class SubtractBackgroundConfig(pexConfig.Config):
"""!Config for SubtractBackgroundTask
@note Many of these fields match fields in lsst.afw.math.BackgroundControl,
the control class for lsst.afw.math.makeBackground
"""
statisticsProperty = pexConfig.ChoiceField(
doc="type of statistic to use for grid points",
dtype=str, default="MEANCLIP",
allowed={
"MEANCLIP": "clipped mean",
"MEAN": "unclipped mean",
"MEDIAN": "median",
}
)
undersampleStyle = pexConfig.ChoiceField(
doc="behaviour if there are too few points in grid for requested interpolation style",
dtype=str, default="REDUCE_INTERP_ORDER",
allowed={
"THROW_EXCEPTION": "throw an exception if there are too few points",
"REDUCE_INTERP_ORDER": "use an interpolation style with a lower order.",
"INCREASE_NXNYSAMPLE": "Increase the number of samples used to make the interpolation grid.",
},
)
binSize = pexConfig.RangeField(
doc="how large a region of the sky should be used for each background point",
dtype=int, default=128, min=1,
)
binSizeX = pexConfig.RangeField(
doc=("Sky region size to be used for each background point in X direction. "
"If 0, the binSize config is used."),
dtype=int, default=0, min=0,
)
binSizeY = pexConfig.RangeField(
doc=("Sky region size to be used for each background point in Y direction. "
"If 0, the binSize config is used."),
dtype=int, default=0, min=0,
)
algorithm = pexConfig.ChoiceField(
doc="how to interpolate the background values. This maps to an enum; see afw::math::Background",
dtype=str, default="AKIMA_SPLINE", optional=True,
allowed={
"CONSTANT": "Use a single constant value",
"LINEAR": "Use linear interpolation",
"NATURAL_SPLINE": "cubic spline with zero second derivative at endpoints",
"AKIMA_SPLINE": "higher-level nonlinear spline that is more robust to outliers",
"NONE": "No background estimation is to be attempted",
},
)
ignoredPixelMask = pexConfig.ListField(
doc="Names of mask planes to ignore while estimating the background",
dtype=str, default=["BAD", "EDGE", "DETECTED", "DETECTED_NEGATIVE", "NO_DATA", ],
itemCheck=lambda x: x in afwImage.Mask().getMaskPlaneDict().keys(),
)
isNanSafe = pexConfig.Field(
doc="Ignore NaNs when estimating the background",
dtype=bool, default=False,
)
useApprox = pexConfig.Field(
doc="Use Approximate (Chebyshev) to model background.",
dtype=bool, default=True,
)
approxOrderX = pexConfig.Field(
doc="Approximation order in X for background Chebyshev (valid only with useApprox=True)",
dtype=int, default=6,
)
# Note: Currently X- and Y-orders must be equal due to a limitation in math::Chebyshev1Function2
# The following is being added so that the weighting attribute can also be configurable for the
# call to afwMath.ApproximateControl
approxOrderY = pexConfig.Field(
doc="Approximation order in Y for background Chebyshev (valid only with useApprox=True)",
dtype=int, default=-1,
)
weighting = pexConfig.Field(
doc="Use inverse variance weighting in calculation (valid only with useApprox=True)",
dtype=bool, default=True,
)
class MatchPessimisticBConfig(pexConfig.Config):
"""Configuration for MatchPessimisticBTask
"""
numBrightStars = pexConfig.RangeField(
doc="Number of bright stars to use. Sets the max number of patterns "
"that can be tested.",
dtype=int,
default=200,
min=2,
)
minMatchedPairs = pexConfig.RangeField(
doc="Minimum number of matched pairs; see also minFracMatchedPairs.",
dtype=int,
default=30,
min=2,
)
minFracMatchedPairs = pexConfig.RangeField(
doc="Minimum number of matched pairs as a fraction of the smaller of "
"the number of reference stars or the number of good sources; "
"the actual minimum is the smaller of this value or "
"minMatchedPairs.",
dtype=float,
default=0.3,
min=0,
max=1,
)
matcherIterations = pexConfig.RangeField(
doc="Number of softening iterations in matcher.",
dtype=int,
default=5,
min=1,
)
maxOffsetPix = pexConfig.RangeField(
doc="Maximum allowed shift of WCS, due to matching (pixel). "
"When changing this value, the "
