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U/pgee/dm 1013 #7

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Jan 21, 2015
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U/pgee/dm 1013 #7

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cfa21d4
DM-1013 - setup flag field for ClassificationExtendedness and set app…
Jan 16, 2015
7f1e14c
Merge branch 'master' into u/pgee/DM-1013
pgee2000 Jan 21, 2015
4e90625
Merge branch 'master' into u/pgee/DM-1013
pgee2000 Jan 21, 2015
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27 changes: 21 additions & 6 deletions python/lsst/meas/base/plugins.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,23 +137,38 @@ def __init__(self, config, name, schema, metadata):
SingleFramePlugin.__init__(self, config, name, schema, metadata)
self.keyProbability = schema.addField(name + "_value", type="D",
doc="Set to 1 for extended sources, 0 for point sources.")
self.keyFlag = schema.addField(name + "_flag", type="Flag", doc="Set to 1 for any fatal failure.")

def measure(self, measRecord, exposure):
modelFlux = measRecord.getModelFlux()
modelFluxErr = measRecord.getModelFluxErr()
modelFluxFlag = measRecord.getModelFluxFlag()
psfFlux = measRecord.getPsfFlux()
psfFluxErr = measRecord.getPsfFluxErr()
flux1 = self.config.fluxRatio*modelFlux + self.config.modelErrFactor*modelFluxErr
flux2 = psfFlux + self.config.psfErrFactor*psfFluxErr
if flux1 < flux2:
measRecord.set(self.keyProbability, 0.0)
psfFluxFlag = measRecord.getPsfFluxFlag()
flux1 = self.config.fluxRatio*modelFlux
if not self.config.modelErrFactor == 0:
flux1 += self.config.modelErrFactor*modelFluxErr
flux2 = psfFlux
if not self.config.psfErrFactor == 0:
flux2 += self.config.psfErrFactor*psfFluxErr
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The logic here appears to have got more complicated for no very clear reason. If self.config.modelErrFactor == 0, then adding something multiplied by it has no effect (and the same for self.config.psfErrFactor, of course). What are the if statements adding?

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This code is basically coding around the possibility that the Err is a NAN.


# A generic failure occurs when either FluxFlag is set to True
# A generic failure also occurs if either calculated flux value is NAN:
# this can occur if the Flux field itself is NAN,
# or the ErrFactor != 0 and the FluxErr is NAN
if numpy.isnan(flux1) or numpy.isnan(flux2) or modelFluxFlag or psfFluxFlag:
self.fail(measRecord)
else:
measRecord.set(self.keyProbability, 1.0);
if flux1 < flux2:
measRecord.set(self.keyProbability, 0.0)
else:
measRecord.set(self.keyProbability, 1.0)

def fail(self, measRecord, error=None):
# Override fail() to do nothing in the case of an exception. We should be setting a flag
# instead.
pass
measRecord.set(self.keyFlag, True)


# --- Forced Plugins ---
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112 changes: 111 additions & 1 deletion tests/testClassification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@

DATA_DIR = os.path.join(os.environ["MEAS_BASE_DIR"], "tests")

class SFMTestCase(lsst.utils.tests.TestCase):
class SFMTestCase(lsst.meas.base.tests.AlgorithmTestCase):

def testAlgorithm(self):

Expand Down Expand Up @@ -75,6 +75,116 @@ def testAlgorithm(self):
else:
self.assertEqual(probability, 1.0)

def testFlags(self):
"""Test all the failure modes of this algorithm, as well as checking that it succeeds when it should.

Since this algorithm depends on having a ModelFlux and a PsfFlux measurement, it is a failure
mode when either is NAN, or when ModelFluxFlag or PsfFluxFlag is True.

When psfFluxFactor != 0, the PsfFluxErr cannot be NAN, but otherwise is ignored

When modelFluxFactor != 0, the ModelFluxErr cannot be NAN, but otherwise is ignored
"""
exp = afwImage.ExposureF()
schema = lsst.afw.table.SourceTable.makeMinimalSchema()

sfm_config = lsst.meas.base.sfm.SingleFrameMeasurementConfig()
sfm_config.plugins = ["base_SdssCentroid", "base_PsfFlux", "base_SincFlux",
"base_ClassificationExtendedness"]
sfm_config.slots.centroid = "base_SdssCentroid"
sfm_config.slots.shape = None
sfm_config.slots.psfFlux = "base_PsfFlux"
sfm_config.slots.modelFlux = "base_SincFlux"
sfm_config.slots.apFlux = None
sfm_config.slots.instFlux = None
task = SingleFrameMeasurementTask(schema, config=sfm_config)
measCat = SourceCatalog(schema)

# Test no error case - all necessary values are set
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
source.set("base_SincFlux_fluxSigma", 2)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertFalse(source.get("base_ClassificationExtendedness_flag"))

# Test psfFlux flag case - failure in PsfFlux
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
source.set("base_SincFlux_fluxSigma", 2)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
source.set("base_PsfFlux_flag", True)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))

# Test modelFlux flag case - falure in ModelFlux
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
source.set("base_SincFlux_fluxSigma", 2)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
source.set("base_SincFlux_flag", True)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))

# Test modelFlux NAN case
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_fluxSigma", 2)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
source.set("base_SincFlux_flag", True)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))

# Test psfFlux NAN case
source = measCat.addNew()
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
source.set("base_SincFlux_fluxSigma", 2)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
source.set("base_SincFlux_flag", True)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))

# Test modelFluxErr NAN case when modelErrFactor is zero and non-zero
sfm_config.plugins["base_ClassificationExtendedness"].modelErrFactor = 0.
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertFalse(source.get("base_ClassificationExtendedness_flag"))

sfm_config.plugins["base_ClassificationExtendedness"].modelErrFactor = 1.
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_PsfFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))

# Test psfFluxErr NAN case when psfErrFactor is zero and non-zero
sfm_config.plugins["base_ClassificationExtendedness"].psfErrFactor = 0.
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_SincFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertFalse(source.get("base_ClassificationExtendedness_flag"))

sfm_config.plugins["base_ClassificationExtendedness"].psfErrFactor = 1.
source = measCat.addNew()
source.set("base_PsfFlux_flux", 100)
source.set("base_SincFlux_fluxSigma", 1)
source.set("base_SincFlux_flux", 200)
task.plugins["base_ClassificationExtendedness"].measure(source, exp)
self.assertTrue(source.get("base_ClassificationExtendedness_flag"))
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You could write the same tests with a lot less code by defining a method which takes the source parameters and expected flag value as arguments and then calling it repeatedly.

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I thought of that too, but it didn't seem like it mattered for a unit test. And there are little differences from test to test.


def suite():
"""Returns a suite containing all the test cases in this module."""

Expand Down