Classes | |
| class | BadSubtractionError |
| class | NoDiaSourcesError |
| class | TooManyCosmicRays |
| class | DetectAndMeasureConnections |
Variables | |
| results : `lsst.pipe.base.Struct` | |
| inputImages : `dict` [`str`, `lsst.images.DifferenceImage`] | |
| science : `lsst.afw.image.ExposureF` | |
| matchedTemplate : `lsst.afw.image.ExposureF` | |
| difference : `lsst.afw.image.ExposureF` | |
| kernelSources : `lsst.afw.table.SourceCatalog`, optional | |
| idFactory : `lsst.afw.table.IdFactory`, optional | |
| measurementResults : `lsst.pipe.base.Struct`, optional | |
| differenceExposure : `lsst.afw.image.ExposureF` | |
| scoreExposure : `lsst.afw.image.ExposureF`, optional | |
| background : `lsst.afw.math.BackgroundList` | |
| sources : `lsst.afw.table.SourceCatalog` | |
| positives : `lsst.afw.table.SourceCatalog`, optional | |
| negatives : `lsst.afw.table.SourceCatalog`, optional | |
| table = afwTable.SourceTable.make(self.schema) | |
| detectResults | |
| differenceBackground | |
| scoreMeasuredExposure | |
| detectedBit = difference.mask.getPlaneBitMask(["DETECTED"]) | |
| int | detectedPix = scoreExposure.mask.array & detectedBit > 0 |
| detectedNegativeBit = difference.mask.getPlaneBitMask(["DETECTED_NEGATIVE"]) | |
| int | detectedNegativePix = scoreExposure.mask.array & detectedNegativeBit > 0 |
| edgeBit = difference.mask.getPlaneBitMask(["EDGE"]) | |
| int | edgePix = scoreExposure.mask.array & edgeBit > 0 |
Variable Documentation
◆ background
| lsst.ip.diffim.detectAndMeasure.background : `lsst.afw.math.BackgroundList` |
Definition at line 862 of file detectAndMeasure.py.
◆ detectedBit
| lsst.ip.diffim.detectAndMeasure.detectedBit = difference.mask.getPlaneBitMask(["DETECTED"]) |
Definition at line 1694 of file detectAndMeasure.py.
◆ detectedNegativeBit
| lsst.ip.diffim.detectAndMeasure.detectedNegativeBit = difference.mask.getPlaneBitMask(["DETECTED_NEGATIVE"]) |
Definition at line 1696 of file detectAndMeasure.py.
◆ detectedNegativePix
| int lsst.ip.diffim.detectAndMeasure.detectedNegativePix = scoreExposure.mask.array & detectedNegativeBit > 0 |
Definition at line 1697 of file detectAndMeasure.py.
◆ detectedPix
| int lsst.ip.diffim.detectAndMeasure.detectedPix = scoreExposure.mask.array & detectedBit > 0 |
Definition at line 1695 of file detectAndMeasure.py.
◆ detectResults
| lsst.ip.diffim.detectAndMeasure.detectResults |
Initial value:
= self.detection.run(
table=table,
exposure=scoreExposure,
doSmooth=False,
background=background,
clearMask=True,
)
Definition at line 1681 of file detectAndMeasure.py.
◆ difference
| lsst.ip.diffim.detectAndMeasure.difference : `lsst.afw.image.ExposureF` |
if measurementResults is None:
measurementResults = pipeBase.Struct()
if idFactory is None:
idFactory = lsst.meas.base.IdGenerator().make_table_id_factory()
# Check image properties and clear detection mask planes.
self._prepareInputs(difference)
if self.config.doSubtractBackground:
background = self._fitAndSubtractBackground(differenceExposure=difference)
else:
background = afwMath.BackgroundList()
if self.config.doFindCosmicRays:
self.findAndMaskCosmicRays(difference)
# Don't use the idFactory until after deblend+merge, so that we aren't
# generating ids that just get thrown away (footprint merge doesn't
# know about past ids).
table = afwTable.SourceTable.make(self.schema)
results = self.detection.run(
table=table,
exposure=difference,
doSmooth=True,
background=background,
clearMask=True,
)
measurementResults.differenceBackground = background
if self.config.doDeblend:
sources, positives, negatives = self._deblend(difference,
results.positive,
results.negative)
else:
positives = afwTable.SourceCatalog(self.schema)
results.positive.makeSources(positives)
negatives = afwTable.SourceCatalog(self.schema)
results.negative.makeSources(negatives)
sources = results.sources
self.processResults(science, matchedTemplate, difference,
sources, idFactory, kernelSources,
positives=positives,
negatives=negatives,
measurementResults=measurementResults)
return measurementResults
def _prepareInputs(self, difference):
Definition at line 737 of file detectAndMeasure.py.
