lsst.pipe.tasks.characterizeImage.CharacterizeImageTask Class Reference

Measure bright sources and use this to estimate background and PSF of an exposure. More...

Inheritance diagram for lsst.pipe.tasks.characterizeImage.CharacterizeImageTask:

Public Member Functions
def	runQuantum (self, butlerQC, inputRefs, outputRefs)
def	__init__ (self, butler=None, refObjLoader=None, schema=None, **kwargs)
	Construct a CharacterizeImageTask. More...
def	getInitOutputDatasets (self)
def	runDataRef (self, dataRef, exposure=None, background=None, doUnpersist=True)
	Characterize a science image and, if wanted, persist the results. More...
def	run (self, exposure, exposureIdInfo=None, background=None)
	Characterize a science image. More...
def	detectMeasureAndEstimatePsf (self, exposure, exposureIdInfo, background)
	Perform one iteration of detect, measure and estimate PSF. More...
def	getSchemaCatalogs (self)
def	display (self, itemName, exposure, sourceCat=None)

Public Attributes
	schema
	algMetadata
	outputSchema

Static Public Attributes
	ConfigClass = CharacterizeImageConfig
	RunnerClass = pipeBase.ButlerInitializedTaskRunner

Detailed Description

Measure bright sources and use this to estimate background and PSF of an exposure.

@anchor CharacterizeImageTask_

@section pipe_tasks_characterizeImage_Contents  Contents

 - @ref pipe_tasks_characterizeImage_Purpose
 - @ref pipe_tasks_characterizeImage_Initialize
 - @ref pipe_tasks_characterizeImage_IO
 - @ref pipe_tasks_characterizeImage_Config
 - @ref pipe_tasks_characterizeImage_Debug


@section pipe_tasks_characterizeImage_Purpose  Description

Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask):
- detect and measure bright sources
- repair cosmic rays
- measure and subtract background
- measure PSF

@section pipe_tasks_characterizeImage_Initialize  Task initialisation

@copydoc \_\_init\_\_

@section pipe_tasks_characterizeImage_IO  Invoking the Task

If you want this task to unpersist inputs or persist outputs, then call
the `runDataRef` method (a thin wrapper around the `run` method).

If you already have the inputs unpersisted and do not want to persist the output
then it is more direct to call the `run` method:

@section pipe_tasks_characterizeImage_Config  Configuration parameters

See @ref CharacterizeImageConfig

@section pipe_tasks_characterizeImage_Debug  Debug variables

The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
`--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.

CharacterizeImageTask has a debug dictionary with the following keys:
<dl>
<dt>frame
<dd>int: if specified, the frame of first debug image displayed (defaults to 1)
<dt>repair_iter
<dd>bool; if True display image after each repair in the measure PSF loop
<dt>background_iter
<dd>bool; if True display image after each background subtraction in the measure PSF loop
<dt>measure_iter
<dd>bool; if True display image and sources at the end of each iteration of the measure PSF loop
    See @ref lsst.meas.astrom.displayAstrometry for the meaning of the various symbols.
<dt>psf
<dd>bool; if True display image and sources after PSF is measured;
    this will be identical to the final image displayed by measure_iter if measure_iter is true
<dt>repair
<dd>bool; if True display image and sources after final repair
<dt>measure
<dd>bool; if True display image and sources after final measurement
</dl>

For example, put something like:
@code{.py}
    import lsstDebug
    def DebugInfo(name):
        di = lsstDebug.getInfo(name)  # N.b. lsstDebug.Info(name) would call us recursively
        if name == "lsst.pipe.tasks.characterizeImage":
            di.display = dict(
                repair = True,
            )

        return di

    lsstDebug.Info = DebugInfo
@endcode
into your `debug.py` file and run `calibrateTask.py` with the `--debug` flag.

Some subtasks may have their own debug variables; see individual Task documentation.

Definition at line 250 of file characterizeImage.py.

Constructor & Destructor Documentation

__init__()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.__init__	(		self,
			butler = None,
			refObjLoader = None,
			schema = None,
		**	kwargs
	)

Construct a CharacterizeImageTask.

    @param[in] butler  A butler object is passed to the refObjLoader constructor in case
        it is needed to load catalogs.  May be None if a catalog-based star selector is
        not used, if the reference object loader constructor does not require a butler,
        or if a reference object loader is passed directly via the refObjLoader argument.
    @param[in] refObjLoader  An instance of LoadReferenceObjectsTasks that supplies an
        external reference catalog to a catalog-based star selector.  May be None if a
        catalog star selector is not used or the loader can be constructed from the
        butler argument.
    @param[in,out] schema  initial schema (an lsst.afw.table.SourceTable), or None
    @param[in,out] kwargs  other keyword arguments for lsst.pipe.base.CmdLineTask

Definition at line 345 of file characterizeImage.py.

    def __init__(self, butler=None, refObjLoader=None, schema=None, **kwargs):
        """!Construct a CharacterizeImageTask
 
        @param[in] butler  A butler object is passed to the refObjLoader constructor in case
            it is needed to load catalogs.  May be None if a catalog-based star selector is
            not used, if the reference object loader constructor does not require a butler,
            or if a reference object loader is passed directly via the refObjLoader argument.
        @param[in] refObjLoader  An instance of LoadReferenceObjectsTasks that supplies an
            external reference catalog to a catalog-based star selector.  May be None if a
            catalog star selector is not used or the loader can be constructed from the
            butler argument.
        @param[in,out] schema  initial schema (an lsst.afw.table.SourceTable), or None
        @param[in,out] kwargs  other keyword arguments for lsst.pipe.base.CmdLineTask
        """
        super().__init__(**kwargs)
 
        if schema is None:
            schema = SourceTable.makeMinimalSchema()
        self.schema = schema
        self.makeSubtask("background")
        self.makeSubtask("installSimplePsf")
        self.makeSubtask("repair")
        self.makeSubtask("measurePsf", schema=self.schema)
        if self.config.doMeasurePsf and self.measurePsf.usesMatches:
            if not refObjLoader:
                self.makeSubtask('refObjLoader', butler=butler)
                refObjLoader = self.refObjLoader
            self.makeSubtask("ref_match", refObjLoader=refObjLoader)
        self.algMetadata = dafBase.PropertyList()
        self.makeSubtask('detection', schema=self.schema)
        if self.config.doDeblend:
            self.makeSubtask("deblend", schema=self.schema)
        self.makeSubtask('measurement', schema=self.schema, algMetadata=self.algMetadata)
        if self.config.doApCorr:
            self.makeSubtask('measureApCorr', schema=self.schema)
            self.makeSubtask('applyApCorr', schema=self.schema)
        self.makeSubtask('catalogCalculation', schema=self.schema)
        self._initialFrame = getDebugFrame(self._display, "frame") or 1
        self._frame = self._initialFrame
        self.schema.checkUnits(parse_strict=self.config.checkUnitsParseStrict)
        self.outputSchema = afwTable.SourceCatalog(self.schema)
 

Member Function Documentation

detectMeasureAndEstimatePsf()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.detectMeasureAndEstimatePsf	(		self,
			exposure,
			exposureIdInfo,
			background
	)

Perform one iteration of detect, measure and estimate PSF.

    Performs the following operations:
    - if config.doMeasurePsf or not exposure.hasPsf():
        - install a simple PSF model (replacing the existing one, if need be)
    - interpolate over cosmic rays with keepCRs=True
    - estimate background and subtract it from the exposure
    - detect, deblend and measure sources, and subtract a refined background model;
    - if config.doMeasurePsf:
        - measure PSF

    @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar)
        The following changes are made:
        - update or set psf
        - update detection and cosmic ray mask planes
        - subtract background
    @param[in] exposureIdInfo  ID info for exposure (an lsst.obs_base.ExposureIdInfo)
    @param[in,out] background  initial model of background already subtracted from exposure
        (an lsst.afw.math.BackgroundList).

    @return pipe_base Struct containing these fields, all from the final iteration
    of detect sources, measure sources and estimate PSF:
    - exposure  characterized exposure; image is repaired by interpolating over cosmic rays,
        mask is updated accordingly, and the PSF model is set
    - sourceCat  detected sources (an lsst.afw.table.SourceCatalog)
    - background  model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
    - psfCellSet  spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)

Definition at line 530 of file characterizeImage.py.

    def detectMeasureAndEstimatePsf(self, exposure, exposureIdInfo, background):
        """!Perform one iteration of detect, measure and estimate PSF
 
        Performs the following operations:
        - if config.doMeasurePsf or not exposure.hasPsf():
            - install a simple PSF model (replacing the existing one, if need be)
        - interpolate over cosmic rays with keepCRs=True
        - estimate background and subtract it from the exposure
        - detect, deblend and measure sources, and subtract a refined background model;
        - if config.doMeasurePsf:
            - measure PSF
 
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar)
            The following changes are made:
            - update or set psf
            - update detection and cosmic ray mask planes
            - subtract background
        @param[in] exposureIdInfo  ID info for exposure (an lsst.obs_base.ExposureIdInfo)
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList).
 
