[91] | 1 | //#--------------------------------------------------------------------------- |
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| 2 | //# SDFitter.cc: A Fitter class for spectra |
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| 3 | //#-------------------------------------------------------------------------- |
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| 4 | //# Copyright (C) 2004 |
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[125] | 5 | //# ATNF |
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[91] | 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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[125] | 31 | #include <casa/aips.h> |
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[91] | 32 | #include <casa/Arrays/ArrayMath.h> |
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| 33 | #include <casa/Arrays/ArrayLogical.h> |
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| 34 | #include <scimath/Fitting.h> |
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| 35 | #include <scimath/Fitting/LinearFit.h> |
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| 36 | #include <scimath/Functionals/CompiledFunction.h> |
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| 37 | #include <scimath/Functionals/CompoundFunction.h> |
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| 38 | #include <scimath/Functionals/Gaussian1D.h> |
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| 39 | #include <scimath/Functionals/Polynomial.h> |
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| 40 | #include <scimath/Mathematics/AutoDiff.h> |
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| 41 | #include <scimath/Mathematics/AutoDiffMath.h> |
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| 42 | #include <scimath/Fitting/NonLinearFitLM.h> |
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| 43 | #include <components/SpectralComponents/SpectralEstimate.h> |
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| 44 | |
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| 45 | #include "SDFitter.h" |
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| 46 | using namespace asap; |
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[125] | 47 | using namespace casa; |
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[91] | 48 | |
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| 49 | SDFitter::SDFitter() |
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| 50 | { |
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| 51 | } |
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| 52 | |
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| 53 | SDFitter::~SDFitter() |
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| 54 | { |
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[517] | 55 | reset(); |
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[91] | 56 | } |
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| 57 | |
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| 58 | void SDFitter::clear() |
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| 59 | { |
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[517] | 60 | for (uInt i=0;i< funcs_.nelements();++i) { |
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| 61 | delete funcs_[i]; funcs_[i] = 0; |
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| 62 | } |
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[610] | 63 | funcs_.resize(0,True); |
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[517] | 64 | parameters_.resize(); |
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| 65 | error_.resize(); |
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| 66 | thefit_.resize(); |
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| 67 | estimate_.resize(); |
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| 68 | chisquared_ = 0.0; |
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[91] | 69 | } |
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[517] | 70 | |
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[91] | 71 | void SDFitter::reset() |
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| 72 | { |
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[517] | 73 | clear(); |
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| 74 | x_.resize(); |
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| 75 | y_.resize(); |
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| 76 | m_.resize(); |
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[91] | 77 | } |
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| 78 | |
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| 79 | |
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| 80 | bool SDFitter::computeEstimate() { |
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[517] | 81 | if (x_.nelements() == 0 || y_.nelements() == 0) |
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| 82 | throw (AipsError("No x/y data specified.")); |
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[91] | 83 | |
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[517] | 84 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) == 0) |
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| 85 | return false; |
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| 86 | uInt n = funcs_.nelements(); |
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| 87 | SpectralEstimate estimator(n); |
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| 88 | estimator.setQ(5); |
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| 89 | Int mn,mx; |
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| 90 | mn = 0; |
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| 91 | mx = m_.nelements()-1; |
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| 92 | for (uInt i=0; i<m_.nelements();++i) { |
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| 93 | if (m_[i]) { |
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| 94 | mn = i; |
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| 95 | break; |
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[108] | 96 | } |
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[517] | 97 | } |
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| 98 | for (uInt j=m_.nelements()-1; j>=0;--j) { |
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| 99 | if (m_[j]) { |
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| 100 | mx = j; |
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| 101 | break; |
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[108] | 102 | } |
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[517] | 103 | } |
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| 104 | mn = 0+x_.nelements()/10; |
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| 105 | mx = x_.nelements()-x_.nelements()/10; |
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| 106 | estimator.setRegion(mn,mx); |
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| 107 | //estimator.setWindowing(True); |
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| 108 | SpectralList listGauss = estimator.estimate(x_, y_); |
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| 109 | parameters_.resize(n*3); |
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| 110 | Gaussian1D<Float>* g = 0; |
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| 111 | for (uInt i=0; i<n;i++) { |
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| 112 | g = dynamic_cast<Gaussian1D<Float>* >(funcs_[i]); |
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| 113 | if (g) { |
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| 114 | (*g)[0] = listGauss[i].getAmpl(); |
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| 115 | (*g)[1] = listGauss[i].getCenter(); |
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| 116 | (*g)[2] = listGauss[i].getFWHM(); |
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[91] | 117 | } |
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[517] | 118 | } |
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| 119 | estimate_.resize(); |
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| 120 | listGauss.evaluate(estimate_,x_); |
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| 121 | return true; |
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[91] | 122 | } |
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| 123 | |
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| 124 | std::vector<float> SDFitter::getEstimate() const |
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| 125 | { |
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[517] | 126 | if (estimate_.nelements() == 0) |
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| 127 | throw (AipsError("No estimate set.")); |
