1 | //#--------------------------------------------------------------------------- |
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2 | //# SDMath.cc: A collection of single dish mathematical operations |
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3 | //#--------------------------------------------------------------------------- |
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4 | //# Copyright (C) 2004 |
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5 | //# ATNF |
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6 | //# |
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7 | //# This program is free software; you can redistribute it and/or modify it |
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8 | //# under the terms of the GNU General Public License as published by the Free |
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9 | //# Software Foundation; either version 2 of the License, or (at your option) |
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10 | //# any later version. |
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11 | //# |
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12 | //# This program is distributed in the hope that it will be useful, but |
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13 | //# WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General |
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15 | //# Public License for more details. |
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16 | //# |
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17 | //# You should have received a copy of the GNU General Public License along |
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18 | //# with this program; if not, write to the Free Software Foundation, Inc., |
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19 | //# 675 Massachusetts Ave, Cambridge, MA 02139, USA. |
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20 | //# |
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21 | //# Correspondence concerning this software should be addressed as follows: |
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22 | //# Internet email: Malte.Marquarding@csiro.au |
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23 | //# Postal address: Malte Marquarding, |
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24 | //# Australia Telescope National Facility, |
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25 | //# P.O. Box 76, |
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26 | //# Epping, NSW, 2121, |
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27 | //# AUSTRALIA |
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28 | //# |
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29 | //# $Id: |
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30 | //#--------------------------------------------------------------------------- |
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31 | #include <vector> |
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32 | |
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33 | #include <casa/aips.h> |
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34 | #include <casa/BasicSL/String.h> |
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35 | #include <casa/Arrays/IPosition.h> |
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36 | #include <casa/Arrays/Array.h> |
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37 | #include <casa/Arrays/ArrayIter.h> |
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38 | #include <casa/Arrays/VectorIter.h> |
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39 | #include <casa/Arrays/ArrayMath.h> |
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40 | #include <casa/Arrays/ArrayLogical.h> |
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41 | #include <casa/Arrays/MaskedArray.h> |
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42 | #include <casa/Arrays/MaskArrMath.h> |
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43 | #include <casa/Arrays/MaskArrLogi.h> |
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44 | #include <casa/Utilities/Assert.h> |
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45 | #include <casa/Exceptions.h> |
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46 | |
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47 | #include <scimath/Mathematics/VectorKernel.h> |
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48 | #include <scimath/Mathematics/Convolver.h> |
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49 | |
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50 | #include <tables/Tables/Table.h> |
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51 | #include <tables/Tables/ScalarColumn.h> |
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52 | #include <tables/Tables/ArrayColumn.h> |
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53 | |
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54 | #include <lattices/Lattices/LatticeUtilities.h> |
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55 | #include <lattices/Lattices/RebinLattice.h> |
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56 | #include <coordinates/Coordinates/SpectralCoordinate.h> |
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57 | #include <coordinates/Coordinates/CoordinateSystem.h> |
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58 | #include <coordinates/Coordinates/CoordinateUtil.h> |
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59 | |
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60 | #include "MathUtils.h" |
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61 | #include "SDContainer.h" |
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62 | #include "SDMemTable.h" |
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63 | |
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64 | #include "SDMath.h" |
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65 | |
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66 | using namespace casa; |
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67 | using namespace asap; |
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68 | |
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69 | |
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70 | SDMath::SDMath() |
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71 | {;} |
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72 | |
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73 | SDMath::SDMath(const SDMath& other) |
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74 | { |
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75 | |
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76 | // No state |
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77 | |
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78 | } |
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79 | |
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80 | SDMath& SDMath::operator=(const SDMath& other) |
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81 | { |
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82 | if (this != &other) { |
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83 | // No state |
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84 | } |
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85 | return *this; |
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86 | } |
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87 | |
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88 | SDMath::~SDMath() |
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89 | {;} |
