def test_solve_centroid(self):
exposure = ExposureF(400, 400)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 100
exposure.getMaskedImage().getVariance().getArray()[203, 203] = np.nan
add_psf_image(exposure, 200.0, 200.0, 600.0)
# exposure.image.array += 20*np.random.randn(*exposure.image.array.shape)
schema = afwTable.SourceTable.makeMinimalSchema()
simultaneousPsfFlux_key = schema.addField(
"crowd_psfFlux_flux_instFlux",
type=np.float64,
doc="PSF Flux from simultaneous fitting")
afwTable.Point2DKey.addFields(schema, "coarse_centroid",
"Detection peak", "pixels")
centroid_key = afwTable.Point2DKey.addFields(
schema, "new_centroid", "Measurement of centroid", "pixels")
schema.getAliasMap().set("slot_Centroid", "coarse_centroid")
schema.getAliasMap().set("slot_PsfFlux", "crowd_psfFlux_flux")
source_catalog = afwTable.SourceCatalog(schema)
child = source_catalog.addNew()
child['coarse_centroid_x'] = 200.0
child['coarse_centroid_y'] = 200.5
# Fit once with fixed positions to get the fluxes
matrix = CrowdedFieldMatrix(exposure,
source_catalog,
simultaneousPsfFlux_key,
fitCentroids=False,
centroidKey=centroid_key)
matrix.solve()
# Now that we have rough fluxes, re-fit and allow
# centroids to move.
matrix = CrowdedFieldMatrix(exposure,
source_catalog,
simultaneousPsfFlux_key,
fitCentroids=True,
centroidKey=centroid_key)
result = matrix.solve()
self.assertFloatsAlmostEqual(source_catalog[0]['new_centroid_x'],
200.0,
atol=5e-2)
self.assertFloatsAlmostEqual(source_catalog[0]['new_centroid_y'],
200.0,
atol=5e-2)
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcSet = SourceCatalog.readFits(os.path.join(testDir, "v695833-e0-c000.xy.fits"))
self.bbox = afwGeom.Box2I(afwGeom.Point2I(0, 0), afwGeom.Extent2I(2048, 4612)) # approximate
# create an exposure with the right metadata; the closest thing we have is
# apparently v695833-e0-c000-a00.sci.fits, which is much too small
smallExposure = ExposureF(os.path.join(testDir, "v695833-e0-c000-a00.sci.fits"))
self.exposure = ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
# copy the pixels we can, in case the user wants a debug display
mi = self.exposure.getMaskedImage()
mi.assign(smallExposure.getMaskedImage(), smallExposure.getBBox())
logLevel = Log.INFO
refCatDir = os.path.join(testDir, "data", "sdssrefcat")
butler = Butler(refCatDir)
refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
astrometryConfig = AstrometryTask.ConfigClass()
self.astrom = AstrometryTask(config=astrometryConfig, refObjLoader=refObjLoader)
self.astrom.log.setLevel(logLevel)
# Since our sourceSelector is a registry object we have to wait for it to be created
# before setting default values.
self.astrom.matcher.sourceSelector.config.minSnr = 0
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcSet = SourceCatalog.readFits(os.path.join(testDir, "v695833-e0-c000.xy.fits"))
self.bbox = lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Extent2I(2048, 4612)) # approximate
# create an exposure with the right metadata; the closest thing we have is
# apparently v695833-e0-c000-a00.sci.fits, which is much too small
smallExposure = ExposureF(os.path.join(testDir, "v695833-e0-c000-a00.sci.fits"))
self.exposure = ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
# copy the pixels we can, in case the user wants a debug display
mi = self.exposure.getMaskedImage()
mi.assign(smallExposure.getMaskedImage(), smallExposure.getBBox())
logLevel = Log.INFO
refCatDir = os.path.join(testDir, "data", "sdssrefcat")
butler = Butler(refCatDir)
refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
astrometryConfig = AstrometryTask.ConfigClass()
self.astrom = AstrometryTask(config=astrometryConfig, refObjLoader=refObjLoader)
self.astrom.log.setLevel(logLevel)
