1 | // ----------------------------------------------------------------------- |
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2 | // cubes.cc: Member functions for the DataArray, Cube and Image classes. |
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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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28 | #include <unistd.h> |
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29 | #include <iostream> |
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30 | #include <iomanip> |
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31 | #include <vector> |
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32 | #include <algorithm> |
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33 | #include <string> |
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34 | #include <math.h> |
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35 | |
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36 | #include <wcslib/wcs.h> |
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37 | |
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38 | #include <duchamp/pgheader.hh> |
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39 | |
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40 | #include <duchamp/duchamp.hh> |
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41 | #include <duchamp/param.hh> |
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42 | #include <duchamp/fitsHeader.hh> |
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43 | #include <duchamp/Cubes/cubes.hh> |
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44 | #include <duchamp/PixelMap/Voxel.hh> |
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45 | #include <duchamp/PixelMap/Object3D.hh> |
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46 | #include <duchamp/Detection/detection.hh> |
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47 | #include <duchamp/Detection/columns.hh> |
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48 | #include <duchamp/Utils/utils.hh> |
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49 | #include <duchamp/Utils/mycpgplot.hh> |
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50 | #include <duchamp/Utils/Statistics.hh> |
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51 | |
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52 | using namespace mycpgplot; |
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53 | using namespace Statistics; |
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54 | using namespace PixelInfo; |
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55 | |
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56 | #ifdef TEST_DEBUG |
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57 | const bool TESTING=true; |
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58 | #else |
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59 | const bool TESTING=false; |
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60 | #endif |
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61 | |
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62 | namespace duchamp |
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63 | { |
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64 | |
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65 | using namespace Column; |
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66 | |
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67 | /****************************************************************/ |
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68 | /////////////////////////////////////////////////// |
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69 | //// Functions for DataArray class: |
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70 | /////////////////////////////////////////////////// |
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71 | |
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72 | DataArray::DataArray(){ |
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73 | /** |
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74 | * Fundamental constructor for DataArray. |
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75 | * Number of dimensions and pixels are set to 0. Nothing else allocated. |
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76 | */ |
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77 | this->numDim=0; |
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78 | this->numPixels=0; |
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79 | this->objectList = new std::vector<Detection>; |
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80 | } |
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81 | //-------------------------------------------------------------------- |
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82 | |
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83 | DataArray::DataArray(short int nDim){ |
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84 | /** |
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85 | * N-dimensional constructor for DataArray. |
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86 | * Number of dimensions defined, and dimension array allocated. |
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87 | * Number of pixels are set to 0. |
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88 | * \param nDim Number of dimensions. |
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89 | */ |
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90 | if(nDim>0) this->axisDim = new long[nDim]; |
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91 | this->numDim=nDim; |
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92 | this->numPixels=0; |
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93 | this->objectList = new std::vector<Detection>; |
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94 | } |
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95 | //-------------------------------------------------------------------- |
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96 | |
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97 | DataArray::DataArray(short int nDim, long size){ |
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98 | /** |
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99 | * N-dimensional constructor for DataArray. |
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100 | * Number of dimensions and number of pixels defined. |
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101 | * Arrays allocated based on these values. |
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102 | * \param nDim Number of dimensions. |
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103 | * \param size Number of pixels. |
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104 | * |
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105 | * Note that we can assign values to the dimension array. |
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106 | */ |
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107 | |
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108 | if(size<0) |
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109 | duchampError("DataArray(nDim,size)", |
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110 | "Negative size -- could not define DataArray"); |
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111 | else if(nDim<0) |
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112 | duchampError("DataArray(nDim,size)", |
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113 | "Negative number of dimensions: could not define DataArray"); |
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114 | else { |
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115 | if(size>0) this->array = new float[size]; |
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116 | this->numPixels = size; |
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117 | if(nDim>0) this->axisDim = new long[nDim]; |
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118 | this->numDim = nDim; |
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119 | } |
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120 | this->objectList = new std::vector<Detection>; |
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121 | } |
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122 | //-------------------------------------------------------------------- |
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123 | |
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124 | DataArray::DataArray(short int nDim, long *dimensions) |
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125 | { |
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126 | /** |
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127 | * Most robust constructor for DataArray. |
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128 | * Number and sizes of dimensions are defined, and hence the number of |
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129 | * pixels. Arrays allocated based on these values. |
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130 | * \param nDim Number of dimensions. |
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131 | * \param dimensions Array giving sizes of dimensions. |
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132 | */ |
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133 | if(nDim<0) |
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134 | duchampError("DataArray(nDim,dimArray)", |
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135 | "Negative number of dimensions: could not define DataArray"); |
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136 | else { |
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137 | int size = dimensions[0]; |
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138 | for(int i=1;i<nDim;i++) size *= dimensions[i]; |
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139 | if(size<0) |
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140 | duchampError("DataArray(nDim,dimArray)", |
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141 | "Negative size: could not define DataArray"); |
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142 | else{ |
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143 | this->numPixels = size; |
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144 | if(size>0) this->array = new float[size]; |
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145 | this->numDim=nDim; |
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146 | if(nDim>0) this->axisDim = new long[nDim]; |
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147 | for(int i=0;i<nDim;i++) this->axisDim[i] = dimensions[i]; |
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148 | } |
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149 | } |
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150 | } |
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151 | //-------------------------------------------------------------------- |
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152 | |
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153 | DataArray::~DataArray() |
