lsst.meas.modelfit  20.0.0+d05b1ddbb0
SemiEmpiricalPrior.h
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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 
34  LSST_CONTROL_FIELD(
35  ellipticitySigma, double,
36  "Width of exponential ellipticity distribution (conformal shear units)."
37  );
38 
39  LSST_CONTROL_FIELD(
40  ellipticityCore, double,
41  "Softened core width for ellipticity distribution (conformal shear units)."
42  );
43 
44  LSST_CONTROL_FIELD(
45  logRadiusMinOuter, double,
46  "Minimum ln(radius)."
47  );
48 
49  LSST_CONTROL_FIELD(
50  logRadiusMinInner, double,
51  "ln(radius) at which the softened cutoff begins towards the minimum"
52  );
53 
54  LSST_CONTROL_FIELD(
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 
60  LSST_CONTROL_FIELD(
61  logRadiusSigma, double,
62  "Width of the Student's T distribution in ln(radius)."
63  );
64 
65  LSST_CONTROL_FIELD(
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  PTR(Impl) _impl;
136 };
137 
138 }}} // namespace lsst::meas::modelfit
139 
140 #endif // !LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
lsst::meas::modelfit::SemiEmpiricalPriorControl::ellipticitySigma
double ellipticitySigma
"Width of exponential ellipticity distribution (conformal shear units)." ;
Definition: SemiEmpiricalPrior.h:37
lsst::meas::modelfit::SemiEmpiricalPriorControl::SemiEmpiricalPriorControl
SemiEmpiricalPriorControl()
Definition: SemiEmpiricalPrior.h:70
Prior.h
lsst::meas::modelfit::SemiEmpiricalPriorControl
Definition: SemiEmpiricalPrior.h:32
lsst::meas::modelfit::Prior
Base class for Bayesian priors.
Definition: Prior.h:36
lsst::meas::modelfit::Scalar
double Scalar
Typedefs to be used for probability and parameter values.
Definition: common.h:44
lsst::meas::modelfit::SemiEmpiricalPriorControl::logRadiusSigma
double logRadiusSigma
"Width of the Student's T distribution in ln(radius)." ;
Definition: SemiEmpiricalPrior.h:63
lsst::meas::modelfit::SemiEmpiricalPrior::maximize
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.
lsst::meas::modelfit::SemiEmpiricalPrior::SemiEmpiricalPrior
SemiEmpiricalPrior(Control const &ctrl=Control())
lsst::meas::modelfit::SemiEmpiricalPriorControl::logRadiusMu
double logRadiusMu
"Mean of the Student's T distribution used for ln(radius) at large radius, and the transition " "poin...
Definition: SemiEmpiricalPrior.h:58
PTR
#define PTR(...)
lsst::meas::modelfit::SemiEmpiricalPrior
A piecewise prior motivated by both real distributions and practical considerations.
Definition: SemiEmpiricalPrior.h:84
lsst::meas::modelfit::Vector
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
Definition: common.h:46
lsst::meas::modelfit::SemiEmpiricalPriorControl::validate
void validate() const
Raise InvalidParameterException if the configuration options are invalid.
lsst::meas::modelfit::SemiEmpiricalPrior::evaluate
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.
lsst::meas::modelfit::SemiEmpiricalPriorControl::logRadiusNu
double logRadiusNu
"Number of degrees of freedom for the Student's T distribution on ln(radius)." ;
Definition: SemiEmpiricalPrior.h:68
lsst::meas::modelfit::SemiEmpiricalPrior::evaluateDerivatives
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.
lsst::meas::modelfit::SemiEmpiricalPriorControl::ellipticityCore
double ellipticityCore
"Softened core width for ellipticity distribution (conformal shear units)." ;
Definition: SemiEmpiricalPrior.h:42
lsst
lsst::meas::modelfit::Matrix
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
Definition: common.h:45
lsst::meas::modelfit::SemiEmpiricalPriorControl::logRadiusMinInner
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
Definition: SemiEmpiricalPrior.h:52
lsst::afw::math::Random
lsst::meas::modelfit::SemiEmpiricalPrior::drawAmplitudes
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.
lsst::meas::modelfit::SemiEmpiricalPrior::Control
SemiEmpiricalPriorControl Control
Definition: SemiEmpiricalPrior.h:87
lsst::meas::modelfit::SemiEmpiricalPriorControl::logRadiusMinOuter
double logRadiusMinOuter
"Minimum ln(radius)." ;
Definition: SemiEmpiricalPrior.h:47
lsst::meas::modelfit::SemiEmpiricalPrior::marginalize
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.