class FgcmMakeLutConnections(pipeBase.PipelineTaskConnections,
dimensions=('instrument', ),
defaultTemplates={}):
camera = connectionTypes.PrerequisiteInput(
doc="Camera instrument",
name="camera",
storageClass="Camera",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
)
transmission_optics = connectionTypes.PrerequisiteInput(
doc="Optics transmission curve information",
name="transmission_optics",
storageClass="TransmissionCurve",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
deferLoad=True,
)
transmission_sensor = connectionTypes.PrerequisiteInput(
doc="Sensor transmission curve information",
name="transmission_sensor",
storageClass="TransmissionCurve",
dimensions=(
"instrument",
"detector",
),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
deferLoad=True,
multiple=True,
)
transmission_filter = connectionTypes.PrerequisiteInput(
doc="Filter transmission curve information",
name="transmission_filter",
storageClass="TransmissionCurve",
dimensions=(
"band",
"instrument",
"physical_filter",
),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
deferLoad=True,
multiple=True,
)
fgcmLookUpTable = connectionTypes.Output(
doc=("Atmosphere + instrument look-up-table for FGCM throughput and "
"chromatic corrections."),
name="fgcmLookUpTable",
storageClass="Catalog",
dimensions=("instrument", ),
)
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 GenerateDonutCatalogWcsTaskConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "visit",
"detector")):
"""
Specify the pipeline connections needed for
GenerateDonutCatalogWcsTask. We
need the reference catalogs and exposures and
will produce donut catalogs for a specified instrument.
"""
refCatalogs = connectionTypes.PrerequisiteInput(
doc="Reference catalog",
storageClass="SimpleCatalog",
dimensions=("htm7", ),
multiple=True,
deferLoad=True,
name="cal_ref_cat",
)
exposure = connectionTypes.Input(
doc="Input exposure to make measurements on",
dimensions=("exposure", "detector", "instrument"),
storageClass="Exposure",
name="postISRCCD",
)
donutCatalog = connectionTypes.Output(
doc="Donut Locations",
dimensions=(
"visit",
"detector",
"instrument",
),
storageClass="DataFrame",
name="donutCatalog",
)
class MergeDefectsConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "detector")):
inputDefects = cT.Input(
name="singleExpDefects",
doc="Measured defect lists.",
storageClass="Defects",
dimensions=("instrument", "detector", "exposure"),
multiple=True,
)
camera = cT.PrerequisiteInput(
name='camera',
doc="Camera associated with these defects.",
storageClass="Camera",
dimensions=("instrument", ),
isCalibration=True,
lookupFunction=lookupStaticCalibration,
)
mergedDefects = cT.Output(
name="defects",
doc="Final merged defects.",
storageClass="Defects",
dimensions=("instrument", "detector"),
multiple=False,
isCalibration=True,
)
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 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 CpVerifyVisitExpMergeConnections(pipeBase.PipelineTaskConnections,
dimensions={"instrument", "visit"},
defaultTemplates={}):
inputStats = cT.Input(
name="detectorStats",
doc="Input statistics to merge.",
storageClass="StructuredDataDict",
dimensions=["instrument", "visit", "detector"],
multiple=True,
)
camera = cT.PrerequisiteInput(
name="camera",
storageClass="Camera",
doc="Input camera.",
dimensions=[
"instrument",
],
isCalibration=True,
)
outputStats = cT.Output(
name="exposureStats",
doc="Output statistics.",
storageClass="StructuredDataDict",
dimensions=["instrument", "visit"],
)
class CpFlatNormalizationConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument",
"physical_filter")):
inputMDs = cT.Input(
name="cpFlatProc_metadata",
doc="Input metadata for each visit/detector in input set.",
storageClass="PropertyList",
