Inheritance diagram for lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask:

Public Member Functions
def	__init__ (self, args, kwargs)

def	getFwhmPix (self, psf)

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

def	matchMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)

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

def	subtractMaskedImages (self, templateMaskedImage, scienceMaskedImage, candidateList, templateFwhmPix=None, scienceFwhmPix=None)

def	getSelectSources (self, exposure, sigma=None, doSmooth=True, idFactory=None)

def	makeCandidateList (self, templateExposure, scienceExposure, kernelSize, candidateList=None)

Public Attributes
	kConfig

	background

	selectSchema

	selectAlgMetadata

	useRegularization

	hMat

Static Public Attributes
	ConfigClass = ImagePsfMatchConfig

Detailed Description

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`,
`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages`,
`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures`, and
`~lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.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:

.. code-block:: py

    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:

.. code-block:: py

    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:

.. code-block:: py

    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:

.. code-block:: py

    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):

.. code-block:: py

    # 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:

.. code-block:: py

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

And finally provide some optional debugging displays:

.. code-block:: py

    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")

Definition at line 80 of file imagePsfMatch.py.

Constructor & Destructor Documentation

◆ init()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.__init__	(	self,
		args,
		kwargs
	)

Create the ImagePsfMatchTask.

Definition at line 317 of file imagePsfMatch.py.

Member Function Documentation

◆ getFwhmPix()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getFwhmPix	(	self,
		psf
	)

Return the FWHM in pixels of a Psf.

Definition at line 332 of file imagePsfMatch.py.

◆ getSelectSources()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.getSelectSources	(	self,
		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

Definition at line 748 of file imagePsfMatch.py.

◆ makeCandidateList()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.makeCandidateList	(	self,
		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

Definition at line 804 of file imagePsfMatch.py.

◆ matchExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchExposures	(	self,
		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

Definition at line 341 of file imagePsfMatch.py.

◆ matchMaskedImages()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.matchMaskedImages	(	self,
		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

Definition at line 455 of file imagePsfMatch.py.

◆ subtractExposures()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractExposures	(	self,
		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``` 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

Definition at line 568 of file imagePsfMatch.py.

◆ subtractMaskedImages()

def lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.subtractMaskedImages	(	self,
		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

Definition at line 679 of file imagePsfMatch.py.

Member Data Documentation

◆ background

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.background

Definition at line 325 of file imagePsfMatch.py.

◆ ConfigClass

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.ConfigClass = ImagePsfMatchConfig

static

Definition at line 315 of file imagePsfMatch.py.

◆ hMat

lsst.ip.diffim.psfMatch.PsfMatchTask.hMat

inherited

Definition at line 658 of file psfMatch.py.

◆ kConfig

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.kConfig

Definition at line 321 of file imagePsfMatch.py.

◆ selectAlgMetadata

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectAlgMetadata

Definition at line 328 of file imagePsfMatch.py.

◆ selectSchema

lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.selectSchema

Definition at line 327 of file imagePsfMatch.py.

◆ useRegularization

lsst.ip.diffim.psfMatch.PsfMatchTask.useRegularization