class DiaPipelineConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit", "detector"),
defaultTemplates={
"coaddName": "deep",
"fakesType": ""
}):
"""Butler connections for DiaPipelineTask.
"""
diaSourceSchema = connTypes.InitInput(
doc="Schema of the DiaSource catalog produced during image "
"differencing",
name="{fakesType}{coaddName}Diff_diaSrc_schema",
storageClass="SourceCatalog",
multiple=True)
diaSourceCat = connTypes.Input(
doc="Catalog of DiaSources produced during image differencing.",
name="{fakesType}{coaddName}Diff_diaSrc",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
)
diffIm = connTypes.Input(
doc="Difference image on which the DiaSources were detected.",
name="{fakesType}{coaddName}Diff_differenceExp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
exposure = connTypes.Input(
doc="Calibrated exposure differenced with a template image during "
"image differencing.",
name="calexp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
warpedExposure = connTypes.Input(
doc="Warped template used to create `subtractedExposure`. Not PSF "
"matched.",
dimensions=("instrument", "visit", "detector"),
storageClass="ExposureF",
name="{fakesType}{coaddName}Diff_warpedExp",
)
apdbMarker = connTypes.Output(
doc="Marker dataset storing the configuration of the Apdb for each "
"visit/detector. Used to signal the completion of the pipeline.",
name="apdb_marker",
storageClass="Config",
dimensions=("instrument", "visit", "detector"),
)
associatedDiaSources = connTypes.Output(
doc="Optional output storing the DiaSource catalog after matching, "
"calibration, and standardization for insertation into the Apdb.",
name="{fakesType}{coaddName}Diff_assocDiaSrc",
storageClass="DataFrame",
dimensions=("instrument", "visit", "detector"),
)
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.doWriteAssociatedSources:
self.outputs.remove("associatedDiaSources")
class InsertFakesConnections(PipelineTaskConnections,
defaultTemplates={"coaddName": "deep",
"fakesType": "fakes_"},
dimensions=("tract", "patch", "band", "skymap")):
image = cT.Input(
doc="Image into which fakes are to be added.",
name="{coaddName}Coadd",
storageClass="ExposureF",
dimensions=("tract", "patch", "band", "skymap")
)
fakeCat = cT.Input(
doc="Catalog of fake sources to draw inputs from.",
name="{fakesType}fakeSourceCat",
storageClass="DataFrame",
dimensions=("tract", "skymap")
)
imageWithFakes = cT.Output(
doc="Image with fake sources added.",
name="{fakesType}{coaddName}Coadd",
storageClass="ExposureF",
dimensions=("tract", "patch", "band", "skymap")
)
class SingleStarCentroidTaskConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit",
"detector")):
inputExp = cT.Input(
name="icExp",
doc="Image-characterize output exposure",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
multiple=False,
)
inputSources = cT.Input(
name="icSrc",
doc="Image-characterize output sources.",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
multiple=False,
)
astromRefCat = cT.PrerequisiteInput(
doc="Reference catalog to use for astrometry",
name="gaia_dr2_20200414",
storageClass="SimpleCatalog",
dimensions=("skypix", ),
deferLoad=True,
multiple=True,
)
atmospecCentroid = cT.Output(
name="atmospecCentroid",
doc="The main star centroid in yaml format.",
storageClass="StructuredDataDict",
dimensions=("instrument", "visit", "detector"),
)
class DeblendCoaddSourcesMultiConnections(PipelineTaskConnections,
dimensions=("tract", "patch",
"skymap"),
defaultTemplates=deblendBaseTemplates
):
inputSchema = cT.InitInput(
doc="Input schema to use in the deblend catalog",
name="{inputCoaddName}Coadd_mergeDet_schema",
storageClass="SourceCatalog")
peakSchema = cT.InitInput(doc="Schema of the footprint peak catalogs",
name="{inputCoaddName}Coadd_peak_schema",
storageClass="PeakCatalog")
mergedDetections = cT.Input(doc="Detection catalog merged across bands",