dimensions=("instrument", "physical_filter", "detector", "exposure"),
multiple=True,
)
camera = cT.PrerequisiteInput(
name="camera",
doc="Input camera to use for gain lookup.",
storageClass="Camera",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibration,
isCalibration=True,
)
outputScales = cT.Output(
name="cpFlatNormScales",
doc="Output combined proposed calibration.",
storageClass="StructuredDataDict",
dimensions=("instrument", "physical_filter"),
)
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,
)
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 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 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 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 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 VircamIsrTaskConnections(IsrTaskConnections):
confidence = cT.PrerequisiteInput(
name="confidence",
doc="Confidence map associated with input exposure to process.",
storageClass="ExposureF",
dimensions=["instrument", "exposure", "detector"],
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config.doVircamConfidence is not True:
self.prerequisiteInputs.discard("confidence")
class EstimateZernikesBaseConnections(pipeBase.PipelineTaskConnections,
dimensions=("detector", "instrument")):
donutCatalog = connectionTypes.Input(
doc="Donut Locations",
dimensions=(
"visit",
"detector",
"instrument",
),
storageClass="DataFrame",
name="donutCatalog",
multiple=True,
)
camera = connectionTypes.PrerequisiteInput(
name="camera",
storageClass="Camera",
doc="Input camera to construct complete exposures.",
dimensions=["instrument"],
isCalibration=True,
lookupFunction=lookupStaticCalibration,
)
donutStampsExtra = connectionTypes.Output(
doc="Extra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsExtra",
multiple=True,
)
donutStampsIntra = connectionTypes.Output(
doc="Intra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsIntra",
multiple=True,
)
outputZernikesRaw = connectionTypes.Output(
doc="Zernike Coefficients from all donuts",
dimensions=("visit", "detector", "instrument"),
storageClass="NumpyArray",
name="zernikeEstimateRaw",
multiple=True,
)
outputZernikesAvg = connectionTypes.Output(
doc="Zernike Coefficients averaged over donuts",
dimensions=("visit", "detector", "instrument"),
storageClass="NumpyArray",
name="zernikeEstimateAvg",
multiple=True,
)
class CutOutDonutsBaseTaskConnections(pipeBase.PipelineTaskConnections,
dimensions=("exposure", "instrument")):
exposures = connectionTypes.Input(
doc="Input exposure to make measurements on",
dimensions=("exposure", "detector", "instrument"),
storageClass="Exposure",
name="postISRCCD",
multiple=True,
)
donutCatalog = connectionTypes.Input(
doc="Donut Locations",
dimensions=(
"visit",
"detector",
"instrument",
),
storageClass="DataFrame",
name="donutCatalog",
multiple=True,
)
camera = connectionTypes.PrerequisiteInput(
name="camera",
storageClass="Camera",
doc="Input camera to construct complete exposures.",
dimensions=["instrument"],
isCalibration=True,
lookupFunction=lookupStaticCalibration,
)
donutStampsExtra = connectionTypes.Output(
doc="Extra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsExtra",
multiple=True,
)
donutStampsIntra = connectionTypes.Output(
doc="Intra-focal Donut Postage Stamp Images",
dimensions=("visit", "detector", "instrument"),
storageClass="StampsBase",
name="donutStampsIntra",
multiple=True,
)
class VisualizeBinExpConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "detector")):
inputExp = cT.Input(
name="calexp",
doc="Input exposure data to mosaic.",
storageClass="ExposureF",
dimensions=("instrument", "detector"),
)
camera = cT.PrerequisiteInput(
name="camera",
doc="Input camera to use for mosaic geometry.",
storageClass="Camera",
dimensions=("instrument", "calibration_label"),
)
outputExp = cT.Output(
name="calexpBin",
