MakeKernelTask¶
MakeKernelTask creates a PSFmatching kernel for two images.
Description¶
Build a Psfmatching kernel using two input images, either as MaskedImages
(in which case they need to be astrometrically aligned) or Exposures (in which
case astrometric alignment will happen by default but may be turned off). This
requires a list of input Sources which may be provided by the calling Task; if
not, the Task will perform a coarse source detection and selection for this
purpose. Sources are vetted for signaltonoise and masked pixels (in both the
template and science image), and substamps around each acceptable source are
extracted and used to create an instance of KernelCandidate. Each
KernelCandidate is then placed within a SpatialCellSet
, which is
used by an ensemble of CandidateVisitor
instances to build the
Psfmatching kernel. These visitors include, in the order that they are
called: BuildSingleKernelVisitor
, KernelSumVisitor
, BuildSpatialKernelVisitor
,
and AssessSpatialKernelVisitor
.
Upon initialization, the kernel configuration is defined by
self.config.kernel.active
. The task creates an Warper
from the
subConfig self.config.kernel.active.warpingConfig
. A schema for the selection
and measurement of candidate KernelCandidates
is defined, and
used to initize subTasks selectDetection (for candidate detection) and
selectMeasurement(for candidate measurement).
Sigma clipping of KernelCandidates is performed as follows:
BuildSingleKernelVisitor, using the substamp diffim residuals from the persource kernel fit, if PsfMatchConfig.singleKernelClipping is True
KernelSumVisitor, using the mean and standard deviation of the kernel sum from all candidates, if PsfMatchConfig.kernelSumClipping is True
AssessSpatialKernelVisitor, using the substamp diffim ressiduals from the spatial kernel fit, if PsfMatchConfig.spatialKernelClipping is True
The actual solving for the kernel (and differential background model) happens
in lsst.ip.diffim.PsfMatchTask._solve
. This involves a loop over the
SpatialCellSet that first builds the percandidate matching kernel for the
requested number of KernelCandidates per cell
(PsfMatchConfig.nStarPerCell
). The quality of this initial percandidate
difference image is examined, using moments of the pixel residuals in the
difference image normalized by the square root of the variance(i.e. sigma);
ideally this should follow a normal (0, 1) distribution, but the rejection
thresholds are set by the config (PsfMatchConfig.candidateResidualMeanMax
and
PsfMatchConfig.candidateResidualStdMax
). All candidates that pass this initial
build are then examined en masse to find the mean/stdev of the kernel sums
across all candidates. Objects that are significantly above or below the mean,
typically due to variability or sources that are saturated in one image but
not the other, are also rejected.This threshold is defined by
PsfMatchConfig.maxKsumSigma
. Finally, a spatial model is built using all
currentlyacceptable candidates, and the spatial model used to derive a second
set of (spatial) residuals which are again used to reject bad candidates,
using the same thresholds as above.
Python API summary¶
from lsst.ip.diffim.makeKernel import MakeKernelTask

class
MakeKernelTask
(*args, **kwargs) Construct a kernel for PSF matching two exposures
...
 attributeconfig
Access configuration fields and retargetable subtasks.

method
run
(template, science, kernelSources, preconvolved=False) Solve for the kernel and background model that best match two Exposures evaluated at the given source locations
...
See also
See the MakeKernelTask
API reference for complete details.
Retargetable subtasks¶
selectDetection¶
 Default
 Field type
Initial detections used to feed stars to kernel fitting
selectMeasurement¶
 Default
 Field type
Initial measurements used to feed stars to kernel fitting
Configuration fields¶
afwBackgroundConfig¶
 Data type
lsst.meas.algorithms.subtractBackground.SubtractBackgroundConfig
 Field type
Controlling the Afw background fitting
badMaskPlanes¶
 Mask planes to ignore when calculating diffim statistics
Options: NO_DATA EDGE SAT BAD CR INTRP
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.
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
candidateResidualMeanMax¶
 Rejects KernelCandidates yielding bad difference image quality.
Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents average over pixels of (image/sqrt(variance)).
candidateResidualStdMax¶
 Rejects KernelCandidates yielding bad difference image quality.
Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents stddev over pixels of (image/sqrt(variance)).
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
conditionNumberType¶
 Default
'EIGENVALUE'
 Field type
str
ChoiceField
(optional) Choices
'SVD'
Use singular values
'EIGENVALUE'
Use eigen values (faster)
None
Field is optional
Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number
constantVarianceWeighting¶
 Use constant variance weighting in single kernel fitting?
