def setDefaults(self):
SingleFrameMeasurementConfig.setDefaults(self)
self.plugins = [
"base_CircularApertureFlux", "base_PixelFlags", "base_SkyCoord",
"base_PsfFlux", "ip_diffim_NaiveDipoleCentroid",
"ip_diffim_NaiveDipoleFlux", "ip_diffim_PsfDipoleFlux"
]
self.slots.calibFlux = None
self.slots.modelFlux = None
self.slots.instFlux = None
self.slots.shape = None
self.slots.centroid = "ip_diffim_NaiveDipoleCentroid"
self.doReplaceWithNoise = False
def setDefaults(self):
SingleFrameMeasurementConfig.setDefaults(self)
self.plugins = ["base_CircularApertureFlux",
"base_PixelFlags",
"base_SkyCoord",
"base_PsfFlux",
"ip_diffim_NaiveDipoleCentroid",
"ip_diffim_NaiveDipoleFlux",
"ip_diffim_PsfDipoleFlux"]
self.slots.calibFlux = None
self.slots.modelFlux = None
self.slots.instFlux = None
self.slots.shape = None
self.slots.centroid = "ip_diffim_NaiveDipoleCentroid"
self.doReplaceWithNoise = False
def test1(self):
#exposure = afwImage.ExposureF('mini-v85408556-fr-R23-S11.fits')
#exposure = afwImage.ExposureF('../afwdata/ImSim/calexp/v85408556-fr/R23/S11.fits')
#bb = afwGeom.Box2I(afwGeom.Point2I(0,0), afwGeom.Point2I(511,511))
#exposure = afwImage.ExposureF('data/goodSeeingCoadd/r/3/113,0/coadd-r-3-113,0.fits', 0, bb)
#exposure.writeFits('mini-r-3-113,0.fits')
fn = os.path.join(os.path.dirname(__file__), 'data',
'mini-r-3-113,0.fits.gz')
print 'Reading image', fn
exposure = afwImage.ExposureF(fn)
exposure.setPsf(afwDetection.GaussianPsf(15, 15, 3))
schema = afwTable.SourceTable.makeMinimalSchema()
idFactory = afwTable.IdFactory.makeSimple()
dconf = measAlg.SourceDetectionConfig()
dconf.reEstimateBackground = False
dconf.includeThresholdMultiplier = 5.
mconf = SingleFrameMeasurementConfig()
aconf = ANetAstrometryConfig()
aconf.forceKnownWcs = True
det = measAlg.SourceDetectionTask(schema=schema, config=dconf)
meas = SingleFrameMeasurementTask(schema, config=mconf)
astrom = ANetAstrometryTask(schema, config=aconf, name='astrom')
astrom.log.setThreshold(pexLog.Log.DEBUG)
inwcs = exposure.getWcs()
print 'inwcs:', inwcs
instr = inwcs.getFitsMetadata().toString()
print 'inwcs:', instr
table = afwTable.SourceTable.make(schema, idFactory)
sources = det.makeSourceCatalog(table, exposure, sigma=1).sources
meas.measure(exposure, sources)
for dosip in [False, True]:
aconf.solver.calculateSip = dosip
ast = astrom.run(sourceCat=sources, exposure=exposure)
outwcs = exposure.getWcs()
outstr = outwcs.getFitsMetadata().toString()
if dosip is False:
self.assertEqual(inwcs, outwcs)
self.assertEqual(instr, outstr)
print 'inwcs:', instr
print 'outwcs:', outstr
print len(ast.matches), 'matches'
self.assertTrue(len(ast.matches) > 10)
def test1(self):
fn = os.path.join(os.path.dirname(__file__), 'data',
'mini-r-3-113,0.fits.gz')
print('Reading image', fn)
exposure = afwImage.ExposureF(fn)
exposure.setPsf(afwDetection.GaussianPsf(15, 15, 3))
schema = afwTable.SourceTable.makeMinimalSchema()
idFactory = afwTable.IdFactory.makeSimple()
dconf = measAlg.SourceDetectionConfig()
dconf.reEstimateBackground = False
dconf.includeThresholdMultiplier = 5.
