lsst.ip.diffim.psfMatch.PsfMatchTask Class Reference

Base class for Psf Matching; should not be called directly. More...

Inheritance diagram for lsst.ip.diffim.psfMatch.PsfMatchTask:

Public Member Functions
def	__init__
	Create the psf-matching Task. More...

Public Attributes
	kConfig
	useRegularization
	hMat

Static Public Attributes
	ConfigClass = PsfMatchConfig

Private Member Functions
def	_diagnostic
	Provide logging diagnostics on quality of spatial kernel fit. More...
def	_displayDebug
	Provide visualization of the inputs and ouputs to the Psf-matching code. More...
def	_createPcaBasis
	Create Principal Component basis. More...
def	_buildCellSet
	Fill a SpatialCellSet with KernelCandidates for the Psf-matching process; override in derived classes. More...
def	_solve
	Solve for the PSF matching kernel. More...

Static Private Attributes
string	_DefaultName = "psfMatch"

Detailed Description

Base class for Psf Matching; should not be called directly.

Contents

• Description
• Task initialization
• Invoking the Task
• Configuration parameters
• Quantities set in Metadata
• Debug variables
• Example code

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Description

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 an lsst.afw.math.KernelList 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.

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Task initialization

Create the psf-matching Task.

Parameters

*args	arguments to be passed to lsst.pipe.base.task.Task.__init__
**kwargs	keyword arguments to be passed to lsst.pipe.base.task.Task.__init__

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.

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Configuration parameters

See PsfMatchConfig, PsfMatchConfigAL, PsfMatchConfigDF, and DetectionConfig.

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Quantities set in Metadata

spatialConditionNum

Condition number of the spatial kernel fit; via PsfMatchTask._diagnostic

spatialKernelSum

Kernel sum (10^{-0.4 * Δ zeropoint}) of the spatial Psf-matching kernel; via PsfMatchTask._diagnostic

ALBasisNGauss

If using sum-of-Gaussian basis, the number of gaussians used; via generateAlardLuptonBasisList

ALBasisDegGauss

If using sum-of-Gaussian basis, the degree of spatial variation of the Gaussians; via generateAlardLuptonBasisList

ALBasisSigGauss

If using sum-of-Gaussian basis, the widths (sigma) of the Gaussians; via generateAlardLuptonBasisList

ALKernelSize

If using sum-of-Gaussian basis, the kernel size; via generateAlardLuptonBasisList

NFalsePositivesTotal

Total number of diaSources; via KernelCandidateQa.aggregate

NFalsePositivesRefAssociated

Number of diaSources that associate with the reference catalog; via KernelCandidateQa.aggregate

NFalsePositivesRefAssociated

Number of diaSources that associate with the source catalog; via KernelCandidateQa.aggregate

NFalsePositivesUnassociated

Number of diaSources that are orphans; via KernelCandidateQa.aggregate

metric_MEAN

Mean value of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals; via KernelCandidateQa.aggregate

metric_MEDIAN

Median value of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals; via KernelCandidateQa.aggregate

metric_STDEV

Standard deviation of substamp diffim quality metrics across all KernelCandidates, for both the per-candidate (LOCAL) and SPATIAL residuals; via KernelCandidateQa.aggregate

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Debug variables

The command line task interface supports a flag -d/–debug to import 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":
        di.display = True                 # enable debug output
        di.maskTransparency = 80          # ds9 mask transparency
        di.displayCandidates = True       # show all the candidates and residuals
        di.displayKernelBasis = False     # show kernel basis functions
        di.displayKernelMosaic = True     # show kernel realized across the image
        di.plotKernelSpatialModel = False # show coefficients of spatial model
        di.showBadCandidates = True       # show the bad candidates (red) along with good (green)
    return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1

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

import lsst.pex.logging as pexLog
pexLog.Trace_setVerbosity('lsst.ip.diffim', 5)

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Example code

As a base class, there is no example code for PsfMatchTask. However, see ImagePsfMatchTask, SnapPsfMatchTask, and ModelPsfMatchTask.

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Definition at line 525 of file psfMatch.py.

Constructor & Destructor Documentation

def lsst.ip.diffim.psfMatch.PsfMatchTask.__init__	(		self,
			args,
			kwargs
	)

Create the psf-matching Task.

Parameters

*args	arguments to be passed to lsst.pipe.base.task.Task.__init__
**kwargs	keyword arguments to be passed to lsst.pipe.base.task.Task.__init__

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.

Definition at line 676 of file psfMatch.py.

    def __init__(self, *args, **kwargs):
        """!Create the psf-matching Task

        \param *args arguments to be passed to lsst.pipe.base.task.Task.__init__
        \param **kwargs keyword arguments to be passed to lsst.pipe.base.task.Task.__init__

        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.
        """
        pipeBase.Task.__init__(self, *args, **kwargs)
        self.kConfig = self.config.kernel.active

        #
        if 'useRegularization' in self.kConfig.keys():
            self.useRegularization = self.kConfig.useRegularization
        else:
            self.useRegularization = False

        if self.useRegularization:
            self.hMat = diffimLib.makeRegularizationMatrix(pexConfig.makePolicy(self.kConfig))