lsst.ip.diffim.detectAndMeasure Namespace Reference

Classes
class	BadSubtractionError
class	DetectAndMeasureConnections
class	NoDiaSourcesError

Variables
	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
	sources : `lsst.afw.table.SourceCatalog`
	positives : `lsst.afw.table.SourceCatalog`, optional
	negatives : `lsst.afw.table.SourceCatalog`, optional
	footprints : `lsst.afw.detection.FootprintSet`
	negative : `bool`
	diaSources : `lsst.afw.table.SourceCatalog`
	trail_parameters : `dict`
	mask : `lsst.afw.image.Mask`
	seed : `int`
	image : `lsst.afw.image.ExposureF`
	wcs : `lsst.afw.geom.SkyWcs`
	template : `bool`
	allow_list : `list` of `int`
	filterdDiaSources : `lsst.afw.table.SourceCatalog`
	streakInfo : `lsst.pipe.base.Struct`
	scoreExposure : `lsst.afw.image.ExposureF`

Variable Documentation

allow_list

lsst.ip.diffim.detectAndMeasure.allow_list : `list` of `int`

Definition at line 1124 of file detectAndMeasure.py.

diaSources

lsst.ip.diffim.detectAndMeasure.diaSources : `lsst.afw.table.SourceCatalog`

sources = afwTable.SourceCatalog(self.schema)
footprints.makeSources(sources)
if negative:
    # Invert the image so the deblender can run on positive peaks
    difference_inverted = difference.clone()
    difference_inverted.image *= -1
    self.deblend.run(exposure=difference_inverted, sources=sources)
    children = sources[sources["parent"] != 0]
    # Set the heavy footprint pixel values back to reality
    for child in children:
        footprint = child.getFootprint()
        array = footprint.getImageArray()
        array *= -1
else:
    self.deblend.run(exposure=difference, sources=sources)
self.setPrimaryFlags.run(sources)
children = sources["detect_isDeblendedSource"] == 1
sources = sources[children].copy(deep=True)
# Clear parents, so that measurement plugins behave correctly.
sources['parent'] = 0
return sources.copy(deep=True)

positives = deblend(positiveFootprints)
negatives = deblend(negativeFootprints, negative=True)

sources = afwTable.SourceCatalog(self.schema)
sources.reserve(len(positives) + len(negatives))
sources.extend(positives, deep=True)
sources.extend(negatives, deep=True)
if len(negatives) > 0:
sources[-len(negatives):]["is_negative"] = True
return sources, positives, negatives

def _removeBadSources(self, diaSources):

selector = np.ones(len(diaSources), dtype=bool)
for flag in self.config.badSourceFlags:
    flags = diaSources[flag]
    nBad = np.count_nonzero(flags)
    if nBad > 0:
        self.log.debug("Found %d unphysical sources with flag %s.", nBad, flag)
        selector &= ~flags
nBadTotal = np.count_nonzero(~selector)
self.metadata["nRemovedBadFlaggedSources"] = nBadTotal
self.log.info("Removed %d unphysical sources.", nBadTotal)
return diaSources[selector].copy(deep=True)

def _find_glint_trails(self, diaSources):

if self.config.doSkySources:
    # Do not include sky sources in glint detection
    candidateDiaSources = diaSources[~diaSources["sky_source"]].copy(deep=True)
else:
    candidateDiaSources = diaSources
trailed_glints = self.findGlints.run(candidateDiaSources)
glint_mask = [True if id in trailed_glints.trailed_ids else False for id in diaSources['id']]
if np.any(glint_mask):
    diaSources['glint_trail'] = np.array(glint_mask)

slopes = np.array([trail.slope for trail in trailed_glints.parameters])
intercepts = np.array([trail.intercept for trail in trailed_glints.parameters])
stderrs = np.array([trail.stderr for trail in trailed_glints.parameters])
lengths = np.array([trail.length for trail in trailed_glints.parameters])
angles = np.array([trail.angle for trail in trailed_glints.parameters])
parameters = {'slopes': slopes, 'intercepts': intercepts, 'stderrs': stderrs, 'lengths': lengths,
              'angles': angles}

trail_parameters = pipeBase.Struct(glintTrailInfo=parameters)

return diaSources, trail_parameters

def addSkySources(self, diaSources, mask, seed,
              subtask=None):

if subtask is None:
    subtask = self.skySources
if subtask.config.nSources <= 0:
    self.metadata[f"n_{subtask.getName()}"] = 0
    return
skySourceFootprints = subtask.run(mask=mask, seed=seed, catalog=diaSources)
self.metadata[f"n_{subtask.getName()}"] = len(skySourceFootprints)

def measureDiaSources(self, diaSources, science, difference, matchedTemplate):

