File BasisLists.h

Subroutines associated with generating, normalising, and regularising Basis functions.

Author

Andrew Becker, University of Washington

namespace lsst

Class for a simple mapping implementing a generic AstrometryTransform.

Remove all non-astronomical counts from the Chunk Exposure’s pixels.

Forward declarations for lsst::utils::Cache

For details on the Cache class, see the Cache.h file.

It uses a template rather than a pointer so that the derived classes can use the specifics of the transform. The class simplePolyMapping overloads a few routines.

A base class for image defects

Numeric constants used by the Integrate.h integrator routines.

Compute Image Statistics

Note

Gauss-Kronrod-Patterson quadrature coefficients for use in quadpack routine qng. These coefficients were calculated with 101 decimal digit arithmetic by L. W. Fullerton, Bell Labs, Nov 1981.

Note

The Statistics class itself can only handle lsst::afw::image::MaskedImage() types. The philosophy has been to handle other types by making them look like lsst::afw::image::MaskedImage() and reusing that code. Users should have no need to instantiate a Statistics object directly, but should use the overloaded makeStatistics() factory functions.

namespace ip
namespace diffim

Functions

lsst::afw::math::KernelList makeDeltaFunctionBasisList(int width, int height)

Build a set of Delta Function basis kernels.

Note

Total number of basis functions is width*height

Parameters

  • width: Width of basis set (cols)

  • height: Height of basis set (rows)

Eigen::MatrixXd makeRegularizationMatrix(lsst::daf::base::PropertySet const &ps)

Build a regularization matrix for Delta function kernels.

Note

Calls either makeForwardDifferenceMatrix or makeCentralDifferenceMatrix based on the PropertySet config.

Parameters

  • ps: PropertySet dictating which type of matrix to make

Eigen::MatrixXd makeForwardDifferenceMatrix(int width, int height, std::vector<int> const &orders, float borderPenalty, bool fitForBackground)

Build a forward difference regularization matrix for Delta function kernels.

Parameters

  • width: Width of basis set you want to regularize

  • height: Height of basis set you want to regularize

  • orders: Which derivatives to penalize (1,2,3)

  • borderPenalty: Amount of penalty (if any) to apply to border pixels; > 0

  • fitForBackground: Fit for differential background?

Eigen::MatrixXd makeCentralDifferenceMatrix(int width, int height, int stencil, float borderPenalty, bool fitForBackground)

Build a central difference Laplacian regularization matrix for Delta function kernels.

Parameters

  • width: Width of basis set you want to regularize

  • height: Height of basis set you want to regularize

  • stencil: Which type of Laplacian approximation to use

  • borderPenalty: Amount of penalty (if any) to apply to border pixels; > 0

  • fitForBackground: Fit for differential background?

lsst::afw::math::KernelList renormalizeKernelList(lsst::afw::math::KernelList const &kernelListIn)

Renormalize a list of basis kernels.

Note

Renormalization means make Ksum_0 = 1.0, Ksum_i = 0.0, K_i.dot.K_i = 1.0

Note

Output list of shared pointers to FixedKernels

Note

Images are checked for their current kernel sum. If it is larger than std::numeric_limits<double>::epsilon(), the kernel is first divided by the kernel sum, giving it a kSum of 1.0, and then the first (normalized) component is subtracted from it, giving it a kSum of 0.0.

Parameters

  • kernelListIn: input list of basis kernels

lsst::afw::math::KernelList makeAlardLuptonBasisList(int halfWidth, int nGauss, std::vector<double> const &sigGauss, std::vector<int> const &degGauss)

Build a set of Alard/Lupton basis kernels.

Note

Should consider implementing as SeparableKernels for additional speed, but this will make the normalization a bit more complicated

Parameters

  • halfWidth: size is 2*N + 1

  • nGauss: number of gaussians

  • sigGauss: Widths of the Gaussian Kernels

  • degGauss: Local spatial variation of bases