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90 | |
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91 | |
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92 | CountedPtr<SDMemTable> SDMath::average(const Block<CountedPtr<SDMemTable> >& in, |
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93 | const Vector<Bool>& mask, Bool scanAv, |
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94 | const std::string& weightStr) |
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95 | // |
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96 | // Weighted averaging of spectra from one or more Tables. |
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97 | // |
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98 | { |
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99 | |
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100 | // Convert weight type |
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101 | |
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102 | WeightType wtType = NONE; |
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103 | convertWeightString(wtType, weightStr); |
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104 | |
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105 | // Create output Table by cloning from the first table |
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106 | |
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107 | SDMemTable* pTabOut = new SDMemTable(*in[0],True); |
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108 | |
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109 | // Setup |
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110 | |
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111 | const uInt axis = 3; // Spectral axis |
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112 | IPosition shp = in[0]->rowAsMaskedArray(0).shape(); // Must not change |
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113 | Array<Float> arr(shp); |
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114 | Array<Bool> barr(shp); |
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115 | const Bool useMask = (mask.nelements() == shp(axis)); |
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116 | |
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117 | // Columns from Tables |
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118 | |
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119 | ROArrayColumn<Float> tSysCol; |
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120 | ROScalarColumn<Double> mjdCol; |
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121 | ROScalarColumn<String> srcNameCol; |
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122 | ROScalarColumn<Double> intCol; |
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123 | ROArrayColumn<uInt> fqIDCol; |
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124 | |
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125 | // Create accumulation MaskedArray. We accumulate for each channel,if,pol,beam |
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126 | // Note that the mask of the accumulation array will ALWAYS remain ALL True. |
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127 | // The MA is only used so that when data which is masked Bad is added to it, |
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128 | // that data does not contribute. |
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129 | |
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130 | Array<Float> zero(shp); |
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131 | zero=0.0; |
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132 | Array<Bool> good(shp); |
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133 | good = True; |
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134 | MaskedArray<Float> sum(zero,good); |
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135 | |
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136 | // Counter arrays |
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137 | |
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138 | Array<Float> nPts(shp); // Number of points |
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139 | nPts = 0.0; |
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140 | Array<Float> nInc(shp); // Increment |
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141 | nInc = 1.0; |
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142 | |
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143 | // Create accumulation Array for variance. We accumulate for |
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144 | // each if,pol,beam, but average over channel. So we need |
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145 | // a shape with one less axis dropping channels. |
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146 | |
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147 | const uInt nAxesSub = shp.nelements() - 1; |
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148 | IPosition shp2(nAxesSub); |
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149 | for (uInt i=0,j=0; i<(nAxesSub+1); i++) { |
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150 | if (i!=axis) { |
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151 | shp2(j) = shp(i); |
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152 | j++; |
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153 | } |
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154 | } |
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155 | Array<Float> sumSq(shp2); |
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156 | sumSq = 0.0; |
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157 | IPosition pos2(nAxesSub,0); // For indexing |
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158 | |
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159 | // Time-related accumulators |
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160 | |
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161 | Double time; |
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162 | Double timeSum = 0.0; |
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163 | Double intSum = 0.0; |
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164 | Double interval = 0.0; |
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165 | |
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166 | // To get the right shape for the Tsys accumulator we need to |
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167 | // access a column from the first table. The shape of this |
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168 | // array must not change |
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169 | |
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170 | Array<Float> tSysSum; |
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171 | { |
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172 | const Table& tabIn = in[0]->table(); |
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173 | tSysCol.attach(tabIn,"TSYS"); |
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174 | tSysSum.resize(tSysCol.shape(0)); |
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175 | } |
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176 | tSysSum =0.0; |
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177 | Array<Float> tSys; |
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178 | |
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179 | // Scan and row tracking |
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180 | |