name="{inputCoaddName}Coadd_mergeDet",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "skymap"))
coadds = cT.Input(doc="Exposure on which to run deblending",
name="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
multiple=True,
dimensions=("tract", "patch", "band", "skymap"))
outputSchema = cT.InitOutput(
doc="Output of the schema used in deblending task",
name="{outputCoaddName}Coadd_deblendedFlux_schema",
storageClass="SourceCatalog")
templateCatalogs = cT.Output(
doc="Template catalogs produced by multiband deblending",
name="{outputCoaddName}Coadd_deblendedFlux",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "band", "skymap"),
multiple=True)
class ExamplePipelineTaskConnections(PipelineTaskConnections,
dimensions=["Visit", "Detector"]):
input1 = cT.Input(name="",
dimensions=["Visit", "Detector"],
storageClass="example",
doc="Input for this task")
input2 = cT.Input(name="",
dimensions=["Visit", "Detector"],
storageClass="example",
doc="Input for this task")
output1 = cT.Output(name="",
dimensions=["Visit", "Detector"],
storageClass="example",
doc="Output for this task")
output2 = cT.Output(name="",
dimensions=["Visit", "Detector"],
storageClass="example",
doc="Output for this task")
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.connections.input2:
self.inputs.remove('input2')
if not config.connections.output2:
self.outputs.remove('output2')
class CalcZernikesTaskConnections(
pipeBase.PipelineTaskConnections, dimensions=("visit", "detector", "instrument")
):
donutStampsExtra = connectionTypes.Input(
doc="Extra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsExtra",
)
donutStampsIntra = connectionTypes.Input(
doc="Intra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsIntra",
)
outputZernikesRaw = connectionTypes.Output(
doc="Zernike Coefficients from all donuts",
dimensions=("visit", "detector", "instrument"),
storageClass="NumpyArray",
name="zernikeEstimateRaw",
)
outputZernikesAvg = connectionTypes.Output(
doc="Zernike Coefficients averaged over donuts",
dimensions=("visit", "detector", "instrument"),
storageClass="NumpyArray",
name="zernikeEstimateAvg",
)
class VisitConnections(PipelineTaskConnections, dimensions={"instrument", "visit"}):
a = connectionTypes.Input(
name="VisitA",
storageClass="StructuredData",
multiple=False,
dimensions={"instrument", "visit"},
)
b = connectionTypes.Input(
name="VisitB",
storageClass="StructuredData",
multiple=False,
dimensions={"instrument", "visit"},
)
outA = connectionTypes.Output(
name="VisitOutA",
storageClass="StructuredData",
multiple=False,
dimensions={"instrument", "visit"},
)
outB = connectionTypes.Output(
name="VisitOutB",
storageClass="StructuredData",
multiple=False,
dimensions={"instrument", "visit"},
)
class ProcessBrightStarsConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit", "detector")):
inputExposure = cT.Input(
doc="Input exposure from which to extract bright star stamps",
name="calexp",
storageClass="ExposureF",
dimensions=("visit", "detector")
)
skyCorr = cT.Input(
doc="Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
name="skyCorr",
storageClass="Background",
dimensions=("instrument", "visit", "detector")
)
refCat = cT.PrerequisiteInput(
doc="Reference catalog that contains bright star positions",
name="gaia_dr2_20200414",
storageClass="SimpleCatalog",
dimensions=("skypix",),
multiple=True,
deferLoad=True
)
brightStarStamps = cT.Output(
doc="Set of preprocessed postage stamps, each centered on a single bright star.",
name="brightStarStamps",
storageClass="BrightStarStamps",
dimensions=("visit", "detector")
)
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.doApplySkyCorr:
self.inputs.remove("skyCorr")
class TransformDiaSourceCatalogConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit",
"detector"),
defaultTemplates={
"coaddName": "deep",
"fakesType": ""