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154 | { |
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155 | /** |
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156 | * Destructor -- arrays deleted if they have been allocated, and the |
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157 | * object list is deleted. |
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158 | */ |
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159 | if(this->numPixels>0) delete [] this->array; |
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160 | if(this->numDim>0) delete [] this->axisDim; |
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161 | delete this->objectList; |
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162 | } |
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163 | //-------------------------------------------------------------------- |
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164 | //-------------------------------------------------------------------- |
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165 | |
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166 | void DataArray::getDim(long &x, long &y, long &z){ |
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167 | /** |
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168 | * The sizes of the first three dimensions (if they exist) are returned. |
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169 | * \param x The first dimension. Defaults to 0 if numDim \f$\le\f$ 0. |
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170 | * \param y The second dimension. Defaults to 1 if numDim \f$\le\f$ 1. |
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171 | * \param z The third dimension. Defaults to 1 if numDim \f$\le\f$ 2. |
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172 | */ |
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173 | if(this->numDim>0) x=this->axisDim[0]; |
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174 | else x=0; |
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175 | if(this->numDim>1) y=this->axisDim[1]; |
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176 | else y=1; |
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177 | if(this->numDim>2) z=this->axisDim[2]; |
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178 | else z=1; |
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179 | } |
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180 | //-------------------------------------------------------------------- |
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181 | |
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182 | void DataArray::getDimArray(long *output){ |
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183 | /** |
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184 | * The axisDim array is written to output. This needs to be defined |
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185 | * beforehand: no checking is done on the memory. |
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186 | * \param output The array that is written to. |
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187 | */ |
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188 | for(int i=0;i<this->numDim;i++) output[i] = this->axisDim[i]; |
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189 | } |
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190 | //-------------------------------------------------------------------- |
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191 | |
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192 | void DataArray::getArray(float *output){ |
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193 | /** |
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194 | * The pixel value array is written to output. This needs to be defined |
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195 | * beforehand: no checking is done on the memory. |
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196 | * \param output The array that is written to. |
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197 | */ |
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198 | for(int i=0;i<this->numPixels;i++) output[i] = this->array[i]; |
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199 | } |
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200 | //-------------------------------------------------------------------- |
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201 | |
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202 | void DataArray::saveArray(float *input, long size){ |
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203 | /** |
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204 | * Saves the array in input to the pixel array DataArray::array. |
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205 | * The size of the array given must be the same as the current number of |
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206 | * pixels, else an error message is returned and nothing is done. |
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207 | * \param input The array of values to be saved. |
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208 | * \param size The size of input. |
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209 | */ |
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210 | if(size != this->numPixels) |
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211 | duchampError("DataArray::saveArray", |
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212 | "Input array different size to existing array. Cannot save."); |
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213 | else { |
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214 | if(this->numPixels>0) delete [] this->array; |
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215 | this->numPixels = size; |
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216 | this->array = new float[size]; |
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217 | for(int i=0;i<size;i++) this->array[i] = input[i]; |
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218 | } |
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219 | } |
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220 | //-------------------------------------------------------------------- |
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221 | |
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222 | void DataArray::addObject(Detection object){ |
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223 | /** |
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224 | * \param object The object to be added to the object list. |
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225 | */ |
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226 | // objectList is a vector, so just use push_back() |
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227 | this->objectList->push_back(object); |
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228 | } |
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229 | //-------------------------------------------------------------------- |
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230 | |
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231 | void DataArray::addObjectList(std::vector <Detection> newlist) { |
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232 | /** |
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233 | * \param newlist The list of objects to be added to the object list. |
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234 | */ |
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235 | for(int i=0;i<newlist.size();i++) this->objectList->push_back(newlist[i]); |
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236 | } |
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237 | //-------------------------------------------------------------------- |
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238 | |
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239 | // void DataArray::addObjectOffsets(){ |
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240 | // /** |
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241 | // * Add the pixel offsets (that is, offsets from the corner of the cube to the |
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242 | // * corner of the utilised part) that are stored in the Param set to the |
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243 | // * coordinate values of each object in the object list. |
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244 | // */ |
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245 | // for(int i=0;i<this->objectList->size();i++){ |
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246 | // for(int p=0;p<this->objectList->at(i).getSize();p++){ |
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247 | // this->objectList->at(i).setX(p,this->objectList->at(i).getX(p)+ |
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248 | // this->par.getXOffset()); |
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249 | // this->objectList->at(i).setY(p,this->objectList->at(i).getY(p)+ |
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250 | // this->par.getYOffset()); |
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251 | // this->objectList->at(i).setZ(p,this->objectList->at(i).getZ(p)+ |
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252 | // this->par.getZOffset()); |
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253 | // } |
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254 | // } |
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255 | // } |
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256 | // //-------------------------------------------------------------------- |
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257 | |
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258 | bool DataArray::isDetection(float value){ |
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259 | /** |
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260 | * Is a given value a detection, based on the statistics in the |
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261 | * DataArray's StatsContainer? |
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262 | * \param value The pixel value to test. |
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263 | */ |
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264 | if(par.isBlank(value)) return false; |
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265 | else return Stats.isDetection(value); |
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266 | } |
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267 | //-------------------------------------------------------------------- |
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268 | |
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269 | bool DataArray::isDetection(long voxel){ |
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270 | /** |
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271 | * Is a given pixel a detection, based on the statistics in the |
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272 | * DataArray's StatsContainer? |
