CalibrateImageTask#

class lsst.pipe.tasks.calibrateImage.CalibrateImageTask(initial_stars_schema=None, **kwargs)#

Bases: PipelineTask

Compute the PSF, aperture corrections, astrometric and photometric calibrations, and summary statistics for a single science exposure, and produce a catalog of brighter stars that were used to calibrate it.

Parameters#

initial_stars_schemalsst.afw.table.Schema: Schema of the initial_stars output catalog.

Methods Summary

`run`(*, exposures[, id_generator, result, ...])	Find stars and perform psf measurement, then do a deeper detection and measurement and calibrate astrometry and photometry from that.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks `run` method.

Methods Documentation

run(*, exposures, id_generator=None, result=None, background_flat=None, illumination_correction=None, camera_model=None)#

Find stars and perform psf measurement, then do a deeper detection and measurement and calibrate astrometry and photometry from that.

Parameters#

exposureslsst.afw.image.Exposure or list [lsst.afw.image.Exposure]: Post-ISR exposure(s), with an initial WCS, VisitInfo, and Filter. Modified in-place during processing if only one is passed. If two exposures are passed, treat them as snaps and combine before doing further processing.
id_generatorlsst.meas.base.IdGenerator, optional: Object that generates source IDs and provides random seeds.
resultlsst.pipe.base.Struct, optional: Result struct that is modified to allow saving of partial outputs for some failure conditions. If the task completes successfully, this is also returned.
background_flatlsst.afw.image.Exposure, optional: Background flat-field image.
illumination_correctionlsst.afw.image.Exposure, optional: Illumination correction image.
camera_modellsst.afw.cameraGeom.Camera, optional: Camera to be used if constructing updated WCS.

Returns#

resultlsst.pipe.base.Struct

Results as a struct with attributes:

exposure: Calibrated exposure, with pixels in nJy units. (lsst.afw.image.Exposure)
stars: Stars that were used to calibrate the exposure, with calibrated fluxes and magnitudes. (astropy.table.Table)
stars_footprints: Footprints of stars that were used to calibrate the exposure. (lsst.afw.table.SourceCatalog)
psf_stars: Stars that were used to determine the image PSF. (astropy.table.Table)
psf_stars_footprints: Footprints of stars that were used to determine the image PSF. (lsst.afw.table.SourceCatalog)
background: Background that was fit to the exposure when detecting stars. (lsst.afw.math.BackgroundList)
applied_photo_calib: Photometric calibration that was fit to the star catalog and applied to the exposure. (lsst.afw.image.PhotoCalib) This is None if config.do_calibrate_pixels is False.
astrometry_matches: Reference catalog stars matches used in the astrometric fit. (list [lsst.afw.table.ReferenceMatch] or lsst.afw.table.BaseCatalog).
photometry_matches: Reference catalog stars matches used in the photometric fit. (list [lsst.afw.table.ReferenceMatch] or lsst.afw.table.BaseCatalog).
mask: Copy of the mask plane of exposure. (lsst.afw.image.Mask)

runQuantum(butlerQC, inputRefs, outputRefs)#

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters#

butlerQCQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefsInputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefsOutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.