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
SoftenedLinearPrior.h
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1 // -*- lsst-c++ -*-
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23 
24 #ifndef LSST_MEAS_MODELFIT_SoftenedLinearPrior_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_SoftenedLinearPrior_h_INCLUDED
26 
27 #include "lsst/pex/config.h"
29 
30 namespace lsst { namespace meas { namespace modelfit {
31 
33 
35  ellipticityMaxOuter, double,
36  "Maximum ellipticity magnitude (conformal shear units)"
37  );
38 
40  ellipticityMaxInner, double,
41  "Ellipticity magnitude (conformal shear units) at which the softened cutoff begins"
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  logRadiusMaxOuter, double,
56  "Maximum ln(radius)"
57  );
58 
60  logRadiusMaxInner, double,
61  "ln(radius) at which the softened cutoff begins towards the maximum"
62  );
63 
65  logRadiusMinMaxRatio, double,
66  "The ratio P(logRadiusMinInner)/P(logRadiusMaxInner)"
67  );
68 
71  logRadiusMinOuter(-6.001), logRadiusMinInner(-6.0),
74  {}
75 
76 };
77 
81 class SoftenedLinearPrior : public Prior {
82 public:
83 
85 
86  explicit SoftenedLinearPrior(Control const & ctrl=Control());
87 
90  ndarray::Array<Scalar const,1,1> const & nonlinear,
91  ndarray::Array<Scalar const,1,1> const & amplitudes
92  ) const override;
93 
96  ndarray::Array<Scalar const,1,1> const & nonlinear,
97  ndarray::Array<Scalar const,1,1> const & amplitudes,
98  ndarray::Array<Scalar,1,1> const & nonlinearGradient,
99  ndarray::Array<Scalar,1,1> const & amplitudeGradient,
100  ndarray::Array<Scalar,2,1> const & nonlinearHessian,
101  ndarray::Array<Scalar,2,1> const & amplitudeHessian,
102  ndarray::Array<Scalar,2,1> const & crossHessian
103  ) const override;
104 
107  Vector const & gradient, Matrix const & hessian,
108  ndarray::Array<Scalar const,1,1> const & nonlinear
109  ) const override;
110 
113  Vector const & gradient, Matrix const & hessian,
114  ndarray::Array<Scalar const,1,1> const & nonlinear,
115  ndarray::Array<Scalar,1,1> const & amplitudes
116  ) const override;
117 
120  Vector const & gradient, Matrix const & fisher,
121  ndarray::Array<Scalar const,1,1> const & nonlinear,
122  afw::math::Random & rng,
123  ndarray::Array<Scalar,2,1> const & amplitudes,
124  ndarray::Array<Scalar,1,1> const & weights,
125  bool multiplyWeights=false
126  ) const override;
127 
128  Control const & getControl() const { return _ctrl; }
129 
130 private:
131 
132  Scalar _evaluate(ndarray::Array<Scalar const,1,1> const & nonlinear) const;
133 
134  Control _ctrl;
135  double _logRadiusP1; // probability value at ln(radius) = ctrl.logRadiusMinInner
136  double _logRadiusSlope;
137  double _logRadiusMinRampFraction;
138  double _logRadiusMaxRampFraction;
139  double _ellipticityMaxRampFraction;
140  Eigen::Matrix<double,4,1,Eigen::DontAlign> _logRadiusPoly1;
141  Eigen::Matrix<double,4,1,Eigen::DontAlign> _logRadiusPoly2;
142  Eigen::Matrix<double,4,1,Eigen::DontAlign> _ellipticityPoly;
143 };
144 
145 }}} // namespace lsst::meas::modelfit
146 
147 #endif // !LSST_MEAS_MODELFIT_SoftenedLinearPrior_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 prior that's linear in radius and flat in ellipticity, with a cubic roll-off at the edges.
SoftenedLinearPrior(Control const &ctrl=Control())
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.
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.
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 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.
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.
#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 logRadiusMinMaxRatio
"The ratio P(logRadiusMinInner)/P(logRadiusMaxInner)" ;
double ellipticityMaxInner
"Ellipticity magnitude (conformal shear units) at which the softened cutoff begins" ;
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
double logRadiusMaxInner
"ln(radius) at which the softened cutoff begins towards the maximum" ;
double ellipticityMaxOuter
"Maximum ellipticity magnitude (conformal shear units)" ;