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isullivan merged 3 commits into from
Jun 4, 2025
Merged

DM-51063: Add error handling if the science image is missing a photoCalib #403

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5927a9c
Add error handling if the science image is missing a photoCalib
isullivan May 23, 2025
9e3094f
Pass in pre-determined PSFs to makeKernel
isullivan May 28, 2025
494d15f
Move psf evaluation into _prepareInputs
isullivan Jun 4, 2025
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92 changes: 48 additions & 44 deletions python/lsst/ip/diffim/makeKernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,7 +105,8 @@ def __init__(self, *args, **kwargs):
self.makeSubtask("selectDetection", schema=self.selectSchema)
self.makeSubtask("selectMeasurement", schema=self.selectSchema, algMetadata=self.selectAlgMetadata)

def run(self, template, science, kernelSources, preconvolved=False):
def run(self, template, science, kernelSources, preconvolved=False,
templateFwhmPix=None, scienceFwhmPix=None):
"""Solve for the kernel and background model that best match two
Exposures evaluated at the given source locations.

Expand All @@ -131,27 +132,28 @@ def run(self, template, science, kernelSources, preconvolved=False):
Spatially varying background-matching function.
"""
kernelCellSet = self._buildCellSet(template.maskedImage, science.maskedImage, kernelSources)
# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
try:
templateFwhmPix = getPsfFwhm(template.psf)
scienceFwhmPix = getPsfFwhm(science.psf)
except (InvalidParameterError, RangeError):
self.log.debug("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
templateFwhmPix = evaluateMeanPsfFwhm(template,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
scienceFwhmPix = evaluateMeanPsfFwhm(science,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
if (scienceFwhmPix is None) or (templateFwhmPix is None):
# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
try:
templateFwhmPix = getPsfFwhm(template.psf)
scienceFwhmPix = getPsfFwhm(science.psf)
except (InvalidParameterError, RangeError):
self.log.debug("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
templateFwhmPix = evaluateMeanPsfFwhm(template,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
scienceFwhmPix = evaluateMeanPsfFwhm(science,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)

if preconvolved:
scienceFwhmPix *= np.sqrt(2)
Expand All @@ -163,7 +165,8 @@ def run(self, template, science, kernelSources, preconvolved=False):
backgroundModel=backgroundModel,
)

def selectKernelSources(self, template, science, candidateList=None, preconvolved=False):
def selectKernelSources(self, template, science, candidateList=None, preconvolved=False,
templateFwhmPix=None, scienceFwhmPix=None):
"""Select sources from a list of candidates, and extract footprints.

Parameters
Expand All @@ -182,27 +185,28 @@ def selectKernelSources(self, template, science, candidateList=None, preconvolve
kernelSources : `lsst.afw.table.SourceCatalog`
Kernel candidates with appropriate sized footprints.
"""
# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
try:
templateFwhmPix = getPsfFwhm(template.psf)
scienceFwhmPix = getPsfFwhm(science.psf)
except (InvalidParameterError, RangeError):
self.log.debug("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
templateFwhmPix = evaluateMeanPsfFwhm(template,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
scienceFwhmPix = evaluateMeanPsfFwhm(science,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
if (scienceFwhmPix is None) or (templateFwhmPix is None):
# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
try:
templateFwhmPix = getPsfFwhm(template.psf)
scienceFwhmPix = getPsfFwhm(science.psf)
except (InvalidParameterError, RangeError):
self.log.debug("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
templateFwhmPix = evaluateMeanPsfFwhm(template,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
scienceFwhmPix = evaluateMeanPsfFwhm(science,
fwhmExposureBuffer=self.config.fwhmExposureBuffer,
fwhmExposureGrid=self.config.fwhmExposureGrid
)
if preconvolved:
scienceFwhmPix *= np.sqrt(2)
kernelSize = self.makeKernelBasisList(templateFwhmPix, scienceFwhmPix)[0].getWidth()
Expand Down
112 changes: 65 additions & 47 deletions python/lsst/ip/diffim/subtractImages.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -374,43 +374,10 @@ def run(self, template, science, sources, visitSummary=None):
"""
self._prepareInputs(template, science, visitSummary=visitSummary)

