|
lsst.fgcmcal g5ec818987f+4ea614f49a
|
Classes | |
| class | FgcmOutputProductsConnections |
Variables | |
| handleDict : `dict` | |
| physicalFilterMap : `dict` | |
| retStruct : `lsst.pipe.base.Struct` | |
| offsets : `np.ndarray` | |
| atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)] | |
| photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)] | |
| tract : `int` | |
| visitCat : `lsst.afw.table.BaseCatalog` | |
| zptCat : `lsst.afw.table.BaseCatalog` | |
| atmCat : `lsst.afw.table.BaseCatalog` | |
| stdCat : `lsst.afw.table.SimpleCatalog` | |
| fgcmBuildStarsConfig : `lsst.fgcmcal.FgcmBuildStarsConfig` | |
| lutCat : `lsst.afw.table.SimpleCatalog` | |
| bands : `list` [`str`] | |
| sourceMapper : `lsst.afw.table.SchemaMapper` | |
| badStarKey : `lsst.afw.table.Key` | |
| b_index : `int` | |
| filterLabel : `lsst.afw.image.FilterLabel` | |
| selected : `numpy.array(dtype=bool)` | |
| refFluxFields : `list` | |
| camera : `lsst.afw.cameraGeom.Camera` | |
| coefficients : `numpy.array` | |
| int | xyMax : `list` of length 2 |
| offset : `float`, optional | |
| scaling : `float`, optional | |
| boundedField : `lsst.afw.math.ChebyshevBoundedField` | |
Make the final fgcmcal output products. This task takes the final output from fgcmFitCycle and produces the following outputs for use in the DM stack: the FGCM standard stars in a reference catalog format; the model atmospheres in "transmission_atmosphere_fgcm" format; and the zeropoints in "fgcm_photoCalib" format. Optionally, the task can transfer the 'absolute' calibration from a reference catalog to put the fgcm standard stars in units of Jansky. This is accomplished by matching stars in a sample of healpix pixels, and applying the median offset per band.
| lsst.fgcmcal.fgcmOutputProducts.atmCat : `lsst.afw.table.BaseCatalog` |
Definition at line 426 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.atmospheres : `generator` [(`int`, `lsst.afw.image.TransmissionCurve`)] |
Definition at line 363 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.b_index : `int` |
Definition at line 636 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.badStarKey : `lsst.afw.table.Key` |
Definition at line 634 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.bands : `list` [`str`] |
Definition at line 498 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.boundedField : `lsst.afw.math.ChebyshevBoundedField` |
Definition at line 839 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.camera : `lsst.afw.cameraGeom.Camera` |
sourceCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema())
sourceCat.reserve(selected.sum())
sourceCat.extend(stdCat[selected], mapper=sourceMapper)
sourceCat['instFlux'] = 10.**(stdCat['mag_std_noabs'][selected, b_index]/(-2.5))
sourceCat['instFluxErr'] = (np.log(10.)/2.5)*(stdCat['magErr_std'][selected, b_index]
* sourceCat['instFlux'])
# Make sure we only use stars that have valid measurements
# (This is perhaps redundant with requirements above that the
# stars be observed in all bands, but it can't hurt)
badStar = (stdCat['mag_std_noabs'][selected, b_index] > 90.0)
for rec in sourceCat[badStar]:
rec.set(badStarKey, True)
exposure = afwImage.ExposureF()
exposure.setFilter(filterLabel)
if refFluxFields[b_index] is None:
# Need to find the flux field in the reference catalog
# to work around limitations of DirectMatch in PhotoCal
ctr = stdCat[0].getCoord()
rad = 0.05*lsst.geom.degrees
refDataTest = self.refObjLoader.loadSkyCircle(ctr, rad, filterLabel.bandLabel)
refFluxFields[b_index] = refDataTest.fluxField
# Make a copy of the config so that we can modify it
calConfig = copy.copy(self.config.photoCal.value)
calConfig.match.referenceSelection.signalToNoise.fluxField = refFluxFields[b_index]
calConfig.match.referenceSelection.signalToNoise.errField = refFluxFields[b_index] + 'Err'
calTask = self.config.photoCal.target(refObjLoader=self.refObjLoader,
config=calConfig,
schema=sourceCat.getSchema())
struct = calTask.run(exposure, sourceCat)
return struct
def _outputZeropoints(self, camera, zptCat, visitCat, offsets, bands,
physicalFilterMap, tract=None):
Definition at line 690 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.coefficients : `numpy.array` |