# Since our sourceSelector is a registry object we have to wait for it to be created
# before setting default values.
self.astrom.sourceSelector.config.minSnr = 0
def test_solve(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
# These last two are to catch edge effects.
add_psf_image(exposure, 5.0, 210.0, 400.0)
add_psf_image(exposure, 300.0, 5.0, 500.0)
matrix = CrowdedFieldMatrix(exposure,
np.array([200.0, 210.0, 5.0, 300.0]),
np.array([400.0, 210.0, 210.0, 5.0]))
status = matrix.solve()
self.assertEqual(status, matrix.SUCCESS)
result = matrix.result()
self.assertFloatsAlmostEqual(result,
np.array([600.0, 300.0, 400.0, 500.0]),
atol=1e-3)
def test_solve_catalog(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
schema = afwTable.SourceTable.makeMinimalSchema()
schema.addField("centroid_x", type=np.float64)
schema.addField("centroid_y", type=np.float64)
flux_key = schema.addField("flux_flux", type=np.float64)
schema.getAliasMap().set("slot_Centroid", "centroid")
testCatalog = afwTable.SourceCatalog(schema)
for x, y in zip([200.0, 210.0], [400.0, 210]):
r = testCatalog.addNew()
r["centroid_x"] = x
r["centroid_y"] = y
matrix = CrowdedFieldMatrix(exposure, testCatalog, flux_key)
result = matrix.solve()
self.assertFloatsAlmostEqual(testCatalog["flux_flux"],
np.array([600.0, 300.0]),
atol=1e-3)
def test_reject_maskedpixels(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
exposure.getMaskedImage().getImage()[200, 400] += 10000
mask_dict = exposure.getMaskedImage().getMask().getMaskPlaneDict()
exposure.getMaskedImage().getMask()[200, 400] |= mask_dict['SAT']
matrix = CrowdedFieldMatrix(exposure, np.array([200.0, 210.0]),
np.array([400.0, 210.0]))
status = matrix.solve()
self.assertEqual(status, matrix.SUCCESS)
result = matrix.result()
self.assertFloatsAlmostEqual(result,
np.array([600.0, 300.0]),
atol=1e-3)
class joinMatchListWithCatalogTestCase(unittest.TestCase):
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcSet = SourceCatalog.readFits(os.path.join(testDir, "v695833-e0-c000.xy.fits"))
self.bbox = afwGeom.Box2I(afwGeom.Point2I(0, 0), afwGeom.Extent2I(2048, 4612)) # approximate
# create an exposure with the right metadata; the closest thing we have is
# apparently v695833-e0-c000-a00.sci.fits, which is much too small
smallExposure = ExposureF(os.path.join(testDir, "v695833-e0-c000-a00.sci.fits"))
self.exposure = ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
# copy the pixels we can, in case the user wants a debug display
mi = self.exposure.getMaskedImage()
mi.assign(smallExposure.getMaskedImage(), smallExposure.getBBox())
logLevel = Log.INFO
refCatDir = os.path.join(testDir, "data", "sdssrefcat")
butler = Butler(refCatDir)
refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
astrometryConfig = AstrometryTask.ConfigClass()
self.astrom = AstrometryTask(config=astrometryConfig, refObjLoader=refObjLoader)
self.astrom.log.setLevel(logLevel)