In some cases this is better for bright star residuals.
detectionConfig¶
 Data type
lsst.ip.diffim.psfMatch.DetectionConfig
 Field type
Controlling the detection of sources for kernel building
fitForBackground¶
Include terms (including kernel cross terms) for background in ip_diffim
fwhmExposureBuffer¶
Fractional buffer margin to be left out of all sides of the image during constructionof grid to compute average PSF FWHM in an exposure
fwhmExposureGrid¶
Grid size to compute the average PSF FWHM in an exposure
iterateSingleKernel¶
 Remake KernelCandidate using better variance estimate after first pass?
Primarily useful when convolving a singledepth image, otherwise not necessary.
kernel¶
 Default
'AL'
 Field type
Singleselection
ConfigChoiceField
 Choices
'AL'
lsst.ip.diffim.psfMatch.PsfMatchConfigAL
'DF'
lsst.ip.diffim.psfMatch.PsfMatchConfigDF
kernel type
kernelBasisSet¶
 Default
'alardlupton'
 Field type
str
ChoiceField
(optional) Choices
'alardlupton'
AlardLupton sumofgaussians basis set, * The first term has no spatial variation * The kernel sum is conserved * You may want to turn off ‘usePcaForSpatialKernel’
'deltafunction'
Deltafunction 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
Type of basis set for PSF matching kernel.
kernelSize¶
 Number of rows/columns in the convolution kernel; should be oddvalued.
Modified by kernelSizeFwhmScaling if scaleByFwhm = true
kernelSizeFwhmScaling¶
Multiplier of the largest AL Gaussian basis sigma to get the kernel bbox (pixel) size.
kernelSizeMax¶
Maximum kernel bbox (pixel) size.
kernelSizeMin¶
Minimum kernel bbox (pixel) size.
kernelSumClipping¶
Do sigma clipping on the ensemble of kernel sums
maxConditionNumber¶
Maximum condition number for a well conditioned matrix
maxKsumSigma¶
 Maximum allowed sigma for outliers from kernel sum distribution.
Used to reject variable objects from the kernel model
maxSpatialConditionNumber¶
Maximum condition number for a well conditioned spatial matrix
maxSpatialIterations¶
Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting
nStarPerCell¶
Number of KernelCandidates in each SpatialCell to use in the spatial fitting
numPrincipalComponents¶
 Number of principal components to use for Pca basis, including the
mean kernel if requested.
scaleByFwhm¶
Scale kernelSize, alardGaussians by input Fwhm
singleKernelClipping¶
Do sigma clipping on each raw kernel candidate
sizeCellX¶
Size (rows) in pixels of each SpatialCell for spatial modeling
sizeCellY¶
Size (columns) in pixels of each SpatialCell for spatial modeling
spatialBgOrder¶
Spatial order of differential background variation
spatialKernelClipping¶
Do sigma clipping after building the spatial model
spatialKernelOrder¶
Spatial order of convolution kernel variation
spatialModelType¶
 Default
'chebyshev1'
 Field type
str
ChoiceField
(optional) Choices
'chebyshev1'
Chebyshev polynomial of the first kind
'polynomial'
Standard x,y polynomial
None
Field is optional
Type of spatial functions for kernel and background
subtractMeanForPca¶
Subtract off the mean feature before doing the Pca
useAfwBackground¶
Use afw background subtraction instead of ip_diffim
useBicForKernelBasis¶
Use Bayesian Information Criterion to select the number of bases going into the kernel
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
usePcaForSpatialKernel¶
 Use Pca to reduce the dimensionality of the kernel basis sets.
This is particularly useful for deltafunction kernels. Functionally, after all Cells have their raw kernels determined, we run a Pca on these Kernels, refit the Cells using the eigenKernels and then fit those for spatial variation using the same technique as for AlardLupton kernels. If this option is used, the first term will have no spatial variation and the kernel sum will be conserved.
warpingConfig¶
 Data type
lsst.afw.math._warper.WarperConfig
 Field type
Config for warping exposures to a common alignment
Debugging¶
The pipetask
command line interface supports a debug
flag to import
debug.py
from your PYTHONPATH; see lsstDebug for more about debug.py
files.
The available variables in MakeKernelTask include:
 display
bool
Enable debug display output.
 maskTransparency
float
Transparency of mask planes in the output display.
 displayCandidates
bool
Show all the candidates and residuals.
 displayKernelBasis
bool
Show kernel basis functions.
 displayKernelMosaic
bool
Show kernel realized across the image.
 plotKernelSpatialModel
bool
Show coefficients of spatial model.
 showBadCandidates
bool
Show the bad candidates (red) along with good (green).
 displayTemplate
bool
Show full (remapped) template.
 displaySciIm
bool
Show science image to match to.
 displaySpatialCells
bool
Show spatial cells.
 displayDiffIm
bool
Show difference image.