doc="Output binned image.",
storageClass="ExposureF",
dimensions=("instrument", "detector"),
)
class VisualizeMosaicExpConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", )):
inputExps = cT.Input(
name="calexpBin",
doc="Input binned images mosaic.",
storageClass="ExposureF",
dimensions=("instrument", "detector"),
multiple=True,
)
camera = cT.PrerequisiteInput(
name="camera",
doc="Input camera to use for mosaic geometry.",
storageClass="Camera",
dimensions=("instrument", "calibration_label"),
)
outputData = cT.Output(
name="calexpFocalPlane",
doc="Output binned mosaicked frame.",
storageClass="ImageF",
dimensions=("instrument", ),
)
class CrosstalkSolveConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "detector")):
inputRatios = cT.Input(
name="crosstalkRatios",
doc="Ratios measured for an input exposure.",
storageClass="StructuredDataDict",
dimensions=("instrument", "exposure", "detector"),
multiple=True,
)
inputFluxes = cT.Input(
name="crosstalkFluxes",
doc="Fluxes of CT source pixels, for nonlinear fits.",
storageClass="StructuredDataDict",
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,
)
outputCrosstalk = cT.Output(
name="crosstalk",
doc="Output proposed crosstalk calibration.",
storageClass="CrosstalkCalib",
dimensions=("instrument", "detector"),
multiple=False,
isCalibration=True,
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config.fluxOrder == 0:
self.inputs.discard("inputFluxes")
class MeasureDefectsConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", "exposure",
"detector")):
inputExp = cT.Input(name="defectExps",
doc="Input ISR-processed exposures to measure.",
storageClass="Exposure",
dimensions=("instrument", "detector", "exposure"),
multiple=False)
camera = cT.PrerequisiteInput(
name='camera',
doc="Camera associated with this exposure.",
storageClass="Camera",
dimensions=("instrument", ),
isCalibration=True,
lookupFunction=lookupStaticCalibration,
)
outputDefects = cT.Output(
name="singleExpDefects",
doc="Output measured defects.",
storageClass="Defects",
dimensions=("instrument", "detector", "exposure"),
)
class CpVerifyBfkConnections(CpVerifyStatsConnections,
dimensions={'instrument', 'visit', 'detector'}):
inputExp = cT.Input(
name="icExp",
doc="Input exposure to calculate statistics for.",
storageClass="ExposureF",
dimensions=["instrument", "visit", "detector"],
)
inputCatalog = cT.Input(
name="icSrc",
doc="Input catalog to calculate statistics from.",
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", "visit", "detector"],
)
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")
def adjustQuantum(self, inputs, outputs, label, data_id):
# Find the data IDs common to all multiple=True inputs.
input_names = ("exposures", "inputCatalogs", "backgrounds")
inputs_by_data_id = []
for name in input_names:
inputs_by_data_id.append(
{ref.dataId: ref
for ref in inputs[name][1]})
# Intersection looks messy because dict_keys only supports |.
# not an "intersection" method.
data_ids_to_keep = functools.reduce(operator.__and__,
(d.keys()
for d in inputs_by_data_id))
# Pull out just the DatasetRefs that are in common in the inputs
# and order them consistently (note that consistent ordering is not
# automatic).
adjusted_inputs = {}
for name, refs in zip(input_names, inputs_by_data_id):
adjusted_inputs[name] = (
inputs[name][0],
[refs[data_id] for data_id in data_ids_to_keep],
)
# Also update the full dict of inputs, so we can pass it to
# super() later.
inputs[name] = adjusted_inputs[name]
# Do the same for the outputs.
outputs_by_data_id = {
ref.dataId: ref
for ref in outputs["outputCatalogs"][1]
}
adjusted_outputs = {
"outputCatalogs": (
outputs["outputCatalogs"][0],
[outputs_by_data_id[data_id] for data_id in data_ids_to_keep],
)
}
outputs["outputCatalogs"] = adjusted_outputs["outputCatalogs"]
