class MakeWarpConfig(pipeBase.PipelineTaskConfig, MakeCoaddTempExpConfig):
calExpList = pipeBase.InputDatasetField(
doc=
"Input exposures to be resampled and optionally PSF-matched onto a SkyMap projection/patch",
name="calexp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"))
backgroundList = pipeBase.InputDatasetField(
doc=
"Input backgrounds to be added back into the calexp if bgSubtracted=False",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"))
skyCorrList = pipeBase.InputDatasetField(
doc=
"Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
name="skyCorr",
storageClass="Background",
dimensions=("instrument", "visit", "detector"))
skyMap = pipeBase.InputDatasetField(
doc=
"Input definition of geometry/bbox and projection/wcs for warped exposures",
nameTemplate="{coaddName}Coadd_skyMap",
storageClass="SkyMap",
dimensions=("skymap", ),
scalar=True)
direct = pipeBase.OutputDatasetField(doc=(
"Output direct warped exposure (previously called CoaddTempExp), produced by resampling ",
"calexps onto the skyMap patch geometry."),
nameTemplate=
"{coaddName}Coadd_directWarp",
storageClass="ExposureF",
dimensions=("tract", "patch",
"skymap", "visit",
"instrument"),
scalar=True)
psfMatched = pipeBase.OutputDatasetField(doc=(
"Output PSF-Matched warped exposure (previously called CoaddTempExp), produced by resampling ",
"calexps onto the skyMap patch geometry and PSF-matching to a model PSF."
),
nameTemplate=
"{coaddName}Coadd_psfMatchedWarp",
storageClass="ExposureF",
dimensions=("tract", "patch",
"skymap", "visit",
"instrument"),
scalar=True)
def setDefaults(self):
super().setDefaults()
self.formatTemplateNames({"coaddName": "deep"})
self.quantum.dimensions = ("tract", "patch", "skymap", "visit")
def validate(self):
super().validate()
# TODO: Remove this constraint after DM-17062
if self.doApplyUberCal:
raise RuntimeError(
"Gen3 MakeWarpTask cannot apply meas_mosaic or jointcal results."
"Please set doApplyUbercal=False.")
class AddConfig(pipeBase.PipelineTaskConfig):
addend = pexConfig.Field(doc="amount to add", dtype=int, default=3)
input = pipeBase.InputDatasetField(name="add_input",
dimensions=["instrument", "visit"],
storageClass="Catalog",
doc="Input dataset type for this task")
output = pipeBase.OutputDatasetField(
name="add_output",
dimensions=["instrument", "visit"],
storageClass="Catalog",
doc="Output dataset type for this task")
def setDefaults(self):
# set dimensions of a quantum, this task uses per-visit quanta and it
# expects dataset dimensions to be the same
self.quantum.dimensions = ["instrument", "visit"]
self.quantum.sql = None
class ConfigWithDatasets(pexConfig.Config):
input1 = pipeBase.InputDatasetField(name="in1",
dimensions=["label"],
storageClass="SCA",
doc="")
input2 = pipeBase.InputDatasetField(
name="in2",
dimensions=["label", "abstract_filter"],
storageClass="SCB",
scalar=True,
doc="")
output = pipeBase.OutputDatasetField(
name="out",
dimensions=["abstract_filter", "skypix"],
storageClass="SCC",
scalar=False,
doc="")
initInput = pipeBase.InitInputDatasetField(name="init_input",
storageClass="SCX",
doc="")
initOutput = pipeBase.InitOutputDatasetField(name="init_output",
storageClass="SCY",
doc="")
class CharacterizeImageConfig(pipeBase.PipelineTaskConfig):
# Gen3 IO options
exposure = pipeBase.InputDatasetField(
doc="Input exposure data",
name="postISRCCD",
scalar=True,
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
characterized = pipeBase.OutputDatasetField(
doc="Output characterized data.",
name="icExp",
scalar=True,
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
sourceCat = pipeBase.OutputDatasetField(
doc="Output source catalog.",
name="icSrc",
scalar=True,
storageClass="SourceCatalog",
dimensions=["instrument", "visit", "detector"],
)
backgroundModel = pipeBase.OutputDatasetField(
doc="Output background model.",
name="icExpBackground",
scalar=True,
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
outputSchema = pipeBase.InitOutputDatasetField(
doc="Schema of the catalog produced by CharacterizeImage",
name="icSrc_schema",
storageClass="SourceCatalog",
)
"""!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
# 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",
]
self.quantum.dimensions = ("instrument", "visit", "detector")
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 InsertFakesConfig(PipelineTaskConfig):
"""Config for inserting fake sources
Notes
-----
The default column names are those from the University of Washington sims database.