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273 | * If the pixel lies outside the valid range for the data array, return false. |
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274 | * \param voxel Location of the DataArray's pixel to be tested. |
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275 | */ |
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276 | if((voxel<0)||(voxel>this->numPixels)) return false; |
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277 | else if(par.isBlank(this->array[voxel])) return false; |
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278 | else return Stats.isDetection(this->array[voxel]); |
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279 | } |
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280 | //-------------------------------------------------------------------- |
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281 | |
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282 | std::ostream& operator<< ( std::ostream& theStream, DataArray &array) |
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283 | { |
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284 | /** |
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285 | * A way to print out the pixel coordinates & flux values of the |
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286 | * list of detected objects belonging to the DataArray. |
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287 | * These are formatted nicely according to the << operator for Detection, |
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288 | * with a line indicating the number of detections at the start. |
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289 | * \param theStream The ostream object to which the output should be sent. |
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290 | * \param array The DataArray containing the list of objects. |
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291 | */ |
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292 | for(int i=0;i<array.numDim;i++){ |
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293 | if(i>0) theStream<<"x"; |
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294 | theStream<<array.axisDim[i]; |
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295 | } |
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296 | theStream<<std::endl; |
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297 | theStream<<array.objectList->size()<<" detections:\n--------------\n"; |
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298 | for(int i=0;i<array.objectList->size();i++){ |
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299 | theStream << "Detection #" << array.objectList->at(i).getID()<<std::endl; |
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300 | Detection *obj = new Detection; |
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301 | *obj = array.objectList->at(i); |
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302 | obj->addOffsets(); |
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303 | theStream<<*obj; |
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304 | delete obj; |
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305 | } |
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306 | theStream<<"--------------\n"; |
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307 | return theStream; |
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308 | } |
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309 | |
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310 | /****************************************************************/ |
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311 | ///////////////////////////////////////////////////////////// |
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312 | //// Functions for Cube class |
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313 | ///////////////////////////////////////////////////////////// |
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314 | |
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315 | Cube::Cube(){ |
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316 | /** |
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317 | * Basic Constructor for Cube class. |
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318 | * numDim set to 3, but numPixels to 0 and all bool flags to false. |
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319 | * No allocation done. |
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320 | */ |
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321 | numPixels=0; numDim=3; |
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322 | reconExists = false; reconAllocated = false; baselineAllocated = false; |
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323 | } |
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324 | //-------------------------------------------------------------------- |
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325 | |
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326 | Cube::Cube(long size){ |
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327 | /** |
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328 | * Alternative Cube constructor, where size is given but not individual |
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329 | * dimensions. Arrays are allocated as appropriate (according to the |
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330 | * relevant flags in Param set), but the Cube::axisDim array is not. |
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331 | */ |
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332 | this->reconAllocated = false; |
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333 | this->baselineAllocated = false; |
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334 | this->numPixels = this->numDim = 0; |
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335 | if(size<0) |
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336 | duchampError("Cube(size)","Negative size -- could not define Cube"); |
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337 | else{ |
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338 | if(size>0){ |
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339 | this->array = new float[size]; |
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340 | if(this->par.getFlagATrous()||this->par.getFlagSmooth()){ |
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341 | this->recon = new float[size]; |
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342 | this->reconAllocated = true; |
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343 | } |
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344 | if(this->par.getFlagBaseline()){ |
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345 | this->baseline = new float[size]; |
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346 | this->baselineAllocated = true; |
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347 | } |
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348 | } |
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349 | this->numPixels = size; |
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350 | this->axisDim = new long[2]; |
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351 | this->numDim = 3; |
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352 | this->reconExists = false; |
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353 | } |
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354 | } |
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355 | //-------------------------------------------------------------------- |
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356 | |
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357 | Cube::Cube(long *dimensions){ |
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358 | /** |
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359 | * Alternative Cube constructor, where sizes of dimensions are given. |
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360 | * Arrays are allocated as appropriate (according to the |
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361 | * relevant flags in Param set), as is the Cube::axisDim array. |
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362 | */ |
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363 | int size = dimensions[0] * dimensions[1] * dimensions[2]; |
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364 | int imsize = dimensions[0] * dimensions[1]; |
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365 | this->reconAllocated = false; |
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366 | this->baselineAllocated = false; |
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367 | this->numPixels = this->numDim = 0; |
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368 | if((size<0) || (imsize<0) ) |
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369 | duchampError("Cube(dimArray)","Negative size -- could not define Cube"); |
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370 | else{ |
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371 | this->numPixels = size; |
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372 | if(size>0){ |
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373 | this->array = new float[size]; |
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374 | this->detectMap = new short[imsize]; |
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375 | if(this->par.getFlagATrous()||this->par.getFlagSmooth()){ |
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376 | this->recon = new float[size]; |
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377 | this->reconAllocated = true; |
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378 | } |
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379 | if(this->par.getFlagBaseline()){ |
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380 | this->baseline = new float[size]; |
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381 | this->baselineAllocated = true; |
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382 | } |
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383 | } |
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384 | this->numDim = 3; |
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385 | this->axisDim = new long[3]; |
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386 | for(int i=0;i<3 ;i++) this->axisDim[i] = dimensions[i]; |
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387 | for(int i=0;i<imsize;i++) this->detectMap[i] = 0; |
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388 | this->reconExists = false; |
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389 | } |
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390 | } |
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391 | //-------------------------------------------------------------------- |
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392 | |
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393 | Cube::~Cube() |
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394 | { |
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395 | /** |