# Select visit/ccds where we have a calibration
# This includes ccds where we were able to interpolate from neighboring
# ccds.
cannot_compute = fgcm.fgcmUtilities.zpFlagDict['CANNOT_COMPUTE_ZEROPOINT']
selected = (((zptCat['fgcmFlag'] & cannot_compute) == 0)
& (zptCat['fgcmZptVar'] > 0.0)
& (zptCat['fgcmZpt'] > FGCM_ILLEGAL_VALUE))
# Log warnings for any visit which has no valid zeropoints
badVisits = np.unique(zptCat['visit'][~selected])
goodVisits = np.unique(zptCat['visit'][selected])
allBadVisits = badVisits[~np.isin(badVisits, goodVisits)]
for allBadVisit in allBadVisits:
self.log.warning(f'No suitable photoCalib for visit {allBadVisit}')
# Get a mapping from filtername to the offsets
offsetMapping = {}
for f in physicalFilterMap:
# Not every filter in the map will necesarily have a band.
if physicalFilterMap[f] in bands:
offsetMapping[f] = offsets[bands.index(physicalFilterMap[f])]
# Get a mapping from "ccd" to the ccd index used for the scaling
ccdMapping = {}
for ccdIndex, detector in enumerate(camera):
ccdMapping[detector.getId()] = ccdIndex
# And a mapping to get the flat-field scaling values
scalingMapping = {}
for rec in visitCat:
scalingMapping[rec['visit']] = rec['scaling']
if self.config.doComposeWcsJacobian:
approxPixelAreaFields = computeApproxPixelAreaFields(camera)
# The zptCat is sorted by visit, which is useful
lastVisit = -1
zptVisitCatalog = None
metadata = dafBase.PropertyList()
metadata.add("COMMENT", "Catalog id is detector id, sorted.")
metadata.add("COMMENT", "Only detectors with data have entries.")
for rec in zptCat[selected]:
# Retrieve overall scaling
scaling = scalingMapping[rec['visit']][ccdMapping[rec['detector']]]
# The postCalibrationOffset describe any zeropoint offsets
# to apply after the fgcm calibration. The first part comes
# from the reference catalog match (used in testing). The
# second part comes from the mean chromatic correction
# (if configured).
postCalibrationOffset = offsetMapping[rec['filtername']]
if self.config.doApplyMeanChromaticCorrection:
postCalibrationOffset += rec['fgcmDeltaChrom']
fgcmSuperStarField = self._getChebyshevBoundedField(rec['fgcmfZptSstarCheb'],
rec['fgcmfZptChebXyMax'])
# Convert from FGCM AB to nJy
fgcmZptField = self._getChebyshevBoundedField((rec['fgcmfZptCheb']*units.AB).to_value(units.nJy),
rec['fgcmfZptChebXyMax'],
offset=postCalibrationOffset,
scaling=scaling)
if self.config.doComposeWcsJacobian:
fgcmField = afwMath.ProductBoundedField([approxPixelAreaFields[rec['detector']],
fgcmSuperStarField,
fgcmZptField])
else:
# The photoCalib is just the product of the fgcmSuperStarField and the
# fgcmZptField
fgcmField = afwMath.ProductBoundedField([fgcmSuperStarField, fgcmZptField])
# The "mean" calibration will be set to the center of the ccd for reference
calibCenter = fgcmField.evaluate(fgcmField.getBBox().getCenter())
calibErr = (np.log(10.0)/2.5)*calibCenter*np.sqrt(rec['fgcmZptVar'])
photoCalib = afwImage.PhotoCalib(calibrationMean=calibCenter,
calibrationErr=calibErr,
calibration=fgcmField,
isConstant=False)
# Return full per-visit exposure catalogs
if rec['visit'] != lastVisit:
# This is a new visit. If the last visit was not -1, yield
# the ExposureCatalog
if lastVisit > -1:
# ensure that the detectors are in sorted order, for fast lookups
zptVisitCatalog.sort()
yield (int(lastVisit), zptVisitCatalog)
else:
# We need to create a new schema
zptExpCatSchema = afwTable.ExposureTable.makeMinimalSchema()
zptExpCatSchema.addField('visit', type='L', doc='Visit number')
# And start a new one
zptVisitCatalog = afwTable.ExposureCatalog(zptExpCatSchema)
zptVisitCatalog.setMetadata(metadata)
lastVisit = int(rec['visit'])
catRecord = zptVisitCatalog.addNew()
catRecord['id'] = int(rec['detector'])
catRecord['visit'] = rec['visit']
catRecord.setPhotoCalib(photoCalib)
# Final output of last exposure catalog
# ensure that the detectors are in sorted order, for fast lookups
zptVisitCatalog.sort()
yield (int(lastVisit), zptVisitCatalog)
def _getChebyshevBoundedField(self, coefficients, xyMax, offset=0.0, scaling=1.0):
Definition at line 828 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.fgcmBuildStarsConfig : `lsst.fgcmcal.FgcmBuildStarsConfig` |
Definition at line 430 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.filterLabel : `lsst.afw.image.FilterLabel` |
Definition at line 638 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.handleDict : `dict` |
physicalFilterMap = pexConfig.DictField(
doc="Mapping from 'physicalFilter' to band.",
keytype=str,
itemtype=str,
default={},
)
# The following fields refer to calibrating from a reference
# catalog, but in the future this might need to be expanded
doReferenceCalibration = pexConfig.Field(
doc=("Transfer 'absolute' calibration from reference catalog? "
"This afterburner step is unnecessary if reference stars "
"were used in the full fit in FgcmFitCycleTask."),
dtype=bool,
default=False,
)
doAtmosphereOutput = pexConfig.Field(
doc="Output atmospheres in transmission_atmosphere_fgcm format",
dtype=bool,
default=True,
)
doZeropointOutput = pexConfig.Field(
doc="Output zeropoints in fgcm_photoCalib format",
dtype=bool,
default=True,
)
doComposeWcsJacobian = pexConfig.Field(
doc="Compose Jacobian of WCS with fgcm calibration for output photoCalib?",
dtype=bool,
default=True,
)
doApplyMeanChromaticCorrection = pexConfig.Field(
doc="Apply the mean chromatic correction to the zeropoints?",
dtype=bool,
default=True,
)
photoCal = pexConfig.ConfigurableField(
target=PhotoCalTask,
doc="task to perform 'absolute' calibration",
)
referencePixelizationNside = pexConfig.Field(
doc="Healpix nside to pixelize catalog to compare to reference catalog",
dtype=int,
default=64,
)
referencePixelizationMinStars = pexConfig.Field(
doc=("Minimum number of stars per healpix pixel to select for comparison"
"to the specified reference catalog"),
dtype=int,
default=200,
)
referenceMinMatch = pexConfig.Field(
doc="Minimum number of stars matched to reference catalog to be used in statistics",
dtype=int,
default=50,
)
referencePixelizationNPixels = pexConfig.Field(
doc=("Number of healpix pixels to sample to do comparison. "
"Doing too many will take a long time and not yield any more "
"precise results because the final number is the median offset "
"(per band) from the set of pixels."),
dtype=int,
default=100,
)
def setDefaults(self):
pexConfig.Config.setDefaults(self)
