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/Exceptions.h> |
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45 | |
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46 | #include <tables/Tables/Table.h> |
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47 | #include <tables/Tables/ScalarColumn.h> |
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48 | #include <tables/Tables/ArrayColumn.h> |
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49 | |
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50 | #include <lattices/Lattices/LatticeUtilities.h> |
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51 | #include <lattices/Lattices/RebinLattice.h> |
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52 | #include <coordinates/Coordinates/SpectralCoordinate.h> |
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53 | #include <coordinates/Coordinates/CoordinateSystem.h> |
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54 | #include <coordinates/Coordinates/CoordinateUtil.h> |
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55 | |
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56 | #include "MathUtils.h" |
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57 | #include "SDContainer.h" |
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58 | #include "SDMemTable.h" |
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59 | |
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60 | #include "SDMath.h" |
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61 | |
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62 | using namespace casa; |
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63 | using namespace asap; |
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64 | //using namespace asap::SDMath; |
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65 | |
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66 | CountedPtr<SDMemTable> SDMath::average(const CountedPtr<SDMemTable>& in) |
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67 | // |
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68 | // Average all rows in Table in time |
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69 | // |
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70 | { |
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71 | Table t = in->table(); |
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72 | ROArrayColumn<Float> tsys(t, "TSYS"); |
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73 | ROScalarColumn<Double> mjd(t, "TIME"); |
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74 | ROScalarColumn<String> srcn(t, "SRCNAME"); |
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75 | ROScalarColumn<Double> integr(t, "INTERVAL"); |
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76 | ROArrayColumn<uInt> freqidc(t, "FREQID"); |
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77 | IPosition ip = in->rowAsMaskedArray(0).shape(); |
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78 | Array<Float> outarr(ip); outarr =0.0; |
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79 | Array<Float> narr(ip);narr = 0.0; |
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80 | Array<Float> narrinc(ip);narrinc = 1.0; |
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81 | |
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82 | Array<Float> tsarr(tsys.shape(0)); |
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83 | Array<Float> outtsarr(tsys.shape(0)); |
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84 | outtsarr =0.0; |
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85 | tsys.get(0, tsarr);// this is probably unneccessary as tsys should |
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86 | Double tme = 0.0; |
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87 | Double inttime = 0.0; |
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88 | |
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89 | // Loop over rows |
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90 | |
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91 | for (uInt i=0; i < t.nrow(); i++) { |
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92 | |
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93 | // Get data and accumulate sums |
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94 | |
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95 | MaskedArray<Float> marr(in->rowAsMaskedArray(i)); |
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96 | outarr += marr; |
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97 | MaskedArray<Float> n(narrinc,marr.getMask()); |
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98 | narr += n; |
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99 | |
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100 | // Accumulkate Tsys |
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101 | |
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102 | tsys.get(i, tsarr);// this is probably unneccessary as tsys should |
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103 | outtsarr += tsarr; // be constant |
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104 | Double tmp; |
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105 | mjd.get(i,tmp); |
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106 | tme += tmp;// average time |
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107 | integr.get(i,tmp); |
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108 | inttime += tmp; |
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109 | } |
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110 | |
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111 | // Average |
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112 | |
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113 | MaskedArray<Float> nma(narr,(narr > Float(0))); |
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114 | outarr /= nma; |
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115 | |
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116 | // Create container and put |
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117 | |
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118 | Array<Bool> outflagsb = !(nma.getMask()); |
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119 | Array<uChar> outflags(outflagsb.shape()); |
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120 | convertArray(outflags,outflagsb); |
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121 | SDContainer sc = in->getSDContainer(); |
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122 | Int n = t.nrow(); |
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123 | outtsarr /= Float(n); |
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124 | sc.timestamp = tme/Double(n); |
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125 | sc.interval = inttime; |
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126 | String tstr; srcn.getScalar(0,tstr);// get sourcename of "mid" point |
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127 | sc.sourcename = tstr; |
