Propagates list of flat pair read noises to ptcDataset

python/lsst/cp/pipe/ptc/cpExtractPtcTask.py

@@ -433,31 +433,6 @@ def run(self, inputExp, inputDims, taskMetadata, inputPhotodiodeData=None):
                 pdCalib.currentScale = self.config.photodiodeCurrentScale
                 monitorDiodeCharge[expId] = pdCalib.integrate()
 
-        # Get read noise.  Try from the exposure, then try
-        # taskMetadata.  This adds a get() for the exposures.
-        readNoiseLists = {}
-        for pairIndex, expRefs in inputExp.items():
-            # This yields an index (exposure_time, seq_num, or flux)
-            # and a pair of references at that index.
-            for expRef, expId in expRefs:
-                # This yields an exposure ref and an exposureId.
-                exposureMetadata = expRef.get(component="metadata")
-                metadataIndex = inputDims.index(expId)
-                thisTaskMetadata = taskMetadata[metadataIndex]
-
-                for ampName in ampNames:
-                    if ampName not in readNoiseLists:
-                        readNoiseLists[ampName] = [self.getReadNoise(exposureMetadata,
-                                                                     thisTaskMetadata, ampName)]
-                    else:
-                        readNoiseLists[ampName].append(self.getReadNoise(exposureMetadata,
-                                                                         thisTaskMetadata, ampName))
-
-        readNoiseDict = {ampName: 0.0 for ampName in ampNames}
-        for ampName in ampNames:
-            # Take median read noise value
-            readNoiseDict[ampName] = np.nanmedian(readNoiseLists[ampName])
-
         # Output list with PTC datasets.
         partialPtcDatasetList = []
         # The number of output references needs to match that of input
@@ -534,6 +509,24 @@ def run(self, inputExp, inputDims, taskMetadata, inputPhotodiodeData=None):
                 im1Area, im2Area, imStatsCtrl, mu1, mu2 = self.getImageAreasMasksStats(exp1, exp2,
                                                                                        region=region)
 
+                # Get the read noise for each exposure
+                readNoise1 = dict()
+                readNoise2 = dict()
+                meanReadNoise = dict()
+
+                expMetadata1 = expRef1.get(component="metadata")
+                metadataIndex1 = inputDims.index(expId1)
+                thisTaskMetadata1 = taskMetadata[metadataIndex1]
+
+                expMetadata2 = expRef2.get(component="metadata")
+                metadataIndex2 = inputDims.index(expId2)
+                thisTaskMetadata2 = taskMetadata[metadataIndex2]
+
+                readNoise1[ampName] = self.getReadNoise(expMetadata1, thisTaskMetadata1, ampName)
+                readNoise2[ampName] = self.getReadNoise(expMetadata2, thisTaskMetadata2, ampName)
+
+                meanReadNoise[ampName] = np.nanmean([readNoise1[ampName], readNoise2[ampName]])
+
                 # We demand that both mu1 and mu2 be finite and greater than 0.
                 if not np.isfinite(mu1) or not np.isfinite(mu2) \
                    or ((np.nan_to_num(mu1) + np.nan_to_num(mu2)/2.) <= 0.0):
@@ -563,7 +556,7 @@ def run(self, inputExp, inputDims, taskMetadata, inputPhotodiodeData=None):
                 if self.config.doGain:
                     gain = self.getGainFromFlatPair(im1Area, im2Area, imStatsCtrl, mu1, mu2,
                                                     correctionType=self.config.gainCorrectionType,
-                                                    readNoise=readNoiseDict[ampName])
+                                                    readNoise=meanReadNoise[ampName])
                 else:
                     gain = np.nan
 
@@ -660,7 +653,7 @@ def run(self, inputExp, inputDims, taskMetadata, inputPhotodiodeData=None):
                     covariance=covArray[0, :, :],
                     covSqrtWeights=covSqrtWeights[0, :, :],
                     gain=gain,
-                    noise=readNoiseDict[ampName],
+                    noise=meanReadNoise[ampName],
                     histVar=histVar,
                     histChi2Dof=histChi2Dof,
                     kspValue=kspValue,

python/lsst/cp/pipe/ptc/cpSolvePtcTask.py

@@ -334,6 +334,7 @@ def run(self, inputCovariances, camera=None, detId=0):
             ptcFitType=self.config.ptcFitType,
             covMatrixSide=self.config.maximumRangeCovariancesAstier,
             covMatrixSideFullCovFit=self.config.maximumRangeCovariancesAstierFullCovFit)
+
         for partialPtcDataset in inputCovariances:
             # Ignore dummy datasets
             if partialPtcDataset.ptcFitType == 'DUMMY':
@@ -350,6 +351,8 @@ def run(self, inputCovariances, camera=None, detId=0):
                                                          partialPtcDataset.rawMeans[ampName][0])
                 datasetPtc.rawVars[ampName] = np.append(datasetPtc.rawVars[ampName],
                                                         partialPtcDataset.rawVars[ampName][0])
+                datasetPtc.noiseList[ampName] = np.append(datasetPtc.noiseList[ampName],
+                                                          partialPtcDataset.noise[ampName])
                 datasetPtc.photoCharges[ampName] = np.append(datasetPtc.photoCharges[ampName],
                                                              partialPtcDataset.photoCharges[ampName][0])
                 datasetPtc.histVars[ampName] = np.append(datasetPtc.histVars[ampName],
@@ -463,6 +466,17 @@ def run(self, inputCovariances, camera=None, detId=0):
             # PhotonTransferCurveDataset object.
             datasetPtc = self.fitMeasurementsToModel(datasetPtc)
 
+        # Initial validation of PTC fit.
+        for ampName in ampNames:
+            noise = np.nanmedian(datasetPtc.noiseList[ampName])**2
+            noiseFitted = datasetPtc.noise[ampName]
+
+            # Check if noise is close to noiseFitted
+            if not np.isclose(noiseFitted, noise, rtol=0.05, atol=0.0):
+                self.log.warning(f"Read noise from PTC fit ({noiseFitted}) is not consistent "
+                                 f"with read noise measured from overscan ({noise}) for "
+                                 f"amplifier {ampName}. Try adjusting the fit range.")
+
         if camera:
             detector = camera[detId]
         else: