lsst.pipe.tasks.selectImages Namespace Reference

Classes
class	BaseExposureInfo
class	BaseSelectImagesTask
class	BestSeeingSelectVisitsConnections
class	DatabaseSelectImagesConfig
class	PsfWcsSelectImagesConnections
class	PsfWcsSelectImagesTask
class	SelectStruct
class	WcsSelectImagesConfig
class	WcsSelectImagesTask

Functions
	_meetsVisitSummaryMinValues (visit, visitSummary, visitSummaryMinValues, logger=None)
	_extractKeyValue (dataList, keys=None)
	validate (self)

Variables
	excludeDetectors
	skyMap : `lsst.afw.table.ExposureCatalog`
	dataId : `dict` of dataId keys
	result : `lsst.sphgeom.ConvexPolygon.convexHull`
	minNPsfStarPerBand

Function Documentation

_extractKeyValue()

lsst.pipe.tasks.selectImages._extractKeyValue ( dataList, keys = None )

protected

Extract the keys and values from a list of dataIds.

The input dataList is a list of objects that have 'dataId' members.
This allows it to be used for both a list of data references and a
list of ExposureInfo.

Parameters
----------
dataList : `Unknown`
keys : `Unknown`

Returns
-------
keys : `Unknown`
values : `Unknown`

Raises
------
RuntimeError
    Raised if DataId keys are inconsistent.

Definition at line 142 of file selectImages.py.

def _extractKeyValue(dataList, keys=None):
    """Extract the keys and values from a list of dataIds.
 
    The input dataList is a list of objects that have 'dataId' members.
    This allows it to be used for both a list of data references and a
    list of ExposureInfo.
 
    Parameters
    ----------
    dataList : `Unknown`
    keys : `Unknown`
 
    Returns
    -------
    keys : `Unknown`
    values : `Unknown`
 
    Raises
    ------
    RuntimeError
        Raised if DataId keys are inconsistent.
    """
    assert len(dataList) > 0
    if keys is None:
        keys = sorted(dataList[0].dataId.keys())
    keySet = set(keys)
    values = list()
    for data in dataList:
        thisKeys = set(data.dataId.keys())
        if thisKeys != keySet:
            raise RuntimeError("DataId keys inconsistent: %s vs %s" % (keySet, thisKeys))
        values.append(tuple(data.dataId[k] for k in keys))
    return keys, values
 
 

_meetsVisitSummaryMinValues()

lsst.pipe.tasks.selectImages._meetsVisitSummaryMinValues ( visit, visitSummary, visitSummaryMinValues, logger = None )

protected

Check if the visitSummary meets minimum values in visitSummaryMinValues.

Parameters
----------
visit : `int`
    Visit number.
visitSummary : `lsst.afw.table.ExposureCatalog`
    Exposure catalog with per-detector summary information.
visitSummaryMinValues : `dict`
    Dictionary with column names as keys and minimum allowed values as values.
logger : `lsst.log.Logger`
    Logger to log debug and warning messages.

Returns
-------
result : `bool`
    True if all columns in visitSummary meet the minimum values specified
    in visitSummaryMinValues, False otherwise.

Definition at line 37 of file selectImages.py.

def _meetsVisitSummaryMinValues(visit, visitSummary, visitSummaryMinValues, logger=None):
    """Check if the visitSummary meets minimum values in visitSummaryMinValues.
 
    Parameters
    ----------
    visit : `int`
        Visit number.
    visitSummary : `lsst.afw.table.ExposureCatalog`
        Exposure catalog with per-detector summary information.
    visitSummaryMinValues : `dict`
        Dictionary with column names as keys and minimum allowed values as values.
    logger : `lsst.log.Logger`
        Logger to log debug and warning messages.
 