# Ensure that the required mask planes are present
for mp in self.config.measurement.plugins["base_PixelFlags"].masksFpAnywhere:
    difference.mask.addMaskPlane(mp)
# Note that this may not be correct if we convolved the science image.
# In the future we may wish to persist the matchedScience image.
self.measurement.run(diaSources, difference, science, matchedTemplate)
if self.config.doApCorr:
    apCorrMap = difference.getInfo().getApCorrMap()
    if apCorrMap is None:
        self.log.warning("Difference image does not have valid aperture correction; skipping.")
    else:
        self.applyApCorr.run(
            catalog=diaSources,
            apCorrMap=apCorrMap,
        )

def measureForcedSources(self, diaSources, image, wcs, template=False):

mask = difference.mask
badPix = (mask.array & mask.getPlaneBitMask(self.config.detection.excludeMaskPlanes)) > 0
self.metadata["nGoodPixels"] = np.sum(~badPix)
self.metadata["nBadPixels"] = np.sum(badPix)
detPosPix = (mask.array & mask.getPlaneBitMask("DETECTED")) > 0
detNegPix = (mask.array & mask.getPlaneBitMask("DETECTED_NEGATIVE")) > 0
self.metadata["nPixelsDetectedPositive"] = np.sum(detPosPix)
self.metadata["nPixelsDetectedNegative"] = np.sum(detNegPix)
detPosPix &= badPix
detNegPix &= badPix
self.metadata["nBadPixelsDetectedPositive"] = np.sum(detPosPix)
self.metadata["nBadPixelsDetectedNegative"] = np.sum(detNegPix)

metricsMaskPlanes = list(mask.getMaskPlaneDict().keys())
for maskPlane in metricsMaskPlanes:
    try:
        self.metadata["%s_mask_fraction"%maskPlane.lower()] = evaluateMaskFraction(mask, maskPlane)
    except InvalidParameterError:
        self.metadata["%s_mask_fraction"%maskPlane.lower()] = -1
        self.log.info("Unable to calculate metrics for mask plane %s: not in image"%maskPlane)

if self.config.doSkySources:
    skySources = diaSources[diaSources["sky_source"]]
else:
    skySources = None
metrics = computeDifferenceImageMetrics(science, difference, kernelSources, sky_sources=skySources)

self.metadata["residualFootprintRatioMean"] = metrics.differenceFootprintRatioMean
self.metadata["residualFootprintRatioStdev"] = metrics.differenceFootprintRatioStdev
self.metadata["differenceFootprintSkyRatioMean"] = metrics.differenceFootprintSkyRatioMean
self.metadata["differenceFootprintSkyRatioStdev"] = metrics.differenceFootprintSkyRatioStdev
self.log.info("Mean, stdev of ratio of difference to science "
              "pixels in star footprints: %5.4f, %5.4f",
              self.metadata["residualFootprintRatioMean"],
              self.metadata["residualFootprintRatioStdev"])
if self.config.raiseOnBadSubtractionRatio:
    if metrics.differenceFootprintRatioMean > self.config.badSubtractionRatioThreshold:
        raise BadSubtractionError(ratio=metrics.differenceFootprintRatioMean,
                                  threshold=self.config.badSubtractionRatioThreshold)
    if metrics.differenceFootprintRatioStdev > self.config.badSubtractionVariationThreshold:
        raise BadSubtractionError(ratio=metrics.differenceFootprintRatioStdev,
                                  threshold=self.config.badSubtractionVariationThreshold)

def getSattleDiaSourceAllowlist(self, diaSources, science):