# In order to transfer the "absolute" calibration from a reference
# catalog to the relatively calibrated FGCM standard stars (one number
# per band), we use the PhotoCalTask to match stars in a sample of healpix
# pixels. These basic settings ensure that only well-measured, good stars
# from the source and reference catalogs are used for the matching.
# applyColorTerms needs to be False if doReferenceCalibration is False,
# as is the new default after DM-16702
self.photoCal.applyColorTerms = False
self.photoCal.fluxField = 'instFlux'
self.photoCal.magErrFloor = 0.003
self.photoCal.match.referenceSelection.doSignalToNoise = True
self.photoCal.match.referenceSelection.signalToNoise.minimum = 10.0
self.photoCal.match.sourceSelection.doSignalToNoise = True
self.photoCal.match.sourceSelection.signalToNoise.minimum = 10.0
self.photoCal.match.sourceSelection.signalToNoise.fluxField = 'instFlux'
self.photoCal.match.sourceSelection.signalToNoise.errField = 'instFluxErr'
self.photoCal.match.sourceSelection.doFlags = True
self.photoCal.match.sourceSelection.flags.good = []
self.photoCal.match.sourceSelection.flags.bad = ['flag_badStar']
self.photoCal.match.sourceSelection.doUnresolved = False
self.photoCal.match.sourceSelection.doRequirePrimary = False
class FgcmOutputProductsTask(pipeBase.PipelineTask):ConfigClass = FgcmOutputProductsConfig
_DefaultName = "fgcmOutputProducts"
def __init__(self, **kwargs):
super().__init__(**kwargs)
def runQuantum(self, butlerQC, inputRefs, outputRefs):
handleDict = {}
handleDict['camera'] = butlerQC.get(inputRefs.camera)
handleDict['fgcmLookUpTable'] = butlerQC.get(inputRefs.fgcmLookUpTable)
handleDict['fgcmVisitCatalog'] = butlerQC.get(inputRefs.fgcmVisitCatalog)
handleDict['fgcmStandardStars'] = butlerQC.get(inputRefs.fgcmStandardStars)
if self.config.doZeropointOutput:
handleDict['fgcmZeropoints'] = butlerQC.get(inputRefs.fgcmZeropoints)
photoCalibRefDict = {photoCalibRef.dataId['visit']:
photoCalibRef for photoCalibRef in outputRefs.fgcmPhotoCalib}
if self.config.doAtmosphereOutput:
handleDict['fgcmAtmosphereParameters'] = butlerQC.get(inputRefs.fgcmAtmosphereParameters)
atmRefDict = {atmRef.dataId['visit']: atmRef for
atmRef in outputRefs.fgcmTransmissionAtmosphere}
if self.config.doReferenceCalibration:
refConfig = LoadReferenceObjectsConfig()
self.refObjLoader = ReferenceObjectLoader(dataIds=[ref.datasetRef.dataId
for ref in inputRefs.refCat],
refCats=butlerQC.get(inputRefs.refCat),
name=self.config.connections.refCat,
log=self.log,
config=refConfig)
else:
self.refObjLoader = None
struct = self.run(handleDict, self.config.physicalFilterMap)
# Output the photoCalib exposure catalogs
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.")
# Output the atmospheres
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.")
if self.config.doReferenceCalibration:
# Turn offset into simple catalog for persistence if necessary
schema = afwTable.Schema()
schema.addField('offset', type=np.float64,
doc="Post-process calibration offset (mag)")
offsetCat = afwTable.BaseCatalog(schema)
offsetCat.resize(len(struct.offsets))
offsetCat['offset'][:] = struct.offsets
butlerQC.put(offsetCat, outputRefs.fgcmOffsets)
return
def run(self, handleDict, physicalFilterMap):stdCat = handleDict['fgcmStandardStars'].get()
md = stdCat.getMetadata()
bands = md.getArray('BANDS')
if self.config.doReferenceCalibration:
lutCat = handleDict['fgcmLookUpTable'].get()
offsets = self._computeReferenceOffsets(stdCat, lutCat, physicalFilterMap, bands)
else:
offsets = np.zeros(len(bands))
del stdCat
if self.config.doZeropointOutput:
zptCat = handleDict['fgcmZeropoints'].get()
visitCat = handleDict['fgcmVisitCatalog'].get()
pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands,
physicalFilterMap)
else:
pcgen = None
if self.config.doAtmosphereOutput:
atmCat = handleDict['fgcmAtmosphereParameters'].get()
atmgen = self._outputAtmospheres(handleDict, atmCat)
else:
atmgen = None
retStruct = pipeBase.Struct(offsets=offsets,
atmospheres=atmgen)
retStruct.photoCalibCatalogs = pcgen
return retStruct
def generateTractOutputProducts(self, handleDict, tract,
visitCat, zptCat, atmCat, stdCat,
fgcmBuildStarsConfig):
orderPlus1 = int(np.sqrt(coefficients.size))
pars = np.zeros((orderPlus1, orderPlus1))
bbox = lsst.geom.Box2I(lsst.geom.Point2I(0.0, 0.0),
lsst.geom.Point2I(*xyMax))
pars[:, :] = (coefficients.reshape(orderPlus1, orderPlus1)
* (10.**(offset/-2.5))*scaling)
boundedField = afwMath.ChebyshevBoundedField(bbox, pars)
return boundedField
def _outputAtmospheres(self, handleDict, atmCat):
Definition at line 335 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.lutCat : `lsst.afw.table.SimpleCatalog` |
Definition at line 494 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.offset : `float`, optional |
Definition at line 832 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.offsets : `np.ndarray` |
Definition at line 361 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.photoCalibCatalogs : `generator` [(`int`, `lsst.afw.table.ExposureCatalog`)] |
Definition at line 365 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.physicalFilterMap : `dict` |
Definition at line 353 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.refFluxFields : `list` |
Definition at line 644 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.retStruct : `lsst.pipe.base.Struct` |
Definition at line 358 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.scaling : `float`, optional |
Definition at line 834 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.selected : `numpy.array(dtype=bool)` |
Definition at line 642 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.sourceMapper : `lsst.afw.table.SchemaMapper` |