"LoadReferenceObjectsConfig.pixelMargin should also be updated.",
dtype=int,
default=300,
max=4000,
)
maxRotationDeg = pexConfig.RangeField(
doc="Rotation angle allowed between sources and position reference "
"objects (degrees).",
dtype=float,
default=1.0,
max=6.0,
)
numPointsForShape = pexConfig.Field(
doc="Number of points to define a shape for matching.",
dtype=int,
default=6,
)
numPointsForShapeAttempt = pexConfig.Field(
doc="Number of points to try for creating a shape. This value should "
"be greater than or equal to numPointsForShape. Besides "
"loosening the signal to noise cut in the 'matcher' SourceSelector, "
"increasing this number will solve CCDs where no match was found.",
dtype=int,
default=6,
)
minMatchDistPixels = pexConfig.RangeField(
doc="Distance in units of pixels to always consider a source-"
"reference pair a match. This prevents the astrometric fitter "
"from over-fitting and removing stars that should be matched and "
"allows for inclusion of new matches as the wcs improves.",
dtype=float,
default=1.0,
min=0.0,
max=6.0,
)
numPatternConsensus = pexConfig.Field(
doc="Number of implied shift/rotations from patterns that must agree "
"before it a given shift/rotation is accepted. This is only used "
"after the first softening iteration fails and if both the "
"number of reference and source objects is greater than "
"numBrightStars.",
dtype=int,
default=3,
)
numRefRequireConsensus = pexConfig.Field(
doc="If the available reference objects exceeds this number, "
"consensus/pessimistic mode will enforced regardless of the "
"number of available sources. Below this optimistic mode ("
"exit at first match rather than requiring numPatternConsensus to "
"be matched) can be used. If more sources are required to match, "
"decrease the signal to noise cut in the sourceSelector.",
dtype=int,
default=1000,
)
maxRefObjects = pexConfig.RangeField(
doc="Maximum number of reference objects to use for the matcher. The "
"absolute maximum allowed for is 2 ** 16 for memory reasons.",
dtype=int,
default=2**16,
min=0,
max=2**16 + 1,
)
def validate(self):
pexConfig.Config.validate(self)
if self.numPointsForShapeAttempt < self.numPointsForShape:
raise ValueError("numPointsForShapeAttempt must be greater than "
"or equal to numPointsForShape.")
if self.numPointsForShape > self.numBrightStars:
raise ValueError("numBrightStars must be greater than "
"numPointsForShape.")
class CharacterizeImageConfig(pipeBase.PipelineTaskConfig,
pipelineConnections=CharacterizeImageConnections
):
"""!Config for CharacterizeImageTask"""
doMeasurePsf = pexConfig.Field(
dtype=bool,
default=True,
doc=
"Measure PSF? If False then for all subsequent operations use either existing PSF "
"model when present, or install simple PSF model when not (see installSimplePsf "
"config options)")
doWrite = pexConfig.Field(
dtype=bool,
default=True,
doc="Persist results?",
)
doWriteExposure = pexConfig.Field(
dtype=bool,
default=True,
doc=
"Write icExp and icExpBackground in addition to icSrc? Ignored if doWrite False.",
)
psfIterations = pexConfig.RangeField(
dtype=int,
default=2,
min=1,
doc="Number of iterations of detect sources, measure sources, "
"estimate PSF. If useSimplePsf is True then 2 should be plenty; "
"otherwise more may be wanted.",
)
background = pexConfig.ConfigurableField(
target=SubtractBackgroundTask,
doc="Configuration for initial background estimation",
)
detection = pexConfig.ConfigurableField(target=SourceDetectionTask,
doc="Detect sources")
doDeblend = pexConfig.Field(dtype=bool,
default=True,
doc="Run deblender input exposure")
deblend = pexConfig.ConfigurableField(
target=SourceDeblendTask,
doc="Split blended source into their components")
measurement = pexConfig.ConfigurableField(
target=SingleFrameMeasurementTask, doc="Measure sources")
doApCorr = pexConfig.Field(
dtype=bool,
default=True,
doc="Run subtasks to measure and apply aperture corrections")
measureApCorr = pexConfig.ConfigurableField(
target=MeasureApCorrTask,