# In the event that getPsfFwhm fails, evaluate the PSF on a grid.
fwhmExposureBuffer = self.config.makeKernel.fwhmExposureBuffer
fwhmExposureGrid = self.config.makeKernel.fwhmExposureGrid

# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
# In the try block below, we catch two exceptions:
# 1. InvalidParameterError, in case the point where we are evaluating
# the PSF lands in a gap in the template.
# 2. RangeError, in case the template coverage is so poor that we end
# up near a region with no data.
try:
self.templatePsfSize = getPsfFwhm(template.psf)
self.sciencePsfSize = getPsfFwhm(science.psf)
except (lsst.pex.exceptions.InvalidParameterError, lsst.pex.exceptions.RangeError):
self.log.info("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
self.templatePsfSize = evaluateMeanPsfFwhm(template,
fwhmExposureBuffer=fwhmExposureBuffer,
fwhmExposureGrid=fwhmExposureGrid
)
self.sciencePsfSize = evaluateMeanPsfFwhm(science,
fwhmExposureBuffer=fwhmExposureBuffer,
fwhmExposureGrid=fwhmExposureGrid
)
self.log.info("Science PSF FWHM: %f pixels", self.sciencePsfSize)
self.log.info("Template PSF FWHM: %f pixels", self.templatePsfSize)
self.metadata["sciencePsfSize"] = self.sciencePsfSize
self.metadata["templatePsfSize"] = self.templatePsfSize

# Calculate estimated image depths, i.e., limiting magnitudes
maglim_science = self._calculateMagLim(science, fallbackPsfSize=self.sciencePsfSize)
if np.isnan(maglim_science):
self.log.warning("Limiting magnitude of the science image is NaN!")
fluxlim_science = (maglim_science*u.ABmag).to_value(u.nJy)
maglim_template = self._calculateMagLim(template, fallbackPsfSize=self.templatePsfSize)
if np.isnan(maglim_template):
Expand All @@ -426,8 +393,8 @@ def run(self, template, science, sources, visitSummary=None):
if self.config.mode == "auto":
convolveTemplate = _shapeTest(template,
science,
fwhmExposureBuffer=fwhmExposureBuffer,
fwhmExposureGrid=fwhmExposureGrid)
fwhmExposureBuffer=self.config.makeKernel.fwhmExposureBuffer,
fwhmExposureGrid=self.config.makeKernel.fwhmExposureGrid)
if convolveTemplate:
if self.sciencePsfSize < self.templatePsfSize:
self.log.info("Average template PSF size is greater, "
Expand Down Expand Up @@ -579,9 +546,13 @@ def runConvolveTemplate(self, template, science, selectSources):
try:
kernelSources = self.makeKernel.selectKernelSources(template, science,
candidateList=selectSources,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
kernelResult = self.makeKernel.run(template, science, kernelSources,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
except Exception as e:
if self.config.allowKernelSourceDetection:
self.log.warning("Error encountered trying to construct the matching kernel"
Expand All @@ -594,9 +565,13 @@ def runConvolveTemplate(self, template, science, selectSources):
sigma=self.sciencePsfSize/sigmaToFwhm)
kernelSources = self.makeKernel.selectKernelSources(template, science,
candidateList=candidateList,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
kernelResult = self.makeKernel.run(template, science, kernelSources,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
else:
raise e