# Only use stars that are observed in all the bands that were actually used
# This will ensure that we use the same healpix pixels for the absolute
# calibration of each band.
minObs = stdCat['ngood'].min(axis=1)
goodStars = (minObs >= 1)
stdCat = stdCat[goodStars]
self.log.info("Found %d stars with at least 1 good observation in each band" %
(len(stdCat)))
# Associate each band with the appropriate physicalFilter and make
# filterLabels
filterLabels = []
lutPhysicalFilters = lutCat[0]['physicalFilters'].split(',')
lutStdPhysicalFilters = lutCat[0]['stdPhysicalFilters'].split(',')
physicalFilterMapBands = list(physicalFilterMap.values())
physicalFilterMapFilters = list(physicalFilterMap.keys())
for band in bands:
# Find a physical filter associated from the band by doing
# a reverse lookup on the physicalFilterMap dict
physicalFilterMapIndex = physicalFilterMapBands.index(band)
physicalFilter = physicalFilterMapFilters[physicalFilterMapIndex]
# Find the appropriate fgcm standard physicalFilter
lutPhysicalFilterIndex = lutPhysicalFilters.index(physicalFilter)
stdPhysicalFilter = lutStdPhysicalFilters[lutPhysicalFilterIndex]
filterLabels.append(afwImage.FilterLabel(band=band,
physical=stdPhysicalFilter))
# We have to make a table for each pixel with flux/fluxErr
# This is a temporary table generated for input to the photoCal task.
# These fluxes are not instFlux (they are top-of-the-atmosphere approximate and
# have had chromatic corrections applied to get to the standard system
# specified by the atmosphere/instrumental parameters), nor are they
# in Jansky (since they don't have a proper absolute calibration: the overall
# zeropoint is estimated from the telescope size, etc.)
sourceMapper = afwTable.SchemaMapper(stdCat.schema)
sourceMapper.addMinimalSchema(afwTable.SimpleTable.makeMinimalSchema())
sourceMapper.editOutputSchema().addField('instFlux', type=np.float64,
doc="instrumental flux (counts)")
sourceMapper.editOutputSchema().addField('instFluxErr', type=np.float64,
doc="instrumental flux error (counts)")
badStarKey = sourceMapper.editOutputSchema().addField('flag_badStar',
type='Flag',
doc="bad flag")
# Split up the stars
# Note that there is an assumption here that the ra/dec coords stored
# on-disk are in radians, and therefore that starObs['coord_ra'] /
# starObs['coord_dec'] return radians when used as an array of numpy float64s.
ipring = hpg.angle_to_pixel(
self.config.referencePixelizationNside,
stdCat['coord_ra'],
stdCat['coord_dec'],
degrees=False,
)
h, rev = fgcm.fgcmUtilities.histogram_rev_sorted(ipring)
gdpix, = np.where(h >= self.config.referencePixelizationMinStars)
self.log.info("Found %d pixels (nside=%d) with at least %d good stars" %
(gdpix.size,
self.config.referencePixelizationNside,
self.config.referencePixelizationMinStars))
if gdpix.size < self.config.referencePixelizationNPixels:
self.log.warning("Found fewer good pixels (%d) than preferred in configuration (%d)" %
(gdpix.size, self.config.referencePixelizationNPixels))
else:
# Sample out the pixels we want to use
gdpix = np.random.choice(gdpix, size=self.config.referencePixelizationNPixels, replace=False)
results = np.zeros(gdpix.size, dtype=[('hpix', 'i4'),
('nstar', 'i4', len(bands)),
('nmatch', 'i4', len(bands)),
('zp', 'f4', len(bands)),
('zpErr', 'f4', len(bands))])
results['hpix'] = ipring[rev[rev[gdpix]]]
