
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| 97 | |
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| 98 | this->SpectralSmooth(); |
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[377] | 99 | |
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| 100 | } |
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| 101 | else if(this->par.getSmoothType()=="spatial"){ |
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| 102 | |
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| 103 | this->SpatialSmooth(); |
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| 104 | |
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| 105 | } |
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[290] | 106 | } |
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| 107 | //----------------------------------------------------------- |
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| 108 | |
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[275] | 109 | void Cube::SpectralSmooth() |
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[201] | 110 | { |
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| 111 | /** |
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[275] | 112 | * A function that smoothes each spectrum in the cube using the |
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[201] | 113 | * Hanning smoothing function. The degree of smoothing is given |
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[272] | 114 | * by the parameter Param::hanningWidth. |
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[201] | 115 | */ |
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| 116 | |
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[275] | 117 | long xySize = this->axisDim[0]*this->axisDim[1]; |
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| 118 | long zdim = this->axisDim[2]; |
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| 119 | ProgressBar bar; |
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| 120 | |
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[277] | 121 | if(!this->reconExists && this->par.getSmoothType()=="spectral"){ |
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[365] | 122 | // if(!this->head.isSpecOK()) |
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| 123 | if(!this->head.canUseThirdAxis()) |
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[277] | 124 | duchampWarning("SpectralSmooth", |
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[377] | 125 | "There is no spectral axis, so cannot do the spectral smoothing.\n"); |
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[277] | 126 | else{ |
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| 127 | |
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| 128 | Hanning hann(this->par.getHanningWidth()); |
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[201] | 129 | |
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[277] | 130 | float *spectrum = new float[this->axisDim[2]]; |
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[275] | 131 | |
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[277] | 132 | if(this->par.isVerbose()) { |
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| 133 | std::cout<<" Smoothing spectrally... "; |
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| 134 | bar.init(xySize); |
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| 135 | } |
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[275] | 136 | |
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[277] | 137 | for(int pix=0;pix<xySize;pix++){ |
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[275] | 138 | |
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[277] | 139 | if( this->par.isVerbose() ) bar.update(pix+1); |
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[275] | 140 | |
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[277] | 141 | for(int z=0;z<zdim;z++){ |
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| 142 | if(this->isBlank(z*xySize+pix)) spectrum[z]=0.; |
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| 143 | else spectrum[z] = this->array[z*xySize+pix]; |
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| 144 | } |
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[275] | 145 | |
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[277] | 146 | float *smoothed = hann.smooth(spectrum,zdim); |
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[275] | 147 | |
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[277] | 148 | for(int z=0;z<zdim;z++){ |
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| 149 | if(this->isBlank(z*xySize+pix)) |
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[285] | 150 | this->recon[z*xySize+pix] = this->array[z*xySize+pix]; |
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[277] | 151 | else |
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| 152 | this->recon[z*xySize+pix] = smoothed[z]; |
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| 153 | } |
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| 154 | delete [] smoothed; |
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[275] | 155 | } |
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[277] | 156 | this->reconExists = true; |
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| 157 | if(this->par.isVerbose()) bar.fillSpace("All Done.\n"); |
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[275] | 158 | |
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[277] | 159 | delete [] spectrum; |
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[275] | 160 | |
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[277] | 161 | } |
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[275] | 162 | } |
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| 163 | } |
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| 164 | //----------------------------------------------------------- |
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| 165 | |
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| 166 | void Cube::SpatialSmooth() |
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| 167 | { |
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| 168 | |
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[277] | 169 | if(!this->reconExists && this->par.getSmoothType()=="spatial"){ |
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[201] | 170 | |
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[277] | 171 | if( this->head.getNumAxes() < 2 ) |
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| 172 | duchampWarning("SpatialSmooth", |
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| 173 | "There are not enough axes to do the spatial smoothing.\n"); |
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| 174 | else{ |
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[201] | 175 | |
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[277] | 176 | long xySize = this->axisDim[0]*this->axisDim[1]; |
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| 177 | long xdim = this->axisDim[0]; |
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| 178 | long ydim = this->axisDim[1]; |
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| 179 | long zdim = this->axisDim[2]; |
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[275] | 180 | |
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[277] | 181 | ProgressBar bar; |
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[372] | 182 | // bool useBar = this->head.canUseThirdAxis(); |
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| 183 | bool useBar = (zdim > 1); |
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| 184 | |
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[285] | 185 | // if kernMin is negative (not defined), make it equal to kernMaj |
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| 186 | if(this->par.getKernMin() < 0) |
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| 187 | this->par.setKernMin(this->par.getKernMaj()); |
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| 188 | |
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[277] | 189 | GaussSmooth gauss(this->par.getKernMaj(), |
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| 190 | this->par.getKernMin(), |
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| 191 | this->par.getKernPA()); |
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[201] | 192 | |
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[277] | 193 | if(this->par.isVerbose()) { |
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| 194 | std::cout<<" Smoothing spatially... " << std::flush; |
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[372] | 195 | if(useBar) bar.init(zdim); |
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[277] | 196 | } |
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| 197 | |
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| 198 | float *image = new float[xySize]; |
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[291] | 199 | |
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[277] | 200 | for(int z=0;z<zdim;z++){ |
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| 201 | |
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[372] | 202 | if( this->par.isVerbose() && useBar ) bar.update(z+1); |
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[275] | 203 | |
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[277] | 204 | for(int pix=0;pix<xySize;pix++) image[pix] = this->array[z*xySize+pix]; |
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[201] | 205 | |
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[291] | 206 | bool *mask = this->par.makeBlankMask(image,xySize); |
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[275] | 207 | |
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[291] | 208 | float *smoothed = gauss.smooth(image,xdim,ydim,mask); |
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[275] | 209 | |
