PsfMatchConfigAL

class lsst.ip.diffim.PsfMatchConfigAL

Bases: lsst.ip.diffim.PsfMatchConfig

!The parameters specific to the “Alard-Lupton” (sum-of-Gaussian) Psf-matching basis

Attributes Summary

afwBackgroundConfig Controlling the Afw background fitting (SubtractBackgroundConfig, default <class 'lsst.meas.algorithms.subtractBackground.SubtractBackgroundConfig'>)
alardDegGauss Polynomial order of spatial modification of Gaussians.
alardDegGaussDeconv Degree of spatial modification of ALL gaussians in AL basis during deconvolution (int, default 3)
alardGaussBeta Default scale factor between Gaussian sigmas (float, default 2.0)
alardMinSig Minimum Sigma (pixels) for Gaussians (float, default 0.7)
alardMinSigDeconv Minimum Sigma (pixels) for Gaussians during deconvolution; make smaller than alardMinSig as this is only indirectly used (float, default 0.4)
alardNGauss Number of Gaussians in alard-lupton basis (int, default 3)
alardNGaussDeconv Number of Gaussians in AL basis during deconvolution (int, default 3)
alardSigGauss Sigma in pixels of Gaussians (FWHM = 2.35 sigma).
badMaskPlanes Mask planes to ignore when calculating diffim statistics Options: NO_DATA EDGE SAT BAD CR INTRP (List, default ('NO_DATA', 'EDGE', 'SAT'))
calculateKernelUncertainty Calculate kernel and background uncertainties for each kernel candidate? This comes from the inverse of the covariance matrix.
candidateCoreRadius Radius for calculation of stats in ‘core’ of KernelCandidate diffim.
candidateResidualMeanMax Rejects KernelCandidates yielding bad difference image quality.
candidateResidualStdMax Rejects KernelCandidates yielding bad difference image quality.
checkConditionNumber Test for maximum condition number when inverting a kernel matrix.
conditionNumberType Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number (str, default 'EIGENVALUE')
constantVarianceWeighting Use constant variance weighting in single kernel fitting? In some cases this is better for bright star residuals.
detectionConfig Controlling the detection of sources for kernel building (DetectionConfig, default <class 'lsst.ip.diffim.psfMatch.DetectionConfig'>)
fitForBackground Include terms (including kernel cross terms) for background in ip_diffim (bool, default False)
iterateSingleKernel Remake KernelCandidate using better variance estimate after first pass? Primarily useful when convolving a single-depth image, otherwise not necessary.
kernelBasisSet Type of basis set for PSF matching kernel.
kernelSize Number of rows/columns in the convolution kernel; should be odd-valued.
kernelSizeFwhmScaling How much to scale the kernel size based on the largest AL Sigma (float, default 6.0)
kernelSizeMax Maximum Kernel Size (int, default 35)
kernelSizeMin Minimum Kernel Size (int, default 21)
kernelSumClipping Do sigma clipping on the ensemble of kernel sums (bool, default True)
maxConditionNumber Maximum condition number for a well conditioned matrix (float, default 50000000.0)
maxKsumSigma Maximum allowed sigma for outliers from kernel sum distribution.
maxSpatialConditionNumber Maximum condition number for a well conditioned spatial matrix (float, default 10000000000.0)
maxSpatialIterations Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting (int, default 3)
nStarPerCell Number of KernelCandidates in each SpatialCell to use in the spatial fitting (int, default 3)
numPrincipalComponents Number of principal components to use for Pca basis, including the mean kernel if requested.
scaleByFwhm Scale kernelSize, alardGaussians by input Fwhm (bool, default True)
singleKernelClipping Do sigma clipping on each raw kernel candidate (bool, default True)
sizeCellX Size (rows) in pixels of each SpatialCell for spatial modeling (int, default 128)
sizeCellY Size (columns) in pixels of each SpatialCell for spatial modeling (int, default 128)
spatialBgOrder Spatial order of differential background variation (int, default 1)
spatialKernelClipping Do sigma clipping after building the spatial model (bool, default True)
spatialKernelOrder Spatial order of convolution kernel variation (int, default 2)
spatialModelType Type of spatial functions for kernel and background (str, default 'chebyshev1')
subtractMeanForPca Subtract off the mean feature before doing the Pca (bool, default True)
useAfwBackground Use afw background subtraction instead of ip_diffim (bool, default False)
useBicForKernelBasis Use Bayesian Information Criterion to select the number of bases going into the kernel (bool, default False)
useCoreStats Use the core of the footprint for the quality statistics, instead of the entire footprint.
usePcaForSpatialKernel Use Pca to reduce the dimensionality of the kernel basis sets.
warpingConfig Config for warping exposures to a common alignment (WarperConfig, default <class 'lsst.afw.math.warper.WarperConfig'>)

Methods Summary

setDefaults() Derived config classes that must compute defaults rather than using the Field defaults should do so here.

Attributes Documentation

afwBackgroundConfig

Controlling the Afw background fitting (SubtractBackgroundConfig, default <class 'lsst.meas.algorithms.subtractBackground.SubtractBackgroundConfig'>)

alardDegGauss

Polynomial order of spatial modification of Gaussians. Must in number equal alardNGauss (List, default (4, 2, 2))

alardDegGaussDeconv

Degree of spatial modification of ALL gaussians in AL basis during deconvolution (int, default 3)

alardGaussBeta

Default scale factor between Gaussian sigmas (float, default 2.0)

alardMinSig

Minimum Sigma (pixels) for Gaussians (float, default 0.7)

alardMinSigDeconv

Minimum Sigma (pixels) for Gaussians during deconvolution; make smaller than alardMinSig as this is only indirectly used (float, default 0.4)

alardNGauss

Number of Gaussians in alard-lupton basis (int, default 3)

alardNGaussDeconv

Number of Gaussians in AL basis during deconvolution (int, default 3)

alardSigGauss

Sigma in pixels of Gaussians (FWHM = 2.35 sigma). Must in number equal alardNGauss (List, default (0.7, 1.5, 3.0))

badMaskPlanes

Mask planes to ignore when calculating diffim statistics Options: NO_DATA EDGE SAT BAD CR INTRP (List, default ('NO_DATA', 'EDGE', 'SAT'))

calculateKernelUncertainty

Calculate kernel and background uncertainties for each kernel candidate? This comes from the inverse of the covariance matrix. Warning: regularization can cause problems for this step. (bool, default False)

candidateCoreRadius

Radius for calculation of stats in ‘core’ of KernelCandidate diffim. Total number of pixels used will be (2*radius)**2. This is used both for ‘core’ diffim quality as well as ranking of KernelCandidates by their total flux in this core (int, default 3)

candidateResidualMeanMax

Rejects KernelCandidates yielding bad difference image quality. Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents average over pixels of (image/sqrt(variance)). (float, default 0.25)

candidateResidualStdMax

Rejects KernelCandidates yielding bad difference image quality. Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents stddev over pixels of (image/sqrt(variance)). (float, default 1.5)

checkConditionNumber

Test for maximum condition number when inverting a kernel matrix. Anything above maxConditionNumber is not used and the candidate is set as BAD. Also used to truncate inverse matrix in estimateBiasedRisk. However, if you are doing any deconvolution you will want to turn this off, or use a large maxConditionNumber (bool, default False)

conditionNumberType

Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number (str, default 'EIGENVALUE')

Allowed values:

'SVD'
Use singular values
'EIGENVALUE'
Use eigen values (faster)
'None'
Field is optional
constantVarianceWeighting

Use constant variance weighting in single kernel fitting? In some cases this is better for bright star residuals. (bool, default True)

detectionConfig

Controlling the detection of sources for kernel building (DetectionConfig, default <class 'lsst.ip.diffim.psfMatch.DetectionConfig'>)

fitForBackground

Include terms (including kernel cross terms) for background in ip_diffim (bool, default False)

iterateSingleKernel

Remake KernelCandidate using better variance estimate after first pass? Primarily useful when convolving a single-depth image, otherwise not necessary. (bool, default False)

kernelBasisSet

Type of basis set for PSF matching kernel. (str, default 'alard-lupton')