"""
raColName = pexConfig.Field(
doc="RA column name used in the fake source catalog.",
dtype=str,
default="raJ2000",
)
decColName = pexConfig.Field(
doc="Dec. column name used in the fake source catalog.",
dtype=str,
default="decJ2000",
)
doCleanCat = pexConfig.Field(
doc="If true removes bad sources from the catalog.",
dtype=bool,
default=True,
)
diskHLR = pexConfig.Field(
doc=
"Column name for the disk half light radius used in the fake source catalog.",
dtype=str,
default="DiskHalfLightRadius",
)
bulgeHLR = pexConfig.Field(
doc=
"Column name for the bulge half light radius used in the fake source catalog.",
dtype=str,
default="BulgeHalfLightRadius",
)
magVar = pexConfig.Field(
doc=
"The column name for the magnitude calculated taking variability into account. In the format "
"``filter name``magVar, e.g. imagVar for the magnitude in the i band.",
dtype=str,
default="%smagVar",
)
nDisk = pexConfig.Field(
doc=
"The column name for the sersic index of the disk component used in the fake source catalog.",
dtype=str,
default="disk_n",
)
nBulge = pexConfig.Field(
doc=
"The column name for the sersic index of the bulge component used in the fake source catalog.",
dtype=str,
default="bulge_n",
)
aDisk = pexConfig.Field(
doc=
"The column name for the semi major axis length of the disk component used in the fake source"
"catalog.",
dtype=str,
default="a_d",
)
aBulge = pexConfig.Field(
doc=
"The column name for the semi major axis length of the bulge component.",
dtype=str,
default="a_b",
)
bDisk = pexConfig.Field(
doc=
"The column name for the semi minor axis length of the disk component.",
dtype=str,
default="b_d",
)
bBulge = pexConfig.Field(
doc=
"The column name for the semi minor axis length of the bulge component used in the fake source "
"catalog.",
dtype=str,
default="b_b",
)
paDisk = pexConfig.Field(
doc=
"The column name for the PA of the disk component used in the fake source catalog.",
dtype=str,
default="pa_disk",
)
paBulge = pexConfig.Field(
doc=
"The column name for the PA of the bulge component used in the fake source catalog.",
dtype=str,
default="pa_bulge",
)
fakeType = pexConfig.Field(
doc=
"What type of fake catalog to use, snapshot (includes variability in the magnitudes calculated "
"from the MJD of the image), static (no variability) or filename for a user defined fits"
"catalog.",
dtype=str,
default="static",
)
calibFluxRadius = pexConfig.Field(
doc="Radius for the calib flux (in pixels).",
dtype=float,
default=12.0,
)
image = pipeBase.InputDatasetField(
doc="Image into which fakes are to be added.",
nameTemplate="{CoaddName}Coadd",
scalar=True,
storageClass="ExposureF",
dimensions=("Tract", "Patch", "AbstractFilter", "SkyMap"))
fakeCat = pipeBase.InputDatasetField(
doc="Catalog of fake sources to draw inputs from.",
nameTemplate="{CoaddName}Coadd_fakeSourceCat",
scalar=True,
storageClass="Parquet",
dimensions=("Tract", "SkyMap"))
imageWithFakes = pipeBase.OutputDatasetField(
doc="Image with fake sources added.",
nameTemplate="fakes_{CoaddName}Coadd",
scalar=True,
storageClass="ExposureF",
dimensions=("Tract", "Patch", "AbstractFilter", "SkyMap"))
coaddName = pexConfig.Field(
doc="The name of the type of coadd used",
dtype=str,
default="deep",
)
def setDefaults(self):
super().setDefaults()
self.quantum.dimensions = ("Tract", "Patch", "AbstractFilter",
"SkyMap")
self.formatTemplateNames({"CoaddName": "deep"})
class SkyCorrectionConfig(pipeBase.PipelineTaskConfig, Config):
"""Configuration for SkyCorrectionTask"""
rawLinker = pipeBase.InputDatasetField(
doc=
"Raw data to provide exp-visit linkage to connect calExp inputs to camera/sky calibs.",
name="raw",
scalar=False,
storageClass="ExposureU",
dimensions=["instrument", "exposure", "detector"],
)
calExpArray = pipeBase.InputDatasetField(
doc="Input exposures to process",
name="calexp",
scalar=False,
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