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181 | Int oldScanID = 0; |
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182 | Int outScanID = 0; |
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183 | Int scanID = 0; |
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184 | Int rowStart = 0; |
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185 | Int nAccum = 0; |
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186 | Int tableStart = 0; |
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187 | |
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188 | // Source and FreqID |
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189 | |
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190 | String sourceName, oldSourceName, sourceNameStart; |
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191 | Vector<uInt> freqID, freqIDStart, oldFreqID; |
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192 | |
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193 | // Loop over tables |
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194 | |
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195 | Float fac = 1.0; |
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196 | const uInt nTables = in.nelements(); |
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197 | for (uInt iTab=0; iTab<nTables; iTab++) { |
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198 | |
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199 | // Attach columns to Table |
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200 | |
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201 | const Table& tabIn = in[iTab]->table(); |
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202 | tSysCol.attach(tabIn, "TSYS"); |
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203 | mjdCol.attach(tabIn, "TIME"); |
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204 | srcNameCol.attach(tabIn, "SRCNAME"); |
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205 | intCol.attach(tabIn, "INTERVAL"); |
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206 | fqIDCol.attach(tabIn, "FREQID"); |
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207 | |
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208 | // Loop over rows in Table |
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209 | |
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210 | const uInt nRows = in[iTab]->nRow(); |
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211 | for (uInt iRow=0; iRow<nRows; iRow++) { |
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212 | |
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213 | // Check conformance |
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214 | |
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215 | IPosition shp2 = in[iTab]->rowAsMaskedArray(iRow).shape(); |
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216 | if (!shp.isEqual(shp2)) { |
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217 | throw (AipsError("Shapes for all rows must be the same")); |
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218 | } |
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219 | |
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220 | // If we are not doing scan averages, make checks for source and |
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221 | // frequency setup and warn if averaging across them |
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222 | |
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223 | // Get copy of Scan Container for this row |
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224 | |
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225 | SDContainer sc = in[iTab]->getSDContainer(iRow); |
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226 | scanID = sc.scanid; |
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227 | |
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228 | // Get quantities from columns |
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229 | |
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230 | srcNameCol.getScalar(iRow, sourceName); |
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231 | mjdCol.get(iRow, time); |
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232 | tSysCol.get(iRow, tSys); |
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233 | intCol.get(iRow, interval); |
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234 | fqIDCol.get(iRow, freqID); |
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235 | |
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236 | // Initialize first source and freqID |
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237 | |
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238 | if (iRow==0 && iTab==0) { |
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239 | sourceNameStart = sourceName; |
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240 | freqIDStart = freqID; |
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241 | } |
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242 | |
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243 | // If we are doing scan averages, see if we are at the end of an |
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244 | // accumulation period (scan). We must check soutce names too, |
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245 | // since we might have two tables with one scan each but different |
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246 | // source names; we shouldn't average different sources together |
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247 | |
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248 | if (scanAv && ( (scanID != oldScanID) || |
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249 | (iRow==0 && iTab>0 && sourceName!=oldSourceName))) { |
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250 | |
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251 | // Normalize data in 'sum' accumulation array according to weighting scheme |
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252 | |
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253 | normalize(sum, sumSq, nPts, wtType, axis, nAxesSub); |
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254 | |
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255 | // Fill scan container. The source and freqID come from the |
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256 | // first row of the first table that went into this average ( |
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257 | // should be the same for all rows in the scan average) |
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258 | |
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259 | Float nR(nAccum); |
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260 | fillSDC(sc, sum.getMask(), sum.getArray(), tSysSum/nR, outScanID, |
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261 | timeSum/nR, intSum, sourceNameStart, freqIDStart); |
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262 | |
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263 | // Write container out to Table |
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264 | |
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265 | pTabOut->putSDContainer(sc); |
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266 | |
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267 | // Reset accumulators |
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268 | |
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269 | sum = 0.0; |