}):
diaSourceSchema = connTypes.InitInput(
doc="Schema for DIASource catalog output by ImageDifference.",
storageClass="SourceCatalog",
name="{fakesType}{coaddName}Diff_diaSrc_schema",
)
diaSourceCat = connTypes.Input(
doc="Catalog of DiaSources produced during image differencing.",
name="{fakesType}{coaddName}Diff_diaSrc",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
)
diffIm = connTypes.Input(
doc="Difference image on which the DiaSources were detected.",
name="{fakesType}{coaddName}Diff_differenceExp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
diaSourceTable = connTypes.Output(
doc=".",
name="{fakesType}{coaddName}Diff_diaSrcTable",
storageClass="DataFrame",
dimensions=("instrument", "visit", "detector"),
)
class SubtractTaskConnections(
pipeBase.PipelineTaskConnections,
dimensions={"instrument", "exposure", "detector"},
defaultTemplates={}):
inputImage = cT.Input(
name="intype", # default dataset type for input image
doc="Input image.",
storageClass="SPHERExImage",
dimensions=["instrument", "exposure", "detector"],
)
subtractImage = cT.Input(
name="subtracttype", # default dataset type for subtract image
doc="Image that will be subtracted from the input image.",
storageClass="SPHERExImage",
dimensions=["instrument", "exposure", "detector"],
)
outputImage = cT.Output(
name='postsubtracttype', # default dataset type for output image
doc="Output image after subtraction.",
storageClass="SPHERExImage",
dimensions=["instrument", "exposure", "detector"],
)
def __init__(self, *, config=None):
super().__init__(config=config)
class BaseMakeQaTractTablesTaskConnections(pipeBase.PipelineTaskConnections,
defaultTemplates={"coaddName": "deep"},
dimensions=("tract", "skymap")):
inputObjCats = cT.Input(
doc="The coadd catalog from which to collect relevant columns.",
storageClass="DataFrame",
name="{coaddName}Coadd_obj",
dimensions=("tract", "patch", "skymap"),
multiple=True,
deferLoad=True,
)
inputObjectTable = cT.Input(
doc=("The objectTable_tract associated with the tract. Only used to ensure the tables persisted "
"have the same row ordering as the objectTable_tract tables for ease of joint use."),
storageClass="DataFrame",
name="objectTable_tract",
dimensions=("tract", "skymap"),
deferLoad=True,
)
qaTractTable = cT.Output(
doc="The collated catalog of measurements with additional columns for QA added.",
storageClass="DataFrame",
name="qaTractTable_forced",
dimensions=("tract", "band", "skymap"),
)
class GridFitConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "exposure", "detector")):
inputCat = cT.Input(
doc="Source catalog produced by characterize spot task.",
name='spotSrc',
storageClass="SourceCatalog",
dimensions=("instrument", "exposure", "detector"))
bbox = cT.Input(doc="Bounding box for CCD.",
name="postISRCCD.bbox",
storageClass="Box2I",
dimensions=("instrument", "exposure", "detector"))
gridCalibTable = cT.PrerequisiteInput(
doc="Calibration table for spot grid.",
name="gridCalibration",
storageClass="AstropyTable",
dimensions=("instrument", "detector"),
isCalibration=True)
gridSourceCat = cT.Output(doc="Source catalog produced by grid fit task.",
name="gridSpotSrc",
storageClass="SourceCatalog",
dimensions=("instrument", "exposure",
"detector"))
def __init__(self, *, config=None):
super().__init__(config=config)
if config.useGridCalibration is not True:
self.prerequisiteInputs.discard("gridCalibTable")
class MatchApFakesConnections(PipelineTaskConnections,
defaultTemplates={
"coaddName": "deep",
"fakesType": "fakes_"
},
dimensions=("tract", "skymap", "instrument",
"visit", "detector")):
fakeCat = connTypes.Input(
doc="Catalog of fake sources to draw inputs from.",
name="{fakesType}fakeSourceCat",
storageClass="DataFrame",
dimensions=("tract", "skymap"))
diffIm = connTypes.Input(
doc="Difference image on which the DiaSources were detected.",
name="{fakesType}{coaddName}Diff_differenceExp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
associatedDiaSources = connTypes.Input(
doc="Optional output storing the DiaSource catalog after matching and "
"SDMification.",
name="{fakesType}{coaddName}Diff_assocDiaSrc",
storageClass="DataFrame",
dimensions=("instrument", "visit", "detector"),
)
matchedDiaSources = connTypes.Output(
doc="",
name="{fakesType}{coaddName}Diff_matchDiaSrc",
storageClass="DataFrame",
dimensions=("instrument", "visit", "detector"),
)
class DeblendCoaddSourceSingleConnections(PipelineTaskConnections,
dimensions=("tract", "patch", "band",
"skymap"),
defaultTemplates=deblendBaseTemplates
):
inputSchema = cT.InitInput(
doc="Input schema to use in the deblend catalog",
name="{inputCoaddName}Coadd_mergeDet_schema",
storageClass="SourceCatalog")
peakSchema = cT.InitInput(doc="Schema of the footprint peak catalogs",
name="{inputCoaddName}Coadd_peak_schema",
storageClass="PeakCatalog")
mergedDetections = cT.Input(doc="Detection catalog merged across bands",
name="{inputCoaddName}Coadd_mergeDet",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "skymap"))
coadd = cT.Input(doc="Exposure on which to run deblending",
name="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
dimensions=("tract", "patch", "band", "skymap"))
measureCatalog = cT.Output(
doc="The output measurement catalog of deblended sources",
name="{outputCoaddName}Coadd_deblendedFlux",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "band", "skymap"))
outputSchema = cT.InitOutput(
doc="Output of the schema used in deblending task",
name="{outputCoaddName}Coadd_deblendedFlux_schema",
storageClass="SourceCatalog")
def setDefaults(self):
super().setDefaults()
self.singleBandDeblend.propagateAllPeaks = True
class ApertureTaskConnections(pipeBase.PipelineTaskConnections,
dimensions=("visit", "detector", "band")):
exposure = connectionTypes.Input(
doc="Input exposure to make measurements on",
dimensions=("visit", "detector", "band"),
storageClass="ExposureF",
name="calexp",
)
inputCatalog = connectionTypes.Input(
doc="Input catalog with existing measurements",
dimensions=("visit", "detector", "band"),
storageClass="SourceCatalog",
name="src",
)
inputCatalog = connectionTypes.Input(
doc="Input catalog with existing measurements",
dimensions=(
"visit",
"detector",
"band",
),
storageClass="SourceCatalog",
name="src",
)
outputCatalog = connectionTypes.Output(
doc="Aperture measurements",
dimensions=("visit", "detector", "band"),
storageClass="SourceCatalog",
name="customAperture",
)
outputSchema = connectionTypes.InitOutput(
doc="Schema created in Aperture PipelineTask",
storageClass="SourceCatalog",
name="customAperture_schema",
)
class ApertureTaskConnections(
pipeBase.PipelineTaskConnections,
defaultTemplates={"outputName": "customAperture"},
dimensions=("visit", "band"),
):
exposures = connectionTypes.Input(
doc="Input exposure to make measurements on",
dimensions=("visit", "detector", "band"),
storageClass="ExposureF",
name="calexp",
multiple=True,
deferLoad=True,
)
backgrounds = connectionTypes.Input(
doc="Background model for the exposure",
storageClass="Background",
name="calexpBackground",
dimensions=("visit", "detector", "band"),
multiple=True,
deferLoad=True,
)
inputCatalogs = connectionTypes.Input(
doc="Input catalog with existing measurements",
dimensions=(
"visit",
"detector",
"band",
),
storageClass="SourceCatalog",
name="src",
multiple=True,
deferLoad=True,
)
outputCatalogs = connectionTypes.Output(
doc="Aperture measurements",
dimensions=("visit", "detector", "band"),
storageClass="SourceCatalog",
name="{outputName}",
multiple=True,
)
outputSchema = connectionTypes.InitOutput(
doc="Schema created in Aperture PipelineTask",
storageClass="SourceCatalog",
name="{outputName}_schema",
)
areaMasks = connectionTypes.PrerequisiteInput(