◆ differenceBackground
| lsst.ip.diffim.detectAndMeasure.differenceBackground |
Definition at line 1688 of file detectAndMeasure.py.
◆ differenceExposure
| lsst.ip.diffim.detectAndMeasure.differenceExposure : `lsst.afw.image.ExposureF` |
# Check that we have a valid PSF now before we do more work
sigma = difference.psf.computeShape(difference.psf.getAveragePosition()).getDeterminantRadius()
if np.isnan(sigma):
raise pipeBase.UpstreamFailureNoWorkFound("Invalid PSF detected! PSF width evaluates to NaN.")
# Ensure that we start with an empty detection and deblended mask.
mask = difference.mask
for mp in self.config.clearMaskPlanes:
if mp not in mask.getMaskPlaneDict():
mask.addMaskPlane(mp)
mask &= ~mask.getPlaneBitMask(self.config.clearMaskPlanes)
def _fitAndSubtractBackground(self, differenceExposure, scoreExposure=None):
Definition at line 853 of file detectAndMeasure.py.
◆ edgeBit
| lsst.ip.diffim.detectAndMeasure.edgeBit = difference.mask.getPlaneBitMask(["EDGE"]) |
Definition at line 1701 of file detectAndMeasure.py.
◆ edgePix
| int lsst.ip.diffim.detectAndMeasure.edgePix = scoreExposure.mask.array & edgeBit > 0 |
Definition at line 1702 of file detectAndMeasure.py.
◆ idFactory
| lsst.ip.diffim.detectAndMeasure.idFactory : `lsst.afw.table.IdFactory`, optional |
Definition at line 741 of file detectAndMeasure.py.
◆ inputImages
| lsst.ip.diffim.detectAndMeasure.inputImages : `dict` [`str`, `lsst.images.DifferenceImage`] |
Definition at line 680 of file detectAndMeasure.py.
◆ kernelSources
| lsst.ip.diffim.detectAndMeasure.kernelSources : `lsst.afw.table.SourceCatalog`, optional |
Definition at line 739 of file detectAndMeasure.py.
◆ matchedTemplate
| lsst.ip.diffim.detectAndMeasure.matchedTemplate : `lsst.afw.image.ExposureF` |
Definition at line 734 of file detectAndMeasure.py.
◆ measurementResults
| lsst.ip.diffim.detectAndMeasure.measurementResults : `lsst.pipe.base.Struct`, optional |
Definition at line 745 of file detectAndMeasure.py.
◆ negatives
| lsst.ip.diffim.detectAndMeasure.negatives : `lsst.afw.table.SourceCatalog`, optional |
Definition at line 925 of file detectAndMeasure.py.
◆ positives
| lsst.ip.diffim.detectAndMeasure.positives : `lsst.afw.table.SourceCatalog`, optional |
Definition at line 923 of file detectAndMeasure.py.
◆ results
| lsst.ip.diffim.detectAndMeasure.results : `lsst.pipe.base.Struct` |
doMerge = pexConfig.Field(
dtype=bool,
default=True,
doc="Merge positive and negative diaSources with grow radius "
"set by growFootprint"
)
doForcedMeasurement = pexConfig.Field(
dtype=bool,
default=True,
doc="Force photometer diaSource locations on PVI?"
)
doAddMetrics = pexConfig.Field(
dtype=bool,
default=False,
doc="Add columns to the source table to hold analysis metrics?"
)
doSubtractBackground = pexConfig.Field(
dtype=bool,
doc="Subtract a background model from the image before detection?",
default=True,
)
doWriteBackground = pexConfig.Field(
dtype=bool,
doc="Persist the fitted background model? Ignored when"
" image_type='future', which records it on the output images.",
default=False,
deprecated="Use image_type='future', which records the background on the output images."
" Will be removed after v31.",
)
doCalculateResidualMetics = pexConfig.Field(
dtype=bool,
doc="Calculate metrics to assess image subtraction quality for the task"
"metadata?",
default=True,
)
subtractInitialBackground = pexConfig.ConfigurableField(
target=lsst.meas.algorithms.SubtractBackgroundTask,
doc="Task to perform intial background subtraction, before first detection pass.",
)
subtractFinalBackground = pexConfig.ConfigurableField(
target=lsst.meas.algorithms.SubtractBackgroundTask,
doc="Task to perform final background subtraction, after first detection pass.",
)
doFindCosmicRays = pexConfig.Field(
dtype=bool,
doc="Detect and mask cosmic rays on the difference image?"
"CRs can be interpolated over by setting cosmicray.keepCRs=False",
default=True,
)
cosmicray = pexConfig.ConfigField(
dtype=FindCosmicRaysConfig,
doc="Options for finding and masking cosmic rays",
)
detection = pexConfig.ConfigurableField(
target=SourceDetectionTask,
doc="Final source detection for diaSource measurement",
)
streakDetection = pexConfig.ConfigurableField(
target=SourceDetectionTask,
doc="Separate source detection used only for streak masking",
)
doDeblend = pexConfig.Field(
dtype=bool,
default=False,
doc="Deblend DIASources after detection?"
)
deblend = pexConfig.ConfigurableField(
target=lsst.meas.deblender.SourceDeblendTask,
doc="Task to split blended sources into their components."
)
measurement = pexConfig.ConfigurableField(
target=DipoleFitTask,
doc="Task to measure sources on the difference image.",
)
doApCorr = lsst.pex.config.Field(
dtype=bool,
default=True,
doc="Run subtask to apply aperture corrections"
)
applyApCorr = lsst.pex.config.ConfigurableField(
target=ApplyApCorrTask,
doc="Task to apply aperture corrections"
)
forcedMeasurement = pexConfig.ConfigurableField(
target=ForcedMeasurementTask,
doc="Task to force photometer science image at diaSource locations.",
)
growFootprint = pexConfig.Field(
dtype=int,
default=2,
doc="Grow positive and negative footprints by this many pixels before merging"
)
diaSourceMatchRadius = pexConfig.Field(
dtype=float,
default=0.5,
doc="Match radius (in arcseconds) for DiaSource to Source association"
)
doSkySources = pexConfig.Field(
dtype=bool,
default=False,
doc="Generate sky sources?",
)
skySources = pexConfig.ConfigurableField(
target=SkyObjectsTask,
doc="Generate sky sources",
)
doMaskStreaks = pexConfig.Field(
dtype=bool,
default=True,
doc="Turn on streak masking",
)
maskStreaks = pexConfig.ConfigurableField(
target=MaskStreaksTask,
doc="Subtask for masking streaks. Only used if doMaskStreaks is True. "
"Adds a mask plane to an exposure, with the mask plane name set by streakMaskName.",
)
streakBinFactor = pexConfig.Field(
dtype=int,
default=4,
doc="Bin scale factor to use when rerunning detection for masking streaks. "
"Only used if doMaskStreaks is True.",
)
writeStreakInfo = pexConfig.Field(
dtype=bool,
default=False,
doc="Record the parameters of any detected streaks. For LSST, this should be turned off except for "
"development work."
)
findGlints = pexConfig.ConfigurableField(
target=FindGlintTrailsTask,
doc="Subtask for finding glint trails, usually caused by satellites or debris."
)
writeGlintInfo = pexConfig.Field(
dtype=bool,
default=True,
doc="Record the parameters of any detected glint trails."
)
setPrimaryFlags = pexConfig.ConfigurableField(
target=SetPrimaryFlagsTask,
doc="Task to add isPrimary and deblending-related flags to the catalog."
)
badSourceFlags = lsst.pex.config.ListField(
dtype=str,
doc="Sources with any of these flags set are removed before writing the output catalog.",
default=("base_PixelFlags_flag_offimage",
"base_PixelFlags_flag_interpolatedCenterAll",
"base_PixelFlags_flag_badCenter",
"base_PixelFlags_flag_edgeCenter",
"base_PixelFlags_flag_nodataCenter",
"base_PixelFlags_flag_saturatedCenter",
"base_PixelFlags_flag_sat_templateCenter",
"base_PixelFlags_flag_spikeCenter",
),
)
clearMaskPlanes = lsst.pex.config.ListField(
dtype=str,
doc="Mask planes to clear before running detection.",
default=("DETECTED", "DETECTED_NEGATIVE", "NOT_DEBLENDED", "STREAK"),
)
doRejectBadMaskPlaneDetections = pexConfig.Field(
dtype=bool,
default=True,
doc="Reject any peaks detected on ``badMaskPlanes`` before measurement."
"These should all be rejected downstream by ``badSourceFlags``, "
"but filtering earlier can save time."
)
badMaskPlanes = lsst.pex.config.ListField(
dtype=str,
doc="Detections whose footprint peak lies on a pixel with any of these"
" mask planes set will be rejected before measurement."
" Any missing mask planes will be silently ignored.",
default=("NO_DATA", "BAD", "SAT", "EDGE"),
)
raiseOnBadSubtractionRatio = pexConfig.Field(
dtype=bool,
default=True,
doc="Raise an error if the ratio of power in detected footprints"
" on the difference image to the power in footprints on the science"
" image exceeds ``badSubtractionRatioThreshold``",
)
badSubtractionRatioThreshold = pexConfig.Field(
dtype=float,
default=0.2,
doc="Maximum ratio of power in footprints on the difference image to"
" the same footprints on the science image."
"Only used if ``raiseOnBadSubtractionRatio`` is set",
)
badSubtractionVariationThreshold = pexConfig.Field(
dtype=float,
default=0.4,
doc="Maximum standard deviation of the ratio of power in footprints on"
" the difference image to the same footprints on the science image."
"Only used if ``raiseOnBadSubtractionRatio`` is set",
)
raiseOnNoDiaSources = pexConfig.Field(
dtype=bool,
default=True,
doc="Raise an algorithm error if no diaSources are detected.",
)
run_sattle = pexConfig.Field(
dtype=bool,
default=False,
doc="If true, dia source bounding boxes will be sent for verification"
"to the sattle service."
)
sattle_historical = pexConfig.Field(
dtype=bool,
default=False,
doc="If re-running a pipeline that requires sattle, this should be set "
"to True. This will populate sattle's cache with the historic data "
"closest in time to the exposure."
)
idGenerator = DetectorVisitIdGeneratorConfig.make_field()
image_type = pexConfig.ChoiceField[str](
"Image type of the input and measured difference images (and, for"
" DetectAndMeasureScoreTask, the input and measured score images).",
allowed={
"legacy": "Read and write lsst.afw.image.ExposureF.",
"future": "Read and write lsst.images.DifferenceImage.",
},
optional=False,
default="legacy",
)
def setDefaults(self):
# Background subtraction
# Use a small binsize for the first pass to reduce detections on glints
# and extended structures. Should not affect the detectability of
# faint diaSources
self.subtractInitialBackground.binSize = 8
self.subtractInitialBackground.useApprox = False
self.subtractInitialBackground.statisticsProperty = "MEDIAN"
self.subtractInitialBackground.doFilterSuperPixels = True
self.subtractInitialBackground.ignoredPixelMask = ["BAD",
"EDGE",
"DETECTED",
"DETECTED_NEGATIVE",
"NO_DATA",
]
# Use a larger binsize for the final background subtraction, to reduce
# over-subtraction of bright objects.
self.subtractFinalBackground.binSize = 40
self.subtractFinalBackground.useApprox = False
self.subtractFinalBackground.statisticsProperty = "MEDIAN"
self.subtractFinalBackground.doFilterSuperPixels = True
self.subtractFinalBackground.ignoredPixelMask = ["BAD",
"EDGE",
"DETECTED",
"DETECTED_NEGATIVE",
"NO_DATA",
]
# Cosmic ray detection
self.cosmicray.keepCRs = True # do not interpolate over detected CRs
# DiaSource Detection
self.detection.thresholdPolarity = "both"
self.detection.thresholdValue = 5.0
self.detection.reEstimateBackground = False
self.detection.thresholdType = "pixel_stdev"
self.detection.excludeMaskPlanes = []
# Copy configs for binned streak detection from the base detection task
self.streakDetection.thresholdType = self.detection.thresholdType
self.streakDetection.reEstimateBackground = False
self.streakDetection.excludeMaskPlanes = self.detection.excludeMaskPlanes
self.streakDetection.thresholdValue = self.detection.thresholdValue
# Only detect positive streaks
self.streakDetection.thresholdPolarity = "positive"
