LSST Applications  21.0.0-172-gfb10e10a+18fedfabac,22.0.0+297cba6710,22.0.0+80564b0ff1,22.0.0+8d77f4f51a,22.0.0+a28f4c53b1,22.0.0+dcf3732eb2,22.0.1-1-g7d6de66+2a20fdde0d,22.0.1-1-g8e32f31+297cba6710,22.0.1-1-geca5380+7fa3b7d9b6,22.0.1-12-g44dc1dc+2a20fdde0d,22.0.1-15-g6a90155+515f58c32b,22.0.1-16-g9282f48+790f5f2caa,22.0.1-2-g92698f7+dcf3732eb2,22.0.1-2-ga9b0f51+7fa3b7d9b6,22.0.1-2-gd1925c9+bf4f0e694f,22.0.1-24-g1ad7a390+a9625a72a8,22.0.1-25-g5bf6245+3ad8ecd50b,22.0.1-25-gb120d7b+8b5510f75f,22.0.1-27-g97737f7+2a20fdde0d,22.0.1-32-gf62ce7b1+aa4237961e,22.0.1-4-g0b3f228+2a20fdde0d,22.0.1-4-g243d05b+871c1b8305,22.0.1-4-g3a563be+32dcf1063f,22.0.1-4-g44f2e3d+9e4ab0f4fa,22.0.1-42-gca6935d93+ba5e5ca3eb,22.0.1-5-g15c806e+85460ae5f3,22.0.1-5-g58711c4+611d128589,22.0.1-5-g75bb458+99c117b92f,22.0.1-6-g1c63a23+7fa3b7d9b6,22.0.1-6-g50866e6+84ff5a128b,22.0.1-6-g8d3140d+720564cf76,22.0.1-6-gd805d02+cc5644f571,22.0.1-8-ge5750ce+85460ae5f3,master-g6e05de7fdc+babf819c66,master-g99da0e417a+8d77f4f51a,w.2021.48
LSST Data Management Base Package
SemiEmpiricalPrior.h
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1 // -*- lsst-c++ -*-
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23 
24 #ifndef LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
26 
27 #include "lsst/pex/config.h"
29 
30 namespace lsst { namespace meas { namespace modelfit {
31 
33 
35  ellipticitySigma, double,
36  "Width of exponential ellipticity distribution (conformal shear units)."
37  );
38 
40  ellipticityCore, double,
41  "Softened core width for ellipticity distribution (conformal shear units)."
42  );
43 
45  logRadiusMinOuter, double,
46  "Minimum ln(radius)."
47  );
48 
50  logRadiusMinInner, double,
51  "ln(radius) at which the softened cutoff begins towards the minimum"
52  );
53 
55  logRadiusMu, double,
56  "Mean of the Student's T distribution used for ln(radius) at large radius, and the transition "
57  "point between a flat distribution and the Student's T."
58  );
59 
61  logRadiusSigma, double,
62  "Width of the Student's T distribution in ln(radius)."
63  );
64 
66  logRadiusNu, double,
67  "Number of degrees of freedom for the Student's T distribution on ln(radius)."
