def _outputStandardStars(self, butler, stdCat, offsets, datasetConfig):
"""
Output standard stars in indexed reference catalog format.
Parameters
----------
butler: `lsst.daf.persistence.Butler`
stdCat: `lsst.afw.table.SimpleCatalog`
FGCM standard star catalog from fgcmFitCycleTask
offsets: `numpy.array` of floats
Per band zeropoint offsets
datasetConfig: `lsst.meas.algorithms.DatasetConfig`
Config for reference dataset
"""
self.log.info("Outputting standard stars to %s" %
(datasetConfig.ref_dataset_name))
# We determine the conversion from the native units (typically radians) to
# degrees for the first star. This allows us to treat coord_ra/coord_dec as
# numpy arrays rather than Angles, which would we approximately 600x slower.
# TODO: Fix this after DM-16524 (HtmIndexer.indexPoints should take coords
# (as Angles) for input
conv = stdCat[0]['coord_ra'].asDegrees() / float(stdCat[0]['coord_ra'])
indices = np.array(
self.indexer.indexPoints(stdCat['coord_ra'] * conv,
stdCat['coord_dec'] * conv))
formattedCat = self._formatCatalog(stdCat, offsets)
# Write the master schema
dataId = self.indexer.makeDataId('master_schema',
datasetConfig.ref_dataset_name)
masterCat = afwTable.SimpleCatalog(formattedCat.schema)
addRefCatMetadata(masterCat)
butler.put(masterCat, 'ref_cat', dataId=dataId)
# Break up the pixels using a histogram
h, rev = esutil.stat.histogram(indices, rev=True)
gd, = np.where(h > 0)
selected = np.zeros(len(formattedCat), dtype=np.bool)
for i in gd:
i1a = rev[rev[i]:rev[i + 1]]
# the formattedCat 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
# Write the individual pixel
dataId = self.indexer.makeDataId(indices[i1a[0]],
datasetConfig.ref_dataset_name)
butler.put(formattedCat[selected], 'ref_cat', dataId=dataId)
# And save the dataset configuration
dataId = self.indexer.makeDataId(None, datasetConfig.ref_dataset_name)
butler.put(datasetConfig, 'ref_cat_config', dataId=dataId)
self.log.info("Done outputting standard stars.")
def process_one(filename, write=False, quiet=False):
"""Convert one file in-place from Jy (or no units) to nJy fluxes.
Parameters
----------
filename : `str`
The file to convert.
write : `bool`, optional
Write the converted catalog out, overwriting the read in catalog?
quiet : `bool`, optional
Do not print messages about files read/written or fields found?
"""
log = lsst.log.Log()
if quiet:
log.setLevel(lsst.log.WARN)
log.info(f"Reading: {filename}")
catalog = lsst.afw.table.SimpleCatalog.readFits(filename)
output = convertToNanojansky(catalog, log, doConvert=write)
if write:
addRefCatMetadata(output)
output.writeFits(filename)
log.info(f"Wrote: {filename}")
def _formatCatalog(self, fgcmStarCat, offsets, bands):
"""
Turn an FGCM-formatted star catalog, applying zeropoint offsets.
Parameters
----------
fgcmStarCat : `lsst.afw.Table.SimpleCatalog`
SimpleCatalog as output by fgcmcal
offsets : `list` with len(self.bands) entries
Zeropoint offsets to apply
bands : `list` [`str`]
List of band names from FGCM output
Returns
-------
formattedCat: `lsst.afw.table.SimpleCatalog`
SimpleCatalog suitable for using as a reference catalog
"""
sourceMapper = afwTable.SchemaMapper(fgcmStarCat.schema)
minSchema = LoadIndexedReferenceObjectsTask.makeMinimalSchema(
bands, addCentroid=False, addIsResolved=True, coordErrDim=0)
sourceMapper.addMinimalSchema(minSchema)
for band in bands:
sourceMapper.editOutputSchema().addField('%s_nGood' % (band),
type=np.int32)
sourceMapper.editOutputSchema().addField('%s_nTotal' % (band),
type=np.int32)
sourceMapper.editOutputSchema().addField('%s_nPsfCandidate' %
(band),
type=np.int32)
formattedCat = afwTable.SimpleCatalog(sourceMapper.getOutputSchema())
formattedCat.reserve(len(fgcmStarCat))
formattedCat.extend(fgcmStarCat, mapper=sourceMapper)
# Note that we don't have to set `resolved` because the default is False
for b, band in enumerate(bands):
mag = fgcmStarCat['mag_std_noabs'][:, b].astype(
np.float64) + offsets[b]
# We want fluxes in nJy from calibrated AB magnitudes
# (after applying offset). Updated after RFC-549 and RFC-575.
flux = (mag * units.ABmag).to_value(units.nJy)
fluxErr = (np.log(10.) /
2.5) * flux * fgcmStarCat['magErr_std'][:, b].astype(
np.float64)
formattedCat['%s_flux' % (band)][:] = flux
formattedCat['%s_fluxErr' % (band)][:] = fluxErr
formattedCat['%s_nGood' % (band)][:] = fgcmStarCat['ngood'][:, b]
formattedCat['%s_nTotal' % (band)][:] = fgcmStarCat['ntotal'][:, b]
formattedCat['%s_nPsfCandidate' %
(band)][:] = fgcmStarCat['npsfcand'][:, b]
addRefCatMetadata(formattedCat)
return formattedCat