class DeblendCoaddSourcesMultiConfig(DeblendCoaddSourcesBaseConfig):
multibandDeblend = ConfigurableField(
target=MultibandDeblendTask,
doc="Task to deblend an images in multiple bands"
)
coadds = InputDatasetField(
doc="Exposure on which to run deblending",
nameTemplate="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
outputSchema = InitOutputDatasetField(
doc="Output of the schema used in deblending task",
nameTemplate="{outputCoaddName}Coadd_deblendedModel_schema",
storageClass="SourceCatalog"
)
fluxCatalogs = OutputDatasetField(
doc="Flux catalogs produced by multiband deblending, not written "
"if conserve flux is turned off",
nameTemplate="{outputCoaddName}Coadd_deblendedFlux",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
templateCatalogs = OutputDatasetField(
doc="Template catalogs produced by multiband deblending",
nameTemplate="{outputCoaddName}Coadd_deblendedModel",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
def setDefaults(self):
super().setDefaults()
self.quantum.dimensions = ("tract", "patch", "skymap")
class PatchSkyMapTaskConfig(PipelineTaskConfig):
coadd = InputDatasetField(
name="deepCoadd_calexp",
dimensions=["SkyMap", "Tract", "Patch", "AbstractFilter"],
storageClass="ExposureF",
scalar=True,
doc="DatasetType for the input image")
inputCatalog = InputDatasetField(
name="deepCoadd_mergeDet",
dimensions=["SkyMap", "Tract", "Patch"],
storageClass="SourceCatalog",
scalar=True,
doc="DatasetType for the input catalog (merged detections).")
outputCatalog = OutputDatasetField(
name="deepCoadd_meas",
dimensions=["SkyMap", "Tract", "Patch", "AbstractFilter"],
storageClass="SourceCatalog",
scalar=True,
doc=("DatasetType for the output catalog "
"(deblended per-band measurements)"))
def setDefaults(self):
# set dimensions of a quantum, this task uses per-tract-patch-filter quanta
self.quantum.dimensions = [
"SkyMap", "Tract", "Patch", "AbstractFilter"
]
class ExamplePipelineTaskConfig(PipelineTaskConfig):
input1 = InputDatasetField(name="",
dimensions=[],
storageClass="example",
doc="Input for this task")
input2 = InputDatasetField(name="",
dimensions=[],
storageClass="example",
doc="Input for this task")
output1 = OutputDatasetField(name="",
dimensions=[],
storageClass="example",
doc="Output for this task")
output2 = OutputDatasetField(name="",
dimensions=[],
storageClass="example",
doc="Output for this task")
class DummyPipelineTaskConfig(PipelineTaskConfig):
input = InputDatasetField(name="raw",
units=["Camera", "Exposure", "Sensor"],
storageClass="DecoratedImageU",
scalar=True,
doc="Input dataset type")
output = OutputDatasetField(name="modified_raw",
units=["Camera", "Exposure", "Sensor"],
storageClass="DecoratedImageU",
scalar=True,
doc="Output dataset type")
def setDefaults(self):
self.quantum.units = ["Camera", "Exposure", "Sensor"]
class RawToCalexpTaskConfig(PipelineTaskConfig):
input = InputDatasetField(name="raw",
dimensions=["Instrument", "Exposure", "Detector"],
storageClass="ExposureU",
doc="Input dataset type for this task")
output = OutputDatasetField(name="calexp",
dimensions=["Instrument", "Visit", "Detector"],
storageClass="ExposureF",
scalar=True,
doc="Output dataset type for this task")
def setDefaults(self):
# set dimensions of a quantum, this task uses per-visit-detector quanta
self.quantum.dimensions = ["Instrument", "Visit", "Detector"]
class Test2Config(PipelineTaskConfig):
input = InputDatasetField(name="input",
dimensions=["Instrument", "Visit"],
storageClass="example",
doc="Input dataset type for this task")
output = OutputDatasetField(name="output",
dimensions=["Tract", "Patch"],
storageClass="example",
scalar=True,
doc="Output dataset type for this task")
def setDefaults(self):