wcs = science.getWcs()
visit_info = science.getInfo().getVisitInfo()
visit_id = visit_info.getId()
sattle_uri_base = os.getenv('SATTLE_URI_BASE')

dia_sources_json = []
for source in diaSources:
    source_bbox = source.getFootprint().getBBox()
    corners = wcs.pixelToSky([lsst.geom.Point2D(c) for c in source_bbox.getCorners()])
    bbox_radec = [[pt.getRa().asDegrees(), pt.getDec().asDegrees()] for pt in corners]
    dia_sources_json.append({"diasource_id": source["id"], "bbox": bbox_radec})

payload = {"visit_id": visit_id, "detector_id": science.getDetector().getId(),
           "diasources": dia_sources_json, "historical": self.config.sattle_historical}

sattle_output = requests.put(f'{sattle_uri_base}/diasource_allow_list',
                             json=payload)

# retry once if visit cache is not populated
if sattle_output.status_code == 404:
    self.log.warning(f'Visit {visit_id} not found in sattle cache, re-sending')
    populate_sattle_visit_cache(visit_info, historical=self.config.sattle_historical)
    sattle_output = requests.put(f'{sattle_uri_base}/diasource_allow_list', json=payload)

sattle_output.raise_for_status()

return sattle_output.json()['allow_list']

def filterSatellites(self, diaSources, science):

Definition at line 900 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()

if self.config.doSubtractBackground:
    # Run background subtraction before clearing the mask planes
    detectionExposure = difference.clone()
    background = self.subtractInitialBackground.run(detectionExposure).background
else:
    detectionExposure = difference
    background = afwMath.BackgroundList()

self._prepareInputs(detectionExposure)

# 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=detectionExposure,
    doSmooth=True,
    background=background
)

if self.config.doSubtractBackground:
    # Run background subtraction again after detecting peaks
    # but before measurement
    # First update the mask using the detection image
    difference.setMask(detectionExposure.mask)
    background = self.subtractFinalBackground.run(difference).background

    # Re-run detection to get final footprints
    table = afwTable.SourceTable.make(self.schema)
    results = self.detection.run(
        table=table,
        exposure=difference,
        doSmooth=True,
        background=background
    )
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):

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))
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 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 _deblend(self, difference, positiveFootprints, negativeFootprints):

allow_list = self.getSattleDiaSourceAllowlist(diaSources, science)

if allow_list:
    allow_set = set(allow_list)
    allowed_ids = [source['id'] in allow_set for source in diaSources]
    diaSources = diaSources[np.array(allowed_ids)].copy(deep=True)
else:
    self.log.warning('Sattle allowlist is empty, all diaSources removed')
    diaSources = diaSources[0:0].copy(deep=True)
return diaSources

def _runStreakMasking(self, difference):

Definition at line 585 of file detectAndMeasure.py.

filterdDiaSources

lsst.ip.diffim.detectAndMeasure.filterdDiaSources : `lsst.afw.table.SourceCatalog`

Definition at line 1172 of file detectAndMeasure.py.

footprints

lsst.ip.diffim.detectAndMeasure.footprints : `lsst.afw.detection.FootprintSet`

def deblend(footprints, negative=False):

Definition at line 854 of file detectAndMeasure.py.

idFactory

lsst.ip.diffim.detectAndMeasure.idFactory : `lsst.afw.table.IdFactory`, optional

Definition at line 589 of file detectAndMeasure.py.

image

lsst.ip.diffim.detectAndMeasure.image : `lsst.afw.image.ExposureF`

Definition at line 1020 of file detectAndMeasure.py.

kernelSources

lsst.ip.diffim.detectAndMeasure.kernelSources : `lsst.afw.table.SourceCatalog`, optional

Definition at line 587 of file detectAndMeasure.py.

mask

lsst.ip.diffim.detectAndMeasure.mask : `lsst.afw.image.Mask`

Definition at line 969 of file detectAndMeasure.py.

matchedTemplate

lsst.ip.diffim.detectAndMeasure.matchedTemplate : `lsst.afw.image.ExposureF`

Definition at line 582 of file detectAndMeasure.py.

measurementResults

lsst.ip.diffim.detectAndMeasure.measurementResults : `lsst.pipe.base.Struct`, optional