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| 128 | std::vector<float> stlout; |
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| 129 | estimate_.tovector(stlout); |
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| 130 | return stlout; |
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[91] | 131 | } |
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| 132 | |
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| 133 | |
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| 134 | bool SDFitter::setExpression(const std::string& expr, int ncomp) |
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| 135 | { |
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[517] | 136 | clear(); |
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| 137 | if (expr == "gauss") { |
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| 138 | if (ncomp < 1) throw (AipsError("Need at least one gaussian to fit.")); |
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| 139 | funcs_.resize(ncomp); |
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| 140 | for (Int k=0; k<ncomp; ++k) { |
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| 141 | funcs_[k] = new Gaussian1D<Float>(); |
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| 142 | } |
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| 143 | } else if (expr == "poly") { |
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| 144 | funcs_.resize(1); |
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| 145 | funcs_[0] = new Polynomial<Float>(ncomp); |
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| 146 | } else { |
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| 147 | cerr << " compiled functions not yet implemented" << endl; |
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| 148 | //funcs_.resize(1); |
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| 149 | //funcs_[0] = new CompiledFunction<Float>(); |
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| 150 | //funcs_[0]->setFunction(String(expr)); |
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| 151 | return false; |
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| 152 | } |
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| 153 | return true; |
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[91] | 154 | } |
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| 155 | |
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| 156 | bool SDFitter::setData(std::vector<float> absc, std::vector<float> spec, |
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| 157 | std::vector<bool> mask) |
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| 158 | { |
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| 159 | x_.resize(); |
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| 160 | y_.resize(); |
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| 161 | m_.resize(); |
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| 162 | // convert std::vector to casa Vector |
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| 163 | Vector<Float> tmpx(absc); |
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| 164 | Vector<Float> tmpy(spec); |
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| 165 | Vector<Bool> tmpm(mask); |
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| 166 | AlwaysAssert(tmpx.nelements() == tmpy.nelements(), AipsError); |
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| 167 | x_ = tmpx; |
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| 168 | y_ = tmpy; |
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| 169 | m_ = tmpm; |
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| 170 | return true; |
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| 171 | } |
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| 172 | |
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| 173 | std::vector<float> SDFitter::getResidual() const |
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| 174 | { |
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| 175 | if (residual_.nelements() == 0) |
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| 176 | throw (AipsError("Function not yet fitted.")); |
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| 177 | std::vector<float> stlout; |
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| 178 | residual_.tovector(stlout); |
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| 179 | return stlout; |
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| 180 | } |
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| 181 | |
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| 182 | std::vector<float> SDFitter::getFit() const |
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| 183 | { |
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| 184 | Vector<Float> out = thefit_; |
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| 185 | std::vector<float> stlout; |
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| 186 | out.tovector(stlout); |
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| 187 | return stlout; |
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| 188 | |
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| 189 | } |
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| 190 | |
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| 191 | std::vector<float> SDFitter::getErrors() const |
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| 192 | { |
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| 193 | Vector<Float> out = error_; |
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| 194 | std::vector<float> stlout; |
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| 195 | out.tovector(stlout); |
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| 196 | return stlout; |
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| 197 | } |
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| 198 | |
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| 199 | bool SDFitter::setParameters(std::vector<float> params) |
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| 200 | { |
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| 201 | Vector<Float> tmppar(params); |
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| 202 | if (funcs_.nelements() == 0) |
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| 203 | throw (AipsError("Function not yet set.")); |
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| 204 | if (parameters_.nelements() > 0 && tmppar.nelements() != parameters_.nelements()) |
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| 205 | throw (AipsError("Number of parameters inconsistent with function.")); |
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| 206 | if (parameters_.nelements() == 0) |
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| 207 | parameters_.resize(tmppar.nelements()); |
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| 208 | fixedpar_.resize(tmppar.nelements()); |
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| 209 | fixedpar_ = False; |
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| 210 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) { |
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| 211 | uInt count = 0; |
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| 212 | for (uInt j=0; j < funcs_.nelements(); ++j) { |
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| 213 | for (uInt i=0; i < funcs_[j]->nparameters(); ++i) { |
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| 214 | (funcs_[j]->parameters())[i] = tmppar[count]; |
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| 215 | parameters_[count] = tmppar[count]; |
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| 216 | ++count; |
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| 217 | } |
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| 218 | } |
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| 219 | } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) { |
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| 220 | for (uInt i=0; i < funcs_[0]->nparameters(); ++i) { |
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| 221 | parameters_[i] = tmppar[i]; |
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| 222 | (funcs_[0]->parameters())[i] = tmppar[i]; |
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| 223 | } |
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| 224 | } |
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| 225 | return true; |
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| 226 | } |
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| 227 | |
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| 228 | bool SDFitter::setFixedParameters(std::vector<bool> fixed) |