doc="A mask of areas to be ignored",
storageClass="Mask",
dimensions=("visit", "detector", "band"),
name="ApAreaMask",
multiple=True,
deferLoad=True,
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config.doLocalBackground is False:
self.inputs.remove("backgrounds")
class MakeWarpConnections(pipeBase.PipelineTaskConnections,
dimensions=("tract", "patch", "skymap", "instrument", "visit"),
defaultTemplates={"coaddName": "deep"}):
calExpList = cT.Input(
doc="Input exposures to be resampled and optionally PSF-matched onto a SkyMap projection/patch",
name="calexp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
multiple=True,
)
backgroundList = cT.Input(
doc="Input backgrounds to be added back into the calexp if bgSubtracted=False",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
multiple=True,
)
skyCorrList = cT.Input(
doc="Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
name="skyCorr",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
multiple=True,
)
skyMap = cT.Input(
doc="Input definition of geometry/bbox and projection/wcs for warped exposures",
name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
storageClass="SkyMap",
dimensions=("skymap",),
)
direct = cT.Output(
doc=("Output direct warped exposure (previously called CoaddTempExp), produced by resampling ",
"calexps onto the skyMap patch geometry."),
name="{coaddName}Coadd_directWarp",
storageClass="ExposureF",
dimensions=("tract", "patch", "skymap", "visit", "instrument"),
)
psfMatched = cT.Output(
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."),
name="{coaddName}Coadd_psfMatchedWarp",
storageClass="ExposureF",
dimensions=("tract", "patch", "skymap", "visit", "instrument"),
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config.bgSubtracted:
self.inputs.remove("backgroundList")
if not config.doApplySkyCorr:
self.inputs.remove("skyCorrList")
if not config.makeDirect:
self.outputs.remove("direct")
if not config.makePsfMatched:
self.outputs.remove("psfMatched")
class DeblendCoaddSourcesMultiConnections(PipelineTaskConnections,
dimensions=("tract", "patch",
"skymap"),
defaultTemplates=deblendBaseTemplates
):
inputSchema = cT.InitInput(
doc="Input schema to use in the deblend catalog",
name="{inputCoaddName}Coadd_mergeDet_schema",
storageClass="SourceCatalog")
peakSchema = cT.InitInput(doc="Schema of the footprint peak catalogs",
name="{inputCoaddName}Coadd_peak_schema",
storageClass="PeakCatalog")
mergedDetections = cT.Input(doc="Detection catalog merged across bands",
name="{inputCoaddName}Coadd_mergeDet",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "skymap"))
coadds = cT.Input(doc="Exposure on which to run deblending",
name="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
multiple=True,
dimensions=("tract", "patch", "band", "skymap"))
outputSchema = cT.InitOutput(
doc="Output of the schema used in deblending task",
name="{outputCoaddName}Coadd_deblendedFlux_schema",
storageClass="SourceCatalog")
fluxCatalogs = cT.Output(
doc="Flux weighted catalogs produced by multiband deblending",
name="{outputCoaddName}Coadd_deblendedFlux",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "band", "skymap"),
multiple=True)
templateCatalogs = cT.Output(
doc="Template catalogs produced by multiband deblending",
name="{outputCoaddName}Coadd_deblendedModel",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "band", "skymap"),
multiple=True)
deblendedCatalog = cT.Output(
doc="Catalogs produced by multiband deblending",
name="{outputCoaddName}Coadd_deblendedCatalog",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "skymap"),
)
scarletModelData = cT.Output(
doc="Multiband scarlet models produced by the deblender",
name="{outputCoaddName}Coadd_scarletModelData",
storageClass="ScarletModelData",
dimensions=("tract", "patch", "skymap"),
)
def __init__(self, *, config=None):
super().__init__(config=config)
