PsfMatchTask#

class lsst.ip.diffim.PsfMatchTask(*args, **kwargs)#

Bases: Task, ABC

Base class for Psf Matching; should not be called directly

Notes#

PsfMatchTask is a base class that implements the core functionality for matching the Psfs of two images using a spatially varying Psf-matching lsst.afw.math.LinearCombinationKernel. The Task requires the user to provide an instance of an lsst.afw.math.SpatialCellSet, filled with lsst.ip.diffim.KernelCandidate instances, and a list of lsst.afw.math.Kernels of basis shapes that will be used for the decomposition. If requested, the Task also performs background matching and returns the differential background model as an lsst.afw.math.Kernel.SpatialFunction.

The initialization sets the Psf-matching kernel configuration using the value of self.config.kernel.active. If the kernel is requested with regularization to moderate the bias/variance tradeoff, currently only used when a delta function kernel basis is provided, it creates a regularization matrix stored as member variable self.hMat.

Invoking the Task

As a base class, this Task is not directly invoked. However, run() methods that are implemented on derived classes will make use of the core _solve() functionality, which defines a sequence of lsst.afw.math.CandidateVisitor classes that iterate through the KernelCandidates, first building up a per-candidate solution and then building up a spatial model from the ensemble of candidates. Sigma clipping is performed using the mean and standard deviation of all kernel sums (to reject variable objects), on the per-candidate substamp diffim residuals (to indicate a bad choice of kernel basis shapes for that particular object), and on the substamp diffim residuals using the spatial kernel fit (to indicate a bad choice of spatial kernel order, or poor constraints on the spatial model). The _diagnostic() method logs information on the quality of the spatial fit, and also modifies the Task metadata.

Quantities set in Metadata#
Parameter	Description
`spatialConditionNum`	Condition number of the spatial kernel fit
`spatialKernelSum`	Kernel sum (10^{-0.4 * `Delta`; zeropoint}) of the spatial Psf-matching kernel
`ALBasisNGauss`	If using sum-of-Gaussian basis, the number of gaussians used
`ALBasisDegGauss`	If using sum-of-Gaussian basis, the deg of spatial variation of the Gaussians
`ALBasisSigGauss`	If using sum-of-Gaussian basis, the widths (sigma) of the Gaussians
`ALKernelSize`	If using sum-of-Gaussian basis, the kernel size
`NFalsePositivesTotal`	Total number of diaSources
`NFalsePositivesRefAssociated`	Number of diaSources that associate with the reference catalog
`NFalsePositivesRefAssociated`	Number of diaSources that associate with the source catalog
`NFalsePositivesUnassociated`	Number of diaSources that are orphans
`metric_MEAN`	Mean value of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals
`metric_MEDIAN`	Median value of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals
`metric_STDEV`	Standard deviation of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals

Debug variables

The pipetask command line interface supports a flag –debug to import @b debug.py from your PYTHONPATH. The relevant contents of debug.py for this Task include:

import sys
import lsstDebug
def DebugInfo(name):
    di = lsstDebug.getInfo(name)
    if name == "lsst.ip.diffim.psfMatch":
        # enable debug output
        di.display = True
        # display mask transparency
        di.maskTransparency = 80
        # show all the candidates and residuals
        di.displayCandidates = True
        # show kernel basis functions
        di.displayKernelBasis = False
        # show kernel realized across the image
        di.displayKernelMosaic = True
        # show coefficients of spatial model
        di.plotKernelSpatialModel = False
        # show fixed and spatial coefficients and coefficient histograms
        di.plotKernelCoefficients = True
        # show the bad candidates (red) along with good (green)
        di.showBadCandidates = True
    return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1

Note that if you want additional logging info, you may add to your scripts:

import lsst.utils.logging as logUtils
logUtils.trace_set_at("lsst.ip.diffim", 4)