# Do not grow detected mask for streaks
self.streakDetection.nSigmaToGrow = 0
# Set the streak mask along the entire fit line, not only where the
# detected mask is set.
self.maskStreaks.onlyMaskDetected = False
# Restrict streak masking from growing too large
self.maskStreaks.maxStreakWidth = 100
# Restrict the number of iterations allowed for fitting streaks
# When the fit is good it should solve quickly, and exit a bad fit quickly
self.maskStreaks.maxFitIter = 10
# Only mask to 2 sigma in width
self.maskStreaks.nSigmaMask = 2
# Threshold for including streaks after the Hough Transform.
# A lower value will detect more features that are less linear.
self.maskStreaks.absMinimumKernelHeight = 2
self.measurement.plugins.names |= ["ext_trailedSources_Naive",
"base_LocalPhotoCalib",
"base_LocalWcs",
"ext_shapeHSM_HsmSourceMoments",
"ext_shapeHSM_HsmPsfMoments",
"base_ClassificationSizeExtendedness",
]
self.measurement.slots.psfShape = "ext_shapeHSM_HsmPsfMoments"
self.measurement.slots.shape = "ext_shapeHSM_HsmSourceMoments"
self.measurement.plugins["base_SdssCentroid"].maxDistToPeak = 5.0
self.forcedMeasurement.plugins = ["base_TransformedCentroid", "base_PsfFlux"]
self.forcedMeasurement.copyColumns = {
"id": "objectId", "parent": "parentObjectId", "coord_ra": "coord_ra", "coord_dec": "coord_dec"}
self.forcedMeasurement.slots.centroid = "base_TransformedCentroid"
self.forcedMeasurement.slots.shape = None
# Keep track of which footprints contain streaks
self.measurement.plugins["base_PixelFlags"].masksFpAnywhere = [
"STREAK", "INJECTED", "INJECTED_TEMPLATE", "HIGH_VARIANCE", "SAT_TEMPLATE", "SPIKE"]
self.measurement.plugins["base_PixelFlags"].masksFpCenter = [
"STREAK", "INJECTED", "INJECTED_TEMPLATE", "HIGH_VARIANCE", "SAT_TEMPLATE", "SPIKE"]
self.skySources.avoidMask = ["DETECTED", "DETECTED_NEGATIVE", "BAD", "NO_DATA", "EDGE"]
def validate(self):
super().validate()
if self.run_sattle:
if not os.getenv("SATTLE_URI_BASE"):
raise pexConfig.FieldValidationError(DetectAndMeasureConfig.run_sattle, self,
"Sattle requested but SATTLE_URI_BASE "
"environment variable not set.")
class DetectAndMeasureTask(lsst.pipe.base.PipelineTask):
ConfigClass = DetectAndMeasureConfig
_DefaultName = "detectAndMeasure"
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.schema = afwTable.SourceTable.makeMinimalSchema()
self.algMetadata = dafBase.PropertyList()
if self.config.doSubtractBackground:
self.makeSubtask("subtractInitialBackground")
self.makeSubtask("subtractFinalBackground")
self.makeSubtask("detection", schema=self.schema)
if self.config.doDeblend:
self.makeSubtask("deblend", schema=self.schema)
self.makeSubtask("setPrimaryFlags", schema=self.schema, isSingleFrame=True)
self.makeSubtask("measurement", schema=self.schema,
algMetadata=self.algMetadata)
if self.config.doApCorr:
self.makeSubtask("applyApCorr", schema=self.measurement.schema)
if self.config.doForcedMeasurement:
self.schema.addField(
"ip_diffim_forced_PsfFlux_instFlux", "D",
"Forced PSF flux measured on the direct image.",
units="count")
self.schema.addField(
"ip_diffim_forced_PsfFlux_instFluxErr", "D",
"Forced PSF flux error measured on the direct image.",
units="count")
self.schema.addField(
"ip_diffim_forced_PsfFlux_area", "F",
"Forced PSF flux effective area of PSF.",
units="pixel")
self.schema.addField(
"ip_diffim_forced_PsfFlux_flag", "Flag",
"Forced PSF flux general failure flag.")
self.schema.addField(
"ip_diffim_forced_PsfFlux_flag_noGoodPixels", "Flag",
"Forced PSF flux not enough non-rejected pixels in data to attempt the fit.")
self.schema.addField(
"ip_diffim_forced_PsfFlux_flag_edge", "Flag",
"Forced PSF flux object was too close to the edge of the image to use the full PSF model.")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_instFlux", "D",
"Forced PSF flux measured on the template image.",
units="count")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_instFluxErr", "D",
"Forced PSF flux error measured on the template image.",
units="count")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_area", "F",
"Forced template PSF flux effective area of PSF.",
units="pixel")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_flag", "Flag",
"Forced template PSF flux general failure flag.")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_flag_noGoodPixels", "Flag",
"Forced template PSF flux not enough non-rejected pixels in data to attempt the fit.")
self.schema.addField(
"ip_diffim_forced_template_PsfFlux_flag_edge", "Flag",
)
self.makeSubtask("forcedMeasurement", refSchema=self.schema)
self.schema.addField("refMatchId", "L", "unique id of reference catalog match")
self.schema.addField("srcMatchId", "L", "unique id of source match")
# Create the sky source task for use by metrics,
# even if sky sources are not added to the diaSource catalog
self.makeSubtask("skySources", schema=self.schema)
if self.config.doMaskStreaks:
self.makeSubtask("maskStreaks")
self.makeSubtask("streakDetection")
self.makeSubtask("findGlints")
self.schema.addField("glint_trail", "Flag", "DiaSource is part of a glint trail.")
self.schema.addField("reliability", type="F", doc="Reliability score of the DiaSource")
self.schema.addField("reliabilityVersion", type=str, size=7, doc="Version of the reliability model")
# To get the "merge_*" fields in the schema; have to re-initialize
# this later, once we have a peak schema post-detection.
lsst.afw.detection.FootprintMergeList(self.schema, ["positive", "negative"])
# Check that the schema and config are consistent
for flag in self.config.badSourceFlags:
if flag not in self.schema:
raise pipeBase.InvalidQuantumError("Field %s not in schema" % flag)
# initialize InitOutputs
self.outputSchema = afwTable.SourceCatalog(self.schema)
self.outputSchema.getTable().setMetadata(self.algMetadata)
def runQuantum(self, butlerQC: pipeBase.QuantumContext,
inputRefs: pipeBase.InputQuantizedConnection,
outputRefs: pipeBase.OutputQuantizedConnection):
inputs = butlerQC.get(inputRefs)
idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
idFactory = idGenerator.make_table_id_factory()
# Input image that each output is measured from, keyed by output name.
inputImages = {}
if self.config.image_type == "future":
dataId = butlerQC.quantum.dataId
for output, connection in (("subtractedMeasuredExposure", "difference"),
("scoreMeasuredExposure", "scoreExposure")):
if connection not in inputs:
continue
image = inputs[connection]
# The output is written back into this image, so it must
# already carry the kernel and the template records.
get_difference_image_provenance(image, "kernel", connection, dataId)
get_difference_image_provenance(image, "templates", connection, dataId)
inputImages[output] = image
inputs[connection] = image.to_legacy()
# Specify the fields that `annotate` and `convert_outputs_to_future`
# need below, to ensure they exist, even as None.
measurementResults = pipeBase.Struct(
subtractedMeasuredExposure=None,
scoreMeasuredExposure=None,
diaSources=None,
maskedStreaks=None,
differenceBackground=None,
)
try:
self.run(**inputs, idFactory=idFactory, measurementResults=measurementResults)
except pipeBase.AlgorithmError as e:
# Annotate before converting
error = pipeBase.AnnotatedPartialOutputsError.annotate(
e,
self,
measurementResults.subtractedMeasuredExposure,
measurementResults.scoreMeasuredExposure,
measurementResults.diaSources,
measurementResults.maskedStreaks,
log=self.log
)
if self.config.image_type == "future":
self.convert_outputs_to_future(measurementResults, inputImages)
butlerQC.put(measurementResults, outputRefs)
raise error from e
if self.config.image_type == "future":
self.convert_outputs_to_future(measurementResults, inputImages)
butlerQC.put(measurementResults, outputRefs)
def convert_outputs_to_future(self, results, inputImages):
Definition at line 678 of file detectAndMeasure.py.