doc="Subtask to measure aperture corrections")
applyApCorr = pexConfig.ConfigurableField(
target=ApplyApCorrTask, doc="Subtask to apply aperture corrections")
# If doApCorr is False, and the exposure does not have apcorrections already applied, the
# active plugins in catalogCalculation almost certainly should not contain the characterization plugin
catalogCalculation = pexConfig.ConfigurableField(
target=CatalogCalculationTask,
doc="Subtask to run catalogCalculation plugins on catalog")
useSimplePsf = pexConfig.Field(
dtype=bool,
default=True,
doc=
"Replace the existing PSF model with a simplified version that has the same sigma "
"at the start of each PSF determination iteration? Doing so makes PSF determination "
"converge more robustly and quickly.",
)
installSimplePsf = pexConfig.ConfigurableField(
target=InstallGaussianPsfTask,
doc="Install a simple PSF model",
)
refObjLoader = pexConfig.ConfigurableField(
target=LoadIndexedReferenceObjectsTask,
doc="reference object loader",
)
ref_match = pexConfig.ConfigurableField(
target=RefMatchTask,
doc=
"Task to load and match reference objects. Only used if measurePsf can use matches. "
"Warning: matching will only work well if the initial WCS is accurate enough "
"to give good matches (roughly: good to 3 arcsec across the CCD).",
)
measurePsf = pexConfig.ConfigurableField(
target=MeasurePsfTask,
doc="Measure PSF",
)
repair = pexConfig.ConfigurableField(
target=RepairTask,
doc="Remove cosmic rays",
)
checkUnitsParseStrict = pexConfig.Field(
doc=
"Strictness of Astropy unit compatibility check, can be 'raise', 'warn' or 'silent'",
dtype=str,
default="raise",
)
def setDefaults(self):
super().setDefaults()
# just detect bright stars; includeThresholdMultipler=10 seems large,
# but these are the values we have been using
self.detection.thresholdValue = 5.0
self.detection.includeThresholdMultiplier = 10.0
self.detection.doTempLocalBackground = False
# do not deblend, as it makes a mess
self.doDeblend = False
# measure and apply aperture correction; note: measuring and applying aperture
# correction are disabled until the final measurement, after PSF is measured
self.doApCorr = True
# minimal set of measurements needed to determine PSF
self.measurement.plugins.names = [
"base_PixelFlags",
"base_SdssCentroid",
"base_SdssShape",
"base_GaussianFlux",
"base_PsfFlux",
"base_CircularApertureFlux",
]
def validate(self):
if self.doApCorr and not self.measurePsf:
raise RuntimeError(
"Must measure PSF to measure aperture correction, "
"because flags determined by PSF measurement are used to identify "
"sources used to measure aperture correction")
class SourceDeblendConfig(pexConf.Config):
edgeHandling = pexConf.ChoiceField(
doc=
'What to do when a peak to be deblended is close to the edge of the image',
dtype=str,
default='ramp',
allowed={
'clip':
'Clip the template at the edge AND the mirror of the edge.',
'ramp': 'Ramp down flux at the image edge by the PSF',
'noclip': 'Ignore the edge when building the symmetric template.',
})
strayFluxToPointSources = pexConf.ChoiceField(
doc='When the deblender should attribute stray flux to point sources',
dtype=str,
default='necessary',
allowed={
'necessary':
'When there is not an extended object in the footprint',
'always':
'Always',
'never':
('Never; stray flux will not be attributed to any deblended child '
'if the deblender thinks all peaks look like point sources'),
})
findStrayFlux = pexConf.Field(
dtype=bool,
default=True,
doc='Find stray flux---flux not claimed by any child in the deblender.'
)
assignStrayFlux = pexConf.Field(
dtype=bool,
default=True,
doc='Assign stray flux to deblend children. Implies findStrayFlux.')
strayFluxRule = pexConf.ChoiceField(
doc='How to split flux among peaks',
dtype=str,
default='trim',
allowed={
'r-to-peak':
'~ 1/(1+R^2) to the peak',
'r-to-footprint':
('~ 1/(1+R^2) to the closest pixel in the footprint. '
'CAUTION: this can be computationally expensive on large footprints!'