        @return pipe_base Struct containing these fields, all from the final iteration
        of detect sources, measure sources and estimate PSF:
        - exposure  characterized exposure; image is repaired by interpolating over cosmic rays,
            mask is updated accordingly, and the PSF model is set
        - sourceCat  detected sources (an lsst.afw.table.SourceCatalog)
        - background  model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
        - psfCellSet  spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
        """
        # install a simple PSF model, if needed or wanted
        if not exposure.hasPsf() or (self.config.doMeasurePsf and self.config.useSimplePsf):
            self.log.info("PSF estimation initialized with 'simple' PSF")
            self.installSimplePsf.run(exposure=exposure)
 
        # run repair, but do not interpolate over cosmic rays (do that elsewhere, with the final PSF model)
        if self.config.requireCrForPsf:
            self.repair.run(exposure=exposure, keepCRs=True)
        else:
            try:
                self.repair.run(exposure=exposure, keepCRs=True)
            except LengthError:
                self.log.warning("Skipping cosmic ray detection: Too many CR pixels (max %0.f)",
                                 self.config.repair.cosmicray.nCrPixelMax)
 
        self.display("repair_iter", exposure=exposure)
 
        if background is None:
            background = BackgroundList()
 
        sourceIdFactory = exposureIdInfo.makeSourceIdFactory()
        table = SourceTable.make(self.schema, sourceIdFactory)
        table.setMetadata(self.algMetadata)
 
        detRes = self.detection.run(table=table, exposure=exposure, doSmooth=True)
        sourceCat = detRes.sources
        if detRes.fpSets.background:
            for bg in detRes.fpSets.background:
                background.append(bg)
 
        if self.config.doDeblend:
            self.deblend.run(exposure=exposure, sources=sourceCat)
 
        self.measurement.run(measCat=sourceCat, exposure=exposure, exposureId=exposureIdInfo.expId)
 
        measPsfRes = pipeBase.Struct(cellSet=None)
        if self.config.doMeasurePsf:
            if self.measurePsf.usesMatches:
                matches = self.ref_match.loadAndMatch(exposure=exposure, sourceCat=sourceCat).matches
            else:
                matches = None
            measPsfRes = self.measurePsf.run(exposure=exposure, sources=sourceCat, matches=matches,
                                             expId=exposureIdInfo.expId)
        self.display("measure_iter", exposure=exposure, sourceCat=sourceCat)
 
        return pipeBase.Struct(
            exposure=exposure,
            sourceCat=sourceCat,
            background=background,
            psfCellSet=measPsfRes.cellSet,
        )
 

display()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.display	(		self,
			itemName,
			exposure,
			sourceCat = None
	)

Display exposure and sources on next frame, if display of itemName has been requested

@param[in] itemName  name of item in debugInfo
@param[in] exposure  exposure to display
@param[in] sourceCat  source catalog to display

Definition at line 618 of file characterizeImage.py.

    def display(self, itemName, exposure, sourceCat=None):
        """Display exposure and sources on next frame, if display of itemName has been requested
 
        @param[in] itemName  name of item in debugInfo
        @param[in] exposure  exposure to display
        @param[in] sourceCat  source catalog to display
        """
        val = getDebugFrame(self._display, itemName)
        if not val:
            return
 
        displayAstrometry(exposure=exposure, sourceCat=sourceCat, frame=self._frame, pause=False)
        self._frame += 1

getInitOutputDatasets()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.getInitOutputDatasets

(

self

)

Definition at line 387 of file characterizeImage.py.

    def getInitOutputDatasets(self):
        outputCatSchema = afwTable.SourceCatalog(self.schema)
        outputCatSchema.getTable().setMetadata(self.algMetadata)
        return {'outputSchema': outputCatSchema}
 

getSchemaCatalogs()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.getSchemaCatalogs

(

self

)

Return a dict of empty catalogs for each catalog dataset produced by this task.

Definition at line 611 of file characterizeImage.py.

    def getSchemaCatalogs(self):
        """Return a dict of empty catalogs for each catalog dataset produced by this task.
        """
        sourceCat = SourceCatalog(self.schema)
        sourceCat.getTable().setMetadata(self.algMetadata)
        return {"icSrc": sourceCat}
 

run()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.run	(		self,
			exposure,
			exposureIdInfo = None,
			background = None
	)

Characterize a science image.

    Peforms the following operations:
    - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
        - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
    - interpolate over cosmic rays
    - perform final measurement

    @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
        The following changes are made:
        - update or set psf
        - set apCorrMap
        - update detection and cosmic ray mask planes
        - subtract background and interpolate over cosmic rays
    @param[in] exposureIdInfo  ID info for exposure (an lsst.obs.base.ExposureIdInfo).
        If not provided, returned SourceCatalog IDs will not be globally unique.
    @param[in,out] background  initial model of background already subtracted from exposure
        (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
        which is typical for image characterization.

    @return pipe_base Struct containing these fields, all from the final iteration
    of detectMeasureAndEstimatePsf:
    - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
        mask is updated accordingly, and the PSF model is set
    - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
    - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
    - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)

Definition at line 445 of file characterizeImage.py.

    def run(self, exposure, exposureIdInfo=None, background=None):
        """!Characterize a science image
 
        Peforms the following operations:
        - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:
            - detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)
        - interpolate over cosmic rays
        - perform final measurement
 
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
            The following changes are made:
            - update or set psf
            - set apCorrMap
            - update detection and cosmic ray mask planes
            - subtract background and interpolate over cosmic rays
        @param[in] exposureIdInfo  ID info for exposure (an lsst.obs.base.ExposureIdInfo).
            If not provided, returned SourceCatalog IDs will not be globally unique.
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
            which is typical for image characterization.
 
        @return pipe_base Struct containing these fields, all from the final iteration
        of detectMeasureAndEstimatePsf:
        - exposure: characterized exposure; image is repaired by interpolating over cosmic rays,
            mask is updated accordingly, and the PSF model is set
        - sourceCat: detected sources (an lsst.afw.table.SourceCatalog)
        - background: model of background subtracted from exposure (an lsst.afw.math.BackgroundList)
        - psfCellSet: spatial cells of PSF candidates (an lsst.afw.math.SpatialCellSet)
        """
        self._frame = self._initialFrame  # reset debug display frame
 
        if not self.config.doMeasurePsf and not exposure.hasPsf():
            self.log.info("CharacterizeImageTask initialized with 'simple' PSF.")
            self.installSimplePsf.run(exposure=exposure)
 
        if exposureIdInfo is None:
            exposureIdInfo = ExposureIdInfo()
 
        # subtract an initial estimate of background level
        background = self.background.run(exposure).background
 
        psfIterations = self.config.psfIterations if self.config.doMeasurePsf else 1
        for i in range(psfIterations):
            dmeRes = self.detectMeasureAndEstimatePsf(
                exposure=exposure,
                exposureIdInfo=exposureIdInfo,
                background=background,
            )
 
            psf = dmeRes.exposure.getPsf()
            psfSigma = psf.computeShape().getDeterminantRadius()
            psfDimensions = psf.computeImage().getDimensions()
            medBackground = np.median(dmeRes.background.getImage().getArray())
            self.log.info("iter %s; PSF sigma=%0.2f, dimensions=%s; median background=%0.2f",
                          i + 1, psfSigma, psfDimensions, medBackground)
 
        self.display("psf", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        # perform final repair with final PSF
        self.repair.run(exposure=dmeRes.exposure)
        self.display("repair", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        # perform final measurement with final PSF, including measuring and applying aperture correction,
        # if wanted
        self.measurement.run(measCat=dmeRes.sourceCat, exposure=dmeRes.exposure,
                             exposureId=exposureIdInfo.expId)
        if self.config.doApCorr:
            apCorrMap = self.measureApCorr.run(exposure=dmeRes.exposure, catalog=dmeRes.sourceCat).apCorrMap
            dmeRes.exposure.getInfo().setApCorrMap(apCorrMap)
            self.applyApCorr.run(catalog=dmeRes.sourceCat, apCorrMap=exposure.getInfo().getApCorrMap())
        self.catalogCalculation.run(dmeRes.sourceCat)
 
        self.display("measure", exposure=dmeRes.exposure, sourceCat=dmeRes.sourceCat)
 
        return pipeBase.Struct(
            exposure=dmeRes.exposure,
            sourceCat=dmeRes.sourceCat,
            background=dmeRes.background,
            psfCellSet=dmeRes.psfCellSet,
 
            characterized=dmeRes.exposure,
            backgroundModel=dmeRes.background
        )
 

runDataRef()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.runDataRef	(		self,
			dataRef,
			exposure = None,
			background = None,
			doUnpersist = True
	)

Characterize a science image and, if wanted, persist the results.

    This simply unpacks the exposure and passes it to the characterize method to do the work.

    @param[in] dataRef: butler data reference for science exposure
    @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
        If None then unpersist from "postISRCCD".
        The following changes are made, depending on the config:
        - set psf to the measured PSF
        - set apCorrMap to the measured aperture correction
        - subtract background
        - interpolate over cosmic rays
        - update detection and cosmic ray mask planes
    @param[in,out] background  initial model of background already subtracted from exposure
        (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
        which is typical for image characterization.
        A refined background model is output.
    @param[in] doUnpersist  if True the exposure is read from the repository
        and the exposure and background arguments must be None;
        if False the exposure must be provided.
        True is intended for running as a command-line task, False for running as a subtask

    @return same data as the characterize method

Definition at line 393 of file characterizeImage.py.

    def runDataRef(self, dataRef, exposure=None, background=None, doUnpersist=True):
        """!Characterize a science image and, if wanted, persist the results
 
        This simply unpacks the exposure and passes it to the characterize method to do the work.
 
        @param[in] dataRef: butler data reference for science exposure
        @param[in,out] exposure  exposure to characterize (an lsst.afw.image.ExposureF or similar).
            If None then unpersist from "postISRCCD".
            The following changes are made, depending on the config:
            - set psf to the measured PSF
            - set apCorrMap to the measured aperture correction
            - subtract background
            - interpolate over cosmic rays
            - update detection and cosmic ray mask planes
        @param[in,out] background  initial model of background already subtracted from exposure
            (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted,
            which is typical for image characterization.
            A refined background model is output.
        @param[in] doUnpersist  if True the exposure is read from the repository
            and the exposure and background arguments must be None;
            if False the exposure must be provided.
            True is intended for running as a command-line task, False for running as a subtask
 
        @return same data as the characterize method
        """
        self._frame = self._initialFrame  # reset debug display frame
        self.log.info("Processing %s", dataRef.dataId)
 
        if doUnpersist:
            if exposure is not None or background is not None:
                raise RuntimeError("doUnpersist true; exposure and background must be None")
            exposure = dataRef.get("postISRCCD", immediate=True)
        elif exposure is None:
            raise RuntimeError("doUnpersist false; exposure must be provided")
 
        exposureIdInfo = dataRef.get("expIdInfo")
 
        charRes = self.run(
            exposure=exposure,
            exposureIdInfo=exposureIdInfo,
            background=background,
        )
 
        if self.config.doWrite:
            dataRef.put(charRes.sourceCat, "icSrc")
            if self.config.doWriteExposure:
                dataRef.put(charRes.exposure, "icExp")
                dataRef.put(charRes.background, "icExpBackground")
 
        return charRes
 

runQuantum()

def lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.runQuantum	(		self,
			butlerQC,
			inputRefs,
			outputRefs
	)

Definition at line 338 of file characterizeImage.py.

    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        if 'exposureIdInfo' not in inputs.keys():
            inputs['exposureIdInfo'] = ExposureIdInfo.fromDataId(butlerQC.quantum.dataId, "visit_detector")
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 

Member Data Documentation

algMetadata

lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.algMetadata

Definition at line 373 of file characterizeImage.py.

ConfigClass

lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.ConfigClass = CharacterizeImageConfig

static

Definition at line 334 of file characterizeImage.py.

outputSchema

lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.outputSchema

Definition at line 385 of file characterizeImage.py.

RunnerClass

lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.RunnerClass = pipeBase.ButlerInitializedTaskRunner

static

Definition at line 336 of file characterizeImage.py.

schema

lsst.pipe.tasks.characterizeImage.CharacterizeImageTask.schema

Definition at line 363 of file characterizeImage.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.7.0/Linux64/pipe_tasks/21.0.0-147-g0e635eb1+1acddb5be5/python/lsst/pipe/tasks/characterizeImage.py