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396 | * The destructor deletes the memory allocated for Cube::detectMap, and, |
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397 | * if these have been allocated, Cube::recon and Cube::baseline. |
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398 | */ |
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399 | delete [] this->detectMap; |
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400 | if(this->reconAllocated) delete [] this->recon; |
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401 | if(this->baselineAllocated) delete [] this->baseline; |
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402 | } |
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403 | //-------------------------------------------------------------------- |
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404 | |
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405 | void Cube::initialiseCube(long *dimensions) |
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406 | { |
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407 | /** |
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408 | * This function will set the sizes of all arrays that will be used by Cube. |
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409 | * It will also define the values of the axis dimensions: this will be done |
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410 | * using the WCS in the FitsHeader class, so the WCS needs to be good and |
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411 | * have three axes. If this is not the case, the axes are assumed to be |
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412 | * ordered in the sense of lng,lat,spc. |
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413 | * |
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414 | * \param dimensions An array of values giving the dimensions (sizes) for |
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415 | * all axes. |
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416 | */ |
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417 | |
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418 | int lng,lat,spc,size,imsize; |
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419 | |
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420 | if(this->head.isWCS() && (this->head.getNumAxes()>=3)){ |
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421 | // if there is a WCS and there is at least 3 axes |
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422 | lng = this->head.WCS().lng; |
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423 | lat = this->head.WCS().lat; |
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424 | spc = this->head.WCS().spec; |
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425 | } |
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426 | else{ |
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427 | // just take dimensions[] at face value |
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428 | lng = 0; |
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429 | lat = 1; |
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430 | spc = 2; |
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431 | } |
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432 | |
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433 | size = dimensions[lng]; |
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434 | if(this->head.getNumAxes()>1) size *= dimensions[lat]; |
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435 | // if(this->head.isSpecOK()) size *= dimensions[spc]; |
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436 | if(this->head.canUseThirdAxis()) size *= dimensions[spc]; |
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437 | imsize = dimensions[lng]; |
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438 | if(this->head.getNumAxes()>1) imsize *= dimensions[lat]; |
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439 | |
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440 | this->reconAllocated = false; |
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441 | this->baselineAllocated = false; |
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442 | |
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443 | if((size<0) || (imsize<0) ) |
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444 | duchampError("Cube::initialiseCube(dimArray)", |
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445 | "Negative size -- could not define Cube.\n"); |
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446 | else{ |
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447 | this->numPixels = size; |
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448 | if(size>0){ |
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449 | this->array = new float[size]; |
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450 | this->detectMap = new short[imsize]; |
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451 | if(this->par.getFlagATrous() || this->par.getFlagSmooth()){ |
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452 | this->recon = new float[size]; |
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453 | this->reconAllocated = true; |
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454 | } |
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455 | if(this->par.getFlagBaseline()){ |
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456 | this->baseline = new float[size]; |
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457 | this->baselineAllocated = true; |
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458 | } |
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459 | } |
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460 | this->numDim = 3; |
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461 | this->axisDim = new long[this->numDim]; |
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462 | this->axisDim[0] = dimensions[lng]; |
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463 | if(this->head.getNumAxes()>1) this->axisDim[1] = dimensions[lat]; |
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464 | else this->axisDim[1] = 1; |
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465 | // if(this->head.isSpecOK()) this->axisDim[2] = dimensions[spc]; |
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466 | if(this->head.canUseThirdAxis()) this->axisDim[2] = dimensions[spc]; |
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467 | else this->axisDim[2] = 1; |
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468 | for(int i=0;i<imsize;i++) this->detectMap[i] = 0; |
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469 | this->reconExists = false; |
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470 | } |
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471 | } |
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472 | //-------------------------------------------------------------------- |
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473 | |
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474 | int Cube::getCube(){ |
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475 | /** |
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476 | * A front-end to the Cube::getCube() function, that does |
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477 | * subsection checks. |
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478 | * Assumes the Param is set up properly. |
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479 | */ |
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480 | std::string fname = par.getImageFile(); |
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481 | if(par.getFlagSubsection()) fname+=par.getSubsection(); |
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482 | return getCube(fname); |
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483 | } |
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484 | //-------------------------------------------------------------------- |
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485 | |
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486 | void Cube::saveArray(float *input, long size){ |
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487 | if(size != this->numPixels){ |
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488 | std::stringstream errmsg; |
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489 | errmsg << "Input array different size to existing array (" |
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490 | << size << " cf. " << this->numPixels << "). Cannot save.\n"; |
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491 | duchampError("Cube::saveArray",errmsg.str()); |
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492 | } |
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493 | else { |
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494 | if(this->numPixels>0) delete [] array; |
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495 | this->numPixels = size; |
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496 | this->array = new float[size]; |
---|
497 | for(int i=0;i<size;i++) this->array[i] = input[i]; |
---|
498 | } |
---|
499 | } |
---|
500 | //-------------------------------------------------------------------- |
---|
501 | |
---|
502 | void Cube::saveRecon(float *input, long size){ |
---|
503 | /** |
---|
504 | * Saves the array in input to the reconstructed array Cube::recon |
---|
505 | * The size of the array given must be the same as the current number of |
---|
506 | * pixels, else an error message is returned and nothing is done. |
---|
507 | * If the recon array has already been allocated, it is deleted first, and |
---|
508 | * then the space is allocated. |
---|
509 | * Afterwards, the appropriate flags are set. |
---|
510 | * \param input The array of values to be saved. |
---|
511 | * \param size The size of input. |
---|
512 | */ |
---|
513 | if(size != this->numPixels){ |
---|
514 | std::stringstream errmsg; |
---|
515 | errmsg << "Input array different size to existing array (" |
---|
516 | << size << " cf. " << this->numPixels << "). Cannot save.\n"; |
---|
517 | duchampError("Cube::saveRecon",errmsg.str()); |
---|
518 | } |
---|
519 | else { |
---|
520 | if(this->numPixels>0){ |
---|
521 | if(this->reconAllocated) delete [] this->recon; |
---|
522 | this->numPixels = size; |
---|
523 | this->recon = new float[size]; |
---|
524 | this->reconAllocated = true; |
---|
525 | for(int i=0;i<size;i++) this->recon[i] = input[i]; |
---|
526 | this->reconExists = true; |
---|
527 | } |
---|
528 | } |
---|
529 | } |
---|
530 | //-------------------------------------------------------------------- |
---|
531 | |
---|
532 | void Cube::getRecon(float *output){ |
---|
533 | /** |
---|
534 | * The reconstructed array is written to output. The output array needs to |
---|
535 | * be defined beforehand: no checking is done on the memory. |
---|
536 | * \param output The array that is written to. |
---|
537 | */ |
---|
538 | // Need check for change in number of pixels! |
---|
539 | for(int i=0;i<this->numPixels;i++){ |
---|
540 | if(this->reconExists) output[i] = this->recon[i]; |
---|
541 | else output[i] = 0.; |
---|
542 | } |
---|