),
'nearest-footprint':
('Assign 100% to the nearest footprint (using L-1 norm aka '
'Manhattan distance)'),
'trim':
('Shrink the parent footprint to pixels that are not assigned to children'
)
})
clipStrayFluxFraction = pexConf.Field(
dtype=float,
default=0.001,
doc=('When splitting stray flux, clip fractions below '
'this value to zero.'))
psfChisq1 = pexConf.Field(
dtype=float,
default=1.5,
optional=False,
doc=('Chi-squared per DOF cut for deciding a source is '
'a PSF during deblending (un-shifted PSF model)'))
psfChisq2 = pexConf.Field(
dtype=float,
default=1.5,
optional=False,
doc=('Chi-squared per DOF cut for deciding a source is '
'PSF during deblending (shifted PSF model)'))
psfChisq2b = pexConf.Field(
dtype=float,
default=1.5,
optional=False,
doc=('Chi-squared per DOF cut for deciding a source is '
'a PSF during deblending (shifted PSF model #2)'))
maxNumberOfPeaks = pexConf.Field(
dtype=int,
default=0,
doc=("Only deblend the brightest maxNumberOfPeaks peaks in the parent"
" (<= 0: unlimited)"))
maxFootprintArea = pexConf.Field(
dtype=int,
default=1000000,
doc=("Maximum area for footprints before they are ignored as large; "
"non-positive means no threshold applied"))
maxFootprintSize = pexConf.Field(
dtype=int,
default=0,
doc=("Maximum linear dimension for footprints before they are ignored "
"as large; non-positive means no threshold applied"))
minFootprintAxisRatio = pexConf.Field(
dtype=float,
default=0.0,
doc=("Minimum axis ratio for footprints before they are ignored "
"as large; non-positive means no threshold applied"))
notDeblendedMask = pexConf.Field(
dtype=str,
default="NOT_DEBLENDED",
optional=True,
doc="Mask name for footprints not deblended, or None")
tinyFootprintSize = pexConf.RangeField(
dtype=int,
default=2,
min=2,
inclusiveMin=True,
doc=('Footprints smaller in width or height than this value will '
'be ignored; minimum of 2 due to PSF gradient calculation.'))
propagateAllPeaks = pexConf.Field(
dtype=bool,
default=False,
doc=('Guarantee that all peaks produce a child source.'))
catchFailures = pexConf.Field(
dtype=bool,
default=False,
doc=(
"If True, catch exceptions thrown by the deblender, log them, "
"and set a flag on the parent, instead of letting them propagate up"
))
maskPlanes = pexConf.ListField(
dtype=str,
default=["SAT", "INTRP", "NO_DATA"],
doc="Mask planes to ignore when performing statistics")
maskLimits = pexConf.DictField(
keytype=str,
itemtype=float,
default={},
doc=
("Mask planes with the corresponding limit on the fraction of masked pixels. "
"Sources violating this limit will not be deblended."),
)
class MatchOptimisticBConfig(pexConfig.Config):
"""Configuration for MatchOptimisticBTask
"""
maxMatchDistArcSec = pexConfig.RangeField(
doc="Maximum separation between reference objects and sources "
"beyond which they will not be considered a match (arcsec)",
dtype=float,
default=3,
min=0,
)
numBrightStars = pexConfig.RangeField(
doc="Number of bright stars to use",
dtype=int,
default=50,
min=2,
)
minMatchedPairs = pexConfig.RangeField(
doc="Minimum number of matched pairs; see also minFracMatchedPairs",
dtype=int,
default=30,
min=2,
)
minFracMatchedPairs = pexConfig.RangeField(
doc="Minimum number of matched pairs as a fraction of the smaller of "
"the number of reference stars or the number of good sources; "
"the actual minimum is the smaller of this value or minMatchedPairs",
dtype=float,
default=0.3,
min=0,
max=1,
)
maxOffsetPix = pexConfig.RangeField(
doc="Maximum allowed shift of WCS, due to matching (pixel). "
"When changing this value, the LoadReferenceObjectsConfig.pixelMargin should also be updated.",
dtype=int,
default=300,
max=4000,
)
maxRotationDeg = pexConfig.RangeField(
doc=
"Rotation angle allowed between sources and position reference objects (degrees)",
dtype=float,
default=1.0,
