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LSST Data Management Base Package
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Public Member Functions | Static Public Attributes | List of all members
lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask Class Reference
Inheritance diagram for lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask:
lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask lsst.ip.diffim.psfMatch.PsfMatchTask

Public Member Functions

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

Static Public Attributes

 ConfigClass = SnapPsfMatchConfig
 

Detailed Description

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 ``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:

.. 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.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.

.. code-block:: py

    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:

.. code-block:: py

    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

.. code-block:: py

    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

.. 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 choose the basis set to use:

.. code-block:: py

    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):

.. 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 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

.. code-block:: py

    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

.. code-block:: py

    # 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:

.. 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 88 of file snapPsfMatch.py.

Member Function Documentation

◆ subtractExposures()

def lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask.subtractExposures (   self,
  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

Reimplemented from lsst.ip.diffim.imagePsfMatch.ImagePsfMatchTask.

Definition at line 302 of file snapPsfMatch.py.

304 candidateList=None):
305 return ImagePsfMatchTask.subtractExposures(self,
306 templateExposure=templateExposure,
307 scienceExposure=scienceExposure,
308 templateFwhmPix=templateFwhmPix,
309 scienceFwhmPix=scienceFwhmPix,
310 candidateList=candidateList,
311 doWarping=self.config.doWarping,
312 )

Member Data Documentation

◆ ConfigClass

lsst.ip.diffim.snapPsfMatch.SnapPsfMatchTask.ConfigClass = SnapPsfMatchConfig
static

Definition at line 299 of file snapPsfMatch.py.


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