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128 | Vector<uInt> tvec; |
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129 | freqidc.get(0,tvec); |
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130 | sc.putFreqMap(tvec); |
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131 | sc.putTsys(outtsarr); |
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132 | sc.scanid = 0; |
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133 | sc.putSpectrum(outarr); |
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134 | sc.putFlags(outflags); |
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135 | SDMemTable* sdmt = new SDMemTable(*in,True); |
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136 | sdmt->putSDContainer(sc); |
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137 | return CountedPtr<SDMemTable>(sdmt); |
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138 | } |
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139 | |
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140 | CountedPtr<SDMemTable> |
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141 | SDMath::quotient(const CountedPtr<SDMemTable>& on, |
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142 | const CountedPtr<SDMemTable>& off) |
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143 | // |
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144 | // Compute quotient spectrum |
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145 | // |
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146 | { |
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147 | const uInt nRows = on->nRow(); |
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148 | if (off->nRow() != nRows) { |
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149 | throw (AipsError("Input Scan Tables must have the same number of rows")); |
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150 | } |
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151 | |
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152 | // Input Tables and columns |
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153 | |
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154 | Table ton = on->table(); |
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155 | Table toff = off->table(); |
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156 | ROArrayColumn<Float> tsys(toff, "TSYS"); |
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157 | ROScalarColumn<Double> mjd(ton, "TIME"); |
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158 | ROScalarColumn<Double> integr(ton, "INTERVAL"); |
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159 | ROScalarColumn<String> srcn(ton, "SRCNAME"); |
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160 | ROArrayColumn<uInt> freqidc(ton, "FREQID"); |
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161 | |
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162 | // Output Table cloned from input |
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163 | |
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164 | SDMemTable* sdmt = new SDMemTable(*on, True); |
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165 | |
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166 | // Loop over rows |
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167 | |
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168 | for (uInt i=0; i<nRows; i++) { |
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169 | MaskedArray<Float> mon(on->rowAsMaskedArray(i)); |
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170 | MaskedArray<Float> moff(off->rowAsMaskedArray(i)); |
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171 | IPosition ipon = mon.shape(); |
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172 | IPosition ipoff = moff.shape(); |
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173 | // |
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174 | Array<Float> tsarr; |
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175 | tsys.get(i, tsarr); |
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176 | if (ipon != ipoff && ipon != tsarr.shape()) { |
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177 | throw(AipsError("on/off not conformant")); |
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178 | } |
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179 | |
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180 | // Compute quotient |
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181 | |
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182 | MaskedArray<Float> tmp = (mon-moff); |
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183 | Array<Float> out(tmp.getArray()); |
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184 | out /= moff; |
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185 | out *= tsarr; |
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186 | Array<Bool> outflagsb = !(mon.getMask() && moff.getMask()); |
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187 | Array<uChar> outflags(outflagsb.shape()); |
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188 | convertArray(outflags,outflagsb); |
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189 | |
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190 | // Fill container for this row |
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191 | |
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192 | SDContainer sc = on->getSDContainer(); |
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193 | sc.putTsys(tsarr); |
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194 | sc.scanid = 0; |
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195 | sc.putSpectrum(out); |
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196 | sc.putFlags(outflags); |
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197 | |
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198 | // Put new row in output Table |
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199 | |
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200 | sdmt->putSDContainer(sc); |
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201 | } |
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202 | // |
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203 | return CountedPtr<SDMemTable>(sdmt); |
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204 | } |
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205 | |
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206 | void SDMath::multiplyInSitu(SDMemTable* in, Float factor) { |
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207 | SDMemTable* sdmt = new SDMemTable(*in); |
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208 | Table t = sdmt->table(); |
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209 | ArrayColumn<Float> spec(t,"SPECTRA"); |
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210 | for (uInt i=0; i < t.nrow(); i++) { |
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211 | MaskedArray<Float> marr(sdmt->rowAsMaskedArray(i)); |
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212 | marr *= factor; |
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213 | spec.put(i, marr.getArray()); |
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214 | } |
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215 | in = sdmt; |
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216 | delete sdmt;sdmt=0; |
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217 | } |
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218 | |
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219 | CountedPtr<SDMemTable> |
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220 | SDMath::multiply(const CountedPtr<SDMemTable>& in, Float factor) |
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221 | // |
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222 | // Multiply values by factor |
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223 | // |
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224 | { |
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225 | SDMemTable* sdmt = new SDMemTable(*in); |
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226 | Table t = sdmt->table(); |
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227 | ArrayColumn<Float> spec(t,"SPECTRA"); |
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228 | |
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229 | for (uInt i=0; i < t.nrow(); i++) { |
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230 | MaskedArray<Float> marr(sdmt->rowAsMaskedArray(i)); |
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231 | marr *= factor; |
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232 | spec.put(i, marr.getArray()); |
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233 | } |
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234 | return CountedPtr<SDMemTable>(sdmt); |
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235 | } |
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236 | |
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237 | CountedPtr<SDMemTable> |
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238 | SDMath::add(const CountedPtr<SDMemTable>& in, Float offset) |
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239 | // |
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240 | // Add offset to values |
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241 | // |
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242 | { |
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243 | SDMemTable* sdmt = new SDMemTable(*in); |
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244 | |
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245 | Table t = sdmt->table(); |
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246 | ArrayColumn<Float> spec(t,"SPECTRA"); |
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247 | |
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248 | for (uInt i=0; i < t.nrow(); i++) { |
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249 | MaskedArray<Float> marr(sdmt->rowAsMaskedArray(i)); |
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250 | marr += offset; |
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251 | spec.put(i, marr.getArray()); |
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252 | } |
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253 | return CountedPtr<SDMemTable>(sdmt); |
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254 | } |
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255 | |
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256 | |
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257 | CountedPtr<SDMemTable> |
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258 | SDMath::hanning(const CountedPtr<SDMemTable>& in) |
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259 | // |
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260 | // Hanning smooth each row |
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261 | // Should Tsys be smoothed ? |
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262 | // |
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263 | { |
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264 | SDMemTable* sdmt = new SDMemTable(*in,True); |
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265 | |
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266 | // Loop over rows in Table |
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267 | |
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268 | for (uInt ri=0; ri < in->nRow(); ++ri) { |
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269 | |
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270 | // Get data |
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271 | |
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272 | const MaskedArray<Float>& marr(in->rowAsMaskedArray(ri)); |
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273 | Array<Float> arr = marr.getArray(); |
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274 | Array<Bool> barr = marr.getMask(); |
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275 | |
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276 | // Smooth along the channels axis |
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277 | |
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278 | uInt axis = 3; |
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279 | VectorIterator<Float> itData(arr, axis); |
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280 | VectorIterator<Bool> itMask(barr, axis); |
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281 | Vector<Float> outv; |
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282 | Vector<Bool> outm; |
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283 | while (!itData.pastEnd()) { |
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284 | mathutil::hanning(outv, outm, itData.vector(), itMask.vector()); |
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285 | itData.vector() = outv; |
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286 | itMask.vector() = outm; |
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287 | // |
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288 | itData.next(); |
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289 | itMask.next(); |
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290 | } |
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291 | |
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292 | // Create and put back |
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293 | |
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294 | Array<uChar> outflags(barr.shape()); |
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295 | convertArray(outflags,!barr); |
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296 | SDContainer sc = in->getSDContainer(ri); |
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297 | sc.putSpectrum(arr); |
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298 | sc.putFlags(outflags); |
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299 | sdmt->putSDContainer(sc); |
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300 | } |