# Remove unused connections.
# TODO: deprecate once RFC-860 passes.
self.outputs -= set(("fluxCatalogs", "templateCatalogs"))
class CpVerifyStatsConnections(
pipeBase.PipelineTaskConnections,
dimensions={"instrument", "exposure", "detector"},
defaultTemplates={}):
inputExp = cT.Input(
name="postISRCCD",
doc="Input exposure to calculate statistics for.",
storageClass="Exposure",
dimensions=["instrument", "exposure", "detector"],
)
taskMetadata = cT.Input(
name="isrTask_metadata",
doc="Input task metadata to extract statistics from.",
storageClass="PropertySet",
dimensions=["instrument", "exposure", "detector"],
)
inputCatalog = cT.Input(
name="src",
doc="Input catalog to calculate statistics for.",
storageClass="SourceCatalog",
dimensions=["instrument", "visit", "detector"],
)
uncorrectedCatalog = cT.Input(
name="uncorrectedSrc",
doc="Input catalog without correction applied.",
storageClass="SourceCatalog",
dimensions=["instrument", "visit", "detector"],
)
camera = cT.PrerequisiteInput(
name="camera",
storageClass="Camera",
doc="Input camera.",
dimensions=[
"instrument",
],
isCalibration=True,
)
outputStats = cT.Output(
name="detectorStats",
doc="Output statistics from cp_verify.",
storageClass="StructuredDataDict",
dimensions=["instrument", "exposure", "detector"],
)
def __init__(self, *, config=None):
super().__init__(config=config)
if len(config.metadataStatKeywords) < 1:
self.inputs.discard('taskMetadata')
if len(config.catalogStatKeywords) < 1:
self.inputs.discard('inputCatalog')
self.inputs.discard('uncorrectedCatalog')
class CalibCombineConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "detector")):
inputExps = cT.Input(
name="cpInputs",
doc="Input pre-processed exposures to combine.",
storageClass="Exposure",
dimensions=("instrument", "detector", "exposure"),
multiple=True,
)
inputScales = cT.Input(
name="cpScales",
doc="Input scale factors to use.",
storageClass="StructuredDataDict",
dimensions=("instrument", ),
multiple=False,
)
outputData = cT.Output(
name="cpProposal",
doc="Output combined proposed calibration.",
storageClass="ExposureF",
dimensions=("instrument", "detector"),
isCalibration=True,
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config and config.exposureScaling != 'InputList':
self.inputs.discard("inputScales")
if config and len(config.calibrationDimensions) != 0:
newDimensions = tuple(config.calibrationDimensions)
newOutputData = cT.Output(
name=self.outputData.name,
doc=self.outputData.doc,
storageClass=self.outputData.storageClass,
dimensions=self.allConnections['outputData'].dimensions +
newDimensions,
isCalibration=True,
)
self.dimensions.update(config.calibrationDimensions)
self.outputData = newOutputData
if config.exposureScaling == 'InputList':
newInputScales = cT.PrerequisiteInput(
name=self.inputScales.name,
doc=self.inputScales.doc,
storageClass=self.inputScales.storageClass,
dimensions=self.allConnections['inputScales'].dimensions +
newDimensions)
self.dimensions.update(config.calibrationDimensions)
self.inputScales = newInputScales
class CrosstalkColumnConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "exposure",
"detector")):
inputExp = cT.Input(
name="crosstalkInputs",
doc="Input post-ISR processed exposure to measure crosstalk from.",
storageClass="Exposure",
dimensions=("instrument", "exposure", "detector"),
multiple=False,
)
rawExp = cT.Input(
name="rawInputs",
doc="Input raw exposure to measure noise covariance from.",
storageClass="Exposure",
dimensions=("instrument", "exposure", "detector"),
multiple=False,
)
outputRatios = cT.Output(
name="crosstalkRatios",
doc="Extracted crosstalk pixel ratios.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
)
outputFluxes = cT.Output(
name="crosstalkFluxes",
doc="Source pixel fluxes used in ratios.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
)
outputZOffsets = cT.Output(
name="crosstalkBackgroundZOffsets",
doc="Z offset parameters used in background model.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
)
outputYTilts = cT.Output(
name="crosstalkBackgroundYTilts",
doc="Y tilt parameters used in background model.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
)
outputXTilts = cT.Output(
name="crosstalkBackgroundXTilts",
