fgcmCalibrateTractTable.py

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"""Class for running fgcmcal on a single tract using sourceTable_visit tables.
"""
import numpy as np
 
import lsst.pipe.base as pipeBase
from lsst.pipe.base import connectionTypes
from lsst.meas.algorithms import ReferenceObjectLoader, LoadReferenceObjectsConfig
import lsst.afw.table as afwTable
 
from .fgcmBuildStarsTable import FgcmBuildStarsTableTask
from .fgcmCalibrateTractBase import (FgcmCalibrateTractConfigBase,
                                     FgcmCalibrateTractBaseTask)
from .utilities import lookupStaticCalibrations
 
__all__ = ['FgcmCalibrateTractTableConfig', 'FgcmCalibrateTractTableTask']
 
 
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 FgcmCalibrateTractTableConfig(FgcmCalibrateTractConfigBase, pipeBase.PipelineTaskConfig,
                                    pipelineConnections=FgcmCalibrateTractTableConnections):
    """Config for FgcmCalibrateTractTable task"""
    def setDefaults(self):
        super().setDefaults()
 
        # For the Table version of CalibrateTract, use the associated
        # Table version of the BuildStars task.
        self.fgcmBuildStarsfgcmBuildStars.retarget(FgcmBuildStarsTableTask)
        # For tract mode, we set a very high effective density cut.
        self.fgcmBuildStarsfgcmBuildStars.densityCutMaxPerPixel = 10000
 
 
class FgcmCalibrateTractTableTask(FgcmCalibrateTractBaseTask):
    """
    Calibrate a single tract using fgcmcal, using sourceTable_visit (parquet)
    input catalogs.
    """
    ConfigClass = FgcmCalibrateTractTableConfig
    _DefaultName = "fgcmCalibrateTractTable"
 
    canMultiprocess = False
 
    def __init__(self, initInputs=None, **kwargs):
        super().__init__(initInputs=initInputs, **kwargs)
        if initInputs is not None:
            self.sourceSchemasourceSchema = initInputs["sourceSchema"].schema
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        handleDict = butlerQC.get(inputRefs)
 
        self.log.info("Running with %d sourceTable_visit handles", (len(handleDict['source_catalogs'])))
 
        # Run the build stars tasks
        tract = butlerQC.quantum.dataId['tract']
 
        handleDict['sourceSchema'] = self.sourceSchemasourceSchema
 
        sourceTableHandles = handleDict['source_catalogs']
        sourceTableHandleDict = {sourceTableHandle.dataId['visit']: sourceTableHandle for
                                 sourceTableHandle in sourceTableHandles}
 
        visitSummaryHandles = handleDict['visitSummary']
        visitSummaryHandleDict = {visitSummaryHandle.dataId['visit']: visitSummaryHandle for
                                  visitSummaryHandle in visitSummaryHandles}
 
        handleDict['sourceTableHandleDict'] = sourceTableHandleDict
        handleDict['visitSummaryHandleDict'] = visitSummaryHandleDict
 
        # And the outputs
        if self.config.fgcmOutputProducts.doZeropointOutput:
            photoCalibRefDict = {photoCalibRef.dataId.byName()['visit']:
                                 photoCalibRef for photoCalibRef in outputRefs.fgcmPhotoCalib}
            handleDict['fgcmPhotoCalibs'] = photoCalibRefDict
 
        if self.config.fgcmOutputProducts.doAtmosphereOutput:
            atmRefDict = {atmRef.dataId.byName()['visit']: atmRef for
                          atmRef in outputRefs.fgcmTransmissionAtmosphere}
            handleDict['fgcmTransmissionAtmospheres'] = atmRefDict
 
        if self.config.fgcmBuildStars.doReferenceMatches:
            refConfig = LoadReferenceObjectsConfig()
            refConfig.filterMap = self.config.fgcmBuildStars.fgcmLoadReferenceCatalog.filterMap
            loader = ReferenceObjectLoader(dataIds=[ref.datasetRef.dataId
                                                    for ref in inputRefs.refCat],
                                           refCats=butlerQC.get(inputRefs.refCat),
                                           config=refConfig,
                                           log=self.log)
            buildStarsRefObjLoader = loader
        else:
            buildStarsRefObjLoader = None
 
        if self.config.fgcmOutputProducts.doReferenceCalibration:
            refConfig = self.config.fgcmOutputProducts.refObjLoader
            loader = ReferenceObjectLoader(dataIds=[ref.datasetRef.dataId
                                                    for ref in inputRefs.refCat],
                                           refCats=butlerQC.get(inputRefs.refCat),
                                           config=refConfig,
                                           log=self.log)
            self.fgcmOutputProducts.refObjLoader = loader
 
        struct = self.runrun(handleDict, tract,
                          buildStarsRefObjLoader=buildStarsRefObjLoader)
 
        if struct.photoCalibCatalogs is not None:
            self.log.info("Outputting photoCalib catalogs.")
            for visit, expCatalog in struct.photoCalibCatalogs:
                butlerQC.put(expCatalog, photoCalibRefDict[visit])
            self.log.info("Done outputting photoCalib catalogs.")
 
        if struct.atmospheres is not None:
            self.log.info("Outputting atmosphere transmission files.")
            for visit, atm in struct.atmospheres:
                butlerQC.put(atm, atmRefDict[visit])
            self.log.info("Done outputting atmosphere files.")
 
        # Turn raw repeatability into simple catalog for persistence
        schema = afwTable.Schema()
        schema.addField('rawRepeatability', type=np.float64,
                        doc="Per-band raw repeatability in FGCM calibration.")
        repeatabilityCat = afwTable.BaseCatalog(schema)
        repeatabilityCat.resize(len(struct.repeatability))
        repeatabilityCat['rawRepeatability'][:] = struct.repeatability
 
        butlerQC.put(repeatabilityCat, outputRefs.fgcmRepeatability)
 
        return