    Returns
    -------
    result : `bool`
        True if all columns in visitSummary meet the minimum values specified
        in visitSummaryMinValues, False otherwise.
    """
    for columnName, minThreshold in visitSummaryMinValues.items():
        # Get values for this column across all detectors with a valid WCS
        values = np.asarray([vs[columnName] for vs in visitSummary if vs.getWcs()])
        meanValue = np.nanmean(values)
        if meanValue < minThreshold:
            if logger is not None:
                logger.info(f'Rejecting visit {visit}: mean {columnName} ({meanValue:.3f}) '
                            f'is below the minimum threshold ({minThreshold:.3f}).')
            return False
    return True
 
 

validate()

lsst.pipe.tasks.selectImages.validate

(

self

)

Definition at line 358 of file selectImages.py.

    def validate(self):
        super().validate()
        if "fallback" not in self.minNPsfStarPerBand:
            msg = ("Must include a \"fallback\" key in the config.minNPsfStarPerBand config dict. "
                   f"It is currenly: {self.minNPsfStarPerBand}.")
            raise pexConfig.FieldValidationError(self.__class__.minNPsfStarPerBand, self, msg)
 
 

Variable Documentation

dataId

lsst.pipe.tasks.selectImages.dataId : `dict` of dataId keys

Definition at line 689 of file selectImages.py.

excludeDetectors

lsst.pipe.tasks.selectImages.excludeDetectors

Initial value:

=  pexConfig.ListField(
        dtype=int,
        default=[],
        doc="Detectors to exclude from selection.",
        optional=True,
    )

Definition at line 351 of file selectImages.py.

minNPsfStarPerBand

lsst.pipe.tasks.selectImages.minNPsfStarPerBand

Definition at line 360 of file selectImages.py.

result

lsst.pipe.tasks.selectImages.result : `lsst.sphgeom.ConvexPolygon.convexHull`

Definition at line 694 of file selectImages.py.

skyMap

lsst.pipe.tasks.selectImages.skyMap : `lsst.afw.table.ExposureCatalog`

if self.config.doConfirmOverlap:
    patchPolygon = self.makePatchPolygon(skyMap, dataId)

inputVisits = [visitSummary.ref.dataId['visit'] for visitSummary in visitSummaries]
fwhmSizes = []
visits = []
for visit, visitSummary in zip(inputVisits, visitSummaries):
    # read in one-by-one and only once. There may be hundreds
    visitSummary = visitSummary.get()

    # mjd is guaranteed to be the same for every detector in the
    # visitSummary.
    mjd = visitSummary[0].getVisitInfo().getDate().get(system=DateTime.MJD)

    pixelScales = np.array([vs['pixelScale'] for vs in visitSummary if vs.getWcs()])
    # psfSigma is PSF model determinant radius at chip center in pixels
    psfSigmas = np.array([vs['psfSigma'] for vs in visitSummary if vs.getWcs()])
    fwhm = np.nanmean(psfSigmas * pixelScales) * np.sqrt(8.*np.log(2.))

    if self.config.maxPsfFwhm and fwhm > self.config.maxPsfFwhm:
        continue
    if self.config.minPsfFwhm and fwhm < self.config.minPsfFwhm:
        continue
    if self.config.minMJD and mjd < self.config.minMJD:
        self.log.debug('MJD %f earlier than %.2f; rejecting', mjd, self.config.minMJD)
        continue
    if self.config.maxMJD and mjd > self.config.maxMJD:
        self.log.debug('MJD %f later than %.2f;  rejecting', mjd, self.config.maxMJD)
        continue
    if self.config.doConfirmOverlap and not self.doesIntersectPolygon(visitSummary, patchPolygon):
        continue
    if (self.config.visitSummaryMinValues
        and not _meetsVisitSummaryMinValues(visit, visitSummary, self.config.visitSummaryMinValues,
                                            self.log)):
        continue

    fwhmSizes.append(fwhm)
    visits.append(visit)

sortedVisits = [ind for (_, ind) in sorted(zip(fwhmSizes, visits))]
if self.config.nVisitsMax < 0:
    output = sortedVisits
else:
    output = sortedVisits[:self.config.nVisitsMax]

if len(output) == 0:
    self.log.info("All images rejected in BestSeeingSelectVisitsTask.")
    raise pipeBase.NoWorkFound(f"No good images found for {dataId}")
else:
    self.log.info(
        "%d images selected with FWHM range of %f--%f arcseconds",
        len(output),
        fwhmSizes[visits.index(output[0])],
        fwhmSizes[visits.index(output[-1])],
    )

# In order to store as a StructuredDataDict, convert list to dict
goodVisits = {key: True for key in output}
return pipeBase.Struct(goodVisits=goodVisits)

def makePatchPolygon(self, skyMap, dataId):

Definition at line 687 of file selectImages.py.