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270 | sumSq = 0.0; |
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271 | nAccum = 0; |
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272 | // |
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273 | tSysSum =0.0; |
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274 | timeSum = 0.0; |
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275 | intSum = 0.0; |
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276 | nPts = 0.0; // reset this too!!! |
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277 | |
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278 | // Increment |
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279 | |
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280 | rowStart = iRow; // First row for next accumulation |
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281 | tableStart = iTab; // First table for next accumulation |
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282 | sourceNameStart = sourceName; // First source name for next accumulation |
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283 | freqIDStart = freqID; // First FreqID for next accumulation |
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284 | // |
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285 | oldScanID = scanID; |
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286 | outScanID += 1; // Scan ID for next accumulation period |
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287 | |
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288 | } |
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289 | |
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290 | // Accumulate |
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291 | |
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292 | accumulate(timeSum, intSum, nAccum, sum, sumSq, nPts, tSysSum, |
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293 | tSys, nInc, mask, time, interval, in, iTab, iRow, axis, |
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294 | nAxesSub, useMask, wtType); |
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295 | // |
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296 | oldSourceName = sourceName; |
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297 | oldFreqID = freqID; |
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298 | } |
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299 | } |
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300 | |
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301 | // OK at this point we have accumulation data which is either |
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302 | // - accumulated from all tables into one row |
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303 | // or |
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304 | // - accumulated from the last scan average |
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305 | // |
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306 | // Normalize data in 'sum' accumulation array according to weighting scheme |
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307 | normalize(sum, sumSq, nPts, wtType, axis, nAxesSub); |
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308 | |
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309 | // Create and fill container. The container we clone will be from |
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310 | // the last Table and the first row that went into the current |
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311 | // accumulation. It probably doesn't matter that much really... |
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312 | |
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313 | Float nR(nAccum); |
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314 | SDContainer sc = in[tableStart]->getSDContainer(rowStart); |
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315 | fillSDC(sc, sum.getMask(), sum.getArray(), tSysSum/nR, outScanID, |
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316 | timeSum/nR, intSum, sourceNameStart, freqIDStart); |
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317 | // |
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318 | pTabOut->putSDContainer(sc); |
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319 | /* |
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320 | cout << endl; |
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321 | cout << "Last accumulation for output scan ID " << outScanID << endl; |
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322 | cout << " The first row in this accumulation is " << rowStart << endl; |
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323 | cout << " The number of rows accumulated is " << nAccum << endl; |
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324 | cout << " The first table in this accumulation is " << tableStart << endl; |
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325 | cout << " The first source in this accumulation is " << sourceNameStart << endl; |
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326 | cout << " The first freqID in this accumulation is " << freqIDStart << endl; |
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327 | cout << " Average time stamp = " << timeSum/nR << endl; |
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328 | cout << " Integrated time = " << intSum << endl; |
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329 | */ |
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330 | return CountedPtr<SDMemTable>(pTabOut); |
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331 | } |
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332 | |
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333 | |
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334 | |
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335 | CountedPtr<SDMemTable> |
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336 | SDMath::quotient(const CountedPtr<SDMemTable>& on, |
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337 | const CountedPtr<SDMemTable>& off) |
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338 | { |
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339 | // |
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340 | // Compute quotient spectrum |
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341 | // |
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342 | const uInt nRows = on->nRow(); |
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343 | if (off->nRow() != nRows) { |
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344 | throw (AipsError("Input Scan Tables must have the same number of rows")); |
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345 | } |
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346 | |
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347 | // Input Tables and columns |
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348 | |
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349 | Table ton = on->table(); |
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350 | Table toff = off->table(); |
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351 | ROArrayColumn<Float> tsys(toff, "TSYS"); |
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352 | ROScalarColumn<Double> mjd(ton, "TIME"); |
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353 | ROScalarColumn<Double> integr(ton, "INTERVAL"); |
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354 | ROScalarColumn<String> srcn(ton, "SRCNAME"); |
