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DM-42863: Add method to evaluate PTC model in ptcDataset #269

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Sep 26, 2024
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DM-42863: Add method to evaluate PTC model in ptcDataset #269

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6052b6d
Fix notest bug.
Alex-Broughton Sep 24, 2024
123c861
Add test cases for PhotonTransferCurveDataset.evalCovModel
Alex-Broughton Sep 24, 2024
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43 changes: 30 additions & 13 deletions tests/test_ptc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ def setUp(self):
(1, self.dataset.covMatrixSide, self.dataset.covMatrixSide)
)

def notest_covAstier(self):
def test_covAstier(self):
"""Test to check getCovariancesAstier

We check that the gain is the same as the imput gain from the
Expand Down Expand Up @@ -414,6 +414,15 @@ def notest_covAstier(self):
self.assertEqual(
ptc.covariancesModelNoB[ampName].shape, covModelNoBShape
)
# Check if evalPtcModel produces expected values
nanMask = ~np.isnan(ptc.finalMeans[ampName])
means = ptc.finalMeans[ampName][nanMask]
covModel = ptc.covariancesModel[ampName][nanMask]
covariancesModel = ptc.evalPtcModel(means, setBtoZero=False)[ampName]
self.assertFloatsAlmostEqual(covariancesModel, covModel, atol=1e-13)
# Note that the PhotoTransferCurveDataset does not store the gain
# fit parameter for FULLCOVARIANCE with b=0, so we can't compare
# exactly.
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Is that something that we need to add?

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Yes we do, but I want to keep this ticket small and then filed this ticket to update the ptc class https://rubinobs.atlassian.net/browse/DM-46509


# And check that this is serializable
with tempfile.NamedTemporaryFile(suffix=".fits") as f:
Expand Down Expand Up @@ -545,7 +554,15 @@ def ptcFitAndCheckPtc(
np.sqrt(self.noiseSq)
)

def notest_lsstcam_samples(self):
# Check if evalColModel produces expected values
if not doFitBootstrap:
nanMask = ~np.isnan(localDataset.finalMeans[ampName])
means = localDataset.finalMeans[ampName][nanMask]
model = localDataset.finalModelVars[ampName][nanMask]
evalVarModel = localDataset.evalPtcModel(means)[ampName]
self.assertFloatsEqual(evalVarModel, model)

def test_lsstcam_samples(self):
for dense in [False, True]:
for mode in ["normal", "upturn", "dip"]:
rawMeans, rawVars, ptcTurnoff = self._getSampleMeanAndVar(dense=dense, mode=mode)
Expand Down Expand Up @@ -592,7 +609,7 @@ def notest_lsstcam_samples(self):
msg=f"Dense: {dense}; Mode: {mode}",
)

def notest_ptcFit(self):
def test_ptcFit(self):
for doLegacy in [False, True]:
for fitType, order in [
("POLYNOMIAL", 2),
Expand All @@ -605,7 +622,7 @@ def notest_ptcFit(self):
doLegacy=doLegacy,
)

def notest_meanVarMeasurement(self):
def test_meanVarMeasurement(self):
task = self.defaultTaskExtract
im1Area, im2Area, imStatsCtrl, mu1, mu2 = task.getImageAreasMasksStats(
self.flatExp1, self.flatExp2
Expand All @@ -615,7 +632,7 @@ def notest_meanVarMeasurement(self):
self.assertLess(self.flatWidth - np.sqrt(varDiff), 1)
self.assertLess(self.flatMean - mu, 1)

def notest_meanVarMeasurementWithNans(self):
def test_meanVarMeasurementWithNans(self):
task = self.defaultTaskExtract

flatExp1 = self.flatExp1.clone()
Expand Down Expand Up @@ -653,7 +670,7 @@ def notest_meanVarMeasurementWithNans(self):
self.assertLess(np.sqrt(expectedVar) - np.sqrt(varDiff), 1)
self.assertLess(expectedMu - mu, 1)

def notest_meanVarMeasurementAllNan(self):
def test_meanVarMeasurementAllNan(self):
task = self.defaultTaskExtract
flatExp1 = self.flatExp1.clone()
flatExp2 = self.flatExp2.clone()
Expand All @@ -673,7 +690,7 @@ def notest_meanVarMeasurementAllNan(self):
self.assertTrue(covDiff is None)
self.assertTrue(np.isnan(rowMeanVariance))

def notest_meanVarMeasurementTooFewPixels(self):
def test_meanVarMeasurementTooFewPixels(self):
task = self.defaultTaskExtract
flatExp1 = self.flatExp1.clone()
flatExp2 = self.flatExp2.clone()
Expand All @@ -699,7 +716,7 @@ def notest_meanVarMeasurementTooFewPixels(self):
self.assertTrue(covDiff is None)
self.assertTrue(np.isnan(rowMeanVariance))

def notest_meanVarMeasurementTooNarrowStrip(self):
def test_meanVarMeasurementTooNarrowStrip(self):
# We need a new config to make sure the second covariance cut is
# triggered.
config = cpPipe.ptc.PhotonTransferCurveExtractTask.ConfigClass()
Expand Down Expand Up @@ -733,7 +750,7 @@ def notest_meanVarMeasurementTooNarrowStrip(self):
self.assertTrue(covDiff is None)
self.assertTrue(np.isnan(rowMeanVariance))

def notest_makeZeroSafe(self):
def test_makeZeroSafe(self):
noZerosArray = [1.0, 20, -35, 45578.98, 90.0, 897, 659.8]
someZerosArray = [1.0, 20, 0, 0, 90, 879, 0]
allZerosArray = [0.0, 0.0, 0, 0, 0.0, 0, 0]
Expand Down Expand Up @@ -768,7 +785,7 @@ def notest_makeZeroSafe(self):
for exp, meas in zip(noZerosArray, measuredNoZerosArray):
self.assertEqual(exp, meas)

def notest_getInitialGoodPoints(self):
def test_getInitialGoodPoints(self):
xs = [1, 2, 3, 4, 5, 6]
ys = [2 * x for x in xs]
points = self.defaultTaskSolve._getInitialGoodPoints(
Expand Down Expand Up @@ -846,15 +863,15 @@ def runGetGainFromFlatPair(self, correctionType="NONE"):
ptc.noiseList[ampName][i], np.sqrt(self.noiseSq) / self.gain,
)

def notest_getGainFromFlatPair(self):
def test_getGainFromFlatPair(self):
for gainCorrectionType in [
"NONE",
"SIMPLE",
"FULL",
]:
self.runGetGainFromFlatPair(gainCorrectionType)

def notest_ptcFitBootstrap(self):
def test_ptcFitBootstrap(self):
"""Test the bootstrap fit option for the PTC"""
for (fitType, order) in [('POLYNOMIAL', 2), ('POLYNOMIAL', 3), ('EXPAPPROXIMATION', None)]:
self.ptcFitAndCheckPtc(fitType=fitType, order=order, doFitBootstrap=True)
Expand Down Expand Up @@ -1666,7 +1683,7 @@ def setUp(self):
"C01": [(123, 234), (345, 456), (567, 678)],
}

def notest_generalBehaviour(self):
def test_generalBehaviour(self):
test = PhotonTransferCurveDataset(["C00", "C01"], " ")
test.inputExpIdPairs = {
"C00": [(123, 234), (345, 456), (567, 678)],
Expand Down
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