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301 | return CountedPtr<SDMemTable>(sdmt); |
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302 | } |
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303 | |
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304 | |
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305 | |
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306 | CountedPtr<SDMemTable> SDMath::averages(const Block<CountedPtr<SDMemTable> >& in, |
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307 | const Vector<Bool>& mask) |
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308 | // |
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309 | // Noise weighted averaging of spectra from many Tables. Tables can have different |
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310 | // number of rows. |
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311 | // |
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312 | { |
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313 | |
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314 | // Setup |
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315 | |
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316 | const uInt axis = 3; // Spectral axis |
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317 | IPosition shp = in[0]->rowAsMaskedArray(0).shape(); |
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318 | Array<Float> arr(shp); |
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319 | Array<Bool> barr(shp); |
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320 | Double sumInterval = 0.0; |
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321 | const Bool useMask = (mask.nelements() == shp(axis)); |
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322 | |
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323 | // Create data accumulation MaskedArray. We accumulate for each |
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324 | // channel,if,pol,beam |
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325 | |
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326 | Array<Float> zero(shp); zero=0.0; |
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327 | Array<Bool> good(shp); good = True; |
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328 | MaskedArray<Float> sum(zero,good); |
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329 | |
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330 | // Create accumulation Array for variance. We accumulate for |
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331 | // each if,pol,beam, but average over channel |
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332 | |
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333 | const uInt nAxesSub = shp.nelements() - 1; |
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334 | IPosition shp2(nAxesSub); |
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335 | for (uInt i=0,j=0; i<(nAxesSub+1); i++) { |
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336 | if (i!=axis) { |
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337 | shp2(j) = shp(i); |
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338 | j++; |
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339 | } |
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340 | } |
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341 | Array<Float> sumSq(shp2); |
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342 | sumSq = 0.0; |
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343 | IPosition pos2(nAxesSub,0); // FOr indexing |
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344 | // |
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345 | Float fac = 1.0; |
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346 | const uInt nTables = in.nelements(); |
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347 | for (uInt iTab=0; iTab<nTables; iTab++) { |
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348 | const uInt nRows = in[iTab]->nRow(); |
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349 | sumInterval += nRows * in[iTab]->getInterval(); // Sum of time intervals |
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350 | // |
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351 | for (uInt iRow=0; iRow<nRows; iRow++) { |
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352 | |
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353 | // Check conforms |
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354 | |
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355 | IPosition shp2 = in[iTab]->rowAsMaskedArray(iRow).shape(); |
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356 | if (!shp.isEqual(shp2)) { |
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357 | throw (AipsError("Shapes for all rows must be the same")); |
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358 | } |
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359 | |
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360 | // Get data and deconstruct |
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361 | |
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362 | MaskedArray<Float> marr(in[iTab]->rowAsMaskedArray(iRow)); |
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363 | Array<Float>& arr = marr.getRWArray(); // writable reference |
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364 | const Array<Bool>& barr = marr.getMask(); // RO reference |
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365 | |
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366 | // We are going to average the data, weighted by the noise for each |
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367 | // pol, beam and IF. So therefore we need to iterate through by |
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368 | // spectra (axis 3) |
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369 | |
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370 | VectorIterator<Float> itData(arr, axis); |
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371 | ReadOnlyVectorIterator<Bool> itMask(barr, axis); |
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372 | while (!itData.pastEnd()) { |
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373 | |
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374 | // Make MaskedArray of Vector, optionally apply OTF mask, and find scaling factor |
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375 | |
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376 | if (useMask) { |
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377 | MaskedArray<Float> tmp(itData.vector(),mask&&itMask.vector()); |
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378 | fac = 1.0/variance(tmp); |
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379 | } else { |
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380 | MaskedArray<Float> tmp(itData.vector(),itMask.vector()); |
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381 | fac = 1.0/variance(tmp); |
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382 | } |
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383 | |
