lsst.ip.diffim  17.0-1-g99531fe
Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.ip.diffim.psfMatch.PsfMatchTask Class Reference
Inheritance diagram for lsst.ip.diffim.psfMatch.PsfMatchTask:
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask lsst.ip.diffim.modelPsfMatch.ModelPsfMatchTask lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask lsst.ip.diffim.zogy.ZogyImagePsfMatchTask

Public Member Functions

def __init__ (self, args, kwargs)
 

Public Attributes

 kConfig
 
 useRegularization
 
 hMat
 

Static Public Attributes

 ConfigClass = PsfMatchConfig
 

Detailed Description

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
            # ds9 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 524 of file psfMatch.py.

Constructor & Destructor Documentation

◆ __init__()

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__``

Notes
-----
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 628 of file psfMatch.py.

Member Data Documentation

◆ ConfigClass

lsst.ip.diffim.psfMatch.PsfMatchTask.ConfigClass = PsfMatchConfig
static

Definition at line 625 of file psfMatch.py.

◆ hMat

lsst.ip.diffim.psfMatch.PsfMatchTask.hMat

Definition at line 655 of file psfMatch.py.

◆ kConfig

lsst.ip.diffim.psfMatch.PsfMatchTask.kConfig

Definition at line 647 of file psfMatch.py.

◆ useRegularization

lsst.ip.diffim.psfMatch.PsfMatchTask.useRegularization

Definition at line 650 of file psfMatch.py.


The documentation for this class was generated from the following file: