ImagePsfMatchTask¶

class lsst.ip.diffim.ImagePsfMatchTask(*args, **kwargs)¶

Bases: lsst.ip.diffim.PsfMatchTask

Psf-match two MaskedImages or Exposures using the sources in the images

Parameters:	args : arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__ kwargs : keyword arguments to be passed to lsst.ip.diffim.PsfMatchTask.__init__

Notes

Upon initialization, the kernel configuration is defined by self.config.kernel.active. The task creates an lsst.afw.math.Warper from the subConfig self.config.kernel.active.warpingConfig. A schema for the selection and measurement of candidate lsst.ip.diffim.KernelCandidates is defined, and used to initize subTasks selectDetection (for candidate detection) and selectMeasurement (for candidate measurement).

Description

Build a Psf-matching 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 signal-to-noise 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 lsst.afw.math.SpatialCellSet, which is used by an ensemble of lsst.afw.math.CandidateVisitor instances to build the Psf-matching kernel. These visitors include, in the order that they are called: BuildSingleKernelVisitor, KernelSumVisitor, BuildSpatialKernelVisitor, and AssessSpatialKernelVisitor.

Sigma clipping of KernelCandidates is performed as follows:

BuildSingleKernelVisitor, using the substamp diffim residuals from the per-source 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 per-candidate matching kernel for the requested number of KernelCandidates per cell (PsfMatchConfig.nStarPerCell). The quality of this initial per-candidate 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 currently-acceptable 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.

Invoking the Task

There is no run() method for this Task. Instead there are 4 methods that may be used to invoke the Psf-matching. These are lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages matchMaskedImages lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages subtractMaskedImages, lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures matchExposures, and lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures subtractExposures.

The methods that operate on lsst.afw.image.MaskedImage require that the images already be astrometrically aligned, and are the same shape. The methods that operate on lsst.afw.image.Exposure allow for the input images to be misregistered and potentially be different sizes; by default a lsst.afw.math.LanczosWarpingKernel is used to perform the astrometric alignment. The methods that “match” images return a Psf-matched image, while the methods that “subtract” images return a Psf-matched and template subtracted image.

See each method’s returned lsst.pipe.base.Struct for more details.

Debug variables

The lsst.pipe.base.cmdLineTask.CmdLineTask 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)
    elif name == "lsst.ip.diffim.imagePsfMatch":
        di.display = True                 # enable debug output
        di.maskTransparency = 30          # ds9 mask transparency
        di.displayTemplate = True         # show full (remapped) template
        di.displaySciIm = True            # show science image to match to
        di.displaySpatialCells = True     # show spatial cells
        di.displayDiffIm = True           # show difference image
        di.showBadCandidates = True       # show the bad candidates (red) along with good (green)
    elif name == "lsst.ip.diffim.diaCatalogSourceSelector":
        di.display = False                # enable debug output
        di.maskTransparency = 30          # ds9 mask transparency
        di.displayExposure = True         # show exposure with candidates indicated
        di.pauseAtEnd = False             # pause when done
    return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1

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

import lsst.log.utils as logUtils
logUtils.traceSetAt("ip.diffim", 4)

Examples

A complete example of using ImagePsfMatchTask

This code is imagePsfMatchTask.py in the examples directory, and can be run as e.g.

..code-block:: none

examples/imagePsfMatchTask.py –debug examples/imagePsfMatchTask.py –debug –mode=”matchExposures” examples/imagePsfMatchTask.py –debug –template /path/to/templateExp.fits –science /path/to/scienceExp.fits

Create a subclass of ImagePsfMatchTask that allows us to either match exposures, or subtract exposures:

..code-block:: none

class MyImagePsfMatchTask(ImagePsfMatchTask):

def __init__(self, args, kwargs):

ImagePsfMatchTask.__init__(self, args, kwargs)

def run(self, templateExp, scienceExp, mode):

if mode == “matchExposures”:

return self.matchExposures(templateExp, scienceExp)

elif mode == “subtractExposures”:

return self.subtractExposures(templateExp, scienceExp)

And allow the user the freedom to either run the script in default mode, or point to their own images on disk. Note that these images must be readable as an lsst.afw.image.Exposure.

We have enabled some minor display debugging in this script via the –debug option. However, if you have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following block checks for this script:

if args.debug:
    try:
        import debug
        # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
        debug.lsstDebug.frame = 3
    except ImportError as e:
        print(e, file=sys.stderr)

Finally, we call a run method that we define below. First set up a Config and modify some of the parameters. E.g. use an “Alard-Lupton” sum-of-Gaussian basis, fit for a differential background, and use low order spatial variation in the kernel and background:

def run(args):
#
# Create the Config and use sum of gaussian basis
#
config = ImagePsfMatchTask.ConfigClass()
config.kernel.name = "AL"
config.kernel.active.fitForBackground = True
config.kernel.active.spatialKernelOrder = 1
config.kernel.active.spatialBgOrder = 0

Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images are sent, make some fake data up for the sake of this example script (have a look at the code if you want more details on generateFakeImages):