543 | } |
---|
544 | //-------------------------------------------------------------------- |
---|
545 | |
---|
546 | void Cube::removeMW() |
---|
547 | { |
---|
548 | /** |
---|
549 | * The channels corresponding to the Milky Way range (as given by the Param |
---|
550 | * set) are all set to 0 in the pixel array. |
---|
551 | * Only done if the appropriate flag is set, and the pixels are not BLANK. |
---|
552 | * \deprecated |
---|
553 | */ |
---|
554 | if(this->par.getFlagMW()){ |
---|
555 | for(int pix=0;pix<this->axisDim[0]*this->axisDim[1];pix++){ |
---|
556 | for(int z=0;z<this->axisDim[2];z++){ |
---|
557 | int pos = z*this->axisDim[0]*this->axisDim[1] + pix; |
---|
558 | if(!this->isBlank(pos) && this->par.isInMW(z)) this->array[pos]=0.; |
---|
559 | } |
---|
560 | } |
---|
561 | } |
---|
562 | } |
---|
563 | //-------------------------------------------------------------------- |
---|
564 | |
---|
565 | void Cube::setCubeStatsOld() |
---|
566 | { |
---|
567 | /** |
---|
568 | * \deprecated |
---|
569 | * |
---|
570 | * Calculates the full statistics for the cube: mean, rms, median, madfm. |
---|
571 | * Only do this if the threshold has not been defined (ie. is still 0., |
---|
572 | * its default). |
---|
573 | * Also work out the threshold and store it in the Param set. |
---|
574 | * |
---|
575 | * For the stats calculations, we ignore BLANKs and MW channels. |
---|
576 | */ |
---|
577 | |
---|
578 | if(!this->par.getFlagFDR() && this->par.getFlagUserThreshold() ){ |
---|
579 | // if the user has defined a threshold, set this in the StatsContainer |
---|
580 | this->Stats.setThreshold( this->par.getThreshold() ); |
---|
581 | } |
---|
582 | else{ |
---|
583 | // only work out the mean etc if we need to. |
---|
584 | // the only reason we don't is if the user has specified a threshold. |
---|
585 | |
---|
586 | std::cout << "Calculating the cube statistics... " << std::flush; |
---|
587 | |
---|
588 | // get number of good pixels; |
---|
589 | int goodSize = 0; |
---|
590 | for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){ |
---|
591 | for(int z=0;z<this->axisDim[2];z++){ |
---|
592 | int vox = z * this->axisDim[0] * this->axisDim[1] + p; |
---|
593 | if(!this->isBlank(vox) && !this->par.isInMW(z)) goodSize++; |
---|
594 | } |
---|
595 | } |
---|
596 | |
---|
597 | float *tempArray = new float[goodSize]; |
---|
598 | |
---|
599 | goodSize=0; |
---|
600 | for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){ |
---|
601 | for(int z=0;z<this->axisDim[2];z++){ |
---|
602 | int vox = z * this->axisDim[0] * this->axisDim[1] + p; |
---|
603 | if(!this->isBlank(vox) && !this->par.isInMW(z)) |
---|
604 | tempArray[goodSize++] = this->array[vox]; |
---|
605 | } |
---|
606 | } |
---|
607 | if(!this->reconExists){ |
---|
608 | // if there's no recon array, calculate everything from orig array |
---|
609 | this->Stats.calculate(tempArray,goodSize); |
---|
610 | } |
---|
611 | else{ |
---|
612 | // just get mean & median from orig array, and rms & madfm from recon |
---|
613 | StatsContainer<float> origStats,reconStats; |
---|
614 | origStats.calculate(tempArray,goodSize); |
---|
615 | goodSize=0; |
---|
616 | for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){ |
---|
617 | for(int z=0;z<this->axisDim[2];z++){ |
---|
618 | int vox = z * this->axisDim[0] * this->axisDim[1] + p; |
---|
619 | if(!this->isBlank(vox) && !this->par.isInMW(z)) |
---|
620 | tempArray[goodSize++] = this->array[vox] - this->recon[vox]; |
---|
621 | } |
---|
622 | } |
---|
623 | reconStats.calculate(tempArray,goodSize); |
---|
624 | |
---|
625 | // Get the "middle" estimators from the original array. |
---|
626 | this->Stats.setMean(origStats.getMean()); |
---|
627 | this->Stats.setMedian(origStats.getMedian()); |
---|
628 | // Get the "spread" estimators from the residual (orig-recon) array |
---|
629 | this->Stats.setStddev(reconStats.getStddev()); |
---|
630 | this->Stats.setMadfm(reconStats.getMadfm()); |
---|
631 | } |
---|
632 | |
---|
633 | delete [] tempArray; |
---|
634 | |
---|
635 | this->Stats.setUseFDR( this->par.getFlagFDR() ); |
---|
636 | // If the FDR method has been requested |
---|
637 | if(this->par.getFlagFDR()) this->setupFDR(); |
---|
638 | else{ |
---|
639 | // otherwise, calculate one based on the requested SNR cut level, and |
---|
640 | // then set the threshold parameter in the Par set. |
---|
641 | this->Stats.setThresholdSNR( this->par.getCut() ); |
---|
642 | this->par.setThreshold( this->Stats.getThreshold() ); |
---|
643 | } |
---|
644 | |
---|
645 | |
---|
646 | } |
---|
647 | std::cout << "Using "; |
---|
648 | if(this->par.getFlagFDR()) std::cout << "effective "; |
---|
649 | std::cout << "flux threshold of: "; |
---|
650 | float thresh = this->Stats.getThreshold(); |
---|
651 | if(this->par.getFlagNegative()) thresh *= -1.; |
---|
652 | std::cout << thresh << std::endl; |
---|
653 | |
---|
654 | } |
---|
655 | //-------------------------------------------------------------------- |
---|
656 | |
---|
657 | void Cube::setCubeStats() |
---|
658 | { |
---|
659 | /** |
---|
660 | * Calculates the full statistics for the cube: |
---|
661 | * mean, rms, median, madfm |
---|
662 | * Only do this if the threshold has not been defined (ie. is still 0., |
---|
663 | * its default). |
---|
664 | * Also work out the threshold and store it in the par set. |
---|
665 | * |
---|
666 | * Different from Cube::setCubeStatsOld() as it doesn't use the |
---|
667 | * getStats functions but has own versions of them hardcoded to |
---|
668 | * ignore BLANKs and MW channels. This saves on memory usage -- necessary |
---|
669 | * for dealing with very big files. |
---|
670 | * |
---|
671 | * Three cases exist: |
---|
672 | * <ul><li>Simple case, with no reconstruction/smoothing: all stats |
---|
673 | * calculated from the original array. |
---|
674 | * <li>Wavelet reconstruction: mean & median calculated from the |
---|
675 | * original array, and stddev & madfm from the residual. |
---|
676 | * <li>Smoothing: all four stats calculated from the recon array |
---|
677 | * (which holds the smoothed data). |
---|
678 | * </ul> |
---|
679 | */ |
---|
680 | |
---|
681 | this->Stats.setRobust(this->par.getFlagRobustStats()); |
---|
682 | |
---|
683 | if(!this->par.getFlagFDR() && this->par.getFlagUserThreshold() ){ |
---|
684 | // if the user has defined a threshold, set this in the StatsContainer |
---|
685 | this->Stats.setThreshold( this->par.getThreshold() ); |
---|
686 | } |
---|
687 | else{ |
---|
688 | // only work out the stats if we need to. |
---|
689 | // the only reason we don't is if the user has specified a threshold. |
---|
690 | |
---|
691 | if(this->par.isVerbose()) |
---|
692 | std::cout << "Calculating the cube statistics... " << std::flush; |
---|
693 | |
---|
694 | long xysize = this->axisDim[0]*this->axisDim[1]; |
---|
695 | |
---|
696 | bool *mask = new bool[this->numPixels]; |
---|
697 | int vox,goodSize = 0; |
---|
698 | for(int x=0;x<this->axisDim[0];x++){ |
---|
699 | for(int y=0;y<this->axisDim[1];y++){ |
---|
700 | for(int z=0;z<this->axisDim[2];z++){ |
---|
701 | vox = z * xysize + y*this->axisDim[0] + x; |
---|
702 | mask[vox] = (!this->isBlank(vox) && |
---|
703 | !this->par.isInMW(z) && |
---|
704 | this->par.isStatOK(x,y,z) ); |
---|
705 | if(mask[vox]) goodSize++; |
---|
706 | } |
---|
707 | } |
---|
708 | } |
---|
709 | |
---|
710 | float mean,median,stddev,madfm; |
---|
711 | if( this->par.getFlagATrous() ){ |
---|
712 | // Case #2 -- wavelet reconstruction |
---|
713 | // just get mean & median from orig array, and rms & madfm from |
---|
714 | // residual recompute array values to be residuals & then find |
---|
715 | // stddev & madfm |
---|
716 | if(!this->reconExists) |
---|
717 | duchampError("setCubeStats", |
---|
718 | "Reconstruction not yet done!\nCannot calculate stats!\n"); |
---|
719 | else{ |
---|
720 | float *tempArray = new float[goodSize]; |
---|
721 | |
---|
722 | goodSize=0; |
---|
723 | for(int x=0;x<this->axisDim[0];x++){ |
---|
724 | for(int y=0;y<this->axisDim[1];y++){ |
---|
725 | for(int z=0;z<this->axisDim[2];z++){ |
---|
726 | vox = z * xysize + y*this->axisDim[0] + x; |
---|
727 | if(mask[vox]) tempArray[goodSize++] = this->array[vox]; |
---|
728 | } |
---|
729 | } |
---|
730 | } |
---|
731 | |
---|
732 | // First, find the mean of the original array. Store it. |
---|
733 | mean = tempArray[0]; |
---|
734 | for(int i=1;i<goodSize;i++) mean += tempArray[i]; |
---|
735 | mean /= float(goodSize); |
---|
736 | mean = findMean(tempArray,goodSize); |
---|
737 | this->Stats.setMean(mean); |
---|
738 | |
---|
739 | // Now sort it and find the median. Store it. |
---|
740 | std::sort(tempArray,tempArray+goodSize); |
---|
741 | if((goodSize%2)==0) |
---|
742 | median = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2; |
---|
743 | else median = tempArray[goodSize/2]; |
---|
744 | this->Stats.setMedian(median); |
---|
745 | |
---|
746 | // Now calculate the residuals and find the mean & median of |
---|
747 | // them. We don't store these, but they are necessary to find |
---|
748 | // the sttdev & madfm. |
---|
749 | goodSize = 0; |
---|
750 | for(int p=0;p<xysize;p++){ |
---|
751 | for(int z=0;z<this->axisDim[2];z++){ |
---|
752 | vox = z * xysize + p; |
---|
753 | if(mask[vox]) |
---|
754 | tempArray[goodSize++] = this->array[vox] - this->recon[vox]; |
---|
755 | } |
---|
756 | } |
---|
757 | mean = tempArray[0]; |
---|
758 | for(int i=1;i<goodSize;i++) mean += tempArray[i]; |
---|
759 | mean /= float(goodSize); |
---|
760 | std::sort(tempArray,tempArray+goodSize); |
---|
761 | if((goodSize%2)==0) |
---|
762 | median = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2; |
---|
763 | else median = tempArray[goodSize/2]; |
---|
764 | |
---|
765 | // Now find the standard deviation of the residuals. Store it. |
---|
766 | stddev = (tempArray[0]-mean) * (tempArray[0]-mean); |
---|
767 | for(int i=1;i<goodSize;i++) |
---|
768 | stddev += (tempArray[i]-mean)*(tempArray[i]-mean); |
---|
769 | stddev = sqrt(stddev/float(goodSize-1)); |
---|
770 | this->Stats.setStddev(stddev); |
---|
771 | |
---|
772 | // Now find the madfm of the residuals. Store it. |
---|
773 | for(int i=0;i<goodSize;i++){ |
---|
774 | if(tempArray[i]>median) tempArray[i] = tempArray[i]-median; |
---|
775 | else tempArray[i] = median - tempArray[i]; |
---|
776 | } |
---|
777 | std::sort(tempArray,tempArray+goodSize); |
---|
778 | if((goodSize%2)==0) |
---|
779 | madfm = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2; |
---|
780 | else madfm = tempArray[goodSize/2]; |
---|
781 | this->Stats.setMadfm(madfm); |
---|
782 | |
---|
783 | delete [] tempArray; |
---|
784 | } |
---|
785 | } |
---|
786 | else if(this->par.getFlagSmooth()) { |
---|
787 | // Case #3 -- smoothing |
---|
788 | // get all four stats from the recon array, which holds the |
---|
789 | // smoothed data. This can just be done with the |
---|
790 | // StatsContainer::calculate function, using the mask generated |
---|
791 | // earlier. |
---|
792 | if(!this->reconExists) |
---|
793 | duchampError("setCubeStats","Smoothing not yet done!\nCannot calculate stats!\n"); |
---|
794 | else this->Stats.calculate(this->recon,this->numPixels,mask); |
---|
795 | } |
---|
796 | else{ |
---|
797 | // Case #1 -- default case, with no smoothing or reconstruction. |
---|