# Delegate to super(); ignore results because they are guaranteed
# to be empty.
super().adjustQuantum(inputs, outputs, label, data_id)
return adjusted_inputs, adjusted_outputs
class FgcmCalibrateTractTableConnections(pipeBase.PipelineTaskConnections,
dimensions=(
"instrument",
"tract",
)):
camera = connectionTypes.PrerequisiteInput(
doc="Camera instrument",
name="camera",
storageClass="Camera",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
)
fgcmLookUpTable = connectionTypes.PrerequisiteInput(
doc=("Atmosphere + instrument look-up-table for FGCM throughput and "
"chromatic corrections."),
name="fgcmLookUpTable",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
sourceSchema = connectionTypes.InitInput(
doc="Schema for source catalogs",
name="src_schema",
storageClass="SourceCatalog",
)
refCat = connectionTypes.PrerequisiteInput(
doc="Reference catalog to use for photometric calibration",
name="cal_ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix", ),
deferLoad=True,
multiple=True,
)
source_catalogs = connectionTypes.Input(
doc="Source table in parquet format, per visit",
name="sourceTable_visit",
storageClass="DataFrame",
dimensions=("instrument", "visit"),
deferLoad=True,
multiple=True,
)
visitSummary = connectionTypes.Input(
doc="Per-visit summary statistics table",
name="visitSummary",
storageClass="ExposureCatalog",
dimensions=("instrument", "visit"),
deferLoad=True,
multiple=True,
)
background = connectionTypes.Input(
doc="Calexp background model",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
deferLoad=True,
multiple=True,
)
fgcmPhotoCalib = connectionTypes.Output(
doc=
"Per-tract, per-visit photoCalib exposure catalogs produced from fgcm calibration",
name="fgcmPhotoCalibTractCatalog",
storageClass="ExposureCatalog",
dimensions=(
"instrument",
"tract",
"visit",
),
multiple=True,
)
fgcmTransmissionAtmosphere = connectionTypes.Output(
doc=
"Per-visit atmosphere transmission files produced from fgcm calibration",
name="transmission_atmosphere_fgcm_tract",
storageClass="TransmissionCurve",
dimensions=(
"instrument",
"tract",
"visit",
),
multiple=True,
)
fgcmRepeatability = connectionTypes.Output(
doc="Per-band raw repeatability numbers in the fgcm tract calibration",
name="fgcmRawRepeatability",
storageClass="Catalog",
dimensions=(
"instrument",
"tract",
),
multiple=False,
)
def __init__(self, *, config=None):
super().__init__(config=config)
# The ref_dataset_name will be deprecated with Gen2
loaderName = config.fgcmBuildStars.fgcmLoadReferenceCatalog.refObjLoader.ref_dataset_name
if config.connections.refCat != loaderName:
raise ValueError(
"connections.refCat must be the same as "
"config.fgcmBuildStars.fgcmLoadReferenceCatalog.refObjLoader.ref_dataset_name"
)
if config.fgcmOutputProducts.doReferenceCalibration:
loaderName = config.fgcmOutputProducts.refObjLoader.ref_dataset_name
if config.connections.refCat != loaderName:
raise ValueError(
"connections.refCat must be the same as "
"config.fgcmOutputProducts.refObjLoader.ref_dataset_name")
if not config.fgcmBuildStars.doModelErrorsWithBackground:
self.inputs.remove("background")
if not config.fgcmOutputProducts.doAtmosphereOutput:
self.prerequisiteInputs.remove("fgcmAtmosphereParameters")
if not config.fgcmOutputProducts.doZeropointOutput:
self.prerequisiteInputs.remove("fgcmZeropoints")
class CalibrateConnections(pipeBase.PipelineTaskConnections, dimensions=("instrument", "visit", "detector"),
defaultTemplates={}):
icSourceSchema = cT.InitInput(
doc="Schema produced by characterize image task, used to initialize this task",
name="icSrc_schema",
storageClass="SourceCatalog",
)
outputSchema = cT.InitOutput(
doc="Schema after CalibrateTask has been initialized",
name="src_schema",
storageClass="SourceCatalog",
)
exposure = cT.Input(
doc="Input image to calibrate",