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[285] | 210 | for(int pix=0;pix<xySize;pix++){ |
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| 211 | if(mask[pix]) |
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| 212 | this->recon[z*xySize+pix] = this->array[z*xySize+pix]; |
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| 213 | else |
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| 214 | this->recon[z*xySize+pix] = smoothed[pix]; |
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| 215 | } |
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[201] | 216 | |
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[291] | 217 | delete [] smoothed; |
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| 218 | delete [] mask; |
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[277] | 219 | } |
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[201] | 220 | |
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[277] | 221 | delete [] image; |
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[275] | 222 | |
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[277] | 223 | this->reconExists = true; |
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[275] | 224 | |
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[372] | 225 | if(par.isVerbose()){ |
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| 226 | if(useBar) bar.fillSpace("All Done."); |
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| 227 | std::cout << "\n"; |
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| 228 | } |
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| 229 | |
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| 230 | } |
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[277] | 231 | } |
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[275] | 232 | } |
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| 233 | //----------------------------------------------------------- |
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| 234 | |
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| 235 | void Cube::SpatialSmoothNSearch() |
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| 236 | { |
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| 237 | |
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| 238 | long xySize = this->axisDim[0]*this->axisDim[1]; |
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| 239 | long xdim = this->axisDim[0]; |
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| 240 | long ydim = this->axisDim[1]; |
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| 241 | long zdim = this->axisDim[2]; |
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| 242 | int numFound=0; |
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| 243 | ProgressBar bar; |
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| 244 | |
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| 245 | GaussSmooth gauss(this->par.getKernMaj(), |
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| 246 | this->par.getKernMin(), |
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| 247 | this->par.getKernPA()); |
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| 248 | |
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| 249 | this->Stats.scaleNoise(1./gauss.getStddevScale()); |
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| 250 | if(this->par.getFlagFDR()) this->setupFDR(); |
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| 251 | |
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| 252 | if(this->par.isVerbose()) { |
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| 253 | std::cout<<" Smoothing spatially & searching... " << std::flush; |
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| 254 | bar.init(zdim); |
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| 255 | } |
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| 256 | |
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| 257 | std::vector <Detection> outputList; |
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| 258 | long *imdim = new long[2]; |
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| 259 | imdim[0] = xdim; imdim[1] = ydim; |
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| 260 | Image *channelImage = new Image(imdim); |
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| 261 | delete [] imdim; |
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| 262 | channelImage->saveParam(this->par); |
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| 263 | channelImage->saveStats(this->Stats); |
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| 264 | channelImage->setMinSize(1); |
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| 265 | |
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| 266 | float *image = new float[xySize]; |
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| 267 | float *smoothed; |
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| 268 | bool *mask; |
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| 269 | float median,madfm;//,threshold; |
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| 270 | for(int z=0;z<zdim;z++){ |
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| 271 | |
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| 272 | if( this->par.isVerbose() ) bar.update(z+1); |
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| 273 | |
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| 274 | if(!this->par.isInMW(z)){ |
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| 275 | |
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| 276 | for(int pix=0;pix<xySize;pix++) image[pix] = this->array[z*xySize+pix]; |
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| 277 | |
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| 278 | mask = this->par.makeBlankMask(image,xySize); |
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| 279 | |
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| 280 | smoothed = gauss.smooth(image,xdim,ydim,mask); |
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| 281 | |
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| 282 | // for(int pix=0;pix<xySize;pix++) |
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| 283 | // this->recon[z*xySize+pix] = smoothed[pix]; |
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| 284 | |
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| 285 | findMedianStats(smoothed,xySize,mask,median,madfm); |
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[277] | 286 | // threshold = median+this->par.getCut()*Statistics::madfmToSigma(madfm); |
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| 287 | // for(int i=0;i<xySize;i++) |
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| 288 | // if(smoothed[i]<threshold) image[i] = this->Stats.getMiddle(); |
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| 289 | // channelImage->saveArray(image,xySize); |
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[275] | 290 | |
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| 291 | |
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[277] | 292 | // channelImage->stats().setMadfm(madfm); |
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| 293 | // channelImage->stats().setMedian(median); |
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| 294 | // channelImage->stats().setThresholdSNR(this->par.getCut()); |
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[275] | 295 | channelImage->saveArray(smoothed,xySize); |
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| 296 | |
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| 297 | std::vector<PixelInfo::Object2D> objlist = channelImage->lutz_detect(); |
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| 298 | numFound += objlist.size(); |
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| 299 | for(int obj=0;obj<objlist.size();obj++){ |
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| 300 | Detection newObject; |
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| 301 | newObject.pixels().addChannel(z,objlist[obj]); |
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| 302 | newObject.setOffsets(this->par); |
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| 303 | mergeIntoList(newObject,outputList,this->par); |
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| 304 | } |
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| 305 | |
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[201] | 306 | } |
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[275] | 307 | |
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[201] | 308 | } |
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| 309 | |
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[275] | 310 | delete [] smoothed; |
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| 311 | delete [] mask; |
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| 312 | delete [] image; |
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| 313 | delete channelImage; |
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| 314 | |
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[277] | 315 | // this->reconExists = true; |
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[275] | 316 | if(par.isVerbose()){ |
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| 317 | bar.fillSpace("Found "); |
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| 318 | std::cout << numFound << ".\n"; |
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| 319 | } |
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| 320 | |
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[291] | 321 | *this->objectList = outputList; |
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[201] | 322 | |
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| 323 | } |
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[378] | 324 | |
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| 325 | } |
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