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.

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.

Table 2 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)

Methods Summary

emptyMetadata()

Empty (clear) the metadata for this Task and all sub-Tasks.

getFullMetadata()

Get metadata for all tasks.

getFullName()

Get the task name as a hierarchical name including parent task names.

getName()

Get the name of the task.

getTaskDict()

Get a dictionary of all tasks as a shallow copy.

makeField(doc)

Make a lsst.pex.config.ConfigurableField for this task.

makeSubtask(name, **keyArgs)

Create a subtask as a new instance as the name attribute of this task.

timer(name[, logLevel])

Context manager to log performance data for an arbitrary block of code.

Methods Documentation

emptyMetadata() None

Empty (clear) the metadata for this Task and all sub-Tasks.

getFullMetadata() TaskMetadata

Get metadata for all tasks.

Returns:
metadataTaskMetadata

The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() str

Get the task name as a hierarchical name including parent task names.

Returns:
fullNamestr

The full name consists of the name of the parent task and each subtask separated by periods. For example:

  • The full name of top-level task “top” is simply “top”.

  • The full name of subtask “sub” of top-level task “top” is “top.sub”.

  • The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() str

Get the name of the task.

Returns:
taskNamestr

Name of the task.

See also

getFullName

Get the full name of the task.

getTaskDict() dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:
taskDictdict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:
docstr

Help text for the field.

Returns:
configurableFieldlsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")
makeSubtask(name: str, **keyArgs: Any) None

Create a subtask as a new instance as the name attribute of this task.

Parameters:
namestr

Brief name of the subtask.

**keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

  • config.

  • parentTask.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

timer(name: str, logLevel: int = 10) Iterator[None]

Context manager to log performance data for an arbitrary block of code.

Parameters:
namestr

Name of code being timed; data will be logged using item name: Start and End.

logLevelint

A logging level constant.

See also

lsst.utils.timer.logInfo

Implementation function.

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time