◆ science
| lsst.ip.diffim.detectAndMeasure.science : `lsst.afw.image.ExposureF` |
background = getattr(results, "differenceBackground", None)
for name, image in inputImages.items():
exposure = getattr(results, name, None)
if exposure is None:
continue
# These are no-ops when the legacy output is a view of the input,
# as returned by `~lsst.images.DifferenceImage.to_legacy`.
image.image.array[...] = exposure.image.array
image.variance.array[...] = exposure.variance.array
image.mask = Mask.from_legacy(exposure.mask, get_legacy_difference_image_mask_planes(),
sky_projection=image.sky_projection)
if background is not None and len(background) > 0:
# The same background is subtracted from the difference image
# and the score image, so it applies to both.
image.backgrounds.add(
"subtracted",
field_from_legacy_background(background, bounds=image.bbox, unit=image.unit),
description="Background subtracted from this image before detection.",
is_subtracted=True,
)
setattr(results, name, image)
if background is not None and len(background) == 0:
# An empty BackgroundList cannot be converted to a field, and means
# that no background was subtracted.
self.log.debug("No background model to attach to the output images.")
@timeMethod
def run(self, science, matchedTemplate, difference, kernelSources=None,
idFactory=None, measurementResults=None):
if scoreExposure is None:
detectionExposure = differenceExposure.clone()
background = self.subtractInitialBackground.run(detectionExposure).background
doSmooth = True
else:
# Use a clone of differenceExposure because the background is
# subtracted in place.
background = self.subtractInitialBackground.run(differenceExposure.clone()).background
detectionExposure = scoreExposure.clone()
detectionExposure.image -= background.getImage()
doSmooth = False
table = afwTable.SourceTable.make(self.schema)
self.detection.run(
table=table,
exposure=detectionExposure,
doSmooth=doSmooth,
background=background,
clearMask=True,
)
# Use the temporary detection mask for the final background subtraction,
# then restore the original mask. The score image variant never
# clears the detection planes of the difference image.
mask = differenceExposure.mask
originalMask = mask.array.copy()
detectedBit = mask.getPlaneBitMask(["DETECTED"])
detectedPix = detectionExposure.mask.array & detectedBit > 0
detectedNegativeBit = mask.getPlaneBitMask(["DETECTED_NEGATIVE"])
detectedNegativePix = detectionExposure.mask.array & detectedNegativeBit > 0
mask.array[detectedPix] |= detectedBit
mask.array[detectedNegativePix] |= detectedNegativeBit
background = self.subtractFinalBackground.run(differenceExposure).background
mask.array[...] = originalMask
if scoreExposure is not None:
# The preconvolution kernel is normalized to 1, so the same
# background level applies to the difference and score images.
scoreExposure.image -= background.getImage()
return background
def processResults(self, science, matchedTemplate, difference, sources, idFactory,
kernelSources=None, positives=None, negatives=None, measurementResults=None):
Definition at line 732 of file detectAndMeasure.py.
◆ scoreExposure
| lsst.ip.diffim.detectAndMeasure.scoreExposure : `lsst.afw.image.ExposureF`, optional |
Definition at line 856 of file detectAndMeasure.py.
◆ scoreMeasuredExposure
| lsst.ip.diffim.detectAndMeasure.scoreMeasuredExposure |
Definition at line 1689 of file detectAndMeasure.py.
◆ sources
| lsst.ip.diffim.detectAndMeasure.sources : `lsst.afw.table.SourceCatalog` |
Definition at line 916 of file detectAndMeasure.py.
