def run():
exposure, srcCat = loadData()
schema = srcCat.getSchema()
#
# Create the astrometry task
#
config = AstrometryTask.ConfigClass()
config.refObjLoader.filterMap = {"_unknown_": "r"}
config.matcher.sourceFluxType = "Psf" # sample catalog does not contain aperture flux
aTask = AstrometryTask(config=config)
#
# And the photometry Task
#
config = PhotoCalTask.ConfigClass()
config.applyColorTerms = False # we don't have any available, so this suppresses a warning
pTask = PhotoCalTask(config=config, schema=schema)
#
# The tasks may have added extra elements to the schema (e.g. AstrometryTask's centroidKey to
# handle distortion; photometryTask's config.outputField). If this is so, we need to add
# these columns to the Source table.
#
# We wouldn't need to do this if we created the schema prior to measuring the exposure,
# but in this case we read the sources from disk
#
if schema != srcCat.getSchema(): # the tasks added fields
print("Adding columns to the source catalogue")
cat = afwTable.SourceCatalog(schema)
cat.table.defineCentroid(srcCat.table.getCentroidDefinition())
cat.table.definePsfFlux(srcCat.table.getPsfFluxDefinition())
scm = afwTable.SchemaMapper(srcCat.getSchema(), schema)
for schEl in srcCat.getSchema():
scm.addMapping(schEl.getKey(), True)
cat.extend(srcCat, True, scm) # copy srcCat to cat, adding new columns
srcCat = cat
del cat
#
# Process the data
#
matches = aTask.run(exposure, srcCat).matches
result = pTask.run(exposure, matches)
calib = result.calib
fm0, fm0Err = calib.getFluxMag0()
print("Used %d calibration sources out of %d matches" %
(len(result.matches), len(matches)))
delta = result.arrays.refMag - result.arrays.srcMag
q25, q75 = np.percentile(delta, [25, 75])
print("RMS error is %.3fmmsg (robust %.3f, Calib says %.3f)" %
(np.std(delta), 0.741 *
(q75 - q25), 2.5 / np.log(10) * fm0Err / fm0))
def run():
exposure, srcCat = loadData()
schema = srcCat.getSchema()
#
# Create the reference catalog loader
#
refCatDir = os.path.join(lsst.utils.getPackageDir('meas_astrom'), 'tests',
'data', 'sdssrefcat')
butler = Butler(refCatDir)
refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
#
# Create the astrometry task
#
config = AstrometryTask.ConfigClass()
config.matcher.sourceFluxType = "Psf" # sample catalog does not contain aperture flux
config.matcher.minSnr = 0 # disable S/N test because sample catalog does not contain flux sigma
aTask = AstrometryTask(config=config, refObjLoader=refObjLoader)
#
# And the photometry Task
#
config = PhotoCalTask.ConfigClass()
config.applyColorTerms = False # we don't have any available, so this suppresses a warning
# The associated data has been prepared on the basis that we use PsfFlux to perform photometry.
config.fluxField = "base_PsfFlux_flux"
pTask = PhotoCalTask(config=config, schema=schema)
#
# The tasks may have added extra elements to the schema (e.g. AstrometryTask's centroidKey to
# handle distortion; photometryTask's config.outputField). If this is so, we need to add
# these columns to the Source table.
#
# We wouldn't need to do this if we created the schema prior to measuring the exposure,
# but in this case we read the sources from disk
#
if schema != srcCat.getSchema(): # the tasks added fields
print("Adding columns to the source catalogue")
cat = afwTable.SourceCatalog(schema)
cat.table.defineCentroid(srcCat.table.getCentroidDefinition())
cat.table.definePsfFlux(srcCat.table.getPsfFluxDefinition())
scm = afwTable.SchemaMapper(srcCat.getSchema(), schema)
for schEl in srcCat.getSchema():
scm.addMapping(schEl.getKey(), True)
cat.extend(srcCat, True, scm) # copy srcCat to cat, adding new columns
srcCat = cat
del cat
#
# Process the data
#
matches = aTask.run(exposure, srcCat).matches
result = pTask.run(exposure, matches)
calib = result.calib
fm0, fm0Err = calib.getFluxMag0()
print("Used %d calibration sources out of %d matches" %
(len(result.matches), len(matches)))
delta = result.arrays.refMag - result.arrays.srcMag
q25, q75 = np.percentile(delta, [25, 75])
print("RMS error is %.3fmmsg (robust %.3f, Calib says %.3f)" %
(np.std(delta), 0.741 *
(q75 - q25), 2.5 / np.log(10) * fm0Err / fm0))