68  );
69 
71  ellipticitySigma(0.3), ellipticityCore(0.001),
72  logRadiusMinOuter(-6.001), logRadiusMinInner(-6.0),
73  logRadiusMu(-1.0), logRadiusSigma(0.45), logRadiusNu(50.0)
74  {}
75 
77  void validate() const;
78 
79 };
80 
84 class SemiEmpiricalPrior : public Prior {
85 public:
86 
88 
89  explicit SemiEmpiricalPrior(Control const & ctrl=Control());
90 
93  ndarray::Array<Scalar const,1,1> const & nonlinear,
94  ndarray::Array<Scalar const,1,1> const & amplitudes
95  ) const override;
96 
99  ndarray::Array<Scalar const,1,1> const & nonlinear,
100  ndarray::Array<Scalar const,1,1> const & amplitudes,
101  ndarray::Array<Scalar,1,1> const & nonlinearGradient,
102  ndarray::Array<Scalar,1,1> const & amplitudeGradient,
103  ndarray::Array<Scalar,2,1> const & nonlinearHessian,
104  ndarray::Array<Scalar,2,1> const & amplitudeHessian,
105  ndarray::Array<Scalar,2,1> const & crossHessian
106  ) const override;
107 
110  Vector const & gradient, Matrix const & hessian,
111  ndarray::Array<Scalar const,1,1> const & nonlinear
112  ) const override;
113 
116  Vector const & gradient, Matrix const & hessian,
117  ndarray::Array<Scalar const,1,1> const & nonlinear,
118  ndarray::Array<Scalar,1,1> const & amplitudes
119  ) const override;
120 
123  Vector const & gradient, Matrix const & fisher,
124  ndarray::Array<Scalar const,1,1> const & nonlinear,
125  afw::math::Random & rng,
126  ndarray::Array<Scalar,2,1> const & amplitudes,
127  ndarray::Array<Scalar,1,1> const & weights,
128  bool multiplyWeights=false
129  ) const override;
130 
131 private:
132 
133  struct Impl;
134 
135  std::shared_ptr<Impl> _impl;
136 };
137 
138 }}} // namespace lsst::meas::modelfit
139 
140 #endif // !LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
table::Key< table::Array< double > > amplitudes
A class that can be used to generate sequences of random numbers according to a number of different a...
Definition: Random.h:57
Base class for Bayesian priors.
Definition: Prior.h:36
A piecewise prior motivated by both real distributions and practical considerations.
Scalar maximize(Vector const &gradient, Matrix const &hessian, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar, 1, 1 > const &amplitudes) const override
Compute the amplitude vector that maximizes the prior x likelihood product.
Scalar marginalize(Vector const &gradient, Matrix const &hessian, ndarray::Array< Scalar const, 1, 1 > const &nonlinear) const override
Return the -log amplitude integral of the prior*likelihood product.
Scalar evaluate(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &amplitudes) const override
Evaluate the prior at the given point in nonlinear and amplitude space.
void drawAmplitudes(Vector const &gradient, Matrix const &fisher, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, afw::math::Random &rng, ndarray::Array< Scalar, 2, 1 > const &amplitudes, ndarray::Array< Scalar, 1, 1 > const &weights, bool multiplyWeights=false) const override
Draw a set of Monte Carlo amplitude vectors.
void evaluateDerivatives(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &amplitudes, ndarray::Array< Scalar, 1, 1 > const &nonlinearGradient, ndarray::Array< Scalar, 1, 1 > const &amplitudeGradient, ndarray::Array< Scalar, 2, 1 > const &nonlinearHessian, ndarray::Array< Scalar, 2, 1 > const &amplitudeHessian, ndarray::Array< Scalar, 2, 1 > const &crossHessian) const override
Evaluate the derivatives of the prior at the given point in nonlinear and amplitude space.
SemiEmpiricalPrior(Control const &ctrl=Control())
#define LSST_CONTROL_FIELD(NAME, TYPE, DOC)
A preprocessor macro used to define fields in C++ "control object" structs.
Definition: config.h:43
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
Definition: common.h:46
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
Definition: common.h:45
double Scalar
Typedefs to be used for probability and parameter values.
Definition: common.h:44
A base class for image defects.
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
double logRadiusNu
"Number of degrees of freedom for the Student's T distribution on ln(radius)." ;
double logRadiusSigma
"Width of the Student's T distribution in ln(radius)." ;
double ellipticityCore
"Softened core width for ellipticity distribution (conformal shear units)." ;
void validate() const
Raise InvalidParameterException if the configuration options are invalid.
double ellipticitySigma
"Width of exponential ellipticity distribution (conformal shear units)." ;
double logRadiusMu
"Mean of the Student's T distribution used for ln(radius) at large radius, and the transition " "poin...