# Run the requested method of the Task
if args.template is not None and args.science is not None:
    if not os.path.isfile(args.template):
        raise Exception("Template image %s does not exist" % (args.template))
    if not os.path.isfile(args.science):
        raise Exception("Science image %s does not exist" % (args.science))
    try:
        templateExp = afwImage.ExposureF(args.template)
    except Exception as e:
        raise Exception("Cannot read template image %s" % (args.template))
    try:
        scienceExp = afwImage.ExposureF(args.science)
    except Exception as e:
        raise Exception("Cannot read science image %s" % (args.science))
else:
    templateExp, scienceExp = generateFakeImages()
    config.kernel.active.sizeCellX = 128
    config.kernel.active.sizeCellY = 128

Create and run the Task:

# Create the Task
psfMatchTask = MyImagePsfMatchTask(config=config)
# Run the Task
result = psfMatchTask.run(templateExp, scienceExp, args.mode)

And finally provide some optional debugging displays:

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getFwhmPix`(psf)	Return the FWHM in pixels of a Psf
`getName`()	Get the name of the task.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getSelectSources`(exposure[, sigma, …])	Get sources to use for Psf-matching
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeCandidateList`(templateExposure, …[, …])	Make a list of acceptable KernelCandidates
`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.
`matchExposures`(templateExposure, scienceExposure)	Warp and PSF-match an exposure to the reference
`matchMaskedImages`(templateMaskedImage, …)	PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage)
`subtractExposures`(templateExposure, …[, …])	Register, Psf-match and subtract two Exposures
`subtractMaskedImages`(templateMaskedImage, …)	Psf-match and subtract two MaskedImages
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

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

getAllSchemaCatalogs()¶

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:	schemacatalogs : `dict` Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata()¶

Get metadata for all tasks.

Returns:	metadata : `lsst.daf.base.PropertySet` The `PropertySet` 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()¶

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

Returns:	fullName : `str` 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”.

getFwhmPix(psf)¶: Return the FWHM in pixels of a Psf

getName()¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implemenation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getSelectSources(exposure, sigma=None, doSmooth=True, idFactory=None)¶

Get sources to use for Psf-matching

This method runs detection and measurement on an exposure. The returned set of sources will be used as candidates for Psf-matching.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure on which to run detection/measurement sigma : `float` Detection threshold doSmooth : `bool` Whether or not to smooth the Exposure with Psf before detection idFactory : Factory for the generation of Source ids
Returns:	selectSources : source catalog containing candidates for the Psf-matching

getTaskDict()¶

Get a dictionary of all tasks as a shallow copy.

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

makeCandidateList(templateExposure, scienceExposure, kernelSize, candidateList=None)¶

Make a list of acceptable KernelCandidates

Accept or generate a list of candidate sources for Psf-matching, and examine the Mask planes in both of the images for indications of bad pixels

Parameters:

templateExposure : lsst.afw.image.Exposure: Exposure that will be convolved
scienceExposure : lsst.afw.image.Exposure: Exposure that will be matched-to
kernelSize : float: Dimensions of the Psf-matching Kernel, used to grow detection footprints
candidateList : list, optional: List of Sources to examine. Elements must be of type afw.table.Source or a type that wraps a Source and has a getSource() method, such as meas.algorithms.PsfCandidateF.

Returns:

candidateList : list of dict: a list of dicts having a “source” and “footprint” field for the Sources deemed to be appropriate for Psf matching

classmethod makeField(doc)¶

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

Parameters:	doc : `str` Help text for the field.
Returns:	configurableField : `lsst.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("a brief description of what this task does")

makeSubtask(name, **keyArgs)¶

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

Parameters:	name : `str` 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 pex_config ConfigurableField or RegistryField.

matchExposures(templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)¶

Warp and PSF-match an exposure to the reference

Do the following, in order:

Warp templateExposure to match scienceExposure,

if doWarping True and their WCSs do not already match
Determine a PSF matching kernel and differential background model

that matches templateExposure to scienceExposure
Convolve templateExposure by PSF matching kernel

Parameters:	templateExposure : `lsst.afw.image.Exposure` Exposure to warp and PSF-match to the reference masked image scienceExposure : `lsst.afw.image.Exposure` Exposure whose WCS and PSF are to be matched to templateFwhmPix :`float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional a list of footprints/maskedImages for kernel candidates; if None then source detection is run. Currently supported: list of Footprints or measAlg.PsfCandidateF doWarping : `bool` what to do if templateExposure’s and scienceExposure’s WCSs do not match: if True then warp templateExposure to match scienceExposure if False then raise an Exception convolveTemplate : `bool` convolve the template image or the science image: if True, templateExposure is warped if doWarping, templateExposure is convolved if False, templateExposure is warped if doWarping, scienceExposure is convolved
Returns:	results : `Struct` a `lsst.pipe.base.Struct` containing these fields: `matchedImage` : the PSF-matched exposure = warped templateExposure convolved by psfMatchingKernel. This has: the same parent bbox, Wcs and Calib as scienceExposure the same filter as templateExposure no Psf (because the PSF-matching process does not compute one) `psfMatchingKernel` : the PSF matching kernel `backgroundModel` : differential background model `kernelCellSet` : SpatialCellSet used to solve for the PSF matching kernel
Raises:	RuntimeError if doWarping is False and templateExposure’s and scienceExposure’s WCSs do not match

matchMaskedImages(templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)¶

PSF-match a MaskedImage (templateMaskedImage) to a reference MaskedImage (scienceMaskedImage)