Expand Down Expand Up @@ -652,9 +627,13 @@ def runConvolveScience(self, template, science, selectSources):
bbox = science.getBBox()
kernelSources = self.makeKernel.selectKernelSources(science, template,
candidateList=selectSources,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
kernelResult = self.makeKernel.run(science, template, kernelSources,
preconvolved=False)
preconvolved=False,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
modelParams = kernelResult.backgroundModel.getParameters()
# We must invert the background model if the matching kernel is solved for the science image.
kernelResult.backgroundModel.setParameters([-p for p in modelParams])
Expand Down Expand Up @@ -756,22 +735,24 @@ def _calculateMagLim(self, exposure, nsigma=5.0, fallbackPsfSize=None):
maglim : `astropy.units.Quantity`
The limiting magnitude of the exposure, or np.nan.
"""
if exposure.photoCalib is None:
return np.nan
try:
psf = exposure.getPsf()
psf_shape = psf.computeShape(psf.getAveragePosition())
except (lsst.pex.exceptions.InvalidParameterError, afwDetection.InvalidPsfError):
if fallbackPsfSize is not None:
self.log.info("Unable to evaluate PSF, using fallback FWHM %f", fallbackPsfSize)
psf_area = np.pi*(fallbackPsfSize/2)**2
zeropoint = exposure.getPhotoCalib().instFluxToMagnitude(1)
zeropoint = exposure.photoCalib.instFluxToMagnitude(1)
maglim = zeropoint - 2.5*np.log10(nsigma*np.sqrt(psf_area))
else:
self.log.info("Unable to evaluate PSF, setting maglim to nan")
maglim = np.nan
else:
# Get a more accurate area than `psf_shape.getArea()` via moments
psf_area = np.pi*np.sqrt(psf_shape.getIxx()*psf_shape.getIyy())
zeropoint = exposure.getPhotoCalib().instFluxToMagnitude(1)
zeropoint = exposure.photoCalib.instFluxToMagnitude(1)
maglim = zeropoint - 2.5*np.log10(nsigma*np.sqrt(psf_area))
finally:
return maglim
Expand Down Expand Up @@ -987,6 +968,39 @@ def _prepareInputs(self, template, science, visitSummary=None):
# We don't want the detection mask from the science image
self.updateMasks(template, science)

# Calling getPsfFwhm on template.psf fails on some rare occasions when
# the template has no input exposures at the average position of the
# stars. So we try getPsfFwhm first on template, and if that fails we
# evaluate the PSF on a grid specified by fwhmExposure* fields.
# To keep consistent definitions for PSF size on the template and
# science images, we use the same method for both.
# In the try block below, we catch two exceptions:
# 1. InvalidParameterError, in case the point where we are evaluating
# the PSF lands in a gap in the template.
# 2. RangeError, in case the template coverage is so poor that we end
# up near a region with no data.
try:
self.templatePsfSize = getPsfFwhm(template.psf)
self.sciencePsfSize = getPsfFwhm(science.psf)
except (lsst.pex.exceptions.InvalidParameterError, lsst.pex.exceptions.RangeError):
self.log.info("Unable to evaluate PSF at the average position. "
"Evaluting PSF on a grid of points."
)
self.templatePsfSize = evaluateMeanPsfFwhm(
template,
fwhmExposureBuffer=self.config.makeKernel.fwhmExposureBuffer,
fwhmExposureGrid=self.config.makeKernel.fwhmExposureGrid
)
self.sciencePsfSize = evaluateMeanPsfFwhm(
science,
fwhmExposureBuffer=self.config.makeKernel.fwhmExposureBuffer,
fwhmExposureGrid=self.config.makeKernel.fwhmExposureGrid
)
self.log.info("Science PSF FWHM: %f pixels", self.sciencePsfSize)
self.log.info("Template PSF FWHM: %f pixels", self.templatePsfSize)
self.metadata["sciencePsfSize"] = self.sciencePsfSize
self.metadata["templatePsfSize"] = self.templatePsfSize

def updateMasks(self, template, science):
"""Update the science and template mask planes before differencing.

Expand Down Expand Up @@ -1196,9 +1210,13 @@ def runPreconvolve(self, template, science, matchedScience, selectSources, preCo

kernelSources = self.makeKernel.selectKernelSources(template[innerBBox], matchedScience[innerBBox],
candidateList=selectSources,
preconvolved=True)
preconvolved=True,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)
kernelResult = self.makeKernel.run(template[innerBBox], matchedScience[innerBBox], kernelSources,
preconvolved=True)
preconvolved=True,
templateFwhmPix=self.templatePsfSize,
scienceFwhmPix=self.sciencePsfSize)

matchedTemplate = self._convolveExposure(template, kernelResult.psfMatchingKernel,
self.convolutionControl,
Expand Down