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384 | // Scale data |
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385 | |
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386 | itData.vector() *= fac; |
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387 | |
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388 | // Accumulate variance per if/pol/beam averaged over spectrum |
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389 | // This method to get pos2 from itData.pos() is only valid |
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390 | // because the spectral axis is the last one (so we can just |
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391 | // copy the first nAXesSub positions out) |
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392 | |
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393 | pos2 = itData.pos().getFirst(nAxesSub); |
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394 | sumSq(pos2) += fac; |
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395 | // |
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396 | itData.next(); |
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397 | itMask.next(); |
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398 | } |
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399 | |
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400 | // Accumulate sums |
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401 | |
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402 | sum += marr; |
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403 | } |
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404 | } |
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405 | |
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406 | // Normalize by the sum of the 1/var. |
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407 | |
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408 | Array<Float>& data = sum.getRWArray(); |
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409 | VectorIterator<Float> itData(data, axis); |
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410 | while (!itData.pastEnd()) { |
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411 | pos2 = itData.pos().getFirst(nAxesSub); // See comments above |
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412 | itData.vector() /= sumSq(pos2); |
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413 | itData.next(); |
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414 | } |
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415 | |
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416 | // Create and fill output |
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417 | |
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418 | Array<uChar> outflags(shp); |
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419 | convertArray(outflags,!(sum.getMask())); |
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420 | // |
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421 | SDContainer sc = in[0]->getSDContainer(); // CLone from first container of first Table |
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422 | sc.putSpectrum(data); |
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423 | sc.putFlags(outflags); |
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424 | sc.interval = sumInterval; |
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425 | // |
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426 | SDMemTable* sdmt = new SDMemTable(*in[0],True); // CLone from first Table |
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427 | sdmt->putSDContainer(sc); |
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428 | return CountedPtr<SDMemTable>(sdmt); |
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429 | } |
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430 | |
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431 | |
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432 | CountedPtr<SDMemTable> |
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433 | SDMath::averagePol(const CountedPtr<SDMemTable>& in, |
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434 | const Vector<Bool>& mask) |
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435 | { |
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436 | const uInt nRows = in->nRow(); |
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437 | const uInt axis = 3; // Spectrum |
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438 | const IPosition axes(2, 2, 3); // pol-channel plane |
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439 | |
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440 | // Create output Table |
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441 | |
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442 | SDMemTable* sdmt = new SDMemTable(*in, True); |
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443 | |
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444 | // Loop over rows |
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445 | |
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446 | for (uInt iRow=0; iRow<nRows; iRow++) { |
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447 | |
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448 | // Get data for this row |
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449 | |
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450 | MaskedArray<Float> marr(in->rowAsMaskedArray(iRow)); |
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451 | Array<Float>& arr = marr.getRWArray(); |
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452 | const Array<Bool>& barr = marr.getMask(); |
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453 | // |
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454 | IPosition shp = marr.shape(); |
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455 | const Bool useMask = (mask.nelements() == shp(axis)); |
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456 | const uInt nChan = shp(axis); |
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457 | |
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458 | // Make iterators to iterate by pol-channel planes |
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459 | |
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460 | ArrayIterator<Float> itDataPlane(arr, axes); |
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461 | ReadOnlyArrayIterator<Bool> itMaskPlane(barr, axes); |
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462 | |
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463 | // Accumulations |
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464 | |
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465 | Float fac = 0.0; |
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466 | Vector<Float> vecSum(nChan,0.0); |
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467 | |
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468 | // Iterate by plane |
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469 | |