max=6.0,
)
allowedNonperpDeg = pexConfig.RangeField(
doc="Allowed non-perpendicularity of x and y (degree)",
dtype=float,
default=3.0,
max=45.0,
)
numPointsForShape = pexConfig.Field(
doc="number of points to define a shape for matching",
dtype=int,
default=6,
)
maxDeterminant = pexConfig.Field(
doc=
"maximum determinant of linear transformation matrix for a usable solution",
dtype=float,
default=0.02,
)
class MatchBackgroundsConfig(pexConfig.Config):
usePolynomial = pexConfig.Field(
dtype=bool,
doc="Fit background difference with Chebychev polynomial interpolation "
"(using afw.math.Approximate)? If False, fit with spline interpolation using afw.math.Background",
default=False)
order = pexConfig.Field(
dtype=int,
doc=
"Order of Chebyshev polynomial background model. Ignored if usePolynomial False",
default=8)
badMaskPlanes = pexConfig.ListField(
doc="Names of mask planes to ignore while estimating the background",
dtype=str,
default=[
"NO_DATA", "DETECTED", "DETECTED_NEGATIVE", "SAT", "BAD", "INTRP",
"CR"
],
itemCheck=lambda x: x in afwImage.Mask().getMaskPlaneDict(),
)
gridStatistic = pexConfig.ChoiceField(
dtype=str,
doc="Type of statistic to estimate pixel value for the grid points",
default="MEAN",
allowed={
"MEAN": "mean",
"MEDIAN": "median",
"MEANCLIP": "clipped mean"
})
undersampleStyle = pexConfig.ChoiceField(
doc=
"Behaviour if there are too few points in grid for requested interpolation style. "
"Note: INCREASE_NXNYSAMPLE only allowed for usePolynomial=True.",
dtype=str,
default="REDUCE_INTERP_ORDER",
allowed={
"THROW_EXCEPTION":
"throw an exception if there are too few points",
"REDUCE_INTERP_ORDER":
"use an interpolation style with a lower order.",
"INCREASE_NXNYSAMPLE":
"Increase the number of samples used to make the interpolation grid.",
})
binSize = pexConfig.Field(
doc=
"Bin size for gridding the difference image and fitting a spatial model",
dtype=int,
default=256)
interpStyle = pexConfig.ChoiceField(
dtype=str,
doc=
"Algorithm to interpolate the background values; ignored if usePolynomial is True"
"Maps to an enum; see afw.math.Background",
default="AKIMA_SPLINE",
allowed={
"CONSTANT": "Use a single constant value",
"LINEAR": "Use linear interpolation",
"NATURAL_SPLINE":
"cubic spline with zero second derivative at endpoints",
"AKIMA_SPLINE":
"higher-level nonlinear spline that is more robust to outliers",
"NONE": "No background estimation is to be attempted",
})
numSigmaClip = pexConfig.Field(
dtype=int,
doc=
"Sigma for outlier rejection; ignored if gridStatistic != 'MEANCLIP'.",
default=3)
numIter = pexConfig.Field(
dtype=int,
doc=
"Number of iterations of outlier rejection; ignored if gridStatistic != 'MEANCLIP'.",
default=2)
bestRefWeightCoverage = pexConfig.RangeField(
dtype=float,
doc=
"Weight given to coverage (number of pixels that overlap with patch), "
"when calculating best reference exposure. Higher weight prefers exposures with high coverage."
"Ignored when reference visit is supplied",
default=0.4,
min=0.,
max=1.)
bestRefWeightVariance = pexConfig.RangeField(
dtype=float,
doc=
"Weight given to image variance when calculating best reference exposure. "
"Higher weight prefers exposures with low image variance. Ignored when reference visit is supplied",
default=0.4,
min=0.,
max=1.)
bestRefWeightLevel = pexConfig.RangeField(
dtype=float,
doc=
"Weight given to mean background level when calculating best reference exposure. "
"Higher weight prefers exposures with low mean background level. "
"Ignored when reference visit is supplied.",
default=0.2,
min=0.,
max=1.)
approxWeighting = pexConfig.Field(
dtype=bool,
doc=
("Use inverse-variance weighting when approximating background offset model? "
"This will fail when the background offset is constant "
"(this is usually only the case in testing with artificial images)."
