Inheritance diagram for lsst.ip.diffim.modelPsfMatch.ModelPsfMatchTask:
Public Member Functions | |
| def | __init__ (self, *args, **kwargs) |
| def | run (self, exposure, referencePsfModel, kernelSum=1.0) |
Public Attributes | |
| kConfig | |
| useRegularization | |
| hMat | |
Static Public Attributes | |
| ConfigClass = ModelPsfMatchConfig | |
Detailed Description
Matching of two model Psfs, and application of the Psf-matching kernel to an input Exposure
Notes
-----
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.
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 # global
di.maskTransparency = 80 # 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 # mask transparency
di.displaySpatialCells = True # show spatial cells before the fit
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 ModelPsfMatchTask
This code is modelPsfMatchTask.py in the examples directory, and can be run as e.g.
.. code-block :: none
examples/modelPsfMatchTask.py
examples/modelPsfMatchTask.py --debug
examples/modelPsfMatchTask.py --debug --template /path/to/templateExp.fits
--science /path/to/scienceExp.fits
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:
.. code-block :: none
class MyModelPsfMatchTask(ModelPsfMatchTask):
def __init__(self, *args, **kwargs):
ModelPsfMatchTask.__init__(self, *args, **kwargs)
def run(self, templateExp, scienceExp):
return ModelPsfMatchTask.run(self, scienceExp, templateExp.getPsf())
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 :: none
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Demonstrate the use of ModelPsfMatchTask")
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.py in your PYTHONPATH you will get additional
debugging displays. The following block checks for this script:
.. code-block :: none
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.
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).
.. code-block :: none
def run(args):
#
# Create the Config and use sum of gaussian basis
#
config = ModelPsfMatchTask.ConfigClass()
config.kernel.active.scaleByFwhm = 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):
.. code-block :: none
# 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 = generateFakeData()
config.kernel.active.sizeCellX = 128
config.kernel.active.sizeCellY = 128
.. code-block :: none
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 :: none
# Create the Task
psfMatchTask = MyModelPsfMatchTask(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 :: none
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.psfMatchedExposure,
title="Example script: Matched Science Image")
Definition at line 93 of file modelPsfMatch.py.
Constructor & Destructor Documentation
◆ __init__()
| def lsst.ip.diffim.modelPsfMatch.ModelPsfMatchTask.__init__ | ( | self, | |
| * | args, | ||
| ** | kwargs | ||
| ) |
Create a ModelPsfMatchTask
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. This Task
does have a run() method, which is the default way to call the Task.
Reimplemented from lsst.ip.diffim.psfMatch.PsfMatchTask.
Definition at line 280 of file modelPsfMatch.py.
Member Function Documentation
◆ run()
| def lsst.ip.diffim.modelPsfMatch.ModelPsfMatchTask.run | ( | self, | |
| exposure, | |||
| referencePsfModel, | |||
kernelSum = 1.0 |
|||
| ) |
Psf-match an exposure to a model Psf
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure to Psf-match to the reference Psf model;
it must return a valid PSF model via exposure.getPsf()
referencePsfModel : `lsst.afw.detection.Psf`
The Psf model to match to
kernelSum : `float`, optional
A multipicative factor to apply to the kernel sum (default=1.0)
Returns
-------
result : `struct`
- ``psfMatchedExposure`` : the Psf-matched Exposure.
This has the same parent bbox, Wcs, PhotoCalib 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
Raises
------
RuntimeError
if the Exposure does not contain a Psf model
Definition at line 299 of file modelPsfMatch.py.
void convolve(OutImageT &convolvedImage, InImageT const &inImage, KernelT const &kernel, ConvolutionControl const &convolutionControl=ConvolutionControl())
Convolve an Image or MaskedImage with a Kernel, setting pixels of an existing output image.
Definition: ConvolveImage.cc:181
def run(self, coaddExposures, bbox, wcs)
Definition: getTemplate.py:603
def makeKernelBasisList(config, targetFwhmPix=None, referenceFwhmPix=None, basisDegGauss=None, basisSigmaGauss=None, metadata=None)
Definition: makeKernelBasisList.py:33
Member Data Documentation
◆ ConfigClass
|
static |
Definition at line 278 of file modelPsfMatch.py.
◆ hMat
|
inherited |
Definition at line 660 of file psfMatch.py.
◆ kConfig
| lsst.ip.diffim.modelPsfMatch.ModelPsfMatchTask.kConfig |
Definition at line 296 of file modelPsfMatch.py.
◆ useRegularization
|
inherited |
Definition at line 655 of file psfMatch.py.
The documentation for this class was generated from the following file:
- /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.7.0/Linux64/ip_diffim/22.0.1-19-gb93eaa13+aab3ef7709/python/lsst/ip/diffim/modelPsfMatch.py