calBkgArray = pipeBase.InputDatasetField(
doc="Input background files to use",
name="calexpBackground",
scalar=False,
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
camera = pipeBase.InputDatasetField(
doc="Input camera to use.",
name="camera",
scalar=True,
storageClass="TablePersistableCamera",
dimensions=["instrument", "calibration_label"],
)
skyCalibs = pipeBase.InputDatasetField(
doc="Input sky calibrations to use.",
name="sky",
scalar=False,
storageClass="ExposureF",
dimensions=[
"instrument", "physical_filter", "detector", "calibration_label"
],
)
calExpCamera = pipeBase.OutputDatasetField(
doc="Output camera image.",
name='calexp_camera',
scalar=True,
storageClass="ImageF",
dimensions=["instrument", "visit"],
)
skyCorr = pipeBase.OutputDatasetField(
doc="Output sky corrected images.",
name='skyCorr',
scalar=False,
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
bgModel = ConfigField(dtype=FocalPlaneBackgroundConfig,
doc="Background model")
bgModel2 = ConfigField(dtype=FocalPlaneBackgroundConfig,
doc="2nd Background model")
sky = ConfigurableField(target=SkyMeasurementTask, doc="Sky measurement")
maskObjects = ConfigurableField(target=MaskObjectsTask, doc="Mask Objects")
doMaskObjects = Field(dtype=bool,
default=True,
doc="Mask objects to find good sky?")
doBgModel = Field(dtype=bool,
default=True,
doc="Do background model subtraction?")
doBgModel2 = Field(dtype=bool,
default=True,
doc="Do cleanup background model subtraction?")
doSky = Field(dtype=bool, default=True, doc="Do sky frame subtraction?")
binning = Field(dtype=int,
default=8,
doc="Binning factor for constructing focal-plane images")
calexpType = Field(
dtype=str,
default="calexp",
doc=
"Should be set to fakes_calexp if you want to process calexps with fakes in."
)
def setDefaults(self):
Config.setDefaults(self)
self.bgModel2.doSmooth = True
self.bgModel2.minFrac = 0.5
self.bgModel2.xSize = 256
self.bgModel2.ySize = 256
self.bgModel2.smoothScale = 1.0
self.quantum.dimensions = ("instrument", "visit")
class ProcessCcdWithFakesConfig(PipelineTaskConfig):
"""Config for inserting fake sources
Notes
-----
The default column names are those from the UW sims database.
"""
useUpdatedCalibs = pexConfig.Field(
doc="Use updated calibs and wcs from jointcal?",
dtype=bool,
default=False,
)
exposure = pipeBase.InputDatasetField(
doc="Exposure into which fakes are to be added.",
name="calexp",
scalar=True,
storageClass="ExposureF",
dimensions=("Instrument", "Visit", "Detector"))
fakeCat = pipeBase.InputDatasetField(
doc="Catalog of fake sources to draw inputs from.",
nameTemplate="{CoaddName}Coadd_fakeSourceCat",
scalar=True,
storageClass="Parquet",
dimensions=("Tract", "SkyMap"))
wcs = pipeBase.InputDatasetField(
doc="WCS information for the input exposure.",
name="jointcal_wcs",
scalar=True,
storageClass="TablePersistableWcs",
dimensions=("Tract", "SkyMap", "Instrument", "Visit", "Detector"))
photoCalib = pipeBase.InputDatasetField(
doc="Calib information for the input exposure.",
name="jointcal_photoCalib",
scalar=True,
storageClass="TablePersistablePhotoCalib",
dimensions=("Tract", "SkyMap", "Instrument", "Visit", "Detector"))
outputExposure = pipeBase.OutputDatasetField(
doc="Exposure with fake sources added.",
name="fakes_calexp",
scalar=True,
storageClass="ExposureF",
dimensions=("Instrument", "Visit", "Detector"))
outputCat = pipeBase.OutputDatasetField(
doc=
"Source catalog produced in calibrate task with fakes also measured.",
name="src",
storageClass="SourceCatalog",
dimensions=("Instrument", "Visit", "Detector"),
scalar=True)
coaddName = pexConfig.Field(
doc="The name of the type of coadd used",
dtype=str,
default="deep",
)
insertFakes = pexConfig.ConfigurableField(
target=InsertFakesTask, doc="Configuration for the fake sources")
detection = pexConfig.ConfigurableField(target=SourceDetectionTask,
doc="The detection task to use.")