# this task combines all selected visits into a tract/patch, on
# input it expects per-visit data, on output it produces per-patch.
# Combining visits "destroys" Visit dimension in a quantum.
self.quantum.dimensions = ["Instrument", "Tract", "Patch"]
class Test1Config(PipelineTaskConfig):
input = InputDatasetField(name="input",
dimensions=["Instrument", "Visit"],
storageClass="example",
scalar=True,
doc="Input dataset type for this task")
output = OutputDatasetField(name="output",
dimensions=["Instrument", "Visit"],
storageClass="example",
scalar=True,
doc="Output dataset type for this task")
def setDefaults(self):
# set dimensions of a quantum, this task uses per-visit quanta and it
# expects datset dimensions to be the same
self.quantum.dimensions = ["Instrument", "Visit"]
class CalexpToCoaddTaskConfig(PipelineTaskConfig):
calexp = InputDatasetField(name="calexp",
dimensions=["Instrument", "Visit", "Detector"],
storageClass="ExposureF",
doc="DatasetType for the input image")
coadd = OutputDatasetField(
name="deepCoadd_calexp",
dimensions=["SkyMap", "Tract", "Patch", "AbstractFilter"],
storageClass="ExposureF",
scalar=True,
doc="DatasetType for the output image")
def setDefaults(self):
# set dimensions of a quantum, this task uses per-tract-patch-filter quanta
self.quantum.dimensions = [
"SkyMap", "Tract", "Patch", "AbstractFilter"
]
class DeblendCoaddSourcesSingleConfig(DeblendCoaddSourcesBaseConfig):
singleBandDeblend = ConfigurableField(
target=SourceDeblendTask,
doc="Task to deblend an image in one band"
)
coadd = InputDatasetField(
doc="Exposure on which to run deblending",
nameTemplate="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
scalar=True,
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
measureCatalog = OutputDatasetField(
doc="The output measurement catalog of deblended sources",
nameTemplate="{outputCoaddName}Coadd_deblendedFlux",
scalar=True,
storageClass="SourceCatalog",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
outputSchema = InitOutputDatasetField(
doc="Output of the schema used in deblending task",
nameTemplate="{outputCoaddName}Coadd_deblendedFlux_schema",
storageClass="SourceCatalog"
)
class CalibrateConfig(PipelineTaskConfig):
"""Config for CalibrateTask"""
doWrite = pexConfig.Field(
dtype=bool,
default=True,
doc="Save calibration results?",
)
doWriteHeavyFootprintsInSources = pexConfig.Field(
dtype=bool,
default=True,
doc="Include HeavyFootprint data in source table? If false then heavy "
"footprints are saved as normal footprints, which saves some space"
)
doWriteMatches = pexConfig.Field(
dtype=bool,
default=True,
doc="Write reference matches (ignored if doWrite false)?",
)
doWriteMatchesDenormalized = pexConfig.Field(
dtype=bool,
default=False,
doc=("Write reference matches in denormalized format? "
"This format uses more disk space, but is more convenient to "
"read. Ignored if doWriteMatches=False or doWrite=False."),
)
doAstrometry = pexConfig.Field(
dtype=bool,
default=True,
doc="Perform astrometric calibration?",
)
astromRefObjLoader = pexConfig.ConfigurableField(
target=LoadIndexedReferenceObjectsTask,
doc="reference object loader for astrometric calibration",
)
photoRefObjLoader = pexConfig.ConfigurableField(
target=LoadIndexedReferenceObjectsTask,
doc="reference object loader for photometric calibration",
)
astrometry = pexConfig.ConfigurableField(
target=AstrometryTask,
doc="Perform astrometric calibration to refine the WCS",
)
requireAstrometry = pexConfig.Field(
dtype=bool,
default=True,
doc=("Raise an exception if astrometry fails? Ignored if doAstrometry "
"false."),
)
doPhotoCal = pexConfig.Field(
dtype=bool,
default=True,
doc="Perform phometric calibration?",
)
requirePhotoCal = pexConfig.Field(
dtype=bool,
default=True,
doc=("Raise an exception if photoCal fails? Ignored if doPhotoCal "
"false."),
)
photoCal = pexConfig.ConfigurableField(
target=PhotoCalTask,
doc="Perform photometric calibration",
)
icSourceFieldsToCopy = pexConfig.ListField(
dtype=str,
default=("calib_psf_candidate", "calib_psf_used", "calib_psf_reserved"),
doc=("Fields to copy from the icSource catalog to the output catalog "
"for matching sources Any missing fields will trigger a "
"RuntimeError exception. Ignored if icSourceCat is not provided.")
)
matchRadiusPix = pexConfig.Field(
dtype=float,
default=3,
doc=("Match radius for matching icSourceCat objects to sourceCat "
"objects (pixels)"),
)
checkUnitsParseStrict = pexConfig.Field(
doc=("Strictness of Astropy unit compatibility check, can be 'raise', "
"'warn' or 'silent'"),
dtype=str,
default="raise",
)
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 sources into their components"
)
measurement = pexConfig.ConfigurableField(
target=SingleFrameMeasurementTask,
doc="Measure sources"
)
doApCorr = pexConfig.Field(
dtype=bool,
default=True,
doc="Run subtask to apply aperture correction"
)
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"
)
doInsertFakes = pexConfig.Field(
dtype=bool,
default=False,
doc="Run fake sources injection task"
)
insertFakes = pexConfig.ConfigurableField(
target=BaseFakeSourcesTask,
doc="Injection of fake sources for testing purposes (must be "
"retargeted)"
)
icSourceSchema = InitInputDatasetField(
doc="Schema produced by characterize image task, used to initialize this task",
name="icSrc_schema",
storageClass="SourceCatalog",
)
outputSchema = InitOutputDatasetField(
doc="Schema after CalibrateTask has been initialized",
name="src_schema",
storageClass="SourceCatalog",
)
exposure = InputDatasetField(
doc="Input image to calibrate",
name="icExp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
background = InputDatasetField(
doc="Backgrounds determined by characterize task",
name="icExpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
icSourceCat = InputDatasetField(
doc="Source catalog created by characterize task",
name="icSrc",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
astromRefCat = InputDatasetField(
doc="Reference catalog to use for astrometry",
name="ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix",),
manualLoad=True
)
photoRefCat = InputDatasetField(
doc="Reference catalog to use for photometric calibration",
name="ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix",),
manualLoad=True
)
outputExposure = OutputDatasetField(
doc="Exposure after running calibration task",
name="calexp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
outputCat = OutputDatasetField(
doc="Source catalog produced in calibrate task",
name="src",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
outputBackground = OutputDatasetField(
doc="Background models estimated in calibration task",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
matches = OutputDatasetField(
doc="Source/refObj matches from the astrometry solver",
name="srcMatch",
storageClass="Catalog",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
matchesDenormalized = OutputDatasetField(
doc="Denormalized matches from astrometry solver",
name="srcMatchFull",
storageClass="Catalog",
dimensions=("instrument", "visit", "detector"),
scalar=True
)
doWriteExposure = pexConfig.Field(
dtype=bool,
default=True,
doc="Write the calexp? If fakes have been added then we do not want to write out the calexp as a "
"normal calexp but as a fakes_calexp."
)
def setDefaults(self):
super().setDefaults()
self.quantum.dimensions = ("instrument", "visit", "detector")