LSST Applications  21.0.0-147-g0e635eb1+1acddb5be5,22.0.0+052faf71bd,22.0.0+1ea9a8b2b2,22.0.0+6312710a6c,22.0.0+729191ecac,22.0.0+7589c3a021,22.0.0+9f079a9461,22.0.1-1-g7d6de66+b8044ec9de,22.0.1-1-g87000a6+536b1ee016,22.0.1-1-g8e32f31+6312710a6c,22.0.1-10-gd060f87+016f7cdc03,22.0.1-12-g9c3108e+df145f6f68,22.0.1-16-g314fa6d+c825727ab8,22.0.1-19-g93a5c75+d23f2fb6d8,22.0.1-19-gb93eaa13+aab3ef7709,22.0.1-2-g8ef0a89+b8044ec9de,22.0.1-2-g92698f7+9f079a9461,22.0.1-2-ga9b0f51+052faf71bd,22.0.1-2-gac51dbf+052faf71bd,22.0.1-2-gb66926d+6312710a6c,22.0.1-2-gcb770ba+09e3807989,22.0.1-20-g32debb5+b8044ec9de,22.0.1-23-gc2439a9a+fb0756638e,22.0.1-3-g496fd5d+09117f784f,22.0.1-3-g59f966b+1e6ba2c031,22.0.1-3-g849a1b8+f8b568069f,22.0.1-3-gaaec9c0+c5c846a8b1,22.0.1-32-g5ddfab5d3+60ce4897b0,22.0.1-4-g037fbe1+64e601228d,22.0.1-4-g8623105+b8044ec9de,22.0.1-5-g096abc9+d18c45d440,22.0.1-5-g15c806e+57f5c03693,22.0.1-7-gba73697+57f5c03693,master-g6e05de7fdc+c1283a92b8,master-g72cdda8301+729191ecac,w.2021.39
LSST Data Management Base Package
LeastSqFitter2d.h
Go to the documentation of this file.
1 // -*- LSST-C++ -*-
2 
3 /*
4  * LSST Data Management System
5  * Copyright 2008, 2009, 2010 LSST Corporation.
6  *
7  * This product includes software developed by the
8  * LSST Project (http://www.lsst.org/).
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24 
25 #ifndef LEAST_SQ_FITTER_2D
26 #define LEAST_SQ_FITTER_2D
27 
28 #include <cstdio>
29 #include <memory>
30 #include <vector>
31 
32 #include "Eigen/Core"
33 #include "Eigen/SVD"
34 
37 
38 namespace lsst {
39 namespace meas {
40 namespace astrom {
41 namespace sip {
42 
64 template <class FittingFunc>
66 public:
68  const std::vector<double> &s, int order);
69 
70  Eigen::MatrixXd getParams();
71  Eigen::MatrixXd getErrors();
72  double valueAt(double x, double y);
74 
75  double getChiSq();
76  double getReducedChiSq();
77 
78 private:
79  void initFunctions();
80 
81  Eigen::MatrixXd expandParams(Eigen::VectorXd const &input) const;
82 
83  double func2d(double x, double y, int term);
84  double func1d(double value, int exponent);
85 
86  std::vector<double> _x, _y, _z, _s;
87  int _order; // Degree of polynomial to fit, e.g 4=> cubic
88  int _nPar; // Number of parameters in fitting eqn, e.g x^2, xy, y^2, x^3,
89  int _nData; // Number of data points, == _x.size()
90 
91  Eigen::JacobiSVD<Eigen::MatrixXd> _svd;
92  Eigen::VectorXd _par;
93 
95 };
96 
97 // The .cc part
98 
108 template <class FittingFunc>
110  const std::vector<double> &z, const std::vector<double> &s,
111  int order)
112  : _x(x), _y(y), _z(z), _s(s), _order(order), _nPar(0), _par(1) {
113  //_nPar, the number of terms to fix (x^2, xy, y^2 etc.) is \Sigma^(order+1) 1
114  _nPar = 0;
115  for (int i = 0; i < order; ++i) {
116  _nPar += i + 1;
117  }
118 
119  // Check input vectors are the same size
120  _nData = _x.size();
121  if (_nData != static_cast<int>(_y.size())) {
122  throw LSST_EXCEPT(pex::exceptions::RuntimeError, "x and y vectors of different lengths");
123  }
124  if (_nData != static_cast<int>(_s.size())) {
125  throw LSST_EXCEPT(pex::exceptions::RuntimeError, "x and s vectors of different lengths");
126  }
127  if (_nData != static_cast<int>(_z.size())) {
128  throw LSST_EXCEPT(pex::exceptions::RuntimeError, "x and z vectors of different lengths");
129  }
130 
131  for (int i = 0; i < _nData; ++i) {
132  if (_s[i] == 0.0) {
133  std::string msg = "Illegal zero value for fit weight encountered.";
135  }
136  }
137 
138  if (_nData < _order) {
139  throw LSST_EXCEPT(pex::exceptions::RuntimeError, "Fewer data points than parameters");
140  }
141 
142  initFunctions();
143 
144  Eigen::MatrixXd design(_nData, _nPar);
145  Eigen::VectorXd rhs(_nData);
146  for (int i = 0; i < _nData; ++i) {
147  rhs[i] = z[i] / s[i];
148  for (int j = 0; j < _nPar; ++j) {
149  design(i, j) = func2d(_x[i], _y[i], j) / _s[i];
150  }
151  }
152  _svd.compute(design, Eigen::ComputeThinU | Eigen::ComputeThinV);
153  _par = _svd.solve(rhs);
154 }
155 
165 template <class FittingFunc>
167  return expandParams(_par);
168 }
169 
171 template <class FittingFunc>
172 Eigen::MatrixXd LeastSqFitter2d<FittingFunc>::expandParams(Eigen::VectorXd const &input) const {
173  Eigen::MatrixXd out = Eigen::MatrixXd::Zero(_order, _order);
174  int count = 0;