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470 | while (!itDataPlane.pastEnd()) { |
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471 | |
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472 | // Iterate through pol-channel plane by spectrum |
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473 | |
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474 | Vector<Float> t1(nChan); t1 = 0.0; |
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475 | Vector<Bool> t2(nChan); t2 = True; |
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476 | MaskedArray<Float> vecSum(t1,t2); |
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477 | Float varSum = 0.0; |
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478 | { |
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479 | ReadOnlyVectorIterator<Float> itDataVec(itDataPlane.array(), 1); |
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480 | ReadOnlyVectorIterator<Bool> itMaskVec(itMaskPlane.array(), 1); |
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481 | while (!itDataVec.pastEnd()) { |
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482 | |
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483 | // Create MA of data & mask (optionally including OTF mask) and get variance |
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484 | |
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485 | if (useMask) { |
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486 | const MaskedArray<Float> spec(itDataVec.vector(),mask&&itMaskVec.vector()); |
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487 | fac = 1.0 / variance(spec); |
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488 | } else { |
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489 | const MaskedArray<Float> spec(itDataVec.vector(),itMaskVec.vector()); |
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490 | fac = 1.0 / variance(spec); |
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491 | } |
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492 | |
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493 | // Normalize spectrum (without OTF mask) and accumulate |
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494 | |
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495 | const MaskedArray<Float> spec(fac*itDataVec.vector(), itMaskVec.vector()); |
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496 | vecSum += spec; |
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497 | varSum += fac; |
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498 | |
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499 | // Next |
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500 | |
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501 | itDataVec.next(); |
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502 | itMaskVec.next(); |
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503 | } |
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504 | } |
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505 | |
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506 | // Normalize summed spectrum |
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507 | |
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508 | vecSum /= varSum; |
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509 | |
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510 | // We have formed the weighted averaged spectrum from all polarizations |
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511 | // for this beam and IF. Now replicate the spectrum to all polarizations |
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512 | |
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513 | { |
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514 | VectorIterator<Float> itDataVec(itDataPlane.array(), 1); // Writes back into 'arr' |
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515 | const Vector<Float>& vecSumData = vecSum.getArray(); // It *is* a Vector |
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516 | // |
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517 | while (!itDataVec.pastEnd()) { |
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518 | itDataVec.vector() = vecSumData; |
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519 | itDataVec.next(); |
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520 | } |
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521 | } |
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522 | |
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523 | // Step to next beam/IF combination |
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524 | |
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525 | itDataPlane.next(); |
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526 | itMaskPlane.next(); |
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527 | } |
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528 | |
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529 | // Generate output container and write it to output table |
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530 | |
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531 | SDContainer sc = in->getSDContainer(); |
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532 | Array<uChar> outflags(barr.shape()); |
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533 | convertArray(outflags,!barr); |
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534 | sc.putSpectrum(arr); |
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535 | sc.putFlags(outflags); |
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536 | sdmt->putSDContainer(sc); |
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537 | } |
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538 | // |
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539 | return CountedPtr<SDMemTable>(sdmt); |
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540 | } |
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541 | |
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542 | |
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543 | CountedPtr<SDMemTable> SDMath::bin(const CountedPtr<SDMemTable>& in, |
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544 | Int width) |
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545 | { |
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546 | SDHeader sh = in->getSDHeader(); |
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547 | SDMemTable* sdmt = new SDMemTable(*in,True); |
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548 | |
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549 | // Bin up SpectralCoordinates |
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550 | |
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551 | IPosition factors(1); |
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552 | factors(0) = width; |
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553 | for (uInt j=0; j<in->nCoordinates(); ++j) { |
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554 | CoordinateSystem cSys; |
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555 | cSys.addCoordinate(in->getCoordinate(j)); |