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355 | ROArrayColumn<uInt> freqidc(ton, "FREQID"); |
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356 | |
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357 | // Output Table cloned from input |
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358 | |
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359 | SDMemTable* pTabOut = new SDMemTable(*on, True); |
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360 | |
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361 | // Loop over rows |
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362 | |
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363 | for (uInt i=0; i<nRows; i++) { |
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364 | MaskedArray<Float> mon(on->rowAsMaskedArray(i)); |
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365 | MaskedArray<Float> moff(off->rowAsMaskedArray(i)); |
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366 | IPosition ipon = mon.shape(); |
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367 | IPosition ipoff = moff.shape(); |
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368 | // |
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369 | Array<Float> tsarr; |
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370 | tsys.get(i, tsarr); |
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371 | if (ipon != ipoff && ipon != tsarr.shape()) { |
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372 | throw(AipsError("on/off not conformant")); |
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373 | } |
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374 | |
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375 | // Compute quotient |
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376 | |
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377 | MaskedArray<Float> tmp = (mon-moff); |
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378 | Array<Float> out(tmp.getArray()); |
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379 | out /= moff; |
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380 | out *= tsarr; |
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381 | Array<Bool> outflagsb = mon.getMask() && moff.getMask(); |
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382 | |
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383 | // Fill container for this row |
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384 | |
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385 | SDContainer sc = on->getSDContainer(i); |
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386 | // |
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387 | putDataInSDC(sc, out, outflagsb); |
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388 | sc.putTsys(tsarr); |
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389 | sc.scanid = i; |
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390 | |
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391 | // Put new row in output Table |
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392 | |
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393 | pTabOut->putSDContainer(sc); |
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394 | } |
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395 | // |
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396 | return CountedPtr<SDMemTable>(pTabOut); |
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397 | } |
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398 | |
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399 | |
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400 | |
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401 | std::vector<float> SDMath::statistic(const CountedPtr<SDMemTable>& in, |
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402 | const std::vector<bool>& mask, |
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403 | const String& which) |
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404 | // |
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405 | // Perhaps iteration over pol/beam/if should be in here |
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406 | // and inside the nrow iteration ? |
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407 | // |
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408 | { |
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409 | const uInt nRow = in->nRow(); |
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410 | std::vector<float> result(nRow); |
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411 | Vector<Bool> msk(mask); |
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412 | |
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413 | // Specify cursor location |
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414 | |
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415 | IPosition start, end; |
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416 | getCursorLocation(start, end, *in); |
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417 | |
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418 | // Loop over rows |
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419 | |
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420 | const uInt nEl = msk.nelements(); |
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421 | for (uInt ii=0; ii < in->nRow(); ++ii) { |
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422 | |
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423 | // Get row and deconstruct |
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424 | |
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425 | MaskedArray<Float> marr(in->rowAsMaskedArray(ii)); |
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426 | Array<Float> arr = marr.getArray(); |
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427 | Array<Bool> barr = marr.getMask(); |
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428 | |
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429 | // Access desired piece of data |
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430 | |
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431 | Array<Float> v((arr(start,end)).nonDegenerate()); |
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432 | Array<Bool> m((barr(start,end)).nonDegenerate()); |
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433 | |
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434 | // Apply OTF mask |
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435 | |
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436 | MaskedArray<Float> tmp; |
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437 | if (m.nelements()==nEl) { |
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438 | tmp.setData(v,m&&msk); |
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439 | } else { |
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440 | tmp.setData(v,m); |
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441 | } |
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442 | |
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443 | // Get statistic |
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444 | |
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445 | result[ii] = mathutil::statistics(which, tmp); |
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446 | } |
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447 | // |
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448 | return result; |
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449 | } |
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450 | |
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451 | |
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452 | SDMemTable* SDMath::bin(const SDMemTable& in, Int width) |
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453 | { |
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454 | SDHeader sh = in.getSDHeader(); |
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455 | SDMemTable* pTabOut = new SDMemTable(in, True); |
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456 | |
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457 | // Bin up SpectralCoordinates |
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458 | |
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459 | IPosition factors(1); |
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460 | factors(0) = width; |
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461 | for (uInt j=0; j<in.nCoordinates(); ++j) { |
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462 | CoordinateSystem cSys; |
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463 | cSys.addCoordinate(in.getCoordinate(j)); |
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464 | CoordinateSystem cSysBin = |
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465 | CoordinateUtil::makeBinnedCoordinateSystem(factors, cSys, False); |
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466 | // |
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467 | SpectralCoordinate sCBin = cSysBin.spectralCoordinate(0); |
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468 | pTabOut->setCoordinate(sCBin, j); |
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469 | } |
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470 | |
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471 | // Use RebinLattice to find shape |
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472 | |
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473 | IPosition shapeIn(1,sh.nchan); |
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474 | IPosition shapeOut = RebinLattice<Float>::rebinShape(shapeIn, factors); |
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475 | sh.nchan = shapeOut(0); |
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476 | pTabOut->putSDHeader(sh); |
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477 | |
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478 | |
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479 | // Loop over rows and bin along channel axis |
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480 | |
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481 | const uInt axis = 3; |
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482 | for (uInt i=0; i < in.nRow(); ++i) { |
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483 | SDContainer sc = in.getSDContainer(i); |
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484 | // |
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485 | Array<Float> tSys(sc.getTsys()); // Get it out before sc changes shape |
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486 | |
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487 | // Bin up spectrum |
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488 | |
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489 | MaskedArray<Float> marr(in.rowAsMaskedArray(i)); |
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490 | MaskedArray<Float> marrout; |
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491 | LatticeUtilities::bin(marrout, marr, axis, width); |
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492 | |
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493 | // Put back the binned data and flags |
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494 | |
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495 | IPosition ip2 = marrout.shape(); |
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496 | sc.resize(ip2); |
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497 | // |
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498 | putDataInSDC(sc, marrout.getArray(), marrout.getMask()); |
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499 | |
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500 | // Bin up Tsys. |
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501 | |
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502 | Array<Bool> allGood(tSys.shape(),True); |
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503 | MaskedArray<Float> tSysIn(tSys, allGood, True); |
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504 | // |
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505 | MaskedArray<Float> tSysOut; |
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506 | LatticeUtilities::bin(tSysOut, tSysIn, axis, width); |
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507 | sc.putTsys(tSysOut.getArray()); |
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508 | // |
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509 | pTabOut->putSDContainer(sc); |
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510 | } |
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511 | return pTabOut; |
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512 | } |
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513 | |
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514 | SDMemTable* SDMath::simpleOperate(const SDMemTable& in, Float val, Bool doAll, |
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515 | uInt what) |
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516 | // |
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517 | // what = 0 Multiply |
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518 | // 1 Add |
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519 | { |
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520 | SDMemTable* pOut = new SDMemTable(in,False); |
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521 | const Table& tOut = pOut->table(); |
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522 | ArrayColumn<Float> spec(tOut,"SPECTRA"); |
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523 | // |
---|
524 | if (doAll) { |
---|
525 | for (uInt i=0; i < tOut.nrow(); i++) { |
---|
526 | |
---|
527 | // Get |
---|
528 | |
---|
529 | MaskedArray<Float> marr(pOut->rowAsMaskedArray(i)); |
---|
530 | |
---|
531 | // Operate |
---|
532 | |
---|
533 | if (what==0) { |
---|
534 | marr *= val; |
---|
535 | } else if (what==1) { |
---|
536 | marr += val; |
---|
537 | } |
---|
538 | |
---|
539 | // Put |
---|
540 | |
---|
541 | spec.put(i, marr.getArray()); |
---|
542 | } |
---|
543 | } else { |
---|
544 | |
---|
545 | // Get cursor location |
---|
546 | |
---|
547 | IPosition start, end; |
---|
548 | getCursorLocation(start, end, in); |
---|
549 | // |
---|
550 | for (uInt i=0; i < tOut.nrow(); i++) { |
---|
551 | |
---|
552 | // Get |
---|
553 | |
---|
554 | MaskedArray<Float> dataIn(pOut->rowAsMaskedArray(i)); |
---|
555 | |
---|
556 | // Modify. More work than we would like to deal with the mask |
---|
557 | |
---|
558 | Array<Float>& values = dataIn.getRWArray(); |
---|
559 | Array<Bool> mask(dataIn.getMask()); |
---|
560 | // |
---|
561 | Array<Float> values2 = values(start,end); |
---|
562 | Array<Bool> mask2 = mask(start,end); |
---|
563 | MaskedArray<Float> t(values2,mask2); |
---|
564 | if (what==0) { |
---|
565 | t *= val; |
---|
566 | } else if (what==1) { |
---|
567 | t += val; |
---|
568 | } |
---|
569 | values(start, end) = t.getArray(); // Write back into 'dataIn' |
---|
570 | |
---|
571 | // Put |
---|
572 | spec.put(i, dataIn.getArray()); |
---|
573 | } |
---|
574 | } |
---|
575 | // |
---|
