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