name="icExp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
background = cT.Input(
doc="Backgrounds determined by characterize task",
name="icExpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
)
icSourceCat = cT.Input(
doc="Source catalog created by characterize task",
name="icSrc",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
)
astromRefCat = cT.PrerequisiteInput(
doc="Reference catalog to use for astrometry",
name="cal_ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix",),
deferLoad=True,
multiple=True,
)
photoRefCat = cT.PrerequisiteInput(
doc="Reference catalog to use for photometric calibration",
name="cal_ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix",),
deferLoad=True,
multiple=True
)
outputExposure = cT.Output(
doc="Exposure after running calibration task",
name="calexp",
storageClass="ExposureF",
dimensions=("instrument", "visit", "detector"),
)
outputCat = cT.Output(
doc="Source catalog produced in calibrate task",
name="src",
storageClass="SourceCatalog",
dimensions=("instrument", "visit", "detector"),
)
outputBackground = cT.Output(
doc="Background models estimated in calibration task",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
)
matches = cT.Output(
doc="Source/refObj matches from the astrometry solver",
name="srcMatch",
storageClass="Catalog",
dimensions=("instrument", "visit", "detector"),
)
matchesDenormalized = cT.Output(
doc="Denormalized matches from astrometry solver",
name="srcMatchFull",
storageClass="Catalog",
dimensions=("instrument", "visit", "detector"),
)
def __init__(self, *, config=None):
super().__init__(config=config)
if config.doAstrometry is False:
self.prerequisiteInputs.remove("astromRefCat")
if config.doPhotoCal is False:
self.prerequisiteInputs.remove("photoRefCat")
if config.doWriteMatches is False or config.doAstrometry is False:
self.outputs.remove("matches")
if config.doWriteMatchesDenormalized is False or config.doAstrometry is False:
self.outputs.remove("matchesDenormalized")
class FgcmBuildStarsTableConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", ),
defaultTemplates={}):
camera = connectionTypes.PrerequisiteInput(
doc="Camera instrument",
name="camera",
storageClass="Camera",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
)
fgcmLookUpTable = connectionTypes.PrerequisiteInput(
doc=("Atmosphere + instrument look-up-table for FGCM throughput and "
"chromatic corrections."),
name="fgcmLookUpTable",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
sourceSchema = connectionTypes.InitInput(
doc="Schema for source catalogs",
name="src_schema",
storageClass="SourceCatalog",
)
refCat = connectionTypes.PrerequisiteInput(
doc="Reference catalog to use for photometric calibration",
name="cal_ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix", ),
deferLoad=True,
multiple=True,
)
sourceTable_visit = connectionTypes.Input(
doc="Source table in parquet format, per visit",
name="sourceTable_visit",
storageClass="DataFrame",
dimensions=("instrument", "visit"),
deferLoad=True,
multiple=True,
)
visitSummary = connectionTypes.Input(
doc=("Per-visit consolidated exposure metadata. These catalogs use "
"detector id for the id and must be sorted for fast lookups of a "
"detector."),
name="visitSummary",
storageClass="ExposureCatalog",
dimensions=("instrument", "visit"),
deferLoad=True,
multiple=True,
)
background = connectionTypes.Input(
doc="Calexp background model",
name="calexpBackground",
storageClass="Background",
dimensions=("instrument", "visit", "detector"),
deferLoad=True,
multiple=True,
)
fgcmVisitCatalog = connectionTypes.Output(
doc="Catalog of visit information for fgcm",
name="fgcmVisitCatalog",
storageClass="Catalog",
dimensions=("instrument", ),
)
fgcmStarObservations = connectionTypes.Output(
doc="Catalog of star observations for fgcm",