"(usePolynomial=True)"),
default=True,
)
gridStdevEpsilon = pexConfig.RangeField(
dtype=float,
doc=
"Tolerance on almost zero standard deviation in a background-offset grid bin. "
"If all bins have a standard deviation below this value, the background offset model "
"is approximated without inverse-variance weighting. (usePolynomial=True)",
default=1e-8,
min=0.)
class PhotoCalConfig(pexConf.Config):
"""Config for PhotoCal"""
magLimit = pexConf.Field(
dtype = float,
default = 22.0,
doc = "Don't use objects fainter than this magnitude",
)
doWriteOutput = pexConf.Field(
dtype = bool,
default = True,
doc = "Write a field name astrom_usedByPhotoCal to the schema",
)
fluxField = pexConf.Field(
dtype = str,
default = "slot_CalibFlux_flux",
doc = ("Name of the source flux field to use. The associated flag field\n"
"('<name>_flags') will be implicitly included in badFlags."),
)
applyColorTerms = pexConf.Field(
dtype = bool,
default = None,
doc = ("Apply photometric color terms to reference stars? One of:\n"
"None: apply if colorterms and photoCatName are not None;\n"
" fail if color term data is not available for the specified ref catalog and filter.\n"
"True: always apply colorterms; fail if color term data is not available for the\n"
" specified reference catalog and filter.\n"
"False: do not apply."),
optional = True,
)
goodFlags = pexConf.ListField(
dtype = str,
default = [],
doc = "List of source flag fields that must be set for a source to be used.",
)
badFlags = pexConf.ListField(
dtype = str,
default = ["base_PixelFlags_flag_edge", "base_PixelFlags_flag_interpolated",
"base_PixelFlags_flag_saturated"],
doc = "List of source flag fields that will cause a source to be rejected when they are set.",
)
sigmaMax = pexConf.Field(
dtype = float,
default = 0.25,
doc = "maximum sigma to use when clipping",
optional = True,
)
nSigma = pexConf.Field(
dtype = float,
default = 3.0,
doc = "clip at nSigma",
)
useMedian = pexConf.Field(
dtype = bool,
default = True,
doc = "use median instead of mean to compute zeropoint",
)
nIter = pexConf.Field(
dtype = int,
default = 20,
doc = "number of iterations",
)
colorterms = pexConf.ConfigField(
dtype = ColortermLibrary,
doc = "Library of photometric reference catalog name: color term dict",
)
photoCatName = pexConf.Field(
dtype = str,
optional = True,
doc = ("Name of photometric reference catalog; used to select a color term dict in colorterms."
" see also applyColorTerms"),
)
magErrFloor = pexConf.RangeField(
dtype = float,
default = 0.0,
doc = "Additional magnitude uncertainty to be added in quadrature with measurement errors.",
min = 0.0,
)
def validate(self):
pexConf.Config.validate(self)
if self.applyColorTerms and self.photoCatName is None:
raise RuntimeError("applyColorTerms=True requires photoCatName is non-None")
if self.applyColorTerms and len(self.colorterms.data) == 0:
raise RuntimeError("applyColorTerms=True requires colorterms be provided")
class SourceDetectionConfig(pexConfig.Config):
"""!Configuration parameters for the SourceDetectionTask
"""
minPixels = pexConfig.RangeField(
doc=
"detected sources with fewer than the specified number of pixels will be ignored",
dtype=int,
optional=False,
default=1,
min=0,
)
isotropicGrow = pexConfig.Field(
doc="Pixels should be grown as isotropically as possible (slower)",
dtype=bool,
optional=False,
default=False,
)
combinedGrow = pexConfig.Field(
doc=
"Grow all footprints at the same time? This allows disconnected footprints to merge.",
dtype=bool,
default=True,
)
nSigmaToGrow = pexConfig.Field(
doc=
"Grow detections by nSigmaToGrow * [PSF RMS width]; if 0 then do not grow",
dtype=float,
default=2.4, # 2.4 pixels/sigma is roughly one pixel/FWHM
)
returnOriginalFootprints = pexConfig.Field(
doc=
"Grow detections to set the image mask bits, but return the original (not-grown) footprints",