Do the following, in order:

Determine a PSF matching kernel and differential background model

that matches templateMaskedImage to scienceMaskedImage
Convolve templateMaskedImage by the PSF matching kernel

Parameters:	templateMaskedImage : `lsst.afw.image.MaskedImage` masked image to PSF-match to the reference masked image; must be warped to match the reference masked image scienceMaskedImage : `lsst.afw.image.MaskedImage` maskedImage whose PSF is to be matched to templateFwhmPix : `float` FWHM (in pixels) of the Psf in the template image (image to convolve) scienceFwhmPix : `float` FWHM (in pixels) of the Psf in the science image candidateList : `list`, optional a list of footprints/maskedImages for kernel candidates; if None then source detection is run. Currently supported: list of Footprints or measAlg.PsfCandidateF
Returns:	result : `callable` a `lsst.pipe.base.Struct` containing these fields: - psfMatchedMaskedImage: the PSF-matched masked image = templateMaskedImage convolved with psfMatchingKernel. This has the same xy0, dimensions and wcs as scienceMaskedImage. - psfMatchingKernel: the PSF matching kernel - backgroundModel: differential background model - kernelCellSet: SpatialCellSet used to solve for the PSF matching kernel
Raises:	RuntimeError if input images have different dimensions

subtractExposures(templateExposure, scienceExposure, templateFwhmPix=None, scienceFwhmPix=None, candidateList=None, doWarping=True, convolveTemplate=True)¶

Register, Psf-match and subtract two Exposures

Do the following, in order:

Warp templateExposure to match scienceExposure, if their WCSs do not already match
Determine a PSF matching kernel and differential background model

that matches templateExposure to scienceExposure
PSF-match templateExposure to scienceExposure
Compute subtracted exposure (see return values for equation).

Parameters:

templateExposure : lsst.afw.image.Exposure

exposure to PSF-match to scienceExposure

scienceExposure : lsst.afw.image.Exposure

reference Exposure

templateFwhmPix : float

FWHM (in pixels) of the Psf in the template image (image to convolve)

scienceFwhmPix : float

FWHM (in pixels) of the Psf in the science image

candidateList : list, optional

a list of footprints/maskedImages for kernel candidates; if None then source detection is run.

Currently supported: list of Footprints or measAlg.PsfCandidateF

doWarping : bool

what to do if templateExposure’s and scienceExposure’s WCSs do not match:

if True then warp templateExposure to match scienceExposure
if False then raise an Exception

convolveTemplate : bool

convolve the template image or the science image

if True, templateExposure is warped if doWarping, templateExposure is convolved
if False, templateExposure is warped if doWarping, scienceExposure is convolved

Returns:

result: `Struct`

a lsst.pipe.base.Struct containing these fields:

subtractedExposure : subtracted Exposure

scienceExposure - (matchedImage + backgroundModel)
matchedImage : templateExposure after warping to match templateExposure (if doWarping true),

and convolving with psfMatchingKernel
psfMatchingKernel : PSF matching kernel
backgroundModel : differential background model
kernelCellSet : SpatialCellSet used to determine PSF matching kernel

subtractMaskedImages(templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)¶

Psf-match and subtract two MaskedImages

Do the following, in order:

PSF-match templateMaskedImage to scienceMaskedImage
Determine the differential background
Return the difference: scienceMaskedImage

((warped templateMaskedImage convolved with psfMatchingKernel) + backgroundModel)

Parameters:

templateMaskedImage : lsst.afw.image.MaskedImage

MaskedImage to PSF-match to scienceMaskedImage

scienceMaskedImage : lsst.afw.image.MaskedImage

reference MaskedImage

templateFwhmPix : float

FWHM (in pixels) of the Psf in the template image (image to convolve)

scienceFwhmPix : float

FWHM (in pixels) of the Psf in the science image

candidateList : list, optional

a list of footprints/maskedImages for kernel candidates; if None then source detection is run.

Currently supported: list of Footprints or measAlg.PsfCandidateF

Returns:

results : Struct

a lsst.pipe.base.Struct containing these fields:

subtractedMaskedImage : scienceMaskedImage - (matchedImage + backgroundModel)
matchedImage : templateMaskedImage convolved with psfMatchingKernel
psfMatchingKernel` : PSF matching kernel
backgroundModel : differential background model
kernelCellSet : SpatialCellSet used to determine PSF matching kernel

timer(name, logLevel=10000)¶

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

Parameters:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `lsst.log` level constant.

Navigation

ImagePsfMatchTask¶