576 | return pOut; |
---|
577 | } |
---|
578 | |
---|
579 | |
---|
580 | |
---|
581 | SDMemTable* SDMath::averagePol(const SDMemTable& in, const Vector<Bool>& mask) |
---|
582 | // |
---|
583 | // Average all polarizations together, weighted by variance |
---|
584 | // |
---|
585 | { |
---|
586 | // WeightType wtType = NONE; |
---|
587 | // convertWeightString(wtType, weight); |
---|
588 | |
---|
589 | const uInt nRows = in.nRow(); |
---|
590 | const uInt polAxis = 2; // Polarization axis |
---|
591 | const uInt chanAxis = 3; // Spectrum axis |
---|
592 | |
---|
593 | // Create output Table and reshape number of polarizations |
---|
594 | |
---|
595 | Bool clear=True; |
---|
596 | SDMemTable* pTabOut = new SDMemTable(in, clear); |
---|
597 | SDHeader header = pTabOut->getSDHeader(); |
---|
598 | header.npol = 1; |
---|
599 | pTabOut->putSDHeader(header); |
---|
600 | |
---|
601 | // Shape of input and output data |
---|
602 | |
---|
603 | const IPosition& shapeIn = in.rowAsMaskedArray(0u, False).shape(); |
---|
604 | IPosition shapeOut(shapeIn); |
---|
605 | shapeOut(polAxis) = 1; // Average all polarizations |
---|
606 | // |
---|
607 | const uInt nChan = shapeIn(chanAxis); |
---|
608 | const IPosition vecShapeOut(4,1,1,1,nChan); // A multi-dim form of a Vector shape |
---|
609 | IPosition start(4), end(4); |
---|
610 | |
---|
611 | // Output arrays |
---|
612 | |
---|
613 | Array<Float> outData(shapeOut, 0.0); |
---|
614 | Array<Bool> outMask(shapeOut, True); |
---|
615 | const IPosition axes(2, 2, 3); // pol-channel plane |
---|
616 | // |
---|
617 | const Bool useMask = (mask.nelements() == shapeIn(chanAxis)); |
---|
618 | |
---|
619 | // Loop over rows |
---|
620 | |
---|
621 | for (uInt iRow=0; iRow<nRows; iRow++) { |
---|
622 | |
---|
623 | // Get data for this row |
---|
624 | |
---|
625 | MaskedArray<Float> marr(in.rowAsMaskedArray(iRow)); |
---|
626 | Array<Float>& arr = marr.getRWArray(); |
---|
627 | const Array<Bool>& barr = marr.getMask(); |
---|
628 | |
---|
629 | // Make iterators to iterate by pol-channel planes |
---|
630 | |
---|
631 | ReadOnlyArrayIterator<Float> itDataPlane(arr, axes); |
---|
632 | ReadOnlyArrayIterator<Bool> itMaskPlane(barr, axes); |
---|
633 | |
---|
634 | // Accumulations |
---|
635 | |
---|
636 | Float fac = 1.0; |
---|
637 | Vector<Float> vecSum(nChan,0.0); |
---|
638 | |
---|
639 | // Iterate through data by pol-channel planes |
---|
640 | |
---|
641 | while (!itDataPlane.pastEnd()) { |
---|
642 | |
---|
643 | // Iterate through plane by polarization and accumulate Vectors |
---|
644 | |
---|
645 | Vector<Float> t1(nChan); t1 = 0.0; |
---|
646 | Vector<Bool> t2(nChan); t2 = True; |
---|
647 | MaskedArray<Float> vecSum(t1,t2); |
---|
648 | Float varSum = 0.0; |
---|
649 | { |
---|
650 | ReadOnlyVectorIterator<Float> itDataVec(itDataPlane.array(), 1); |
---|
651 | ReadOnlyVectorIterator<Bool> itMaskVec(itMaskPlane.array(), 1); |
---|
652 | while (!itDataVec.pastEnd()) { |
---|
653 | |
---|
654 | // Create MA of data & mask (optionally including OTF mask) and get variance |
---|
655 | |
---|
656 | if (useMask) { |
---|
657 | const MaskedArray<Float> spec(itDataVec.vector(),mask&&itMaskVec.vector()); |
---|
658 | fac = 1.0 / variance(spec); |
---|
659 | } else { |
---|
660 | const MaskedArray<Float> spec(itDataVec.vector(),itMaskVec.vector()); |
---|
661 | fac = 1.0 / variance(spec); |
---|
662 | } |
---|
663 | |
---|
664 | // Normalize spectrum (without OTF mask) and accumulate |
---|
665 | |
---|
666 | const MaskedArray<Float> spec(fac*itDataVec.vector(), itMaskVec.vector()); |
---|
667 | vecSum += spec; |
---|
668 | varSum += fac; |
---|
669 | |
---|
670 | // Next |
---|
671 | |
---|
672 | itDataVec.next(); |
---|
673 | itMaskVec.next(); |
---|
674 | } |
---|
675 | } |
---|
676 | |
---|
677 | // Normalize summed spectrum |
---|
678 | |
---|
679 | vecSum /= varSum; |
---|
680 | |
---|
681 | // FInd position in input data array. We are iterating by pol-channel |
---|
682 | // plane so all that will change is beam and IF and that's what we want. |
---|
683 | |
---|
684 | IPosition pos = itDataPlane.pos(); |
---|
685 | |
---|
686 | // Write out data. This is a bit messy. We have to reform the Vector |
---|
687 | // accumulator into an Array of shape (1,1,1,nChan) |
---|
688 | |
---|
689 | start = pos; |
---|
690 | end = pos; |
---|
691 | end(chanAxis) = nChan-1; |
---|
692 | outData(start,end) = vecSum.getArray().reform(vecShapeOut); |
---|
693 | outMask(start,end) = vecSum.getMask().reform(vecShapeOut); |
---|
694 | |
---|
695 | // Step to next beam/IF combination |
---|
696 | |
---|
697 | itDataPlane.next(); |
---|
698 | itMaskPlane.next(); |
---|
699 | } |
---|
700 | |
---|
701 | // Generate output container and write it to output table |
---|
702 | |
---|
703 | SDContainer sc = in.getSDContainer(); |
---|
704 | sc.resize(shapeOut); |
---|
705 | // |
---|
706 | putDataInSDC(sc, outData, outMask); |
---|
707 | pTabOut->putSDContainer(sc); |
---|
708 | } |
---|
709 | // |
---|
710 | return pTabOut; |
---|
711 | } |
---|
712 | |
---|
713 | |
---|
714 | SDMemTable* SDMath::smooth(const SDMemTable& in, |
---|
715 | const casa::String& kernelType, |
---|
716 | casa::Float width, Bool doAll) |
---|
717 | { |
---|
718 | |
---|
719 | // Number of channels |
---|
720 | |
---|
721 | const uInt chanAxis = 3; // Spectral axis |
---|
722 | SDHeader sh = in.getSDHeader(); |
---|
723 | const uInt nChan = sh.nchan; |
---|
724 | |
---|
725 | // Generate Kernel |
---|
726 | |
---|
727 | VectorKernel::KernelTypes type = VectorKernel::toKernelType(kernelType); |
---|
728 | Vector<Float> kernel = VectorKernel::make(type, width, nChan, True, False); |
---|
729 | |
---|
730 | // Generate Convolver |
---|
731 | |
---|
732 | IPosition shape(1,nChan); |
---|
733 | Convolver<Float> conv(kernel, shape); |
---|
734 | |
---|
735 | // New Table |
---|
736 | |
---|
737 | SDMemTable* pTabOut = new SDMemTable(in,True); |
---|
738 | |
---|
739 | // Get cursor location |
---|
740 | |
---|
741 | IPosition start, end; |
---|
742 | getCursorLocation(start, end, in); |
---|
743 | // |
---|
744 | IPosition shapeOut(4,1); |
---|
745 | |
---|
746 | // Output Vectors |
---|
747 | |
---|
748 | Vector<Float> valuesOut(nChan); |
---|
749 | Vector<Bool> maskOut(nChan); |
---|
750 | |
---|
751 | // Loop over rows in Table |
---|
752 | |
---|
753 | for (uInt ri=0; ri < in.nRow(); ++ri) { |
---|
754 | |
---|
755 | // Get copy of data |
---|
756 | |
---|
757 | const MaskedArray<Float>& dataIn(in.rowAsMaskedArray(ri)); |
---|
758 | AlwaysAssert(dataIn.shape()(chanAxis)==nChan, AipsError); |
---|
759 | // |
---|
760 | Array<Float> valuesIn = dataIn.getArray(); |
---|
761 | Array<Bool> maskIn = dataIn.getMask(); |
---|
762 | |
---|
763 | // Branch depending on whether we smooth all locations or just |
---|
764 | // those pointed at by the current selection cursor |
---|
765 | |
---|
766 | if (doAll) { |
---|
767 | uInt axis = 3; |
---|
768 | VectorIterator<Float> itValues(valuesIn, axis); |
---|
769 | VectorIterator<Bool> itMask(maskIn, axis); |
---|
770 | while (!itValues.pastEnd()) { |
---|
771 | |
---|
772 | // Smooth |
---|
773 | if (kernelType==VectorKernel::HANNING) { |
---|
774 | mathutil::hanning(valuesOut, maskOut, itValues.vector(), itMask.vector()); |
---|
775 | itMask.vector() = maskOut; |
---|
776 | } else { |
---|
777 | mathutil::replaceMaskByZero(itValues.vector(), itMask.vector()); |
---|
778 | conv.linearConv(valuesOut, itValues.vector()); |
---|
779 | } |
---|
780 | // |
---|
781 | itValues.vector() = valuesOut; |
---|
782 | // |
---|
783 | itValues.next(); |
---|
784 | itMask.next(); |
---|
785 | } |
---|
786 | } else { |
---|
787 | |
---|
788 | // Set multi-dim Vector shape |