mconf = SingleFrameMeasurementConfig()
aconf = ANetAstrometryConfig()
aconf.forceKnownWcs = True
det = measAlg.SourceDetectionTask(schema=schema, config=dconf)
meas = SingleFrameMeasurementTask(schema, config=mconf)
astrom = ANetAstrometryTask(schema, config=aconf, name='astrom')
astrom.log.setLevel(astrom.log.TRACE)
inwcs = exposure.getWcs()
print('inwcs:', inwcs)
instr = inwcs.getFitsMetadata().toString()
print('inwcs:', instr)
table = afwTable.SourceTable.make(schema, idFactory)
sources = det.makeSourceCatalog(table, exposure, sigma=1).sources
meas.measure(sources, exposure)
for dosip in [False, True]:
aconf.solver.calculateSip = dosip
ast = astrom.run(sourceCat=sources, exposure=exposure)
outwcs = exposure.getWcs()
outstr = outwcs.getFitsMetadata().toString()
if not dosip:
self.assertEqual(inwcs, outwcs)
self.assertEqual(instr, outstr)
print('inwcs:', instr)
print('outwcs:', outstr)
print(len(ast.matches), 'matches')
self.assertGreater(len(ast.matches), 10)
def getClumps(self, sigma=1.0, display=False):
if self._num <= 0:
raise RuntimeError("No candidate PSF sources")
psfImage = self.getImage()
#
# Embed psfImage into a larger image so we can smooth when measuring it
#
width, height = psfImage.getWidth(), psfImage.getHeight()
largeImg = psfImage.Factory(afwGeom.ExtentI(2 * width, 2 * height))
largeImg.set(0)
bbox = afwGeom.BoxI(afwGeom.PointI(width, height),
afwGeom.ExtentI(width, height))
largeImg.assign(psfImage, bbox, afwImage.LOCAL)
#
# Now measure that image, looking for the highest peak. Start by building an Exposure
#
msk = afwImage.MaskU(largeImg.getDimensions())
msk.set(0)
var = afwImage.ImageF(largeImg.getDimensions())
var.set(1)
mpsfImage = afwImage.MaskedImageF(largeImg, msk, var)
mpsfImage.setXY0(afwGeom.PointI(-width, -height))
del msk
del var
exposure = afwImage.makeExposure(mpsfImage)
#
# Next run an object detector
#
maxVal = afwMath.makeStatistics(psfImage, afwMath.MAX).getValue()
threshold = maxVal - sigma * math.sqrt(maxVal)
if threshold <= 0.0:
threshold = maxVal
threshold = afwDetection.Threshold(threshold)
ds = afwDetection.FootprintSet(mpsfImage, threshold, "DETECTED")
#
# And measure it. This policy isn't the one we use to measure
# Sources, it's only used to characterize this PSF histogram
#
schema = SourceTable.makeMinimalSchema()
psfImageConfig = SingleFrameMeasurementConfig()
psfImageConfig.slots.centroid = "base_SdssCentroid"
psfImageConfig.plugins["base_SdssCentroid"].doFootprintCheck = False
psfImageConfig.slots.psfFlux = None # "base_PsfFlux"
psfImageConfig.slots.apFlux = "base_CircularApertureFlux_3_0"
psfImageConfig.slots.modelFlux = None
psfImageConfig.slots.instFlux = None
psfImageConfig.slots.calibFlux = None
psfImageConfig.slots.shape = "base_SdssShape"
# Formerly, this code had centroid.sdss, flux.psf, flux.naive,
# flags.pixel, and shape.sdss
psfImageConfig.algorithms.names = [
"base_SdssCentroid", "base_CircularApertureFlux", "base_SdssShape"
]
psfImageConfig.algorithms["base_CircularApertureFlux"].radii = [3.0]
psfImageConfig.validate()
task = SingleFrameMeasurementTask(schema, config=psfImageConfig)
sourceCat = SourceCatalog(schema)
gaussianWidth = 1.5 # Gaussian sigma for detection convolution
exposure.setPsf(algorithmsLib.DoubleGaussianPsf(11, 11, gaussianWidth))