798 | // get all four stats from the original array. This can just be |
---|
799 | // done with the StatsContainer::calculate function, using the |
---|
800 | // mask generated earlier. |
---|
801 | this->Stats.calculate(this->array,this->numPixels,mask); |
---|
802 | } |
---|
803 | |
---|
804 | this->Stats.setUseFDR( this->par.getFlagFDR() ); |
---|
805 | // If the FDR method has been requested, define the P-value |
---|
806 | // threshold |
---|
807 | if(this->par.getFlagFDR()) this->setupFDR(); |
---|
808 | else{ |
---|
809 | // otherwise, calculate threshold based on the requested SNR cut |
---|
810 | // level, and then set the threshold parameter in the Par set. |
---|
811 | this->Stats.setThresholdSNR( this->par.getCut() ); |
---|
812 | this->par.setThreshold( this->Stats.getThreshold() ); |
---|
813 | } |
---|
814 | |
---|
815 | delete [] mask; |
---|
816 | |
---|
817 | } |
---|
818 | |
---|
819 | if(this->par.isVerbose()){ |
---|
820 | std::cout << "Using "; |
---|
821 | if(this->par.getFlagFDR()) std::cout << "effective "; |
---|
822 | std::cout << "flux threshold of: "; |
---|
823 | float thresh = this->Stats.getThreshold(); |
---|
824 | if(this->par.getFlagNegative()) thresh *= -1.; |
---|
825 | std::cout << thresh << std::endl; |
---|
826 | } |
---|
827 | |
---|
828 | } |
---|
829 | //-------------------------------------------------------------------- |
---|
830 | |
---|
831 | void Cube::setupFDR() |
---|
832 | { |
---|
833 | /** |
---|
834 | * Call the setupFDR(float *) function on the pixel array of the |
---|
835 | * cube. This is the usual way of running it. |
---|
836 | * |
---|
837 | * However, if we are in smoothing mode, we calculate the FDR |
---|
838 | * parameters using the recon array, which holds the smoothed |
---|
839 | * data. Gives an error message if the reconExists flag is not set. |
---|
840 | * |
---|
841 | */ |
---|
842 | if(this->par.getFlagSmooth()) |
---|
843 | if(this->reconExists) this->setupFDR(this->recon); |
---|
844 | else{ |
---|
845 | duchampError("setupFDR", |
---|
846 | "Smoothing not done properly! Using original array for defining threshold.\n"); |
---|
847 | this->setupFDR(this->array); |
---|
848 | } |
---|
849 | else if( this->par.getFlagATrous() ){ |
---|
850 | this->setupFDR(this->recon); |
---|
851 | } |
---|
852 | else{ |
---|
853 | this->setupFDR(this->array); |
---|
854 | } |
---|
855 | } |
---|
856 | //-------------------------------------------------------------------- |
---|
857 | |
---|
858 | void Cube::setupFDR(float *input) |
---|
859 | { |
---|
860 | /** |
---|
861 | * Determines the critical Probability value for the False |
---|
862 | * Discovery Rate detection routine. All pixels in the given arry |
---|
863 | * with Prob less than this value will be considered detections. |
---|
864 | * |
---|
865 | * Note that the Stats of the cube need to be calculated first. |
---|
866 | * |
---|
867 | * The Prob here is the probability, assuming a Normal |
---|
868 | * distribution, of obtaining a value as high or higher than the |
---|
869 | * pixel value (ie. only the positive tail of the PDF). |
---|
870 | * |
---|
871 | * The probabilities are calculated using the |
---|
872 | * StatsContainer::getPValue(), which calculates the z-statistic, |
---|
873 | * and then the probability via |
---|
874 | * \f$0.5\operatorname{erfc}(z/\sqrt{2})\f$ -- giving the positive |
---|
875 | * tail probability. |
---|
876 | */ |
---|
877 | |
---|
878 | // first calculate p-value for each pixel -- assume Gaussian for now. |
---|
879 | |
---|
880 | float *orderedP = new float[this->numPixels]; |
---|
881 | int count = 0; |
---|
882 | for(int x=0;x<this->axisDim[0];x++){ |
---|
883 | for(int y=0;y<this->axisDim[1];y++){ |
---|
884 | for(int z=0;z<this->axisDim[2];z++){ |
---|
885 | int pix = z * this->axisDim[0]*this->axisDim[1] + |
---|
886 | y*this->axisDim[0] + x; |
---|
887 | |
---|
888 | if(!(this->par.isBlank(this->array[pix])) && !this->par.isInMW(z)){ |
---|
889 | // only look at non-blank, valid pixels |
---|
890 | // orderedP[count++] = this->Stats.getPValue(this->array[pix]); |
---|
891 | orderedP[count++] = this->Stats.getPValue(input[pix]); |
---|
892 | } |
---|
893 | } |
---|
894 | } |
---|
895 | } |
---|
896 | |
---|
897 | // now order them |
---|
898 | std::stable_sort(orderedP,orderedP+count); |
---|
899 | |
---|
900 | // now find the maximum P value. |
---|
901 | int max = 0; |
---|
902 | float cN = 0.; |
---|
903 | int numVox = int(ceil(this->par.getBeamSize())); |
---|
904 | // if(this->head.isSpecOK()) numVox *= 2; |
---|
905 | if(this->head.canUseThirdAxis()) numVox *= 2; |
---|
906 | // why beamSize*2? we are doing this in 3D, so spectrally assume just the |
---|
907 | // neighbouring channels are correlated, but spatially all those within |
---|
908 | // the beam, so total number of voxels is 2*beamSize |
---|
909 | for(int psfCtr=1;psfCtr<=numVox;psfCtr++) cN += 1./float(psfCtr); |
---|
910 | |
---|
911 | double slope = this->par.getAlpha()/cN; |
---|
912 | for(int loopCtr=0;loopCtr<count;loopCtr++) { |
---|
913 | if( orderedP[loopCtr] < (slope * double(loopCtr+1)/ double(count)) ){ |
---|
914 | max = loopCtr; |
---|
915 | } |
---|
916 | } |
---|
917 | |
---|
918 | this->Stats.setPThreshold( orderedP[max] ); |
---|
919 | |
---|
920 | |
---|
921 | // Find real value of the P threshold by finding the inverse of the |
---|
922 | // error function -- root finding with brute force technique |
---|
923 | // (relatively slow, but we only do it once). |
---|
924 | double zStat = 0.; |
---|
925 | double deltaZ = 0.1; |
---|
926 | double tolerance = 1.e-6; |
---|
927 | double initial = 0.5 * erfc(zStat/M_SQRT2) - this->Stats.getPThreshold(); |
---|
928 | do{ |
---|
929 | zStat+=deltaZ; |
---|
930 | double current = 0.5 * erfc(zStat/M_SQRT2) - this->Stats.getPThreshold(); |
---|
931 | if((initial*current)<0.){ |
---|
932 | zStat-=deltaZ; |
---|
933 | deltaZ/=2.; |
---|
934 | } |
---|
935 | }while(deltaZ>tolerance); |
---|
936 | this->Stats.setThreshold( zStat*this->Stats.getSpread() + |
---|
937 | this->Stats.getMiddle() ); |
---|
938 | |
---|
939 | /////////////////////////// |
---|
940 | // if(TESTING){ |
---|
941 | // std::stringstream ss; |
---|
942 | // float *xplot = new float[2*max]; |
---|
943 | // for(int i=0;i<2*max;i++) xplot[i]=float(i)/float(count); |
---|
944 | // cpgopen("latestFDR.ps/vcps"); |
---|
945 | // cpgpap(8.,1.); |
---|
946 | // cpgslw(3); |
---|
947 | // cpgenv(0,float(2*max)/float(count),0,orderedP[2*max],0,0); |
---|
948 | // cpglab("i/N (index)", "p-value",""); |
---|
949 | // cpgpt(2*max,xplot,orderedP,DOT); |
---|
950 | |
---|
951 | // ss.str(""); |
---|
952 | // ss << "\\gm = " << this->Stats.getMiddle(); |
---|
953 | // cpgtext(max/(4.*count),0.9*orderedP[2*max],ss.str().c_str()); |
---|
954 | // ss.str(""); |
---|
955 | // ss << "\\gs = " << this->Stats.getSpread(); |
---|
956 | // cpgtext(max/(4.*count),0.85*orderedP[2*max],ss.str().c_str()); |
---|
957 | // ss.str(""); |
---|
958 | // ss << "Slope = " << slope; |
---|
959 | // cpgtext(max/(4.*count),0.8*orderedP[2*max],ss.str().c_str()); |
---|
960 | // ss.str(""); |
---|
961 | // ss << "Alpha = " << this->par.getAlpha(); |
---|
962 | // cpgtext(max/(4.*count),0.75*orderedP[2*max],ss.str().c_str()); |
---|
963 | // ss.str(""); |
---|
964 | // ss << "c\\dN\\u = " << cN; |
---|
965 | // cpgtext(max/(4.*count),0.7*orderedP[2*max],ss.str().c_str()); |
---|
966 | // ss.str(""); |
---|
967 | // ss << "max = "<<max << " (out of " << count << ")"; |
---|
968 | // cpgtext(max/(4.*count),0.65*orderedP[2*max],ss.str().c_str()); |
---|
969 | // ss.str(""); |
---|
970 | // ss << "Threshold = "<<zStat*this->Stats.getSpread()+this->Stats.getMiddle(); |
---|
971 | // cpgtext(max/(4.*count),0.6*orderedP[2*max],ss.str().c_str()); |
---|
972 | |
---|
973 | // cpgslw(1); |
---|
974 | // cpgsci(RED); |
---|
975 | // cpgmove(0,0); |
---|
976 | // cpgdraw(1,slope); |
---|
977 | // cpgsci(BLUE); |
---|
978 | // cpgsls(DOTTED); |
---|
979 | // cpgmove(0,orderedP[max]); |
---|
980 | // cpgdraw(2*max/float(count),orderedP[max]); |
---|
981 | // cpgmove(max/float(count),0); |
---|
982 | // cpgdraw(max/float(count),orderedP[2*max]); |
---|
983 | // cpgsci(GREEN); |
---|
984 | // cpgsls(SOLID); |
---|
985 | // for(int i=1;i<=10;i++) { |
---|
986 | // ss.str(""); |
---|
987 | // ss << float(i)/2. << "\\gs"; |
---|
988 | // float prob = 0.5*erfc((float(i)/2.)/M_SQRT2); |
---|
989 | // cpgtick(0, 0, 0, orderedP[2*max], |
---|
990 | // prob/orderedP[2*max], |
---|
991 | // 0, 1, 1.5, 90., ss.str().c_str()); |
---|
992 | // } |
---|
993 | // cpgend(); |
---|
994 | // delete [] xplot; |
---|
995 | // } |
---|
996 | delete [] orderedP; |
---|
997 | |
---|
998 | } |
---|
999 | //-------------------------------------------------------------------- |
---|
1000 | |
---|
1001 | bool Cube::isDetection(long x, long y, long z) |
---|
1002 | { |
---|
1003 | /** |
---|
1004 | * Is a given voxel at position (x,y,z) a detection, based on the statistics |
---|
1005 | * in the Cube's StatsContainer? |
---|
1006 | * If the pixel lies outside the valid range for the data array, |
---|
1007 | * return false. |
---|
1008 | * \param x X-value of the Cube's voxel to be tested. |
---|
1009 | * \param y Y-value of the Cube's voxel to be tested. |
---|
1010 | * \param z Z-value of the Cube's voxel to be tested. |
---|
1011 | */ |
---|
1012 | long voxel = z*axisDim[0]*axisDim[1] + y*axisDim[0] + x; |
---|
1013 | return DataArray::isDetection(array[voxel]); |
---|
1014 | } |
---|
1015 | //-------------------------------------------------------------------- |
---|
1016 | |
---|
1017 | void Cube::calcObjectFluxes() |
---|
1018 | { |
---|
1019 | /** |
---|
1020 | * A function to calculate the fluxes and centroids for each |
---|
1021 | * object in the Cube's list of detections. Uses |
---|
1022 | * Detection::calcFluxes() for each object. |
---|
1023 | */ |
---|
1024 | std::vector<Detection>::iterator obj; |
---|
1025 | for(obj=this->objectList->begin();obj<this->objectList->end();obj++){ |
---|
1026 | obj->calcFluxes(this->array, this->axisDim); |
---|
1027 | if(this->par.getFlagUserThreshold()) |
---|
1028 | obj->setPeakSNR( obj->getPeakFlux() / this->Stats.getThreshold() ); |
---|
1029 | else |
---|
1030 | obj->setPeakSNR( (obj->getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() ); |
---|
1031 | } |
---|
1032 | } |
---|
1033 | //-------------------------------------------------------------------- |
---|
1034 | |
---|
1035 | void Cube::calcObjectWCSparams() |
---|
1036 | { |
---|
1037 | /** |
---|
1038 | * A function that calculates the WCS parameters for each object in the |
---|
1039 | * Cube's list of detections. |
---|
1040 | * Each object gets an ID number assigned to it (which is simply its order |
---|
1041 | * in the list), and if the WCS is good, the WCS paramters are calculated. |
---|
1042 | */ |
---|
1043 | |
---|
1044 | for(int i=0;i<this->objectList->size();i++){ |
---|
1045 | this->objectList->at(i).setID(i+1); |
---|
1046 | this->objectList->at(i).setCentreType(this->par.getPixelCentre()); |
---|
1047 | this->objectList->at(i).calcFluxes(this->array,this->axisDim); |
---|
1048 | // this->objectList->at(i).calcWCSparams(this->array,this->axisDim,this->head); |
---|
1049 | this->objectList->at(i).calcWCSparams(this->head); |
---|
1050 | this->objectList->at(i).calcIntegFlux(this->array,this->axisDim,this->head); |
---|
1051 | |
---|
1052 | if(this->par.getFlagUserThreshold()) |
---|
1053 | this->objectList->at(i).setPeakSNR( this->objectList->at(i).getPeakFlux() / this->Stats.getThreshold() ); |
---|
1054 | else |
---|
1055 | this->objectList->at(i).setPeakSNR( (this->objectList->at(i).getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() ); |
---|
1056 | |
---|
1057 | } |
---|
1058 | |
---|
1059 | if(!this->head.isWCS()){ |
---|
1060 | // if the WCS is bad, set the object names to Obj01 etc |
---|
1061 | int numspaces = int(log10(this->objectList->size())) + 1; |
---|
1062 | std::stringstream ss; |
---|
1063 | for(int i=0;i<this->objectList->size();i++){ |
---|
1064 | ss.str(""); |
---|
1065 | ss << "Obj" << std::setfill('0') << std::setw(numspaces) << i+1; |
---|
1066 | this->objectList->at(i).setName(ss.str()); |
---|
1067 | } |
---|
1068 | } |
---|
1069 | |
---|
1070 | } |
---|
1071 | //-------------------------------------------------------------------- |
---|
1072 | |
---|
1073 | void Cube::calcObjectWCSparams(std::vector< std::vector<PixelInfo::Voxel> > bigVoxList) |
---|
1074 | { |
---|
1075 | /** |
---|
1076 | * A function that calculates the WCS parameters for each object in the |
---|
1077 | * Cube's list of detections. |
---|
1078 | * Each object gets an ID number assigned to it (which is simply its order |
---|
1079 | * in the list), and if the WCS is good, the WCS paramters are calculated. |
---|
1080 | * |
---|
1081 | * This version uses vectors of Voxels to define the fluxes. |
---|
1082 | * |
---|
1083 | * \param bigVoxList A vector of vectors of Voxels, with the same |
---|
1084 | * number of elements as this->objectList, where each element is a |
---|
1085 | * vector of Voxels corresponding to the same voxels in each |
---|
1086 | * detection and indicating the flux of each voxel. |
---|
1087 | */ |
---|
1088 | |
---|
1089 | for(int i=0;i<this->objectList->size();i++){ |
---|
1090 | this->objectList->at(i).setID(i+1); |
---|
1091 | this->objectList->at(i).setCentreType(this->par.getPixelCentre()); |
---|
1092 | this->objectList->at(i).calcFluxes(bigVoxList[i]); |
---|
1093 | this->objectList->at(i).calcWCSparams(this->head); |
---|
1094 | this->objectList->at(i).calcIntegFlux(bigVoxList[i],this->head); |
---|
1095 | |
---|
1096 | if(this->par.getFlagUserThreshold()) |
---|
1097 | this->objectList->at(i).setPeakSNR( this->objectList->at(i).getPeakFlux() / this->Stats.getThreshold() ); |
---|
1098 | else |
---|
1099 | this->objectList->at(i).setPeakSNR( (this->objectList->at(i).getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() ); |
---|
1100 | } |
---|
1101 | |
---|
1102 | if(!this->head.isWCS()){ |
---|
1103 | // if the WCS is bad, set the object names to Obj01 etc |
---|
1104 | int numspaces = int(log10(this->objectList->size())) + 1; |
---|
1105 | std::stringstream ss; |
---|
1106 | for(int i=0;i<this->objectList->size();i++){ |
---|
1107 | ss.str(""); |
---|
1108 | ss << "Obj" << std::setfill('0') << std::setw(numspaces) << i+1; |
---|
1109 | this->objectList->at(i).setName(ss.str()); |
---|
1110 | } |
---|
1111 | } |
---|
1112 | |
---|
1113 | } |
---|
1114 | //-------------------------------------------------------------------- |
---|
1115 | |
---|
1116 | void Cube::updateDetectMap() |
---|
1117 | { |
---|
1118 | /** |
---|
1119 | * A function that, for each detected object in the cube's list, increments |
---|
1120 | * the cube's detection map by the required amount at each pixel. |
---|
1121 | */ |
---|
1122 | |
---|
1123 | Scan temp; |
---|
1124 | for(int obj=0;obj<this->objectList->size();obj++){ |
---|
1125 | long numZ=this->objectList->at(obj).pixels().getNumChanMap(); |
---|
1126 | for(int iz=0;iz<numZ;iz++){ // for each channel map |
---|
1127 | Object2D *chanmap = new Object2D; |
---|
1128 | *chanmap = this->objectList->at(obj).pixels().getChanMap(iz).getObject(); |
---|
1129 | for(int iscan=0;iscan<chanmap->getNumScan();iscan++){ |
---|
1130 | temp = chanmap->getScan(iscan); |
---|
1131 | for(int x=temp.getX(); x <= temp.getXmax(); x++) |
---|
1132 | this->detectMap[temp.getY()*this->axisDim[0] + x]++; |
---|
1133 | } // end of loop over scans |
---|
1134 | delete chanmap; |
---|
1135 | } // end of loop over channel maps |
---|
1136 | } // end of loop over objects. |
---|
1137 | |
---|
1138 | } |
---|
1139 | //-------------------------------------------------------------------- |
---|
1140 | |
---|
1141 | void Cube::updateDetectMap(Detection obj) |
---|
1142 | { |
---|
1143 | /** |
---|
1144 | * A function that, for the given object, increments the cube's |
---|
1145 | * detection map by the required amount at each pixel. |
---|
1146 | * |
---|
1147 | * \param obj A Detection object that is being incorporated into the map. |
---|
1148 | */ |
---|
1149 | |
---|
1150 | Scan temp; |
---|
1151 | long numZ=obj.pixels().getNumChanMap(); |
---|
1152 | for(int iz=0;iz<numZ;iz++){ // for each channel map |
---|
1153 | Object2D chanmap = obj.pixels().getChanMap(iz).getObject(); |
---|
1154 | for(int iscan=0;iscan<chanmap.getNumScan();iscan++){ |
---|
1155 | temp = chanmap.getScan(iscan); |
---|
1156 | for(int x=temp.getX(); x <= temp.getXmax(); x++) |
---|
1157 | this->detectMap[temp.getY()*this->axisDim[0] + x]++; |
---|
1158 | } // end of loop over scans |
---|
1159 | } // end of loop over channel maps |
---|
1160 | |
---|
1161 | } |
---|
1162 | //-------------------------------------------------------------------- |
---|
1163 | |
---|
1164 | float Cube::enclosedFlux(Detection obj) |
---|
1165 | { |
---|
1166 | /** |
---|
1167 | * A function to calculate the flux enclosed by the range |
---|
1168 | * of pixels detected in the object obj (not necessarily all |
---|
1169 | * pixels will have been detected). |
---|
1170 | * |
---|
1171 | * \param obj The Detection under consideration. |
---|
1172 | */ |
---|
1173 | obj.calcFluxes(this->array, this->axisDim); |
---|
1174 | int xsize = obj.getXmax()-obj.getXmin()+1; |
---|
1175 | int ysize = obj.getYmax()-obj.getYmin()+1; |
---|
1176 | int zsize = obj.getZmax()-obj.getZmin()+1; |
---|
1177 | std::vector <float> fluxArray(xsize*ysize*zsize,0.); |
---|
1178 | for(int x=0;x<xsize;x++){ |
---|
1179 | for(int y=0;y<ysize;y++){ |
---|
1180 | for(int z=0;z<zsize;z++){ |
---|
1181 | fluxArray[x+y*xsize+z*ysize*xsize] = |
---|
1182 | this->getPixValue(x+obj.getXmin(), |
---|
1183 | y+obj.getYmin(), |
---|
1184 | z+obj.getZmin()); |
---|
1185 | if(this->par.getFlagNegative()) |
---|
1186 | fluxArray[x+y*xsize+z*ysize*xsize] *= -1.; |
---|
1187 | } |
---|
1188 | } |
---|
1189 | } |
---|
1190 | float sum = 0.; |
---|
1191 | for(int i=0;i<fluxArray.size();i++) |
---|
1192 | if(!this->par.isBlank(fluxArray[i])) sum+=fluxArray[i]; |
---|
1193 | return sum; |
---|
1194 | } |
---|
1195 | //-------------------------------------------------------------------- |
---|
1196 | |
---|
1197 | void Cube::setupColumns() |
---|
1198 | { |
---|
1199 | /** |
---|
1200 | * A front-end to the two setup routines in columns.cc. |
---|
1201 | * |
---|
1202 | * This first gets the starting precisions, which may be from |
---|
1203 | * the input parameters. It then sets up the columns (calculates |
---|
1204 | * their widths and precisions and so on based on the values |
---|
1205 | * within). The precisions are also stored in each Detection |
---|
1206 | * object. |
---|
1207 | * |
---|
1208 | * Need to have called calcObjectWCSparams() somewhere |
---|
1209 | * beforehand. |
---|
1210 | */ |
---|
1211 | |
---|
1212 | std::vector<Detection>::iterator obj; |
---|
1213 | for(obj=this->objectList->begin();obj<this->objectList->end();obj++){ |
---|
1214 | obj->setVelPrec( this->par.getPrecVel() ); |
---|
1215 | obj->setFpeakPrec( this->par.getPrecFlux() ); |
---|
1216 | obj->setXYZPrec( Column::prec[Column::prXYZ] ); |
---|
1217 | obj->setPosPrec( Column::prec[Column::prWPOS] ); |
---|
1218 | obj->setFintPrec( this->par.getPrecFlux() ); |
---|
1219 | obj->setSNRPrec( this->par.getPrecSNR() ); |
---|
1220 | } |
---|
1221 | |
---|
1222 | this->fullCols.clear(); |
---|
1223 | this->fullCols = getFullColSet(*(this->objectList), this->head); |
---|
1224 | |
---|
1225 | this->logCols.clear(); |
---|
1226 | this->logCols = getLogColSet(*(this->objectList), this->head); |
---|
1227 | |
---|
1228 | int vel,fpeak,fint,pos,xyz,snr; |
---|
1229 | vel = fullCols[VEL].getPrecision(); |
---|
1230 | fpeak = fullCols[FPEAK].getPrecision(); |
---|
1231 | snr = fullCols[SNRPEAK].getPrecision(); |
---|
1232 | xyz = fullCols[X].getPrecision(); |
---|
1233 | xyz = std::max(xyz, fullCols[Y].getPrecision()); |
---|
1234 | xyz = std::max(xyz, fullCols[Z].getPrecision()); |
---|
1235 | if(this->head.isWCS()) fint = fullCols[FINT].getPrecision(); |
---|
1236 | else fint = fullCols[FTOT].getPrecision(); |
---|
1237 | pos = fullCols[WRA].getPrecision(); |
---|
1238 | pos = std::max(pos, fullCols[WDEC].getPrecision()); |
---|
1239 | |
---|
1240 | for(obj=this->objectList->begin();obj<this->objectList->end();obj++){ |
---|
1241 | obj->setVelPrec(vel); |
---|
1242 | obj->setFpeakPrec(fpeak); |
---|
1243 | obj->setXYZPrec(xyz); |
---|
1244 | obj->setPosPrec(pos); |
---|
1245 | obj->setFintPrec(fint); |
---|
1246 | obj->setSNRPrec(snr); |
---|
1247 | } |
---|
1248 | |
---|
1249 | } |
---|
1250 | //-------------------------------------------------------------------- |
---|
1251 | |
---|
1252 | bool Cube::objAtSpatialEdge(Detection obj) |
---|
1253 | { |
---|
1254 | /** |
---|
1255 | * A function to test whether the object obj |
---|
1256 | * lies at the edge of the cube's spatial field -- |
---|
1257 | * either at the boundary, or next to BLANKs. |
---|
1258 | * |
---|
1259 | * \param obj The Detection under consideration. |
---|
1260 | */ |
---|
1261 | |
---|
1262 | bool atEdge = false; |
---|
1263 | |
---|
1264 | int pix = 0; |
---|
1265 | std::vector<Voxel> voxlist = obj.pixels().getPixelSet(); |
---|
1266 | while(!atEdge && pix<voxlist.size()){ |
---|
1267 | // loop over each pixel in the object, until we find an edge pixel. |
---|
1268 | for(int dx=-1;dx<=1;dx+=2){ |
---|
1269 | if( ((voxlist[pix].getX()+dx)<0) || |
---|
1270 | ((voxlist[pix].getX()+dx)>=this->axisDim[0]) ) |
---|
1271 | atEdge = true; |
---|
1272 | else if(this->isBlank(voxlist[pix].getX()+dx, |
---|
1273 | voxlist[pix].getY(), |
---|
1274 | voxlist[pix].getZ())) |
---|
1275 | atEdge = true; |
---|
1276 | } |
---|
1277 | for(int dy=-1;dy<=1;dy+=2){ |
---|
1278 | if( ((voxlist[pix].getY()+dy)<0) || |
---|
1279 | ((voxlist[pix].getY()+dy)>=this->axisDim[1]) ) |
---|
1280 | atEdge = true; |
---|
1281 | else if(this->isBlank(voxlist[pix].getX(), |
---|
1282 | voxlist[pix].getY()+dy, |
---|
1283 | voxlist[pix].getZ())) |
---|
1284 | atEdge = true; |
---|
1285 | } |
---|
1286 | pix++; |
---|
1287 | } |
---|
1288 | |
---|
1289 | return atEdge; |
---|
1290 | } |
---|
1291 | //-------------------------------------------------------------------- |
---|
1292 | |
---|
1293 | bool Cube::objAtSpectralEdge(Detection obj) |
---|
1294 | { |
---|
1295 | /** |
---|
1296 | * A function to test whether the object obj |
---|
1297 | * lies at the edge of the cube's spectral extent -- |
---|
1298 | * either at the boundary, or next to BLANKs. |
---|
1299 | * |
---|
1300 | * /param obj The Detection under consideration. |
---|