175  for (int i = 0; i < _order; ++i) {
176  for (int j = 0; j < _order - i; ++j) {
177  out(i, j) = input[count++];
178  }
179  }
180  return out;
181 }
182 
185 template <class FittingFunc>
187  double chisq = 0;
188  for (int i = 0; i < _nData; ++i) {
189  double val = _z[i] - valueAt(_x[i], _y[i]);
190  val /= _s[i];
191  chisq += pow(val, 2);
192  }
193 
194  return chisq;
195 }
196 
202 template <class FittingFunc>
204  return getChiSq() / (double)(_nData - _nPar);
205 }
206 
208 template <class FittingFunc>
209 double LeastSqFitter2d<FittingFunc>::valueAt(double x, double y) {
210  double val = 0;
211 
212  // Sum the values of the different orders to get the value of the fitted function
213  for (int i = 0; i < _nPar; ++i) {
214  val += _par[i] * func2d(x, y, i);
215  }
216  return val;
217 }
218 
221 template <class FittingFunc>
224  out.reserve(_nData);
225 
226  for (int i = 0; i < _nData; ++i) {
227  out.push_back(_z[i] - valueAt(_x[i], _y[i]));
228  }
229 
230  return out;
231 }
232 
234 template <class FittingFunc>
236  Eigen::ArrayXd variance(_nPar);
237  for (int i = 0; i < _nPar; ++i) {
238  variance[i] = _svd.matrixV().row(i).dot(
239  (_svd.singularValues().array().inverse().square() * _svd.matrixV().col(i).array()).matrix());
240  }
241  return expandParams(variance.sqrt().matrix());
242 }
243 
244 template <class FittingFunc>
246  // Initialise the array of functions. _funcArray[i] is a object of type math::Function1 of order i
247  _funcArray.reserve(_order);
248 
250  coeff.reserve(_order);
251 
252  coeff.push_back(1);
253  for (int i = 0; i < _order; ++i) {
254  std::shared_ptr<FittingFunc> p(new FittingFunc(coeff));
255  _funcArray.push_back(p);
256 
257  coeff[i] = 0;
258  coeff.push_back(1); // coeff now looks like [0,0,...,0,1]
259  }
260 }
261 
262 // The ith term in the fitting polynomial is of the form x^a * y^b. This function figures
263 // out the value of a and b, then calculates the value of the ith term at the given x and y
264 template <class FittingFunc>
265 double LeastSqFitter2d<FittingFunc>::func2d(double x, double y, int term) {
266  int yexp = 0; // y exponent
267  int xexp = 0; // x exponent
268 
269  for (int i = 0; i < term; ++i) {
270  yexp = (yexp + 1) % (_order - xexp);
271  if (yexp == 0) {
272  xexp++;
273  }
274  }
275 
276  double xcomp = func1d(x, xexp); // x component of polynomial
277  double ycomp = func1d(y, yexp); // y component
278 
279 #if 0 // A useful debugging printf statement
280  printf("The %i(th) function: x^%i * y^%i = %.1f * %.1f\n", term, xexp, yexp, xcomp, ycomp);
281 #endif
282 
283  return xcomp * ycomp;
284 }
285 
286 template <class FittingFunc>
287 double LeastSqFitter2d<FittingFunc>::func1d(double value, int exponent) {
288  return (*_funcArray[exponent])(value);
289 }
290 
291 } // namespace sip
292 } // namespace astrom
293 } // namespace meas
294 } // namespace lsst
295 
296 #endif
double x
#define LSST_EXCEPT(type,...)
Create an exception with a given type.
Definition: Exception.h:48
afw::table::Key< afw::table::Array< VariancePixelT > > variance
double z
Definition: Match.cc:44
int y
Definition: SpanSet.cc:48
Fit an lsst::afw::math::Function1 object to a set of data points in two dimensions.
Eigen::MatrixXd getErrors()
Companion function to getParams(). Returns uncertainties in the parameters as a matrix.
double getChiSq()
Return a measure of the goodness of fit.
double valueAt(double x, double y)
Return the value of the best fit function at a given position (x,y)
double getReducedChiSq()
Return a measure of the goodness of fit.
std::vector< double > residuals()
Return a vector of residuals of the fit (i.e the difference between the input z values,...
LeastSqFitter2d(const std::vector< double > &x, const std::vector< double > &y, const std::vector< double > &z, const std::vector< double > &s, int order)
Fit a 2d polynomial to a set of data points z(x, y)
Eigen::MatrixXd getParams()
Build up a triangular matrix of the parameters.
Reports errors that are due to events beyond the control of the program.
Definition: Runtime.h:104
A base class for image defects.
T push_back(T... args)
T reserve(T... args)
T size(T... args)
int exponent
Definition: orientation.cc:41
ImageT val
Definition: CR.cc:146
table::Key< table::Array< double > > coeff
Definition: PsfexPsf.cc:362
table::Key< int > order