doc="X tilt parameters used in background model.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
)
def __init__(self, *, config=None):
super().__init__(config=config)
class SkyCorrectionConnections(pipeBase.PipelineTaskConnections, dimensions=("instrument", "visit")):
rawLinker = cT.Input(
doc="Raw data to provide exp-visit linkage to connect calExp inputs to camera/sky calibs.",
name="raw",
multiple=True,
deferLoad=True,
storageClass="ExposureU",
dimensions=["instrument", "exposure", "detector"],
)
calExpArray = cT.Input(
doc="Input exposures to process",
name="calexp",
multiple=True,
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
calBkgArray = cT.Input(
doc="Input background files to use",
multiple=True,
name="calexpBackground",
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
camera = cT.PrerequisiteInput(
doc="Input camera to use.",
name="camera",
storageClass="Camera",
dimensions=["instrument", "calibration_label"],
)
skyCalibs = cT.PrerequisiteInput(
doc="Input sky calibrations to use.",
name="sky",
multiple=True,
storageClass="ExposureF",
dimensions=["instrument", "physical_filter", "detector", "calibration_label"],
)
calExpCamera = cT.Output(
doc="Output camera image.",
name='calexp_camera',
storageClass="ImageF",
dimensions=["instrument", "visit"],
)
skyCorr = cT.Output(
doc="Output sky corrected images.",
name='skyCorr',
multiple=True,
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
class DeblendCoaddSourcesMultiConnections(PipelineTaskConnections,
dimensions=("tract", "patch", "skymap"),
defaultTemplates=deblendBaseTemplates):
inputSchema = cT.InitInput(
doc="Input schema to use in the deblend catalog",
name="{inputCoaddName}Coadd_mergeDet_schema",
storageClass="SourceCatalog"
)
peakSchema = cT.InitInput(
doc="Schema of the footprint peak catalogs",
name="{inputCoaddName}Coadd_peak_schema",
storageClass="PeakCatalog"
)
mergedDetections = cT.Input(
doc="Detection catalog merged across bands",
name="{inputCoaddName}Coadd_mergeDet",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "skymap")
)
coadds = cT.Input(
doc="Exposure on which to run deblending",
name="{inputCoaddName}Coadd_calexp",
storageClass="ExposureF",
multiple=True,
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
outputSchema = cT.InitOutput(
doc="Output of the schema used in deblending task",
name="{outputCoaddName}Coadd_deblendedModel_schema",
storageClass="SourceCatalog"
)
fluxCatalogs = cT.Output(
doc="Flux catalogs produced by multiband deblending, not written "
"if conserve flux is turned off",
name="{outputCoaddName}Coadd_deblendedFlux",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
templateCatalogs = cT.Output(
doc="Template catalogs produced by multiband deblending",
name="{outputCoaddName}Coadd_deblendedModel",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "abstract_filter", "skymap")
)
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.multibandDeblend.conserveFlux:
self.outputs -= set(("fluxCatalogs",))
class CharacterizeImageConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit",
"detector")):
exposure = cT.Input(
doc="Input exposure data",
name="postISRCCD",
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
characterized = cT.Output(
doc="Output characterized data.",
name="icExp",
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
sourceCat = cT.Output(
doc="Output source catalog.",
name="icSrc",
storageClass="SourceCatalog",
dimensions=["instrument", "visit", "detector"],
)
backgroundModel = cT.Output(
doc="Output background model.",
name="icExpBackground",
storageClass="Background",
dimensions=["instrument", "visit", "detector"],
)
outputSchema = cT.InitOutput(
doc="Schema of the catalog produced by CharacterizeImage",
name="icSrc_schema",
storageClass="SourceCatalog",
)
class ApdbMetricConnections(
MetricConnections,
dimensions={"instrument"},
):
"""An abstract connections class defining a database input.
Notes
-----
``ApdbMetricConnections`` defines the following dataset templates:
``package``
Name of the metric's namespace. By
:ref:`verify_metrics <verify-metrics-package>` convention, this is
the name of the package the metric is most closely
associated with.
``metric``
Name of the metric, excluding any namespace.
