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.
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.
.. list-table:: Quantities set in Metadata
:header-rows: 1
* - 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 lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a
flag -d/--debug to import @b debug.py from your PYTHONPATH. The relevant contents of debug.py
for this Task include:
.. code-block:: py
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 the bad candidates (red) along with good (green)
di.showBadCandidates = True
return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1
Note that if you want addional logging info, you may add to your scripts:
.. code-block:: py
import lsst.log.utils as logUtils
logUtils.traceSetAt("ip.diffim", 4)
Definition at line 523 of file psfMatch.py.