def _runTask(self):
"""All the common setup to actually test the results"""
task = PhotoCalTask(self.refObjLoader,
config=self.config,
schema=self.srcCat.schema)
pCal = task.run(exposure=self.exposure, sourceCat=self.srcCat)
matches = pCal.matches
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff = []
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = u.Quantity(refFlux, u.nJy).to_value(u.ABmag)
instFlux = m[1].getPsfInstFlux() # Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.photoCalib.instFluxToMagnitude(
instFlux) # Instrumental mag
diff.append(instMag - refMag)
self.diff = np.array(diff)
# Differences of matched objects that were used in the fit.
self.zp = pCal.photoCalib.instFluxToMagnitude(1.)
self.fitdiff = pCal.arrays.srcMag + self.zp - pCal.arrays.refMag
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 astrometry task
#
config = measAstrom.LoadAstrometryNetObjectsTask.ConfigClass()
config.filterMap = {"_unknown_": "r"}
refObjLoader = measAstrom.LoadAstrometryNetObjectsTask(config=config)
config = measAstrom.AstrometryTask.ConfigClass()
config.matcher.sourceFluxType = "Psf" # sample catalog does not contain aperture flux
aTask = measAstrom.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
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 testFlags(self):
"""test that all the calib_photometry flags are set to reasonable values"""
schema = self.srcCat.schema
task = PhotoCalTask(self.refObjLoader, config=self.config, schema=schema)
mapper = afwTable.SchemaMapper(self.srcCat.schema, schema)
cat = afwTable.SourceCatalog(schema)
for name in self.srcCat.schema.getNames():
mapper.addMapping(self.srcCat.schema.find(name).key)
cat.extend(self.srcCat, mapper=mapper)
# test that by default, no stars are reserved and all used are candidates
task.run(exposure=self.exposure, sourceCat=cat)
used = 0
for source in cat:
if source.get("calib_photometry_used"):
used += 1
self.assertFalse(source.get("calib_photometry_reserved"))
# test that some are actually used
self.assertGreater(used, 0)
def _runTask(self):
"""All the common setup to actually test the results"""
task = PhotoCalTask(self.refObjLoader, config=self.config, schema=self.srcCat.schema)
pCal = task.run(exposure=self.exposure, sourceCat=self.srcCat)
matches = pCal.matches
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff = []
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = u.Quantity(refFlux, u.nJy).to_value(u.ABmag)
instFlux = m[1].getPsfInstFlux() # Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.photoCalib.instFluxToMagnitude(instFlux) # Instrumental mag
diff.append(instMag - refMag)
self.diff = np.array(diff)
# Differences of matched objects that were used in the fit.
self.zp = pCal.photoCalib.instFluxToMagnitude(1.)
self.fitdiff = pCal.arrays.srcMag + self.zp - pCal.arrays.refMag
def _runTask(self):
"""All the common setup to actually test the results"""
task = PhotoCalTask(self.refObjLoader, config=self.config, schema=None)
pCal = task.run(exposure=self.exposure, sourceCat=self.srcCat)
matches = pCal.matches
print("Ref flux fields list =", pCal.arrays.refFluxFieldList)
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff = []
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = afwImage.abMagFromFlux(refFlux) # reference catalog mag
instFlux = m[1].getPsfFlux() # Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.calib.getMagnitude(instFlux) # Instrumental mag
diff.append(instMag - refMag)
self.diff = np.array(diff)
# Differences of matched objects that were used in the fit.
self.zp = pCal.calib.getMagnitude(1.)
self.fitdiff = pCal.arrays.srcMag + self.zp - pCal.arrays.refMag
def testFlags(self):
"""test that all the calib_photometry flags are set to reasonable values"""
schema = self.srcCat.schema
task = PhotoCalTask(self.refObjLoader,
config=self.config,
schema=schema)
mapper = afwTable.SchemaMapper(self.srcCat.schema, schema)
cat = afwTable.SourceCatalog(schema)
for name in self.srcCat.schema.getNames():
mapper.addMapping(self.srcCat.schema.find(name).key)
cat.extend(self.srcCat, mapper=mapper)
# test that by default, no stars are reserved and used < candidates
task.run(exposure=self.exposure, sourceCat=cat)
candidates = 0
used = 0
reserved = 0
for source in cat:
if source.get("calib_photometryCandidate"):
candidates += 1
if source.get("calib_photometryUsed"):
used += 1
if source.get("calib_photometryReserved"):
reserved += 1
self.assertLessEqual(used, candidates)
self.assertEqual(reserved, 0)