Definition at line 593 of file detectAndMeasure.py.

negative

lsst.ip.diffim.detectAndMeasure.negative : `bool`

Definition at line 855 of file detectAndMeasure.py.

negatives

lsst.ip.diffim.detectAndMeasure.negatives : `lsst.afw.table.SourceCatalog`, optional

Definition at line 718 of file detectAndMeasure.py.

positives

lsst.ip.diffim.detectAndMeasure.positives : `lsst.afw.table.SourceCatalog`, optional

Definition at line 716 of file detectAndMeasure.py.

science

lsst.ip.diffim.detectAndMeasure.science : `lsst.afw.image.ExposureF`

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?",
    default=False,
)
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.",
)
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_edge",
             "base_PixelFlags_flag_interpolatedCenterAll",
             "base_PixelFlags_flag_badCenter",
             "base_PixelFlags_flag_edgeCenter",
             "base_PixelFlags_flag_nodataCenter",
             "base_PixelFlags_flag_saturatedCenter",
             "base_PixelFlags_flag_saturated_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"),
)
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()

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",
                                                     ]
    # 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", "SATURATED_TEMPLATE", "SPIKE"]
    self.measurement.plugins["base_PixelFlags"].masksFpCenter = [
        "STREAK", "INJECTED", "INJECTED_TEMPLATE", "HIGH_VARIANCE", "SATURATED_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")

    # 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()
    # Specify the fields that `annotate` needs below, to ensure they
    # exist, even as None.
    measurementResults = pipeBase.Struct(
        subtractedMeasuredExposure=None,
        diaSources=None,
        maskedStreaks=None,
        differenceBackground=None,
    )
    try:
        self.run(**inputs, idFactory=idFactory, measurementResults=measurementResults)
    except pipeBase.AlgorithmError as e:
        error = pipeBase.AnnotatedPartialOutputsError.annotate(
            e,
            self,
            measurementResults.subtractedMeasuredExposure,
            measurementResults.diaSources,
            measurementResults.maskedStreaks,
            log=self.log
        )
        butlerQC.put(measurementResults, outputRefs)
        raise error from e
    butlerQC.put(measurementResults, outputRefs)

@timeMethod
def run(self, science, matchedTemplate, difference, kernelSources=None,
        idFactory=None, measurementResults=None):

# 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 processResults(self, science, matchedTemplate, difference, sources, idFactory,
               kernelSources=None, positives=None, negatives=None, measurementResults=None):

# Run forced psf photometry on the image at the diaSource locations.
# Copy the measured flux and error into the diaSource.
forcedSources = self.forcedMeasurement.generateMeasCat(image, diaSources, wcs)
self.forcedMeasurement.run(forcedSources, image, diaSources, wcs)

if template:
    base_key = 'ip_diffim_forced_template_PsfFlux'
else:
    base_key = 'ip_diffim_forced_PsfFlux'
mapper = afwTable.SchemaMapper(forcedSources.schema, diaSources.schema)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFlux")[0],
                  f"{base_key}_instFlux", True)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFluxErr")[0],
                  f"{base_key}_instFluxErr", True)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_area")[0],
                  f"{base_key}_area", True)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag")[0],
                  f"{base_key}_flag", True)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_noGoodPixels")[0],
                  f"{base_key}_flag_noGoodPixels", True)
mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_edge")[0],
                  f"{base_key}_flag_edge", True)
for diaSource, forcedSource in zip(diaSources, forcedSources):
    diaSource.assign(forcedSource, mapper)

def calculateMetrics(self, science, difference, diaSources, kernelSources):