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556 | CoordinateSystem cSysBin = |
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557 | CoordinateUtil::makeBinnedCoordinateSystem (factors, cSys, False); |
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558 | // |
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559 | SpectralCoordinate sCBin = cSysBin.spectralCoordinate(0); |
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560 | sdmt->setCoordinate(sCBin, j); |
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561 | } |
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562 | |
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563 | // Use RebinLattice to find shape |
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564 | |
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565 | IPosition shapeIn(1,sh.nchan); |
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566 | IPosition shapeOut = RebinLattice<Float>::rebinShape (shapeIn, factors); |
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567 | sh.nchan = shapeOut(0); |
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568 | sdmt->putSDHeader(sh); |
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569 | |
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570 | |
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571 | // Loop over rows and bin along channel axis |
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572 | |
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573 | const uInt axis = 3; |
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574 | for (uInt i=0; i < in->nRow(); ++i) { |
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575 | SDContainer sc = in->getSDContainer(i); |
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576 | // |
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577 | Array<Float> tSys(sc.getTsys()); // Get it out before sc changes shape |
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578 | |
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579 | // Bin up spectrum |
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580 | |
---|
581 | MaskedArray<Float> marr(in->rowAsMaskedArray(i)); |
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582 | MaskedArray<Float> marrout; |
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583 | LatticeUtilities::bin(marrout, marr, axis, width); |
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584 | |
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585 | // Put back the binned data and flags |
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586 | |
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587 | IPosition ip2 = marrout.shape(); |
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588 | sc.resize(ip2); |
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589 | sc.putSpectrum(marrout.getArray()); |
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590 | // |
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591 | Array<uChar> outflags(ip2); |
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592 | convertArray(outflags,!(marrout.getMask())); |
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593 | sc.putFlags(outflags); |
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594 | |
---|
595 | // Bin up Tsys. |
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596 | |
---|
597 | Array<Bool> allGood(tSys.shape(),True); |
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598 | MaskedArray<Float> tSysIn(tSys, allGood, True); |
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599 | // |
---|
600 | MaskedArray<Float> tSysOut; |
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601 | LatticeUtilities::bin(tSysOut, tSysIn, axis, width); |
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602 | sc.putTsys(tSysOut.getArray()); |
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603 | sdmt->putSDContainer(sc); |
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604 | } |
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605 | return CountedPtr<SDMemTable>(sdmt); |
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606 | } |
---|
607 | |
---|
608 | |
---|
609 | |
---|
610 | std::vector<float> SDMath::statistic (const CountedPtr<SDMemTable>& in, |
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611 | const std::vector<bool>& mask, |
---|
612 | const std::string& which) |
---|
613 | // |
---|
614 | // Perhaps iteration over pol/beam/if should be in here |
---|
615 | // and inside the nrow iteration ? |
---|
616 | // |
---|
617 | { |
---|
618 | const uInt nRow = in->nRow(); |
---|
619 | std::vector<float> result(nRow); |
---|
620 | Vector<Bool> msk(mask); |
---|
621 | |
---|
622 | // Specify cursor location |
---|
623 | |
---|
624 | uInt i = in->getBeam(); |
---|
625 | uInt j = in->getIF(); |
---|
626 | uInt k = in->getPol(); |
---|
627 | IPosition start(4,i,j,k,0); |
---|
628 | IPosition end(4,i,j,k,in->nChan()-1); |
---|
629 | |
---|
630 | // Loop over rows |
---|
631 | |
---|
632 | const uInt nEl = msk.nelements(); |
---|
633 | for (uInt ii=0; ii < in->nRow(); ++ii) { |
---|
634 | |
---|
635 | // Get row and deconstruct |
---|
636 | |
---|
637 | MaskedArray<Float> marr(in->rowAsMaskedArray(ii)); |
---|
638 | Array<Float> arr = marr.getArray(); |
---|
639 | Array<Bool> barr = marr.getMask(); |
---|
640 | |
---|
641 | // Access desired piece of data |
---|
642 | |
---|
643 | Array<Float> v((arr(start,end)).nonDegenerate()); |
---|
644 | Array<Bool> m((barr(start,end)).nonDegenerate()); |
---|
645 | |
---|
646 | // Apply OTF mask |
---|
647 | |
---|
648 | MaskedArray<Float> tmp; |
---|
649 | if (m.nelements()==nEl) { |
---|
650 | tmp.setData(v,m&&msk); |
---|
651 | } else { |
---|
652 | tmp.setData(v,m); |
---|
653 | } |
---|
654 | |
---|
655 | // Get statistic |
---|
656 | |
---|
657 | result[ii] = SDMath::theStatistic(which, tmp); |
---|
658 | } |
---|
659 | // |
---|
660 | return result; |
---|
661 | } |
---|
662 | |
---|
663 | |
---|
664 | float SDMath::theStatistic(const std::string& which, const casa::MaskedArray<Float>& data) |
---|
665 | { |
---|
666 | String str(which); |
---|
667 | str.upcase(); |
---|
668 | if (str.contains(String("MIN"))) { |
---|
669 | return min(data); |
---|
670 | } else if (str.contains(String("MAX"))) { |
---|
671 | return max(data); |
---|
672 | } else if (str.contains(String("SUMSQ"))) { |
---|
673 | return sumsquares(data); |
---|
674 | } else if (str.contains(String("SUM"))) { |
---|
675 | return sum(data); |
---|
676 | } else if (str.contains(String("MEAN"))) { |
---|
677 | return mean(data); |
---|
678 | } else if (str.contains(String("VAR"))) { |
---|
679 | return variance(data); |
---|
680 | } else if (str.contains(String("STDDEV"))) { |
---|
681 | return stddev(data); |
---|
682 | } else if (str.contains(String("AVDEV"))) { |
---|
683 | return avdev(data); |
---|
684 | } else if (str.contains(String("RMS"))) { |
---|
685 | uInt n = data.nelementsValid(); |
---|
686 | return sqrt(sumsquares(data)/n); |
---|
687 | } else if (str.contains(String("MED"))) { |
---|
688 | return median(data); |
---|
689 | } |
---|
690 | } |
---|