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| 229 | { |
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| 230 | Vector<Bool> tmp(fixed); |
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| 231 | if (funcs_.nelements() == 0) |
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| 232 | throw (AipsError("Function not yet set.")); |
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| 233 | if (fixedpar_.nelements() > 0 && tmp.nelements() != fixedpar_.nelements()) |
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| 234 | throw (AipsError("Number of mask elements inconsistent with function.")); |
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| 235 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) { |
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| 236 | uInt count = 0; |
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| 237 | for (uInt j=0; j < funcs_.nelements(); ++j) { |
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| 238 | for (uInt i=0; i < funcs_[j]->nparameters(); ++i) { |
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| 239 | funcs_[j]->mask(i) = !tmp[count]; |
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| 240 | fixedpar_[count] = !tmp[count]; |
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| 241 | ++count; |
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| 242 | } |
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| 243 | } |
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| 244 | } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) { |
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| 245 | for (uInt i=0; i < funcs_[0]->nparameters(); ++i) { |
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| 246 | fixedpar_[i] = tmp[i]; |
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| 247 | funcs_[0]->mask(i) = tmp[i]; |
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| 248 | } |
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| 249 | } |
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| 250 | //fixedpar_ = !tmpmsk; |
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| 251 | return true; |
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| 252 | } |
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| 253 | |
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| 254 | std::vector<float> SDFitter::getParameters() const { |
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| 255 | Vector<Float> out = parameters_; |
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| 256 | std::vector<float> stlout; |
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| 257 | out.tovector(stlout); |
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| 258 | return stlout; |
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| 259 | } |
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| 260 | |
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| 261 | std::vector<bool> SDFitter::getFixedParameters() const { |
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[108] | 262 | Vector<Bool> out(parameters_.nelements()); |
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| 263 | if (fixedpar_.nelements() == 0) { |
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| 264 | out = False; |
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| 265 | //throw (AipsError("No parameter mask set.")); |
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| 266 | } else { |
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| 267 | out = fixedpar_; |
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| 268 | } |
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| 269 | std::vector<bool> stlout; |
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| 270 | out.tovector(stlout); |
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| 271 | return stlout; |
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[91] | 272 | } |
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| 273 | |
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| 274 | float SDFitter::getChisquared() const { |
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| 275 | return chisquared_; |
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| 276 | } |
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| 277 | |
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| 278 | bool SDFitter::fit() { |
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[517] | 279 | NonLinearFitLM<Float> fitter; |
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| 280 | CompoundFunction<Float> func; |
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[610] | 281 | |
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| 282 | uInt n = funcs_.nelements(); |
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[517] | 283 | for (uInt i=0; i<n; ++i) { |
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| 284 | func.addFunction(*funcs_[i]); |
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| 285 | } |
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[610] | 286 | |
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[517] | 287 | fitter.setFunction(func); |
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| 288 | fitter.setMaxIter(50+n*10); |
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| 289 | // Convergence criterium |
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| 290 | fitter.setCriteria(0.001); |
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[610] | 291 | |
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[517] | 292 | // Fit |
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| 293 | Vector<Float> sigma(x_.nelements()); |
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| 294 | sigma = 1.0; |
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| 295 | |
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| 296 | parameters_.resize(); |
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| 297 | parameters_ = fitter.fit(x_, y_, sigma, &m_); |
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| 298 | std::vector<float> ps; |
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| 299 | parameters_.tovector(ps); |
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| 300 | setParameters(ps); |
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[610] | 301 | |
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[517] | 302 | error_.resize(); |
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| 303 | error_ = fitter.errors(); |
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[610] | 304 | |
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[517] | 305 | chisquared_ = fitter.getChi2(); |
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| 306 | |
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| 307 | residual_.resize(); |
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| 308 | residual_ = y_; |
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| 309 | fitter.residual(residual_,x_); |
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[610] | 310 | |
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[517] | 311 | // use fitter.residual(model=True) to get the model |
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| 312 | thefit_.resize(x_.nelements()); |
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| 313 | fitter.residual(thefit_,x_,True); |
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| 314 | return true; |
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| 315 | } |
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[483] | 316 | |
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| 317 | |
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[517] | 318 | std::vector<float> SDFitter::evaluate(int whichComp) const |
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| 319 | { |
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| 320 | std::vector<float> stlout; |
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| 321 | uInt idx = uInt(whichComp); |
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| 322 | Float y; |
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| 323 | if ( idx < funcs_.nelements() ) { |
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| 324 | for (uInt i=0; i<x_.nelements(); ++i) { |
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| 325 | y = (*funcs_[idx])(x_[i]); |
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| 326 | stlout.push_back(float(y)); |
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| 327 | } |
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| 328 | } |
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| 329 | return stlout; |
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| 330 | } |
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[483] | 331 | |
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