# Since our sourceSelector is a registry object we have to wait for it to be created
# before setting default values.
self.astrom.matcher.sourceSelector.config.minSnr = 0
def tearDown(self):
del self.srcSet
del self.bbox
del self.exposure
del self.astrom
def getAstrometrySolution(self):
return self.astrom.solve(exposure=self.exposure, sourceCat=self.srcSet)
def testJoin(self):
res = self.getAstrometrySolution()
matches = res.matches
matchmeta = res.matchMeta
normalized = packMatches(matches)
normalized.table.setMetadata(matchmeta)
matches2 = self.astrom.refObjLoader.joinMatchListWithCatalog(normalized, self.srcSet)
self.assertEqual(len(matches2), len(matches))
for i in range(len(matches)):
self.assertEqual(matches2[i].second.table, matches[i].second.table)
self.assertEqual(matches2[i].second.getId(), matches[i].second.getId())
self.assertEqual(matches2[i].second, matches[i].second) # no deep copying, so we can compare ptrs
self.assertEqual(matches2[i].first.getId(), matches[i].first.getId())
self.assertEqual(matches2[i].first.getRa().asDegrees(), matches[i].first.getRa().asDegrees())
self.assertEqual(matches2[i].first.getDec().asDegrees(), matches[i].first.getDec().asDegrees())
self.assertEqual(matches2[i].first.get("i_flux"), matches[i].first.get("i_flux"))
def testJoinAllFluxes(self):
"""Test that we can read all the fluxes back from an a.n.d catalogue"""
res = self.getAstrometrySolution()
matches = res.matches
matchmeta = res.matchMeta
normalized = packMatches(matches)
normalized.table.setMetadata(matchmeta)
matches2 = self.astrom.refObjLoader.joinMatchListWithCatalog(normalized, self.srcSet)
self.assertGreater(len(matches2), 0)
ref = matches2[0][0]
names = ref.getSchema().getNames()
for b in ("u", "g", "r", "i", "z"):
self.assertIn("%s_flux" % (b,), names)
self.assertIn("%s_fluxSigma" % (b,), names)
class JoinMatchListWithCatalogTestCase(unittest.TestCase):
def setUp(self):
# Load sample input from disk
testDir = os.path.dirname(__file__)
self.srcSet = SourceCatalog.readFits(os.path.join(testDir, "v695833-e0-c000.xy.fits"))
self.bbox = lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Extent2I(2048, 4612)) # approximate
# create an exposure with the right metadata; the closest thing we have is
# apparently v695833-e0-c000-a00.sci.fits, which is much too small
smallExposure = ExposureF(os.path.join(testDir, "v695833-e0-c000-a00.sci.fits"))
self.exposure = ExposureF(self.bbox)
self.exposure.setWcs(smallExposure.getWcs())
self.exposure.setFilter(smallExposure.getFilter())
# copy the pixels we can, in case the user wants a debug display
mi = self.exposure.getMaskedImage()
mi.assign(smallExposure.getMaskedImage(), smallExposure.getBBox())
logLevel = Log.INFO
refCatDir = os.path.join(testDir, "data", "sdssrefcat")
butler = Butler(refCatDir)
refObjLoader = LoadIndexedReferenceObjectsTask(butler=butler)
astrometryConfig = AstrometryTask.ConfigClass()
self.astrom = AstrometryTask(config=astrometryConfig, refObjLoader=refObjLoader)
self.astrom.log.setLevel(logLevel)
# Since our sourceSelector is a registry object we have to wait for it to be created
# before setting default values.
self.astrom.sourceSelector.config.minSnr = 0
def tearDown(self):
del self.srcSet
del self.bbox
del self.exposure
del self.astrom
def getAstrometrySolution(self):
return self.astrom.solve(exposure=self.exposure, sourceCat=self.srcSet)
def testJoin(self):
res = self.getAstrometrySolution()
matches = res.matches
matchmeta = res.matchMeta
normalized = packMatches(matches)
normalized.table.setMetadata(matchmeta)
matches2 = self.astrom.refObjLoader.joinMatchListWithCatalog(normalized, self.srcSet)
self.assertEqual(len(matches2), len(matches))
for i in range(len(matches)):
self.assertEqual(matches2[i].second.table, matches[i].second.table)
self.assertEqual(matches2[i].second.getId(), matches[i].second.getId())
self.assertEqual(matches2[i].second, matches[i].second) # no deep copying, so we can compare ptrs
self.assertEqual(matches2[i].first.getId(), matches[i].first.getId())
self.assertEqual(matches2[i].first.getRa().asDegrees(), matches[i].first.getRa().asDegrees())
self.assertEqual(matches2[i].first.getDec().asDegrees(), matches[i].first.getDec().asDegrees())
self.assertEqual(matches2[i].first.get("i_flux"), matches[i].first.get("i_flux"))
def testJoinAllFluxes(self):
"""Test that we can read all the fluxes from a reference catalog"""
res = self.getAstrometrySolution()
matches = res.matches
matchmeta = res.matchMeta
normalized = packMatches(matches)
normalized.table.setMetadata(matchmeta)
matches2 = self.astrom.refObjLoader.joinMatchListWithCatalog(normalized, self.srcSet)
self.assertGreater(len(matches2), 0)
ref = matches2[0][0]
refSchema = ref.getSchema()
for b in ("u", "g", "r", "i", "z"):
self.assertIn("%s_flux" % (b,), refSchema)
self.assertIn("%s_fluxErr" % (b,), refSchema)
class CrowdedFieldMatrixTestCase(lsst.utils.tests.TestCase):
def setUp(self):
self.exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=self.exposure)
variance_image = self.exposure.getMaskedImage().getVariance()
variance_image += 50
def test_singleSource(self):
matrix = CrowdedFieldMatrix(self.exposure, np.array([200.0]),
np.array([200.0]))
self.assertGreater(len(matrix.getMatrixEntries()), 0)
def test_multipleSources(self):
x_arr = np.linspace(20.0, 60.0, 20)
y_arr = np.linspace(30.0, 70.0, 20)
matrix = CrowdedFieldMatrix(self.exposure, x_arr, y_arr)
self.assertGreater(len(matrix.getMatrixEntries()), 0)
def test_unequalLengths(self):
x_arr = np.linspace(20.0, 60.0, 20)
y_arr = np.linspace(30.0, 70.0, 21) # Note 21 != 20
with self.assertRaises(lsst.pex.exceptions.wrappers.LengthError):
matrix = CrowdedFieldMatrix(self.exposure, x_arr, y_arr)
def test_createFromCatalog(self):
schema = afwTable.SourceTable.makeMinimalSchema()
schema.addField("centroid_x", type=np.float64)
schema.addField("centroid_y", type=np.float64)
flux_key = schema.addField("flux_flux", type=np.float64)
schema.getAliasMap().set("slot_Centroid", "centroid")
testCatalog = afwTable.SourceCatalog(schema)
for n in range(20):
r = testCatalog.addNew()
r["centroid_x"] = n * 3
r["centroid_y"] = n * 3
matrix = CrowdedFieldMatrix(self.exposure, testCatalog, flux_key)
self.assertGreater(len(matrix.getMatrixEntries()), 0)
@unittest.skip
def test_renameMatrixRows(self):
#
# Haven't figured out how to get access to pre-renaming matrix
# to do the comparision with the post-renaming one.
#
pre_rename_entries = matrix.getMatrixEntries()
matrix.renameMatrixRows()
matrix = CrowdedFieldMatrix(self.exposure, np.array([200.0, 200.0]),
np.array([204.0, 204.0]))
post_rename_entries = matrix.getMatrixEntries()
self.assertEqual(len(pre_rename_entries), len(post_rename_entries))
# We check that the count of rows that appear a given number of times
# remains the same, since this is invariant under renaming.
pre_counter = Counter(x[1] for x in pre_rename_entries)
post_counter = Counter(x[1] for x in post_rename_entries)
pre_counts = list(pre_counter.values())
post_counts = list(post_counter.values())
pre_counts.sort()
post_counts.sort()
self.assertListEqual(pre_counts, post_counts)