◆ table
| lsst.ip.diffim.detectAndMeasure.table = afwTable.SourceTable.make(self.schema) |
if measurementResults is None:
measurementResults = pipeBase.Struct()
self.metadata["nUnmergedDiaSources"] = len(sources)
if self.config.doMerge:
# preserve peak schema, if there are any footprints
if len(positives) > 0:
peakSchema = positives[0].getFootprint().peaks.schema
elif len(negatives) > 0:
peakSchema = negatives[0].getFootprint().peaks.schema
else:
peakSchema = afwDetection.PeakTable.makeMinimalSchema()
mergeList = afwDetection.FootprintMergeList(self.schema,
["positive", "negative"], peakSchema)
initialDiaSources = afwTable.SourceCatalog(self.schema)
# Start with positive, as FootprintMergeList will self-merge the
# subsequent added catalogs, and we want to try to preserve
# deblended positive sources.
mergeList.addCatalog(initialDiaSources.table, positives, "positive", minNewPeakDist=0)
mergeList.addCatalog(initialDiaSources.table, negatives, "negative", minNewPeakDist=0)
mergeList.getFinalSources(initialDiaSources)
# Flag as negative those sources that *only* came from the negative
# footprint set.
initialDiaSources["is_negative"] = initialDiaSources["merge_footprint_negative"] & \
~initialDiaSources["merge_footprint_positive"]
self.log.info("Merging detections into %d sources", len(initialDiaSources))
# All positive peaks were added before any negative peaks, so
# re-order the peaks in each footprint by significance.
self._reorderPeaksBySignificance(initialDiaSources)
else:
initialDiaSources = sources
# Assign source ids at the end: deblend/merge mean that we don't keep
# track of parents and children, we only care about the final ids.
for source in initialDiaSources:
source.setId(idFactory())
# Ensure sources added after this get correct ids.
initialDiaSources.getTable().setIdFactory(idFactory)
initialDiaSources.setMetadata(self.algMetadata)
self.metadata["nMergedDiaSources"] = len(initialDiaSources)
if self.config.doMaskStreaks:
streakInfo = self._runStreakMasking(difference)
if self.config.doSkySources:
self.addSkySources(initialDiaSources, difference.mask, difference.info.id)
if self.config.doRejectBadMaskPlaneDetections:
# Save time by rejecting peaks on bad mask planes prior to
# measurement.
initialDiaSources = self._rejectBadMaskedDetections(initialDiaSources, difference.mask)
if not initialDiaSources.isContiguous():
initialDiaSources = initialDiaSources.copy(deep=True)
self.measureDiaSources(initialDiaSources, science, difference, matchedTemplate)
# Remove unphysical diaSources per config.badSourceFlags
diaSources = self._removeBadSources(initialDiaSources)
if self.config.run_sattle:
diaSources = self.filterSatellites(diaSources, science)
# Flag diaSources in glint trails, but do not remove them
diaSources, trail_parameters = self._find_glint_trails(diaSources)
if self.config.writeGlintInfo:
measurementResults.mergeItems(trail_parameters, 'glintTrailInfo')
if self.config.doForcedMeasurement:
self.measureForcedSources(diaSources, science, difference.getWcs())
self.measureForcedSources(diaSources, matchedTemplate, difference.getWcs(),
template=True)
# Clear the image plane for regions with NO_DATA.
# These regions are most often caused by insufficient template coverage.
# Do this for the final difference image after detection and measurement
# since the subtasks should all be configured to handle NO_DATA properly
difference.image.array[difference.mask.array & difference.mask.getPlaneBitMask('NO_DATA') > 0] = 0
measurementResults.subtractedMeasuredExposure = difference
if self.config.doMaskStreaks and self.config.writeStreakInfo:
measurementResults.maskedStreaks = streakInfo.maskedStreaks
if kernelSources is not None:
self.calculateMetrics(science, difference, diaSources, kernelSources)
if np.count_nonzero(~diaSources["sky_source"]) > 0:
measurementResults.diaSources = diaSources
elif self.config.raiseOnNoDiaSources:
raise NoDiaSourcesError()
elif len(diaSources) > 0:
# This option allows returning sky sources,
# even if there are no diaSources
measurementResults.diaSources = diaSources
self.log.info("Measured %d diaSources and %d sky sources",
np.count_nonzero(~diaSources["sky_source"]),
np.count_nonzero(diaSources["sky_source"])
)
return measurementResults
def _reorderPeaksBySignificance(self, diaSources):
Definition at line 1680 of file detectAndMeasure.py.
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