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DM-37951: Allow PTC analysis to split each amp into subregions #251

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DM-37951: Allow PTC analysis to split each amp into subregions #251

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2ea34b4
Initial push. Still not working
craiglagegit Feb 11, 2023
15e60ee
Changes to deal with larger arrays. cpExtract now runs, but still fa…
craiglagegit Feb 12, 2023
76b5d50
More fixes and clean-up. Seems to be working now
craiglagegit Feb 12, 2023
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29 changes: 17 additions & 12 deletions python/lsst/ip/isr/ptcDataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,6 @@ def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np
covArrayModel=[], covSqrtWeights=[], aMatrix=[], bMatrix=[], covArrayModelNoB=[],
aMatrixNoB=[], finalVar=[np.nan], finalModelVar=[np.nan], finalMean=[np.nan]):
"""Function to initialize an amp of a PhotonTransferCurveDataset.

Notes
-----
The parameters are all documented in `init`.
Expand All @@ -227,12 +226,13 @@ def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np
bMatrix = nanMatrix
if len(aMatrixNoB) == 0:
aMatrixNoB = nanMatrix

self.inputExpIdPairs[ampName] = inputExpIdPair
self.expIdMask[ampName] = expIdMask
self.rawExpTimes[ampName] = rawExpTime
self.rawMeans[ampName] = rawMean
self.rawVars[ampName] = rawVar
# Modifications here for the subregions.
# New data is appended to the existing data
self.inputExpIdPairs[ampName] += inputExpIdPair
self.expIdMask[ampName] += expIdMask
self.rawExpTimes[ampName] += rawExpTime
self.rawMeans[ampName] += rawMean
self.rawVars[ampName] += rawVar
self.photoCharge[ampName] = photoCharge
self.gain[ampName] = gain
self.gainErr[ampName] = gainErr
Expand All @@ -242,10 +242,16 @@ def setAmpValues(self, ampName, inputExpIdPair=[(np.nan, np.nan)], expIdMask=[np
self.ptcFitParsError[ampName] = ptcFitParsError
self.ptcFitChiSq[ampName] = ptcFitChiSq
self.ptcTurnoff[ampName] = ptcTurnoff
self.covariances[ampName] = covArray
self.covariancesSqrtWeights[ampName] = covSqrtWeights
self.covariancesModel[ampName] = covArrayModel
self.covariancesModelNoB[ampName] = covArrayModelNoB
if len(self.covariances[ampName]) == 0:
self.covariances[ampName] = np.array(covArray)
else:
self.covariances[ampName] = np.concatenate((self.covariances[ampName],np.array(covArray)))
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I'm concerned about dimensionality here. Doesn't this take the NxN covariances and stack them to create an `(m_subregion*N)xN array?

if len(self.covariancesSqrtWeights[ampName]) == 0:
self.covariancesSqrtWeights[ampName] = np.array(covSqrtWeights)
else:
self.covariancesSqrtWeights[ampName] = np.concatenate((self.covariancesSqrtWeights[ampName],np.array(covSqrtWeights)))
self.covariancesModel[ampName] = np.full_like(self.covariances[ampName], np.nan)
self.covariancesModelNoB[ampName] = np.full_like(self.covariances[ampName], np.nan)
self.aMatrix[ampName] = aMatrix
self.bMatrix[ampName] = bMatrix
self.aMatrixNoB[ampName] = aMatrixNoB
Expand Down Expand Up @@ -526,7 +532,6 @@ def toTable(self):
for i, ampName in enumerate(self.ampNames):
nPadPoints[ampName] = nSignalPoints - len(list(self.covariances.values())[i])
covMatrixSide = self.covMatrixSide

catalog = Table([{'AMPLIFIER_NAME': ampName,
'PTC_FIT_TYPE': self.ptcFitType,
'COV_MATRIX_SIDE': self.covMatrixSide,
Expand Down