"""
dbInfo = connectionTypes.Input(
name="apdb_marker",
doc="The dataset from which an APDB instance can be constructed by "
"`dbLoader`. By default this is assumed to be a marker produced "
"by AP processing.",
storageClass="Config",
multiple=True,
dimensions={"instrument", "visit", "detector"},
)
# Replaces MetricConnections.measurement, which is detector-level
measurement = connectionTypes.Output(
name="metricvalue_{package}_{metric}",
doc="The metric value computed by this task.",
storageClass="MetricValue",
dimensions={"instrument"},
)
class SingleMetadataMetricConnections(
MetricConnections,
dimensions={"instrument", "exposure", "detector"},
defaultTemplates={
"labelName": "",
"package": None,
"metric": None
}):
"""An abstract connections class defining a metadata input.
Notes
-----
``SingleMetadataMetricConnections`` defines the following dataset
templates:
``package``
Name of the metric's namespace. By
:ref:`verify_metrics <verify-metrics-package>` convention, this is
the name of the package the metric is most closely
associated with.
``metric``
Name of the metric, excluding any namespace.
``labelName``
Pipeline label of the `~lsst.pipe.base.PipelineTask` or name of
the `~lsst.pipe.base.CmdLineTask` whose metadata are being read.
"""
metadata = connectionTypes.Input(
name="{labelName}_metadata",
doc="The target top-level task's metadata. The name must be set to "
"the metadata's butler type, such as 'processCcd_metadata'.",
storageClass="PropertySet",
dimensions={"Instrument", "Exposure", "Detector"},
multiple=False,
)
class MergeMeasurementsConnections(PipelineTaskConnections,
dimensions=("skymap", "tract", "patch"),
defaultTemplates={"inputCoaddName": "deep",
"outputCoaddName": "deep"}):
inputSchema = cT.InitInput(
doc="Schema for the output merged measurement catalog.",
name="{inputCoaddName}Coadd_meas_schema",
storageClass="SourceCatalog",
)
outputSchema = cT.InitOutput(
doc="Schema for the output merged measurement catalog.",
name="{outputCoaddName}Coadd_ref_schema",
storageClass="SourceCatalog",
)
catalogs = cT.Input(
doc="Input catalogs to merge.",
name="{inputCoaddName}Coadd_meas",
multiple=True,
storageClass="SourceCatalog",
dimensions=["band", "skymap", "tract", "patch"],
)
mergedCatalog = cT.Output(
doc="Output merged catalog.",
name="{outputCoaddName}Coadd_ref",
storageClass="SourceCatalog",
dimensions=["skymap", "tract", "patch"],
)
class PhotonTransferCurveSolveConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "detector")
):
inputCovariances = cT.Input(
name="ptcCovariances",
doc="Tuple with measured covariances from flats.",
storageClass="PhotonTransferCurveDataset",
dimensions=("instrument", "exposure", "detector"),
multiple=True,
)
camera = cT.PrerequisiteInput(
name="camera",
doc="Camera the input data comes from.",
storageClass="Camera",
dimensions=("instrument", ),
isCalibration=True,
lookupFunction=lookupStaticCalibration,
)
outputPtcDataset = cT.Output(
name="ptcDatsetProposal",
doc="Output proposed ptc dataset.",
storageClass="PhotonTransferCurveDataset",
dimensions=("instrument", "detector"),
multiple=False,
isCalibration=True,
)
class PatchConnections(PipelineTaskConnections, dimensions={"skymap", "tract"}):
a = connectionTypes.Input(
name="PatchA",
storageClass="StructuredData",
multiple=True,
dimensions={"skymap", "tract", "patch"},
)
b = connectionTypes.PrerequisiteInput(
name="PatchB",
storageClass="StructuredData",
multiple=False,
dimensions={"skymap", "tract"},
)
out = connectionTypes.Output(
name="PatchOut",
storageClass="StructuredData",
multiple=True,
dimensions={"skymap", "tract", "patch"},
)
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.doUseB:
self.prerequisiteInputs.remove("b")
class NoDimensionsTestConnections(PipelineTaskConnections, dimensions=set()):
input = connectionTypes.Input(name="input",
doc="some dict-y input data for testing",
storageClass="StructuredDataDict")
output = connectionTypes.Output(name="output",
doc="some dict-y output data for testing",
storageClass="StructuredDataDict")