---|
789 | |
---|
790 | shapeOut(chanAxis) = valuesIn.shape()(chanAxis); |
---|
791 | |
---|
792 | // Stuff about with shapes so that we don't have conformance run-time errors |
---|
793 | |
---|
794 | Vector<Float> valuesIn2 = valuesIn(start,end).nonDegenerate(); |
---|
795 | Vector<Bool> maskIn2 = maskIn(start,end).nonDegenerate(); |
---|
796 | |
---|
797 | // Smooth |
---|
798 | |
---|
799 | if (kernelType==VectorKernel::HANNING) { |
---|
800 | mathutil::hanning(valuesOut, maskOut, valuesIn2, maskIn2); |
---|
801 | maskIn(start,end) = maskOut.reform(shapeOut); |
---|
802 | } else { |
---|
803 | mathutil::replaceMaskByZero(valuesIn2, maskIn2); |
---|
804 | conv.linearConv(valuesOut, valuesIn2); |
---|
805 | } |
---|
806 | // |
---|
807 | valuesIn(start,end) = valuesOut.reform(shapeOut); |
---|
808 | } |
---|
809 | |
---|
810 | // Create and put back |
---|
811 | |
---|
812 | SDContainer sc = in.getSDContainer(ri); |
---|
813 | putDataInSDC(sc, valuesIn, maskIn); |
---|
814 | // |
---|
815 | pTabOut->putSDContainer(sc); |
---|
816 | } |
---|
817 | // |
---|
818 | return pTabOut; |
---|
819 | } |
---|
820 | |
---|
821 | |
---|
822 | |
---|
823 | |
---|
824 | |
---|
825 | // 'private' functions |
---|
826 | |
---|
827 | void SDMath::fillSDC(SDContainer& sc, |
---|
828 | const Array<Bool>& mask, |
---|
829 | const Array<Float>& data, |
---|
830 | const Array<Float>& tSys, |
---|
831 | Int scanID, Double timeStamp, |
---|
832 | Double interval, const String& sourceName, |
---|
833 | const Vector<uInt>& freqID) |
---|
834 | { |
---|
835 | // Data and mask |
---|
836 | |
---|
837 | putDataInSDC(sc, data, mask); |
---|
838 | |
---|
839 | // TSys |
---|
840 | |
---|
841 | sc.putTsys(tSys); |
---|
842 | |
---|
843 | // Time things |
---|
844 | |
---|
845 | sc.timestamp = timeStamp; |
---|
846 | sc.interval = interval; |
---|
847 | sc.scanid = scanID; |
---|
848 | // |
---|
849 | sc.sourcename = sourceName; |
---|
850 | sc.putFreqMap(freqID); |
---|
851 | } |
---|
852 | |
---|
853 | void SDMath::normalize(MaskedArray<Float>& sum, |
---|
854 | const Array<Float>& sumSq, |
---|
855 | const Array<Float>& nPts, |
---|
856 | WeightType wtType, Int axis, |
---|
857 | Int nAxesSub) |
---|
858 | { |
---|
859 | IPosition pos2(nAxesSub,0); |
---|
860 | // |
---|
861 | if (wtType==NONE) { |
---|
862 | |
---|
863 | // We just average by the number of points accumulated. |
---|
864 | // We need to make a MA out of nPts so that no divide by |
---|
865 | // zeros occur |
---|
866 | |
---|
867 | MaskedArray<Float> t(nPts, (nPts>Float(0.0))); |
---|
868 | sum /= t; |
---|
869 | } else if (wtType==VAR) { |
---|
870 | |
---|
871 | // Normalize each spectrum by sum(1/var) where the variance |
---|
872 | // is worked out for each spectrum |
---|
873 | |
---|
874 | Array<Float>& data = sum.getRWArray(); |
---|
875 | VectorIterator<Float> itData(data, axis); |
---|
876 | while (!itData.pastEnd()) { |
---|
877 | pos2 = itData.pos().getFirst(nAxesSub); |
---|
878 | itData.vector() /= sumSq(pos2); |
---|
879 | itData.next(); |
---|
880 | } |
---|
881 | } else if (wtType==TSYS) { |
---|
882 | } |
---|
883 | } |
---|
884 | |
---|
885 | |
---|
886 | void SDMath::accumulate(Double& timeSum, Double& intSum, Int& nAccum, |
---|
887 | MaskedArray<Float>& sum, Array<Float>& sumSq, |
---|
888 | Array<Float>& nPts, Array<Float>& tSysSum, |
---|
889 | const Array<Float>& tSys, const Array<Float>& nInc, |
---|
890 | const Vector<Bool>& mask, Double time, Double interval, |
---|
891 | const Block<CountedPtr<SDMemTable> >& in, |
---|
892 | uInt iTab, uInt iRow, uInt axis, |
---|
893 | uInt nAxesSub, Bool useMask, |
---|
894 | WeightType wtType) |
---|
895 | { |
---|
896 | |
---|
897 | // Get data |
---|
898 | |
---|
899 | MaskedArray<Float> dataIn(in[iTab]->rowAsMaskedArray(iRow)); |
---|
900 | Array<Float>& valuesIn = dataIn.getRWArray(); // writable reference |
---|
901 | const Array<Bool>& maskIn = dataIn.getMask(); // RO reference |
---|
902 | // |
---|
903 | if (wtType==NONE) { |
---|
904 | const MaskedArray<Float> n(nInc,dataIn.getMask()); |
---|
905 | nPts += n; // Only accumulates where mask==T |
---|
906 | } else if (wtType==VAR) { |
---|
907 | |
---|
908 | // We are going to average the data, weighted by the noise for each pol, beam and IF. |
---|
909 | // So therefore we need to iterate through by spectrum (axis 3) |
---|
910 | |
---|
911 | VectorIterator<Float> itData(valuesIn, axis); |
---|
912 | ReadOnlyVectorIterator<Bool> itMask(maskIn, axis); |
---|
913 | Float fac = 1.0; |
---|
914 | IPosition pos(nAxesSub,0); |
---|
915 | // |
---|
916 | while (!itData.pastEnd()) { |
---|
917 | |
---|
918 | // Make MaskedArray of Vector, optionally apply OTF mask, and find scaling factor |
---|
919 | |
---|
920 | if (useMask) { |
---|
921 | MaskedArray<Float> tmp(itData.vector(),mask&&itMask.vector()); |
---|
922 | fac = 1.0/variance(tmp); |
---|
923 | } else { |
---|
924 | MaskedArray<Float> tmp(itData.vector(),itMask.vector()); |
---|
925 | fac = 1.0/variance(tmp); |
---|
926 | } |
---|
927 | |
---|
928 | // Scale data |
---|
929 | |
---|
930 | itData.vector() *= fac; // Writes back into 'dataIn' |
---|
931 | // |
---|
932 | // Accumulate variance per if/pol/beam averaged over spectrum |
---|
933 | // This method to get pos2 from itData.pos() is only valid |
---|
934 | // because the spectral axis is the last one (so we can just |
---|
935 | // copy the first nAXesSub positions out) |
---|
936 | |
---|
937 | pos = itData.pos().getFirst(nAxesSub); |
---|
938 | sumSq(pos) += fac; |
---|
939 | // |
---|
940 | itData.next(); |
---|
941 | itMask.next(); |
---|
942 | } |
---|
943 | } else if (wtType==TSYS) { |
---|
944 | } |
---|
945 | |
---|
946 | // Accumulate sum of (possibly scaled) data |
---|
947 | |
---|
948 | sum += dataIn; |
---|
949 | |
---|
950 | // Accumulate Tsys, time, and interval |
---|
951 | |
---|
952 | tSysSum += tSys; |
---|
953 | timeSum += time; |
---|
954 | intSum += interval; |
---|
955 | nAccum += 1; |
---|
956 | } |
---|
957 | |
---|
958 | |
---|
959 | |
---|
960 | |
---|
961 | void SDMath::getCursorLocation(IPosition& start, IPosition& end, |
---|
962 | const SDMemTable& in) |
---|
963 | { |
---|
964 | const uInt nDim = 4; |
---|
965 | const uInt i = in.getBeam(); |
---|
966 | const uInt j = in.getIF(); |
---|
967 | const uInt k = in.getPol(); |
---|
968 | const uInt n = in.nChan(); |
---|
969 | // |
---|
970 | start.resize(nDim); |
---|
971 | start(0) = i; |
---|
972 | start(1) = j; |
---|
973 | start(2) = k; |
---|
974 | start(3) = 0; |
---|
975 | // |
---|
976 | end.resize(nDim); |
---|
977 | end(0) = i; |
---|
978 | end(1) = j; |
---|
979 | end(2) = k; |
---|
980 | end(3) = n-1; |
---|
981 | } |
---|
982 | |
---|
983 | |
---|
984 | void SDMath::convertWeightString(WeightType& wtType, const std::string& weightStr) |
---|
985 | { |
---|
986 | String tStr(weightStr); |
---|
987 | tStr.upcase(); |
---|
988 | if (tStr.contains(String("NONE"))) { |
---|
989 | wtType = NONE; |
---|
990 | } else if (tStr.contains(String("VAR"))) { |
---|
991 | wtType = VAR; |
---|
992 | } else if (tStr.contains(String("TSYS"))) { |
---|
993 | wtType = TSYS; |
---|
994 | throw(AipsError("T_sys weighting not yet implemented")); |
---|
995 | } else { |
---|
996 | throw(AipsError("Unrecognized weighting type")); |
---|
997 | } |
---|
998 | } |
---|
999 | |
---|
1000 | void SDMath::putDataInSDC(SDContainer& sc, const Array<Float>& data, |
---|
1001 | const Array<Bool>& mask) |
---|
1002 | { |
---|
1003 | sc.putSpectrum(data); |
---|
1004 | // |
---|
1005 | Array<uChar> outflags(data.shape()); |
---|
1006 | convertArray(outflags,!mask); |
---|
1007 | sc.putFlags(outflags); |
---|
1008 | } |
---|