SnapPsfMatchTask¶

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

Bases: lsst.ip.diffim.ImagePsfMatchTask

Image-based Psf-matching of two subsequent snaps from the same visit

Notes

This Task differs from ImagePsfMatchTask in that it matches two Exposures assuming that the images have been acquired very closely in time. Under this assumption, the astrometric misalignments and/or relative distortions should be within a pixel, and the Psf-shapes should be very similar. As a consequence, the default configurations for this class assume a very simple solution.

The spatial variation in the kernel (SnapPsfMatchConfig.spatialKernelOrder) is assumed to be zero
With no spatial variation, we turn of the spatial

clipping loops (SnapPsfMatchConfig.spatialKernelClipping)
The differential background is not fit for (SnapPsfMatchConfig.fitForBackground)
The kernel is expected to be appx.

a delta function, and has a small size (SnapPsfMatchConfig.kernelSize)

The sub-configurations for the Alard-Lupton (SnapPsfMatchConfigAL) and delta-function (SnapPsfMatchConfigDF) bases also are designed to generate a small, simple kernel.

Task initialization

Initialization is the same as base class ImagePsfMatch.__init__, with the difference being that the Task’s ConfigClass is SnapPsfMatchConfig.

Invoking the Task

The Task is only configured to have a subtractExposures method, which in turn calls ImagePsfMatchTask.subtractExposures.

Configuration parameters

See SnapPsfMatchConfig, which uses either SnapPsfMatchConfigDF and SnapPsfMatchConfigAL as its active configuration.

Debug variables

The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a flag -d/–debug to importdebug.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          # display 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          # display 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          # display 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.utils.logging as logUtils
logUtils.trace_set_at("lsst.ip.diffim", 4)

Examples

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

examples/snapPsfMatchTask.py
examples/snapPsfMatchTask.py --debug
examples/snapPsfMatchTask.py --debug --template /path/to/templateExp.fits
--science /path/to/scienceExp.fits

First, create a subclass of SnapPsfMatchTask that accepts two exposures. Ideally these exposures would have been taken back-to-back, such that the pointing/background/Psf does not vary substantially between the two:

class MySnapPsfMatchTask(SnapPsfMatchTask):
    def __init__(self, *args, **kwargs):
        SnapPsfMatchTask.__init__(self, *args, **kwargs)
    def run(self, templateExp, scienceExp):
        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

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Demonstrate the use of ImagePsfMatchTask")
    parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False)
    parser.add_argument("--template", "-t", help="Template Exposure to use", default=None)
    parser.add_argument("--science", "-s", help="Science Exposure to use", default=None)
    args = parser.parse_args()

We have enabled some minor display debugging in this script via the –debug option. However, if you have an lsstDebug debug.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 choose the basis set to use:

def run(args):
    #
    # Create the Config and use sum of gaussian basis
    #
    config = SnapPsfMatchTask.ConfigClass()
    config.doWarping = True
    config.kernel.name = "AL"

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; as a detail of how the fake images were made, you do have to fit for a differential background):

# 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 FileNotFoundError("Template image %s does not exist" % (args.template))
    if not os.path.isfile(args.science):
        raise FileNotFoundError("Science image %s does not exist" % (args.science))
    try:
        templateExp = afwImage.ExposureF(args.template)
    except Exception as e:
        raise RuntimeError("Cannot read template image %s" % (args.template))
    try:
        scienceExp = afwImage.ExposureF(args.science)
    except Exception as e:
        raise RuntimeError("Cannot read science image %s" % (args.science))
else:
    templateExp, scienceExp = generateFakeImages()
    config.kernel.active.fitForBackground = True
    config.kernel.active.spatialBgOrder = 0
    config.kernel.active.sizeCellX = 128
    config.kernel.active.sizeCellY = 128

Display the two images if -debug

if args.debug:
    afwDisplay.Display(frame=1).mtv(templateExp, title="Example script: Input Template")
    afwDisplay.Display(frame=2).mtv(scienceExp, title="Example script: Input Science Image")

Create and run the Task

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

And finally provide optional debugging display of the Psf-matched (via the Psf models) science image:

if args.debug:
    # See if the LSST debug has incremented the frame number; if not start with frame 3
    try:
        frame = debug.lsstDebug.frame + 1
    except Exception:
        frame = 3
    afwDisplay.Display(frame=frame).mtv(result.matchedExposure,
                                    title="Example script: Matched Template Image")
    if "subtractedExposure" in result.getDict():
        afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure,
                                                title="Example script: Subtracted Image")

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[, position])	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.
`makeKernelBasisList`([targetFwhmPix, …])	Wrapper to set log messages for `lsst.ip.diffim.makeKernelBasisList`.
`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() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]¶

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() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` 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:	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, position=None)¶: Return the FWHM in pixels of a Psf.

getName() → str¶

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 implementation 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() → Dict[str, weakref]¶

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: str) → lsst.pex.config.configurableField.ConfigurableField¶

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("brief description of task")

makeKernelBasisList(targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None)¶

Wrapper to set log messages for lsst.ip.diffim.makeKernelBasisList.

Parameters:

targetFwhmPix : float, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
referenceFwhmPix : float, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
basisDegGauss : list of int, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
basisSigmaGauss : list of int, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.
metadata : lsst.daf.base.PropertySet, optional: Passed on to lsst.ip.diffim.generateAlardLuptonBasisList. Not used for delta function basis sets.

Returns:

basisList: list of lsst.afw.math.kernel.FixedKernel: List of basis kernels.

makeSubtask(name: str, **keyArgs) → None¶

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 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` and `scienceExposure` WCSs do not match: if `True` then warp `templateExposure` to match `scienceExposure` if `False` then raise an Exception convolveTemplate : `bool` Whether to 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 : `lsst.pipe.base.Struct` An `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 PhotoCalib 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 Raised if doWarping is False and `templateExposure` and `scienceExposure` 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
An 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: Raised if input images have different dimensions

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

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.ExposureF

Exposure to PSF-match to scienceExposure

scienceExposure : lsst.afw.image.ExposureF

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` and scienceExposure 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 : lsst.pipe.base.Struct

An 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 : lsst.pipe.base.Struct

An 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: str, logLevel: int = 10) → Iterator[None]¶

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 `logging` level constant.

Navigation

SnapPsfMatchTask¶