def checkExposure(original, doTempLocalBackground, doTempWideBackground):
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
config.thresholdType = "pixel_stdev"
config.doTempLocalBackground = doTempLocalBackground
config.doTempWideBackground = doTempWideBackground
schema = afwTable.SourceTable.makeMinimalSchema()
task = SourceDetectionTask(config=config, schema=schema)
exposure = original.clone()
task.detectFootprints(exposure, sigma=3.21)
self.assertFloatsEqual(exposure.image.array, original.image.array)
# Mask is permitted to vary: DETECTED bit gets set
self.assertFloatsEqual(exposure.variance.array, original.variance.array)
def testBasics(self):
bbox = afwGeom.Box2I(afwGeom.Point2I(256, 100),
afwGeom.Extent2I(128, 127))
minCounts = 5000
maxCounts = 50000
starSigma = 1.5
numX = 5
numY = 5
coordList = self.makeCoordList(
bbox=bbox,
numX=numX,
numY=numY,
minCounts=minCounts,
maxCounts=maxCounts,
sigma=starSigma,
)
kwid = 11
sky = 2000
addPoissonNoise = True
exposure = plantSources(bbox=bbox,
kwid=kwid,
sky=sky,
coordList=coordList,
addPoissonNoise=addPoissonNoise)
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
task = SourceDetectionTask(config=config, schema=schema)
for doSmooth in (False, True):
taskSigma = 2.2
res = task.detectFootprints(exposure,
doSmooth=doSmooth,
sigma=taskSigma)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
self.assertEqual(task.metadata.get("sigma"), taskSigma)
self.assertEqual(task.metadata.get("doSmooth"), doSmooth)
self.assertEqual(task.metadata.get("nGrow"),
int(taskSigma * config.nSigmaToGrow + 0.5))
res = task.detectFootprints(exposure,
doSmooth=doSmooth,
sigma=None)
taskSigma = task.metadata.get("sigma")
self.assertTrue(abs(taskSigma - starSigma) < 0.1)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
def test_pixel_stdev(self):
"""Test that sources are detected on a simulated image with
thresholdType='pixel_stdev', and that they have the right significance.
"""
exposure, schema, numX, numY, starSigma = self._create_exposure()
config = SourceDetectionTask.ConfigClass()
config.thresholdType = "pixel_stdev"
config.reEstimateBackground = False
# TempLocalBackground changes the peak value of the faintest peak,
# so disable it for this test so that we can calculate an expected
# answer without having to try to deal with backgrounds.
config.doTempLocalBackground = False
task = SourceDetectionTask(config=config, schema=schema)
# Don't smooth, so that we can directly calculate the s/n from the exposure.
result = task.detectFootprints(exposure, doSmooth=False)
self.assertEqual(result.numPos, numX * numY)
self.assertEqual(result.numNeg, 0)
# Significance values for `pixel_stdev` should match image/sqrt(variance).
for footprint in result.positive.getFootprints():
for peak in footprint.peaks:
point = lsst.geom.Point2I(peak.getIx(), peak.getIy())
value = exposure.image[point]
stddev = np.sqrt(exposure.variance[point])
with self.subTest(str(point)):
self.assertFloatsAlmostEqual(peak["significance"],
value / stddev,
rtol=1e-7,
msg=str(point))
def test_significance(self):
"""Test that negative peaks have the right significance for
thresholdType='stdev' for the non-convolved, non-local-background case.
"""
exposure, numX, numY = self._create_exposure()
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.thresholdPolarity = 'both'
# don't modify the image after detection.
config.reEstimateBackground = False
config.doTempLocalBackground = False
detection = SourceDetectionTask(config=config, schema=schema)
result = detection.detectFootprints(exposure, doSmooth=False)
bad = exposure.mask.getPlaneBitMask(config.statsMask)
sctrl = afwMath.StatisticsControl()
sctrl.setAndMask(bad)
stats = afwMath.makeStatistics(exposure.maskedImage, afwMath.STDEVCLIP,
sctrl)
stddev = stats.getValue(afwMath.STDEVCLIP)
# Don't bother checking positive footprints: those are tested more
# thoroughly in test_detection.py.
for footprint in result.negative.getFootprints():
for peak in footprint.peaks:
point = lsst.geom.Point2I(peak.getIx(), peak.getIy())
value = exposure.image[point]
with self.subTest(str(point)):
self.assertFloatsAlmostEqual(
peak["significance"],
-value / stddev, # S/N for negative peak
rtol=1e-7,
msg=str(point))
def checkExposure(original, doTempLocalBackground,
doTempWideBackground):
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
config.thresholdType = "pixel_stdev"
config.doTempLocalBackground = doTempLocalBackground
config.doTempWideBackground = doTempWideBackground
schema = afwTable.SourceTable.makeMinimalSchema()
task = SourceDetectionTask(config=config, schema=schema)
exposure = original.clone()
task.detectFootprints(exposure, sigma=3.21)
self.assertFloatsEqual(exposure.image.array, original.image.array)
# Mask is permitted to vary: DETECTED bit gets set
self.assertFloatsEqual(exposure.variance.array,
original.variance.array)
def testBasics(self):
bbox = lsst.geom.Box2I(lsst.geom.Point2I(256, 100), lsst.geom.Extent2I(128, 127))
minCounts = 5000
maxCounts = 50000
starSigma = 1.5
numX = 5
numY = 5
coordList = self.makeCoordList(
bbox=bbox,
numX=numX,
numY=numY,
minCounts=minCounts,
maxCounts=maxCounts,
sigma=starSigma,
)
kwid = 11
sky = 2000
addPoissonNoise = True
exposure = plantSources(bbox=bbox, kwid=kwid, sky=sky, coordList=coordList,
addPoissonNoise=addPoissonNoise)
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
task = SourceDetectionTask(config=config, schema=schema)
for doSmooth in (False, True):
taskSigma = 2.2
res = task.detectFootprints(exposure, doSmooth=doSmooth, sigma=taskSigma)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
self.assertEqual(task.metadata.getScalar("sigma"), taskSigma)
self.assertEqual(task.metadata.getScalar("doSmooth"), doSmooth)
self.assertEqual(task.metadata.getScalar("nGrow"), int(taskSigma * config.nSigmaToGrow + 0.5))
res = task.detectFootprints(exposure, doSmooth=doSmooth, sigma=None)
taskSigma = task.metadata.getScalar("sigma")
self.assertLess(abs(taskSigma - starSigma), 0.1)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
