ModelPsfMatchTask

class lsst.ip.diffim.ModelPsfMatchTask(*args, **kwargs)

Bases: lsst.ip.diffim.PsfMatchTask

! @anchor ModelPsfMatchTask

@brief Matching of two model Psfs, and application of the Psf-matching kernel to an input Exposure

@section ip_diffim_modelpsfmatch_Contents Contents

  • @ref ip_diffim_modelpsfmatch_Purpose
  • @ref ip_diffim_modelpsfmatch_Initialize
  • @ref ip_diffim_modelpsfmatch_IO
  • @ref ip_diffim_modelpsfmatch_Config
  • @ref ip_diffim_modelpsfmatch_Metadata
  • @ref ip_diffim_modelpsfmatch_Debug
  • @ref ip_diffim_modelpsfmatch_Example

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Purpose Description

This Task differs from ImagePsfMatchTask in that it matches two Psf _models_, by realizing them in an Exposure-sized SpatialCellSet and then inserting each Psf-image pair into KernelCandidates. Because none of the pairs of sources that are to be matched should be invalid, all sigma clipping is turned off in ModelPsfMatchConfig. And because there is no tracked _variance_ in the Psf images, the debugging and logging QA info should be interpreted with caution.

One item of note is that the sizes of Psf models are fixed (e.g. its defined as a 21x21 matrix). When the Psf-matching kernel is being solved for, the Psf “image” is convolved with each kernel basis function, leading to a loss of information around the borders. This pixel loss will be problematic for the numerical stability of the kernel solution if the size of the convolution kernel (set by ModelPsfMatchConfig.kernelSize) is much bigger than: psfSize//2. Thus the sizes of Psf-model matching kernels are typically smaller than their image-matching counterparts. If the size of the kernel is too small, the convolved stars will look “boxy”; if the kernel is too large, the kernel solution will be “noisy”. This is a trade-off that needs careful attention for a given dataset.

The primary use case for this Task is in matching an Exposure to a constant-across-the-sky Psf model for the purposes of image coaddition. It is important to note that in the code, the “template” Psf is the Psf that the science image gets matched to. In this sense the order of template and science image are reversed, compared to ImagePsfMatchTask, which operates on the template image.

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Initialize Task initialization

@copydoc __init__

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_IO Invoking the Task

@copydoc run

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Config Configuration parameters

See @ref ModelPsfMatchConfig

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Metadata Quantities set in Metadata

See @ref ip_diffim_psfmatch_Metadata “PsfMatchTask”

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Debug Debug variables

The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag @c -d/–debug to import @b debug.py from your @c PYTHONPATH. The relevant contents of debug.py for this Task include:

@code{.py}

import sys import lsstDebug def DebugInfo(name):

di = lsstDebug.getInfo(name) if name == “lsst.ip.diffim.psfMatch”:

di.display = True # global 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.modelPsfMatch”:
di.display = True # global di.maskTransparency = 30 # ds9 mask transparency di.displaySpatialCells = True # show spatial cells before the fit

return di

lsstDebug.Info = DebugInfo lsstDebug.frame = 1

@endcode

Note that if you want addional logging info, you may add to your scripts: @code{.py} import lsst.log.utils as logUtils logUtils.traceSetAt(“ip.diffim”, 4) @endcode

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

@section ip_diffim_modelpsfmatch_Example A complete example of using ModelPsfMatchTask

This code is modelPsfMatchTask.py in the examples directory, and can be run as @em e.g. @code examples/modelPsfMatchTask.py examples/modelPsfMatchTask.py –debug examples/modelPsfMatchTask.py –debug –template /path/to/templateExp.fits –science /path/to/scienceExp.fits @endcode

@dontinclude modelPsfMatchTask.py Create a subclass of ModelPsfMatchTask that accepts two exposures. Note that the “template” exposure contains the Psf that will get matched to, and the “science” exposure is the one that will be convolved: @skip MyModelPsfMatchTask @until return

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: @skip main @until parse_args

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: @skip args.debug @until sys.stderr

@dontinclude modelPsfMatchTask.py Finally, we call a run method that we define below. First set up a Config and modify some of the parameters. In particular we don’t want to “grow” the sizes of the kernel or KernelCandidates, since we are operating with fixed–size images (i.e. the size of the input Psf models). @skip run(args) @until False

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 generateFakeData): @skip requested @until sizeCellY

Display the two images if –debug: @skip args.debug @until Science

Create and run the Task: @skip Create @until result

And finally provide optional debugging display of the Psf-matched (via the Psf models) science image: @skip args.debug @until result.psfMatchedExposure

#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Methods Summary

run(exposure, referencePsfModel[, kernelSum]) !Psf-match an exposure to a model Psf

Methods Documentation

run(exposure, referencePsfModel, kernelSum=1.0)

!Psf-match an exposure to a model Psf

@param exposure: Exposure to Psf-match to the reference Psf model;
it must return a valid PSF model via exposure.getPsf()

@param referencePsfModel: The Psf model to match to (an lsst.afw.detection.Psf) @param kernelSum: A multipicative factor to apply to the kernel sum (default=1.0)

@return - psfMatchedExposure: the Psf-matched Exposure. This has the same parent bbox, Wcs, Calib and

Filter as the input Exposure but no Psf. In theory the Psf should equal referencePsfModel but the match is likely not exact.
  • psfMatchingKernel: the spatially varying Psf-matching kernel
  • kernelCellSet: SpatialCellSet used to solve for the Psf-matching kernel
  • referencePsfModel: Validated and/or modified reference model used

Raise a RuntimeError if the Exposure does not contain a Psf model