maskedImage = difference.maskedImage
# Bin the diffim to enhance low surface brightness streaks
binnedMaskedImage = afwMath.binImage(maskedImage,
                                     self.config.streakBinFactor,
                                     self.config.streakBinFactor)
binnedExposure = afwImage.ExposureF(binnedMaskedImage.getBBox())
binnedExposure.setMaskedImage(binnedMaskedImage)
# Clear the DETECTED mask plane before streak detection
binnedExposure.mask &= ~binnedExposure.mask.getPlaneBitMask('DETECTED')
# Rerun detection to set the DETECTED mask plane on binnedExposure
sigma = difference.psf.computeShape(difference.psf.getAveragePosition()).getDeterminantRadius()
_table = afwTable.SourceTable.make(afwTable.SourceTable.makeMinimalSchema())
self.streakDetection.run(table=_table, exposure=binnedExposure, doSmooth=True,
                         sigma=sigma/self.config.streakBinFactor)
binnedDetectedMaskPlane = binnedExposure.mask.array & binnedExposure.mask.getPlaneBitMask('DETECTED')
rescaledDetectedMaskPlane = binnedDetectedMaskPlane.repeat(self.config.streakBinFactor,
                                                           axis=0).repeat(self.config.streakBinFactor,
                                                                          axis=1)
# Create new version of a diffim with DETECTED based on binnedExposure
streakMaskedImage = maskedImage.clone()
ysize, xsize = rescaledDetectedMaskPlane.shape
streakMaskedImage.mask.array[:ysize, :xsize] |= rescaledDetectedMaskPlane
# Detect streaks on this new version of the diffim
streaks = self.maskStreaks.run(streakMaskedImage)
streakMaskPlane = streakMaskedImage.mask.array & streakMaskedImage.mask.getPlaneBitMask('STREAK')
# Apply the new STREAK mask to the original diffim
maskedImage.mask.array |= streakMaskPlane

if self.config.writeStreakInfo:
    rhos = np.array([line.rho for line in streaks.lines])
    thetas = np.array([line.theta for line in streaks.lines])
    sigmas = np.array([line.sigma for line in streaks.lines])
    chi2s = np.array([line.reducedChi2 for line in streaks.lines])
    modelMaximums = np.array([line.modelMaximum for line in streaks.lines])
    streakInfo = {'rho': rhos, 'theta': thetas, 'sigma': sigmas, 'reducedChi2': chi2s,
                  'modelMaximum': modelMaximums}
else:
    streakInfo = {'rho': np.array([]), 'theta': np.array([]), 'sigma': np.array([]),
                  'reducedChi2': np.array([]), 'modelMaximum': np.array([])}
return pipeBase.Struct(maskedStreaks=streakInfo)


class DetectAndMeasureScoreConnections(DetectAndMeasureConnections):
scoreExposure = pipeBase.connectionTypes.Input(
doc="Maximum likelihood image for detection.",
dimensions=("instrument", "visit", "detector"),
storageClass="ExposureF",
name="{fakesType}{coaddName}Diff_scoreExp",
)


class DetectAndMeasureScoreConfig(DetectAndMeasureConfig,
                          pipelineConnections=DetectAndMeasureScoreConnections):
pass


class DetectAndMeasureScoreTask(DetectAndMeasureTask):

ConfigClass = DetectAndMeasureScoreConfig
_DefaultName = "detectAndMeasureScore"

@timeMethod
def run(self, science, matchedTemplate, difference, scoreExposure, kernelSources,
        idFactory=None):

Definition at line 580 of file detectAndMeasure.py.

scoreExposure

lsst.ip.diffim.detectAndMeasure.scoreExposure : `lsst.afw.image.ExposureF`

Definition at line 1302 of file detectAndMeasure.py.

seed

lsst.ip.diffim.detectAndMeasure.seed : `int`

Definition at line 971 of file detectAndMeasure.py.

sources

lsst.ip.diffim.detectAndMeasure.sources : `lsst.afw.table.SourceCatalog`

Definition at line 709 of file detectAndMeasure.py.

streakInfo

lsst.ip.diffim.detectAndMeasure.streakInfo : `lsst.pipe.base.Struct`

Definition at line 1205 of file detectAndMeasure.py.

template

lsst.ip.diffim.detectAndMeasure.template : `bool`

Definition at line 1024 of file detectAndMeasure.py.

trail_parameters

lsst.ip.diffim.detectAndMeasure.trail_parameters : `dict`

Definition at line 935 of file detectAndMeasure.py.

wcs

lsst.ip.diffim.detectAndMeasure.wcs : `lsst.afw.geom.SkyWcs`

Definition at line 1022 of file detectAndMeasure.py.