# set the reserve fraction, and see if the right proportion are reserved.
self.config.reserveFraction = .3
task.run(exposure=self.exposure, sourceCat=cat)
candidates = 0
reserved = 0
used = 0
for source in cat:
if source.get("calib_photometryCandidate"):
candidates += 1
if source.get("calib_photometryUsed"):
used += 1
if source.get("calib_photometryReserved"):
reserved += 1
self.assertEqual(reserved, int(.3 * candidates))
self.assertLessEqual(used, (candidates - reserved))
def test1(self):
res = self.getAstrometrySolution()
matches = res.matches
print 'Test1'
logLevel = Log.DEBUG
log = Log(Log.getDefaultLog(),
'testPhotoCal',
logLevel)
schema = matches[0].second.schema
config = PhotoCalConfig()
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
config.fluxField = "base_PsfFlux_flux"
config.doWriteOutput = False # schema is fixed because we already loaded the data
task = PhotoCalTask(config=config, schema=schema)
pCal = task.run(exposure=self.exposure, matches=matches)
print "Ref flux fields list =", pCal.arrays.refFluxFieldList
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff=[]
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = afwImage.abMagFromFlux(refFlux) # reference catalog mag
instFlux = m[1].getPsfFlux() #Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.calib.getMagnitude(instFlux) #Instrumental mag
diff.append(instMag - refMag)
diff = np.array(diff)
self.assertGreater(len(diff), 50)
log.info('%i magnitude differences; mean difference %g; mean abs diff %g' %
(len(diff), np.mean(diff), np.mean(np.abs(diff))))
self.assertLess(np.mean(diff), 0.6)
# Differences of matched objects that were used in the fit.
zp = pCal.calib.getMagnitude(1.)
log.logdebug('zeropoint: %g' % zp)
fitdiff = pCal.arrays.srcMag + zp - pCal.arrays.refMag
log.logdebug('number of sources used in fit: %i' % len(fitdiff))
log.logdebug('rms diff: %g' % np.mean(fitdiff**2)**0.5)
log.logdebug('median abs(diff): %g' % np.median(np.abs(fitdiff)))
# zeropoint: 31.3145
# number of sources used in fit: 65
# median diff: -0.009681
# mean diff: 0.00331871
# median abs(diff): 0.0368904
# mean abs(diff): 0.0516589
self.assertLess(abs(zp - 31.3145), 0.05)
self.assertGreater(len(fitdiff), 50)
# Tolerances are somewhat arbitrary; they're set simply to avoid regressions, and
# are not based on we'd expect to get given the data quality.
self.assertLess(np.mean(fitdiff**2)**0.5, 0.07) # rms difference
self.assertLess(np.median(np.abs(fitdiff)), 0.06) # median absolution difference
def test1(self):
res = self.getAstrometrySolution()
matches = res.matches
print 'Test1'
logLevel = Log.DEBUG
log = Log(Log.getDefaultLog(), 'testPhotoCal', logLevel)
schema = matches[0].second.schema
config = PhotoCalConfig()
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
config.fluxField = "base_PsfFlux_flux"
config.doWriteOutput = False # schema is fixed because we already loaded the data
task = PhotoCalTask(config=config, schema=schema)
pCal = task.run(exposure=self.exposure, matches=matches)
print "Ref flux fields list =", pCal.arrays.refFluxFieldList
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff = []
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = afwImage.abMagFromFlux(refFlux) # reference catalog mag
instFlux = m[1].getPsfFlux() #Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.calib.getMagnitude(instFlux) #Instrumental mag
diff.append(instMag - refMag)
diff = np.array(diff)
self.assertGreater(len(diff), 50)
log.info(
'%i magnitude differences; mean difference %g; mean abs diff %g' %
(len(diff), np.mean(diff), np.mean(np.abs(diff))))
self.assertLess(np.mean(diff), 0.6)
# Differences of matched objects that were used in the fit.
zp = pCal.calib.getMagnitude(1.)
log.logdebug('zeropoint: %g' % zp)
fitdiff = pCal.arrays.srcMag + zp - pCal.arrays.refMag
log.logdebug('number of sources used in fit: %i' % len(fitdiff))
log.logdebug('rms diff: %g' % np.mean(fitdiff**2)**0.5)
log.logdebug('median abs(diff): %g' % np.median(np.abs(fitdiff)))
# zeropoint: 31.3145
# number of sources used in fit: 65
# median diff: -0.009681
# mean diff: 0.00331871
# median abs(diff): 0.0368904
# mean abs(diff): 0.0516589
self.assertLess(abs(zp - 31.3145), 0.05)
self.assertGreater(len(fitdiff), 50)
# Tolerances are somewhat arbitrary; they're set simply to avoid regressions, and
# are not based on we'd expect to get given the data quality.
self.assertLess(np.mean(fitdiff**2)**0.5, 0.07) # rms difference
self.assertLess(np.median(np.abs(fitdiff)),
0.06) # median absolution difference
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))
