def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcCat = afwTable.SourceCatalog.readFits(
os.path.join(testDir, "data", "v695833-e0-c000.xy.fits"))
self.srcCat["slot_ApFlux_fluxSigma"] = 1
self.srcCat["slot_PsfFlux_fluxSigma"] = 1
# The .xy.fits file has sources in the range ~ [0,2000],[0,4500]
# which is bigger than the exposure
self.bbox = afwGeom.Box2I(afwGeom.Point2I(0, 0),
afwGeom.Extent2I(2048, 4612))
smallExposure = afwImage.ExposureF(
os.path.join(testDir, "data", "v695833-e0-c000-a00.sci.fits"))
self.exposure = afwImage.ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
self.exposure.setCalib(smallExposure.getCalib())
# Make a reference loader
butler = Butler(RefCatDir)
self.refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
logLevel = Log.TRACE
self.log = Log.getLogger('testPhotoCal')
self.log.setLevel(logLevel)
self.config = PhotoCalConfig()
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
self.config.fluxField = "base_PsfFlux_flux"
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcCat = afwTable.SourceCatalog.readFits(
os.path.join(testDir, "data", "v695833-e0-c000.xy.fits"))
self.srcCat["slot_ApFlux_instFluxErr"] = 1
self.srcCat["slot_PsfFlux_instFluxErr"] = 1
# The .xy.fits file has sources in the range ~ [0,2000],[0,4500]
# which is bigger than the exposure
self.bbox = geom.Box2I(geom.Point2I(0, 0), geom.Extent2I(2048, 4612))
smallExposure = afwImage.ExposureF(
os.path.join(testDir, "data", "v695833-e0-c000-a00.sci.fits"))
self.exposure = afwImage.ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(
afwImage.FilterLabel(band="i", physical="test-i"))
self.exposure.setPhotoCalib(smallExposure.getPhotoCalib())
coordKey = self.srcCat.getCoordKey()
centroidKey = self.srcCat.getCentroidSlot().getMeasKey()
wcs = self.exposure.getWcs()
for src in self.srcCat:
src.set(coordKey, wcs.pixelToSky(src.get(centroidKey)))
# Make a reference loader
filenames = sorted(
glob.glob(
os.path.join(RefCatDir, 'ref_cats', 'cal_ref_cat',
'??????.fits')))
self.refObjLoader = MockReferenceObjectLoaderFromFiles(filenames,
htmLevel=8)
self.log = logging.getLogger('lsst.testPhotoCal')
self.log.setLevel(TRACE)
self.config = PhotoCalConfig()
self.config.match.matchRadius = 0.5
self.config.match.referenceSelection.doMagLimit = True
self.config.match.referenceSelection.magLimit.maximum = 22.0
self.config.match.referenceSelection.magLimit.fluxField = "i_flux"
self.config.match.referenceSelection.doFlags = True
self.config.match.referenceSelection.flags.good = ['photometric']
self.config.match.referenceSelection.flags.bad = ['resolved']
self.config.match.sourceSelection.doUnresolved = False # Don't have star/galaxy in the srcCat
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
self.config.fluxField = "base_PsfFlux_instFlux"
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcCat = afwTable.SourceCatalog.readFits(
os.path.join(testDir, "data", "v695833-e0-c000.xy.fits"))
self.srcCat["slot_ApFlux_fluxSigma"] = 1
self.srcCat["slot_PsfFlux_fluxSigma"] = 1
# The .xy.fits file has sources in the range ~ [0,2000],[0,4500]
# which is bigger than the exposure
self.bbox = afwGeom.Box2I(afwGeom.Point2I(0, 0),
afwGeom.Extent2I(2048, 4612))
smallExposure = afwImage.ExposureF(
os.path.join(testDir, "data", "v695833-e0-c000-a00.sci.fits"))
self.exposure = afwImage.ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
self.exposure.setCalib(smallExposure.getCalib())
coordKey = self.srcCat.getCoordKey()
centroidKey = self.srcCat.getCentroidKey()
wcs = self.exposure.getWcs()
for src in self.srcCat:
src.set(coordKey, wcs.pixelToSky(src.get(centroidKey)))
# Make a reference loader
butler = Butler(RefCatDir)
self.refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
logLevel = Log.TRACE
self.log = Log.getLogger('testPhotoCal')
self.log.setLevel(logLevel)
self.config = PhotoCalConfig()
self.config.match.matchRadius = 0.5
self.config.match.referenceSelection.doMagLimit = True
self.config.match.referenceSelection.magLimit.maximum = 22.0
self.config.match.referenceSelection.magLimit.fluxField = "i_flux"
self.config.match.referenceSelection.doFlags = True
self.config.match.referenceSelection.flags.good = ['photometric']
self.config.match.referenceSelection.flags.bad = ['resolved']
self.config.match.sourceSelection.doUnresolved = False # Don't have star/galaxy in the srcCat
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
self.config.fluxField = "base_PsfFlux_flux"
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