dtype=bool,
optional=False,
default=False,
)
thresholdValue = pexConfig.RangeField(
doc=
"Threshold for footprints; exact meaning and units depend on thresholdType.",
dtype=float,
optional=False,
default=5.0,
min=0.0,
)
includeThresholdMultiplier = pexConfig.RangeField(
doc="Include threshold relative to thresholdValue",
dtype=float,
default=1.0,
min=0.0,
)
thresholdType = pexConfig.ChoiceField(
doc="specifies the desired flavor of Threshold",
dtype=str,
optional=False,
default="stdev",
allowed={
"variance": "threshold applied to image variance",
"stdev": "threshold applied to image std deviation",
"value": "threshold applied to image value",
"pixel_stdev": "threshold applied to per-pixel std deviation",
},
)
thresholdPolarity = pexConfig.ChoiceField(
doc=
"specifies whether to detect positive, or negative sources, or both",
dtype=str,
optional=False,
default="positive",
allowed={
"positive": "detect only positive sources",
"negative": "detect only negative sources",
"both": "detect both positive and negative sources",
},
)
adjustBackground = pexConfig.Field(
dtype=float,
doc="Fiddle factor to add to the background; debugging only",
default=0.0,
)
reEstimateBackground = pexConfig.Field(
dtype=bool,
doc="Estimate the background again after final source detection?",
default=True,
optional=False,
)
background = pexConfig.ConfigurableField(
doc="Background re-estimation; ignored if reEstimateBackground false",
target=SubtractBackgroundTask,
)
tempLocalBackground = pexConfig.ConfigurableField(
doc=
("A local (small-scale), temporary background estimation step run between "
"detecting above-threshold regions and detecting the peaks within "
"them; used to avoid detecting spuerious peaks in the wings."),
target=SubtractBackgroundTask,
)
doTempLocalBackground = pexConfig.Field(
dtype=bool,
doc=
"Enable temporary local background subtraction? (see tempLocalBackground)",
default=True,
)
tempWideBackground = pexConfig.ConfigurableField(
doc=
("A wide (large-scale) background estimation and removal before footprint and peak detection. "
"It is added back into the image after detection. The purpose is to suppress very large "
"footprints (e.g., from large artifacts) that the deblender may choke on."
),
target=SubtractBackgroundTask,
)
doTempWideBackground = pexConfig.Field(
dtype=bool,
doc=
"Do temporary wide (large-scale) background subtraction before footprint detection?",
default=False,
)
nPeaksMaxSimple = pexConfig.Field(
dtype=int,
doc=("The maximum number of peaks in a Footprint before trying to "
"replace its peaks using the temporary local background"),
default=1,
)
nSigmaForKernel = pexConfig.Field(
dtype=float,
doc=
("Multiple of PSF RMS size to use for convolution kernel bounding box size; "
"note that this is not a half-size. The size will be rounded up to the nearest odd integer"
),
default=7.0,
)
statsMask = pexConfig.ListField(
dtype=str,
doc=
"Mask planes to ignore when calculating statistics of image (for thresholdType=stdev)",
default=['BAD', 'SAT', 'EDGE', 'NO_DATA'],
)
def setDefaults(self):
self.tempLocalBackground.binSize = 64
self.tempLocalBackground.algorithm = "AKIMA_SPLINE"
self.tempLocalBackground.useApprox = False
# Background subtraction to remove a large-scale background (e.g., scattered light); restored later.
# Want to keep it from exceeding the deblender size limit of 1 Mpix, so half that is reasonable.
self.tempWideBackground.binSize = 512
self.tempWideBackground.algorithm = "AKIMA_SPLINE"
self.tempWideBackground.useApprox = False
# Ensure we can remove even bright scattered light that is DETECTED
for maskPlane in ("DETECTED", "DETECTED_NEGATIVE"):
if maskPlane in self.tempWideBackground.ignoredPixelMask:
self.tempWideBackground.ignoredPixelMask.remove(maskPlane)