ds.makeSources(sourceCat)
#
# Show us the Histogram
#
if display:
frame = 1
dispImage = mpsfImage.Factory(
mpsfImage,
afwGeom.BoxI(afwGeom.PointI(width, height),
afwGeom.ExtentI(width, height)), afwImage.LOCAL)
ds9.mtv(dispImage, title="PSF Selection Image", frame=frame)
clumps = list() # List of clumps, to return
e = None # thrown exception
IzzMin = 1.0 # Minimum value for second moments
IzzMax = (
self._xSize /
8.0)**2 # Max value ... clump radius should be < clumpImgSize/8
apFluxes = []
task.run(
sourceCat,
exposure) # notes that this is backwards for the new framework
for i, source in enumerate(sourceCat):
if source.getCentroidFlag():
continue
x, y = source.getX(), source.getY()
apFluxes.append(source.getApFlux())
val = mpsfImage.getImage().get(int(x) + width, int(y) + height)
psfClumpIxx = source.getIxx()
psfClumpIxy = source.getIxy()
psfClumpIyy = source.getIyy()
if display:
if i == 0:
ds9.pan(x, y, frame=frame)
ds9.dot("+", x, y, ctype=ds9.YELLOW, frame=frame)
ds9.dot("@:%g,%g,%g" % (psfClumpIxx, psfClumpIxy, psfClumpIyy),
x,
y,
ctype=ds9.YELLOW,
frame=frame)
if psfClumpIxx < IzzMin or psfClumpIyy < IzzMin:
psfClumpIxx = max(psfClumpIxx, IzzMin)
psfClumpIyy = max(psfClumpIyy, IzzMin)
if display:
ds9.dot("@:%g,%g,%g" %
(psfClumpIxx, psfClumpIxy, psfClumpIyy),
x,
y,
ctype=ds9.RED,
frame=frame)
det = psfClumpIxx * psfClumpIyy - psfClumpIxy * psfClumpIxy
try:
a, b, c = psfClumpIyy / det, -psfClumpIxy / det, psfClumpIxx / det
except ZeroDivisionError:
a, b, c = 1e4, 0, 1e4
clumps.append(
Clump(peak=val,
x=x,
y=y,
a=a,
b=b,
c=c,
ixx=psfClumpIxx,
ixy=psfClumpIxy,
iyy=psfClumpIyy))
if len(clumps) == 0:
msg = "Failed to determine center of PSF clump"
if e:
msg += ": %s" % e
raise RuntimeError(msg)
# if it's all we got return it
if len(clumps) == 1:
return clumps
# which clump is the best?
# if we've undistorted the moments, stars should only have 1 clump
# use the apFlux from the clump measurement, and take the highest
# ... this clump has more psf star candidate neighbours than the others.
# get rid of any that are huge, and thus poorly defined
goodClumps = []
for clump in clumps:
if clump.ixx < IzzMax and clump.iyy < IzzMax:
goodClumps.append(clump)
# if culling > IzzMax cost us all clumps, we'll have to take what we have
if len(goodClumps) == 0:
goodClumps = clumps
# use the 'brightest' clump
iBestClump = numpy.argsort(apFluxes)[0]
clumps = [clumps[iBestClump]]
return clumps
def getClumps(self, sigma=1.0, display=False):
if self._num <= 0:
raise RuntimeError("No candidate PSF sources")
psfImage = self.getImage()
#
# Embed psfImage into a larger image so we can smooth when measuring it
#
width, height = psfImage.getWidth(), psfImage.getHeight()
largeImg = psfImage.Factory(afwGeom.ExtentI(2*width, 2*height))
largeImg.set(0)
bbox = afwGeom.BoxI(afwGeom.PointI(width, height), afwGeom.ExtentI(width, height))
largeImg.assign(psfImage, bbox, afwImage.LOCAL)
#
# Now measure that image, looking for the highest peak. Start by building an Exposure
#
msk = afwImage.MaskU(largeImg.getDimensions())
msk.set(0)
var = afwImage.ImageF(largeImg.getDimensions())
var.set(1)
mpsfImage = afwImage.MaskedImageF(largeImg, msk, var)
mpsfImage.setXY0(afwGeom.PointI(-width, -height))