def test_solve(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
# These last two are to catch edge effects.
add_psf_image(exposure, 5.0, 210.0, 400.0)
add_psf_image(exposure, 300.0, 5.0, 500.0)
matrix = CrowdedFieldMatrix(exposure,
np.array([200.0, 210.0, 5.0, 300.0]),
np.array([400.0, 210.0, 210.0, 5.0]))
status = matrix.solve()
self.assertEqual(status, matrix.SUCCESS)
result = matrix.result()
self.assertFloatsAlmostEqual(result,
np.array([600.0, 300.0, 400.0, 500.0]),
atol=1e-3)
def test_solve_catalog(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
schema = afwTable.SourceTable.makeMinimalSchema()
schema.addField("centroid_x", type=np.float64)
schema.addField("centroid_y", type=np.float64)
flux_key = schema.addField("flux_flux", type=np.float64)
schema.getAliasMap().set("slot_Centroid", "centroid")
testCatalog = afwTable.SourceCatalog(schema)
for x, y in zip([200.0, 210.0], [400.0, 210]):
r = testCatalog.addNew()
r["centroid_x"] = x
r["centroid_y"] = y
matrix = CrowdedFieldMatrix(exposure, testCatalog, flux_key)
result = matrix.solve()
self.assertFloatsAlmostEqual(testCatalog["flux_flux"],
np.array([600.0, 300.0]),
atol=1e-3)
def test_reject_maskedpixels(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
add_psf_image(exposure, 200.0, 400.0, 600.0)
add_psf_image(exposure, 210.0, 210.0, 300.0)
exposure.getMaskedImage().getImage()[200, 400] += 10000
mask_dict = exposure.getMaskedImage().getMask().getMaskPlaneDict()
exposure.getMaskedImage().getMask()[200, 400] |= mask_dict['SAT']
matrix = CrowdedFieldMatrix(exposure, np.array([200.0, 210.0]),
np.array([400.0, 210.0]))
status = matrix.solve()
self.assertEqual(status, matrix.SUCCESS)
result = matrix.result()
self.assertFloatsAlmostEqual(result,
np.array([600.0, 300.0]),
atol=1e-3)
@unittest.skip
def test_solve_subimage(self):
exposure = ExposureF(1000, 1000)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 50
self.add_psf_image(exposure, 200.0, 400.0, 600.0)
self.add_psf_image(exposure, 210.0, 210.0, 300.0)
matrix = CrowdedFieldMatrix(exposure, np.array([200.0, 210.0]),
np.array([400.0, 210.0]))
status = matrix.solve()
self.assertEqual(status, matrix.SUCCESS)
result = matrix.result()
self.assertFloatsAlmostEqual(result,
np.array([600.0, 300.0]),
atol=1e-3)
def test_solve_centroid(self):
exposure = ExposureF(400, 400)
psfConfig = InstallGaussianPsfConfig()
psfConfig.fwhm = 4
psfTask = InstallGaussianPsfTask(config=psfConfig)
psfTask.run(exposure=exposure)
variance_image = exposure.getMaskedImage().getVariance()
variance_image += 100
exposure.getMaskedImage().getVariance().getArray()[203, 203] = np.nan
add_psf_image(exposure, 200.0, 200.0, 600.0)
# exposure.image.array += 20*np.random.randn(*exposure.image.array.shape)
schema = afwTable.SourceTable.makeMinimalSchema()
simultaneousPsfFlux_key = schema.addField(
"crowd_psfFlux_flux_instFlux",
type=np.float64,
doc="PSF Flux from simultaneous fitting")
afwTable.Point2DKey.addFields(schema, "coarse_centroid",
"Detection peak", "pixels")
centroid_key = afwTable.Point2DKey.addFields(
schema, "new_centroid", "Measurement of centroid", "pixels")
schema.getAliasMap().set("slot_Centroid", "coarse_centroid")
schema.getAliasMap().set("slot_PsfFlux", "crowd_psfFlux_flux")
source_catalog = afwTable.SourceCatalog(schema)
child = source_catalog.addNew()
child['coarse_centroid_x'] = 200.0
child['coarse_centroid_y'] = 200.5
# Fit once with fixed positions to get the fluxes
matrix = CrowdedFieldMatrix(exposure,
source_catalog,
simultaneousPsfFlux_key,
fitCentroids=False,
centroidKey=centroid_key)
matrix.solve()
# Now that we have rough fluxes, re-fit and allow
# centroids to move.
matrix = CrowdedFieldMatrix(exposure,
source_catalog,
simultaneousPsfFlux_key,
fitCentroids=True,
centroidKey=centroid_key)
result = matrix.solve()
self.assertFloatsAlmostEqual(source_catalog[0]['new_centroid_x'],
200.0,
atol=5e-2)
self.assertFloatsAlmostEqual(source_catalog[0]['new_centroid_y'],
200.0,
atol=5e-2)