Allowed values:

'alard-lupton'
Alard-Lupton sum-of-gaussians basis set,
  • The first term has no spatial variation
  • The kernel sum is conserved
  • You may want to turn off ‘usePcaForSpatialKernel’
'delta-function'
Delta-function kernel basis set,
  • You may enable the option useRegularization
  • You should seriously consider usePcaForSpatialKernel, which will also enable kernel sum conservation for the delta function kernels
'None'
Field is optional
kernelSize

Number of rows/columns in the convolution kernel; should be odd-valued. Modified by kernelSizeFwhmScaling if scaleByFwhm = true (int, default 21)

kernelSizeFwhmScaling

How much to scale the kernel size based on the largest AL Sigma (float, default 6.0)

kernelSizeMax

Maximum Kernel Size (int, default 35)

kernelSizeMin

Minimum Kernel Size (int, default 21)

kernelSumClipping

Do sigma clipping on the ensemble of kernel sums (bool, default True)

maxConditionNumber

Maximum condition number for a well conditioned matrix (float, default 50000000.0)

maxKsumSigma

Maximum allowed sigma for outliers from kernel sum distribution. Used to reject variable objects from the kernel model (float, default 3.0)

maxSpatialConditionNumber

Maximum condition number for a well conditioned spatial matrix (float, default 10000000000.0)

maxSpatialIterations

Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting (int, default 3)

nStarPerCell

Number of KernelCandidates in each SpatialCell to use in the spatial fitting (int, default 3)

numPrincipalComponents

Number of principal components to use for Pca basis, including the mean kernel if requested. (int, default 5)

scaleByFwhm

Scale kernelSize, alardGaussians by input Fwhm (bool, default True)

singleKernelClipping

Do sigma clipping on each raw kernel candidate (bool, default True)

sizeCellX

Size (rows) in pixels of each SpatialCell for spatial modeling (int, default 128)

sizeCellY

Size (columns) in pixels of each SpatialCell for spatial modeling (int, default 128)

spatialBgOrder

Spatial order of differential background variation (int, default 1)

spatialKernelClipping

Do sigma clipping after building the spatial model (bool, default True)

spatialKernelOrder

Spatial order of convolution kernel variation (int, default 2)

spatialModelType

Type of spatial functions for kernel and background (str, default 'chebyshev1')

Allowed values:

'chebyshev1'
Chebyshev polynomial of the first kind
'polynomial'
Standard x,y polynomial
'None'
Field is optional
subtractMeanForPca

Subtract off the mean feature before doing the Pca (bool, default True)

useAfwBackground

Use afw background subtraction instead of ip_diffim (bool, default False)

useBicForKernelBasis

Use Bayesian Information Criterion to select the number of bases going into the kernel (bool, default False)

useCoreStats

Use the core of the footprint for the quality statistics, instead of the entire footprint. WARNING: if there is deconvolution we probably will need to turn this off (bool, default False)

usePcaForSpatialKernel

Use Pca to reduce the dimensionality of the kernel basis sets. This is particularly useful for delta-function kernels. Functionally, after all Cells have their raw kernels determined, we run a Pca on these Kernels, re-fit the Cells using the eigenKernels and then fit those for spatial variation using the same technique as for Alard-Lupton kernels. If this option is used, the first term will have no spatial variation and the kernel sum will be conserved. (bool, default False)

warpingConfig

Config for warping exposures to a common alignment (WarperConfig, default <class 'lsst.afw.math.warper.WarperConfig'>)

Methods Documentation

setDefaults()

Derived config classes that must compute defaults rather than using the Field defaults should do so here. To correctly use inherited defaults, implementations of setDefaults() must call their base class’ setDefaults()