1301 | */ |
---|
1302 | |
---|
1303 | bool atEdge = false; |
---|
1304 | |
---|
1305 | int pix = 0; |
---|
1306 | std::vector<Voxel> voxlist = obj.pixels().getPixelSet(); |
---|
1307 | while(!atEdge && pix<voxlist.size()){ |
---|
1308 | // loop over each pixel in the object, until we find an edge pixel. |
---|
1309 | for(int dz=-1;dz<=1;dz+=2){ |
---|
1310 | if( ((voxlist[pix].getZ()+dz)<0) || |
---|
1311 | ((voxlist[pix].getZ()+dz)>=this->axisDim[2])) |
---|
1312 | atEdge = true; |
---|
1313 | else if(this->isBlank(voxlist[pix].getX(), |
---|
1314 | voxlist[pix].getY(), |
---|
1315 | voxlist[pix].getZ()+dz)) |
---|
1316 | atEdge = true; |
---|
1317 | } |
---|
1318 | pix++; |
---|
1319 | } |
---|
1320 | |
---|
1321 | return atEdge; |
---|
1322 | } |
---|
1323 | //-------------------------------------------------------------------- |
---|
1324 | |
---|
1325 | void Cube::setObjectFlags() |
---|
1326 | { |
---|
1327 | /** |
---|
1328 | * A function to set any warning flags for all the detected objects |
---|
1329 | * associated with the cube. |
---|
1330 | * Flags to be looked for: |
---|
1331 | * <ul><li> Negative enclosed flux (N) |
---|
1332 | * <li> Detection at edge of field (spatially) (E) |
---|
1333 | * <li> Detection at edge of spectral region (S) |
---|
1334 | * </ul> |
---|
1335 | */ |
---|
1336 | |
---|
1337 | for(int i=0;i<this->objectList->size();i++){ |
---|
1338 | |
---|
1339 | if( this->enclosedFlux(this->objectList->at(i)) < 0. ) |
---|
1340 | this->objectList->at(i).addToFlagText("N"); |
---|
1341 | |
---|
1342 | if( this->objAtSpatialEdge(this->objectList->at(i)) ) |
---|
1343 | this->objectList->at(i).addToFlagText("E"); |
---|
1344 | |
---|
1345 | if( this->objAtSpectralEdge(this->objectList->at(i)) && |
---|
1346 | (this->axisDim[2] > 2)) |
---|
1347 | this->objectList->at(i).addToFlagText("S"); |
---|
1348 | |
---|
1349 | } |
---|
1350 | |
---|
1351 | } |
---|
1352 | //-------------------------------------------------------------------- |
---|
1353 | |
---|
1354 | |
---|
1355 | |
---|
1356 | /****************************************************************/ |
---|
1357 | ///////////////////////////////////////////////////////////// |
---|
1358 | //// Functions for Image class |
---|
1359 | ///////////////////////////////////////////////////////////// |
---|
1360 | |
---|
1361 | Image::Image(long size){ |
---|
1362 | // need error handling in case size<0 !!! |
---|
1363 | this->numPixels = this->numDim = 0; |
---|
1364 | if(size<0) |
---|
1365 | duchampError("Image(size)","Negative size -- could not define Image"); |
---|
1366 | else{ |
---|
1367 | if(size>0) this->array = new float[size]; |
---|
1368 | this->numPixels = size; |
---|
1369 | this->axisDim = new long[2]; |
---|
1370 | this->numDim = 2; |
---|
1371 | } |
---|
1372 | } |
---|
1373 | //-------------------------------------------------------------------- |
---|
1374 | |
---|
1375 | Image::Image(long *dimensions){ |
---|
1376 | this->numPixels = this->numDim = 0; |
---|
1377 | int size = dimensions[0] * dimensions[1]; |
---|
1378 | if(size<0) |
---|
1379 | duchampError("Image(dimArray)","Negative size -- could not define Image"); |
---|
1380 | else{ |
---|
1381 | this->numPixels = size; |
---|
1382 | if(size>0) this->array = new float[size]; |
---|
1383 | this->numDim=2; |
---|
1384 | this->axisDim = new long[2]; |
---|
1385 | for(int i=0;i<2;i++) this->axisDim[i] = dimensions[i]; |
---|
1386 | } |
---|
1387 | } |
---|
1388 | //-------------------------------------------------------------------- |
---|
1389 | //-------------------------------------------------------------------- |
---|
1390 | |
---|
1391 | void Image::saveArray(float *input, long size) |
---|
1392 | { |
---|
1393 | /** |
---|
1394 | * Saves the array in input to the pixel array Image::array. |
---|
1395 | * The size of the array given must be the same as the current number of |
---|
1396 | * pixels, else an error message is returned and nothing is done. |
---|
1397 | * \param input The array of values to be saved. |
---|
1398 | * \param size The size of input. |
---|
1399 | */ |
---|
1400 | if(size != this->numPixels) |
---|
1401 | duchampError("Image::saveArray", |
---|
1402 | "Input array different size to existing array. Cannot save."); |
---|
1403 | else { |
---|
1404 | if(this->numPixels>0) delete [] array; |
---|
1405 | this->numPixels = size; |
---|
1406 | if(this->numPixels>0) this->array = new float[size]; |
---|
1407 | for(int i=0;i<size;i++) this->array[i] = input[i]; |
---|
1408 | } |
---|
1409 | } |
---|
1410 | //-------------------------------------------------------------------- |
---|
1411 | |
---|
1412 | void Image::extractSpectrum(float *Array, long *dim, long pixel) |
---|
1413 | { |
---|
1414 | /** |
---|
1415 | * A function to extract a 1-D spectrum from a 3-D array. |
---|
1416 | * The array is assumed to be 3-D with the third dimension the spectral one. |
---|
1417 | * The spectrum extracted is the one lying in the spatial pixel referenced |
---|
1418 | * by the third argument. |
---|
1419 | * The extracted spectrum is stored in the pixel array Image::array. |
---|
1420 | * \param Array The array containing the pixel values, from which |
---|
1421 | * the spectrum is extracted. |
---|
1422 | * \param dim The array of dimension values. |
---|
1423 | * \param pixel The spatial pixel that contains the desired spectrum. |
---|
1424 | */ |
---|
1425 | if((pixel<0)||(pixel>=dim[0]*dim[1])) |
---|
1426 | duchampError("Image::extractSpectrum", |
---|
1427 | "Requested spatial pixel outside allowed range. Cannot save."); |
---|
1428 | else if(dim[2] != this->numPixels) |
---|
1429 | duchampError("Image::extractSpectrum", |
---|
1430 | "Input array different size to existing array. Cannot save."); |
---|
1431 | else { |
---|
1432 | if(this->numPixels>0) delete [] array; |
---|
1433 | this->numPixels = dim[2]; |
---|
1434 | if(this->numPixels>0) this->array = new float[dim[2]]; |
---|
1435 | for(int z=0;z<dim[2];z++) this->array[z] = Array[z*dim[0]*dim[1] + pixel]; |
---|
1436 | } |
---|
1437 | } |
---|
1438 | //-------------------------------------------------------------------- |
---|
1439 | |
---|
1440 | void Image::extractSpectrum(Cube &cube, long pixel) |
---|
1441 | { |
---|
1442 | /** |
---|
1443 | * A function to extract a 1-D spectrum from a Cube class |
---|
1444 | * The spectrum extracted is the one lying in the spatial pixel referenced |
---|
1445 | * by the second argument. |
---|
1446 | * The extracted spectrum is stored in the pixel array Image::array. |
---|
1447 | * \param cube The Cube containing the pixel values, from which the spectrum is extracted. |
---|
1448 | * \param pixel The spatial pixel that contains the desired spectrum. |
---|
1449 | */ |
---|
1450 | long zdim = cube.getDimZ(); |
---|
1451 | long spatSize = cube.getDimX()*cube.getDimY(); |
---|
1452 | if((pixel<0)||(pixel>=spatSize)) |
---|
1453 | duchampError("Image::extractSpectrum", |
---|
1454 | "Requested spatial pixel outside allowed range. Cannot save."); |
---|
1455 | else if(zdim != this->numPixels) |
---|
1456 | duchampError("Image::extractSpectrum", |
---|
1457 | "Input array different size to existing array. Cannot save."); |
---|
1458 | else { |
---|
1459 | if(this->numPixels>0) delete [] array; |
---|
1460 | this->numPixels = zdim; |
---|
1461 | if(this->numPixels>0) this->array = new float[zdim]; |
---|
1462 | for(int z=0;z<zdim;z++) |
---|
1463 | this->array[z] = cube.getPixValue(z*spatSize + pixel); |
---|
1464 | } |
---|
1465 | } |
---|
1466 | //-------------------------------------------------------------------- |
---|
1467 | |
---|
1468 | void Image::extractImage(float *Array, long *dim, long channel) |
---|
1469 | { |
---|
1470 | /** |
---|
1471 | * A function to extract a 2-D image from a 3-D array. |
---|
1472 | * The array is assumed to be 3-D with the third dimension the spectral one. |
---|
1473 | * The dimensions of the array are in the dim[] array. |
---|
1474 | * The image extracted is the one lying in the channel referenced |
---|
1475 | * by the third argument. |
---|
1476 | * The extracted image is stored in the pixel array Image::array. |
---|
1477 | * \param Array The array containing the pixel values, from which the image is extracted. |
---|
1478 | * \param dim The array of dimension values. |
---|
1479 | * \param channel The spectral channel that contains the desired image. |
---|
1480 | */ |
---|
1481 | |
---|
1482 | long spatSize = dim[0]*dim[1]; |
---|
1483 | if((channel<0)||(channel>=dim[2])) |
---|
1484 | duchampError("Image::extractImage", |
---|
1485 | "Requested channel outside allowed range. Cannot save."); |
---|
1486 | else if(spatSize != this->numPixels) |
---|
1487 | duchampError("Image::extractImage", |
---|
1488 | "Input array different size to existing array. Cannot save."); |
---|
1489 | else { |
---|
1490 | if(this->numPixels>0) delete [] array; |
---|
1491 | this->numPixels = spatSize; |
---|
1492 | if(this->numPixels>0) this->array = new float[spatSize]; |
---|
1493 | for(int npix=0; npix<spatSize; npix++) |
---|
1494 | this->array[npix] = Array[channel*spatSize + npix]; |
---|
1495 | } |
---|
1496 | } |
---|
1497 | //-------------------------------------------------------------------- |
---|
1498 | |
---|
1499 | void Image::extractImage(Cube &cube, long channel) |
---|
1500 | { |
---|
1501 | /** |
---|
1502 | * A function to extract a 2-D image from Cube class. |
---|
1503 | * The image extracted is the one lying in the channel referenced |
---|
1504 | * by the second argument. |
---|
1505 | * The extracted image is stored in the pixel array Image::array. |
---|
1506 | * \param cube The Cube containing the pixel values, from which the image is extracted. |
---|
1507 | * \param channel The spectral channel that contains the desired image. |
---|
1508 | */ |
---|
1509 | long spatSize = cube.getDimX()*cube.getDimY(); |
---|
1510 | if((channel<0)||(channel>=cube.getDimZ())) |
---|
1511 | duchampError("Image::extractImage", |
---|
1512 | "Requested channel outside allowed range. Cannot save."); |
---|
1513 | else if(spatSize != this->numPixels) |
---|
1514 | duchampError("Image::extractImage", |
---|
1515 | "Input array different size to existing array. Cannot save."); |
---|
1516 | else { |
---|
1517 | if(this->numPixels>0) delete [] array; |
---|
1518 | this->numPixels = spatSize; |
---|
1519 | if(this->numPixels>0) this->array = new float[spatSize]; |
---|
1520 | for(int npix=0; npix<spatSize; npix++) |
---|
1521 | this->array[npix] = cube.getPixValue(channel*spatSize + npix); |
---|
1522 | } |
---|
1523 | } |
---|
1524 | //-------------------------------------------------------------------- |
---|
1525 | |
---|
1526 | void Image::removeMW() |
---|
1527 | { |
---|
1528 | /** |
---|
1529 | * A function to remove the Milky Way range of channels from a 1-D spectrum. |
---|
1530 | * The array in this Image is assumed to be 1-D, with only the first axisDim |
---|
1531 | * equal to 1. |
---|
1532 | * The values of the MW channels are set to 0, unless they are BLANK. |
---|
1533 | */ |
---|
1534 | if(this->par.getFlagMW() && (this->axisDim[1]==1) ){ |
---|
1535 | for(int z=0;z<this->axisDim[0];z++){ |
---|
1536 | if(!this->isBlank(z) && this->par.isInMW(z)) this->array[z]=0.; |
---|
1537 | } |
---|
1538 | } |
---|
1539 | } |
---|
1540 | |
---|
1541 | } |
---|