del msk
del var
exposure = afwImage.makeExposure(mpsfImage)
#
# Next run an object detector
#
maxVal = afwMath.makeStatistics(psfImage, afwMath.MAX).getValue()
threshold = maxVal - sigma*math.sqrt(maxVal)
if threshold <= 0.0:
threshold = maxVal
threshold = afwDetection.Threshold(threshold)
ds = afwDetection.FootprintSet(mpsfImage, threshold, "DETECTED")
#
# And measure it. This policy isn't the one we use to measure
# Sources, it's only used to characterize this PSF histogram
#
schema = SourceTable.makeMinimalSchema()
psfImageConfig = SingleFrameMeasurementConfig()
psfImageConfig.doApplyApCorr = "no"
psfImageConfig.slots.centroid = "base_SdssCentroid"
psfImageConfig.slots.psfFlux = None #"base_PsfFlux"
psfImageConfig.slots.apFlux = "base_CircularApertureFlux_3_0"
psfImageConfig.slots.modelFlux = None
psfImageConfig.slots.instFlux = None
psfImageConfig.slots.calibFlux = None
psfImageConfig.slots.shape = "base_SdssShape"
# Formerly, this code had centroid.sdss, flux.psf, flux.naive,
# flags.pixel, and shape.sdss
psfImageConfig.algorithms.names = ["base_SdssCentroid", "base_CircularApertureFlux", "base_SdssShape"]
psfImageConfig.algorithms["base_CircularApertureFlux"].radii = [3.0]
psfImageConfig.validate()
task = SingleFrameMeasurementTask(schema, config=psfImageConfig)
sourceCat = SourceCatalog(schema)
gaussianWidth = 1.5 # Gaussian sigma for detection convolution
exposure.setPsf(algorithmsLib.DoubleGaussianPsf(11, 11, gaussianWidth))
ds.makeSources(sourceCat)
#
# Show us the Histogram
#
if display:
frame = 1
dispImage = mpsfImage.Factory(mpsfImage, afwGeom.BoxI(afwGeom.PointI(width, height),
afwGeom.ExtentI(width, height)),
afwImage.LOCAL)
ds9.mtv(dispImage,title="PSF Selection Image", frame=frame)
clumps = list() # List of clumps, to return
e = None # thrown exception
IzzMin = 1.0 # Minimum value for second moments
IzzMax = (self._xSize/8.0)**2 # Max value ... clump r < clumpImgSize/8
# diameter should be < 1/4 clumpImgSize
apFluxes = []
task.run(exposure, sourceCat) # notes that this is backwards for the new framework
for i, source in enumerate(sourceCat):
if source.getCentroidFlag():
continue
x, y = source.getX(), source.getY()
apFluxes.append(source.getApFlux())
val = mpsfImage.getImage().get(int(x) + width, int(y) + height)
psfClumpIxx = source.getIxx()
psfClumpIxy = source.getIxy()
psfClumpIyy = source.getIyy()
if display:
if i == 0:
ds9.pan(x, y, frame=frame)
ds9.dot("+", x, y, ctype=ds9.YELLOW, frame=frame)
ds9.dot("@:%g,%g,%g" % (psfClumpIxx, psfClumpIxy, psfClumpIyy), x, y,
ctype=ds9.YELLOW, frame=frame)
if psfClumpIxx < IzzMin or psfClumpIyy < IzzMin:
psfClumpIxx = max(psfClumpIxx, IzzMin)
psfClumpIyy = max(psfClumpIyy, IzzMin)
if display:
ds9.dot("@:%g,%g,%g" % (psfClumpIxx, psfClumpIxy, psfClumpIyy), x, y,
ctype=ds9.RED, frame=frame)
det = psfClumpIxx*psfClumpIyy - psfClumpIxy*psfClumpIxy
try:
a, b, c = psfClumpIyy/det, -psfClumpIxy/det, psfClumpIxx/det
except ZeroDivisionError:
a, b, c = 1e4, 0, 1e4
clumps.append(Clump(peak=val, x=x, y=y, a=a, b=b, c=c,
ixx=psfClumpIxx, ixy=psfClumpIxy, iyy=psfClumpIyy))
if len(clumps) == 0:
msg = "Failed to determine center of PSF clump"
if e:
msg += ": %s" % e
raise RuntimeError(msg)
# if it's all we got return it
if len(clumps) == 1:
return clumps
# which clump is the best?
# if we've undistorted the moments, stars should only have 1 clump
# use the apFlux from the clump measurement, and take the highest
# ... this clump has more psf star candidate neighbours than the others.
# get rid of any that are huge, and thus poorly defined
goodClumps = []
for clump in clumps:
if clump.ixx < IzzMax and clump.iyy < IzzMax:
goodClumps.append(clump)
# if culling > IzzMax cost us all clumps, we'll have to take what we have
if len(goodClumps) == 0:
goodClumps = clumps
# use the 'brightest' clump
iBestClump = numpy.argsort(apFluxes)[0]
clumps = [clumps[iBestClump]]
return clumps
def detect_and_deblend(*, exp, log):
log = lsst.log.Log.getLogger("LSSTMEDSifier")
thresh = 5.0
loglevel = 'INFO'
# This schema holds all the measurements that will be run within the
# stack It needs to be constructed before running anything and passed
# to algorithms that make additional measurents.
schema = afw_table.SourceTable.makeMinimalSchema()
# Setup algorithms to run
meas_config = SingleFrameMeasurementConfig()
meas_config.plugins.names = [
"base_SdssCentroid",
"base_PsfFlux",
"base_SkyCoord",
# "modelfit_ShapeletPsfApprox",
"modelfit_DoubleShapeletPsfApprox",
"modelfit_CModel",
# "base_SdssShape",
# "base_LocalBackground",
]
# set these slots to none because we aren't running these algorithms
meas_config.slots.apFlux = None
meas_config.slots.gaussianFlux = None
meas_config.slots.calibFlux = None
meas_config.slots.modelFlux = None
# goes with SdssShape above
meas_config.slots.shape = None
# fix odd issue where it things things are near the edge
meas_config.plugins['base_SdssCentroid'].binmax = 1
meas_task = SingleFrameMeasurementTask(
config=meas_config,
schema=schema,
)
# setup detection config
detection_config = SourceDetectionConfig()
detection_config.reEstimateBackground = False
detection_config.thresholdValue = thresh
detection_task = SourceDetectionTask(config=detection_config)
detection_task.log.setLevel(getattr(lsst.log, loglevel))
deblend_config = SourceDeblendConfig()
deblend_task = SourceDeblendTask(config=deblend_config, schema=schema)
deblend_task.log.setLevel(getattr(lsst.log, loglevel))
# Detect objects
table = afw_table.SourceTable.make(schema)
result = detection_task.run(table, exp)
sources = result.sources
# run the deblender
deblend_task.run(exp, sources)
# Run on deblended images
noise_replacer_config = NoiseReplacerConfig()
footprints = {
record.getId(): (record.getParent(), record.getFootprint())
for record in result.sources
}
# This constructor will replace all detected pixels with noise in the
# image
replacer = NoiseReplacer(
noise_replacer_config,
exposure=exp,
footprints=footprints,
)
nbad = 0
ntry = 0
kept_sources = []
for record in result.sources:
# Skip parent objects where all children are inserted
if record.get('deblend_nChild') != 0:
continue
ntry += 1
# This will insert a single source into the image
replacer.insertSource(record.getId()) # Get the peak as before
# peak = record.getFootprint().getPeaks()[0]
# The bounding box will be for the parent object
# bbox = record.getFootprint().getBBox()
meas_task.callMeasure(record, exp)
# Remove object
replacer.removeSource(record.getId())
if record.getCentroidFlag():
nbad += 1
kept_sources.append(record)
# Insert all objects back into image
replacer.end()
if ntry > 0:
log.debug('nbad center: %d frac: %d' % (nbad, nbad / ntry))
nkeep = len(kept_sources)
ntot = len(result.sources)
log.debug('kept %d/%d non parents' % (nkeep, ntot))
return kept_sources