deblend = pexConfig.ConfigurableField(target=SourceDeblendTask,
doc="The deblending task to use.")
measurement = pexConfig.ConfigurableField(
target=SingleFrameMeasurementTask, doc="The measurement task to use")
applyApCorr = pexConfig.ConfigurableField(
target=ApplyApCorrTask,
doc="The apply aperture correction task to use.")
catalogCalculation = pexConfig.ConfigurableField(
target=CatalogCalculationTask,
doc="The catalog calculation ask to use.")
def setDefaults(self):
self.detection.reEstimateBackground = False
super().setDefaults()
self.quantum.dimensions = ("Instrument", "Visit", "Detector")
self.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append(
"FAKE")
self.measurement.plugins["base_PixelFlags"].masksFpCenter.append(
"FAKE")
class MergeMeasurementsConfig(pipeBase.PipelineTaskConfig):
"""!
@anchor MergeMeasurementsConfig_
@brief Configuration parameters for the MergeMeasurementsTask
"""
# Gen 3 options
inputSchema = pipeBase.InitInputDatasetField(
doc="Schema for the input measurement catalogs.",
nameTemplate="{inputCoaddName}Coadd_meas_schema",
storageClass="SourceCatalog",
)
outputSchema = pipeBase.InitOutputDatasetField(
doc="Schema for the output merged measurement catalog.",
nameTemplate="{outputCoaddName}Coadd_ref_schema",
storageClass="SourceCatalog",
)
catalogs = pipeBase.InputDatasetField(
doc="Input catalogs to merge.",
nameTemplate="{inputCoaddName}Coadd_meas",
scalar=False,
storageClass="SourceCatalog",
dimensions=["abstract_filter", "skymap", "tract", "patch"],
)
mergedCatalog = pipeBase.OutputDatasetField(
doc="Output merged catalog.",
nameTemplate="{outputCoaddName}Coadd_ref",
scalar=True,
storageClass="SourceCatalog",
dimensions=["skymap", "tract", "patch"],
)
# Task configuration options
pseudoFilterList = pexConfig.ListField(
dtype=str,
default=["sky"],
doc="Names of filters which may have no associated detection\n"
"(N.b. should include MergeDetectionsConfig.skyFilterName)"
)
snName = pexConfig.Field(
dtype=str,
default="base_PsfFlux",
doc="Name of flux measurement for calculating the S/N when choosing the reference band."
)
minSN = pexConfig.Field(
dtype=float,
default=10.,
doc="If the S/N from the priority band is below this value (and the S/N "
"is larger than minSNDiff compared to the priority band), use the band with "
"the largest S/N as the reference band."
)
minSNDiff = pexConfig.Field(
dtype=float,
default=3.,
doc="If the difference in S/N between another band and the priority band is larger "
"than this value (and the S/N in the priority band is less than minSN) "
"use the band with the largest S/N as the reference band"
)
flags = pexConfig.ListField(
dtype=str,
doc="Require that these flags, if available, are not set",
default=["base_PixelFlags_flag_interpolatedCenter", "base_PsfFlux_flag",
"ext_photometryKron_KronFlux_flag", "modelfit_CModel_flag", ]
)
priorityList = pexConfig.ListField(
dtype=str,
default=[],
doc="Priority-ordered list of bands for the merge."
)
coaddName = pexConfig.Field(
dtype=str,
default="deep",
doc="Name of coadd"
)
def validate(self):
super().validate()
if len(self.priorityList) == 0:
raise RuntimeError("No priority list provided")
def setDefaults(self):
super().setDefaults()
self.formatTemplateNames({"inputCoaddName": "deep",
"outputCoaddName": "deep"})
self.quantum.dimensions = ("skymap", "tract", "patch")