name="fgcmStarObservations",
storageClass="Catalog",
dimensions=("instrument", ),
)
fgcmStarIds = connectionTypes.Output(
doc="Catalog of fgcm calibration star IDs",
name="fgcmStarIds",
storageClass="Catalog",
dimensions=("instrument", ),
)
fgcmStarIndices = connectionTypes.Output(
doc="Catalog of fgcm calibration star indices",
name="fgcmStarIndices",
storageClass="Catalog",
dimensions=("instrument", ),
)
fgcmReferenceStars = connectionTypes.Output(
doc="Catalog of fgcm-matched reference stars",
name="fgcmReferenceStars",
storageClass="Catalog",
dimensions=("instrument", ),
)
def __init__(self, *, config=None):
super().__init__(config=config)
if not config.doReferenceMatches:
self.prerequisiteInputs.remove("refCat")
self.prerequisiteInputs.remove("fgcmLookUpTable")
if not config.doModelErrorsWithBackground:
self.inputs.remove("background")
if not config.doReferenceMatches:
self.outputs.remove("fgcmReferenceStars")
class FgcmOutputProductsConnections(pipeBase.PipelineTaskConnections,
dimensions=("instrument", ),
defaultTemplates={"cycleNumber": "0"}):
camera = connectionTypes.PrerequisiteInput(
doc="Camera instrument",
name="camera",
storageClass="Camera",
dimensions=("instrument", ),
lookupFunction=lookupStaticCalibrations,
isCalibration=True,
)
fgcmLookUpTable = connectionTypes.PrerequisiteInput(
doc=("Atmosphere + instrument look-up-table for FGCM throughput and "
"chromatic corrections."),
name="fgcmLookUpTable",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
fgcmVisitCatalog = connectionTypes.Input(
doc="Catalog of visit information for fgcm",
name="fgcmVisitCatalog",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
fgcmStandardStars = connectionTypes.Input(
doc="Catalog of standard star data from fgcm fit",
name="fgcmStandardStars{cycleNumber}",
storageClass="SimpleCatalog",
dimensions=("instrument", ),
deferLoad=True,
)
fgcmZeropoints = connectionTypes.Input(
doc="Catalog of zeropoints from fgcm fit",
name="fgcmZeropoints{cycleNumber}",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
fgcmAtmosphereParameters = connectionTypes.Input(
doc="Catalog of atmosphere parameters from fgcm fit",
name="fgcmAtmosphereParameters{cycleNumber}",
storageClass="Catalog",
dimensions=("instrument", ),
deferLoad=True,
)
refCat = connectionTypes.PrerequisiteInput(
doc="Reference catalog to use for photometric calibration",
name="cal_ref_cat",
storageClass="SimpleCatalog",
dimensions=("skypix", ),
deferLoad=True,
multiple=True,
)
fgcmPhotoCalib = connectionTypes.Output(
doc=(
"Per-visit photometric calibrations derived from fgcm calibration. "
"These catalogs use detector id for the id and are sorted for "
"fast lookups of a detector."),
name="fgcmPhotoCalibCatalog",
storageClass="ExposureCatalog",
dimensions=(
"instrument",
"visit",
),
multiple=True,
)
fgcmTransmissionAtmosphere = connectionTypes.Output(
doc=
"Per-visit atmosphere transmission files produced from fgcm calibration",
name="transmission_atmosphere_fgcm",
storageClass="TransmissionCurve",
dimensions=(
"instrument",
"visit",
),
multiple=True,
)
fgcmOffsets = connectionTypes.Output(
doc="Per-band offsets computed from doReferenceCalibration",
name="fgcmReferenceCalibrationOffsets",
storageClass="Catalog",
dimensions=("instrument", ),
multiple=False,
)
def __init__(self, *, config=None):
super().__init__(config=config)
if str(int(config.connections.cycleNumber)
) != config.connections.cycleNumber:
raise ValueError("cycleNumber must be of integer format")
if not config.doReferenceCalibration:
self.prerequisiteInputs.remove("refCat")
if not config.doAtmosphereOutput:
self.inputs.remove("fgcmAtmosphereParameters")
if not config.doZeropointOutput:
self.inputs.remove("fgcmZeropoints")
if not config.doReferenceCalibration:
self.outputs.remove("fgcmOffsets")