# We need a boolean index to deal with catalogs...
selected = np.zeros(len(stdCat), dtype=bool)
refFluxFields = [None]*len(bands)
for p_index, pix in enumerate(gdpix):
i1a = rev[rev[pix]: rev[pix + 1]]
# the stdCat afwTable can only be indexed with boolean arrays,
# and not numpy index arrays (see DM-16497). This little trick
# converts the index array into a boolean array
selected[:] = False
selected[i1a] = True
for b_index, filterLabel in enumerate(filterLabels):
struct = self._computeOffsetOneBand(sourceMapper, badStarKey, b_index,
filterLabel, stdCat,
selected, refFluxFields)
results['nstar'][p_index, b_index] = len(i1a)
results['nmatch'][p_index, b_index] = len(struct.arrays.refMag)
results['zp'][p_index, b_index] = struct.zp
results['zpErr'][p_index, b_index] = struct.sigma
# And compute the summary statistics
offsets = np.zeros(len(bands))
for b_index, band in enumerate(bands):
# make configurable
ok, = np.where(results['nmatch'][:, b_index] >= self.config.referenceMinMatch)
offsets[b_index] = np.median(results['zp'][ok, b_index])
# use median absolute deviation to estimate Normal sigma
# see https://en.wikipedia.org/wiki/Median_absolute_deviation
madSigma = 1.4826*np.median(np.abs(results['zp'][ok, b_index] - offsets[b_index]))
self.log.info("Reference catalog offset for %s band: %.12f +/- %.12f",
band, offsets[b_index], madSigma)
return offsets
def _computeOffsetOneBand(self, sourceMapper, badStarKey,
b_index, filterLabel, stdCat, selected, refFluxFields):
Definition at line 632 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.stdCat : `lsst.afw.table.SimpleCatalog` |
physicalFilterMap = fgcmBuildStarsConfig.physicalFilterMap
md = stdCat.getMetadata()
bands = md.getArray('BANDS')
if self.config.doComposeWcsJacobian and not fgcmBuildStarsConfig.doApplyWcsJacobian:
raise RuntimeError("Cannot compose the WCS jacobian if it hasn't been applied "
"in fgcmBuildStarsTask.")
if not self.config.doComposeWcsJacobian and fgcmBuildStarsConfig.doApplyWcsJacobian:
self.log.warning("Jacobian was applied in build-stars but doComposeWcsJacobian is not set.")
if self.config.doReferenceCalibration:
lutCat = handleDict['fgcmLookUpTable'].get()
offsets = self._computeReferenceOffsets(stdCat, lutCat, bands, physicalFilterMap)
else:
offsets = np.zeros(len(bands))
if self.config.doZeropointOutput:
pcgen = self._outputZeropoints(handleDict['camera'], zptCat, visitCat, offsets, bands,
physicalFilterMap)
else:
pcgen = None
if self.config.doAtmosphereOutput:
atmgen = self._outputAtmospheres(handleDict, atmCat)
else:
atmgen = None
retStruct = pipeBase.Struct(offsets=offsets,
atmospheres=atmgen)
retStruct.photoCalibCatalogs = pcgen
return retStruct
def _computeReferenceOffsets(self, stdCat, lutCat, physicalFilterMap, bands):
Definition at line 428 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.tract : `int` |
Definition at line 420 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.visitCat : `lsst.afw.table.BaseCatalog` |
Definition at line 422 of file fgcmOutputProducts.py.
| int lsst.fgcmcal.fgcmOutputProducts.xyMax : `list` of length 2 |
Definition at line 830 of file fgcmOutputProducts.py.
| lsst.fgcmcal.fgcmOutputProducts.zptCat : `lsst.afw.table.BaseCatalog` |
Definition at line 424 of file fgcmOutputProducts.py.