Fix PTC fit type saving

Also, ensure POLYNOMIAL is always used regardless of
upstream default, and improve error message on failure.

python/lsst/cp/pipe/makeBrighterFatterKernel.py

@@ -529,6 +529,8 @@ def runDataRef(self, dataRef, visitPairs):
             ptcConfig = MeasurePhotonTransferCurveTaskConfig()
             ptcConfig.isrForbiddenSteps = []
             ptcConfig.doFitBootstrap = True
+            ptcConfig.ptcFitType = 'POLYNOMIAL'  # default Astier doesn't work for gain correction
+            ptcConfig.polynomialFitDegree = 3
             ptcConfig.minMeanSignal = self.config.minMeanSignal
             ptcConfig.maxMeanSignal = self.config.maxMeanSignal
             ptcTask = MeasurePhotonTransferCurveTask(config=ptcConfig)
@@ -671,9 +673,9 @@ def _applyGains(self, means, xcorrs, ptcData):
             mask = ptcData.visitMask[ampName]
             gain = ptcData.gain[ampName]
 
-            fitType = ptcData.ptcFitType
+            fitType = ptcData.ptcFitType[ampName]
             if fitType != 'POLYNOMIAL':
-                raise RuntimeError("Only polynomial fit types supported currently")
+                raise RuntimeError(f"Only polynomial fit types supported currently, found {fitType}")
             ptcFitPars = ptcData.ptcFitPars[ampName]
             # polynomial params go in ascending order, so this is safe w.r.t.
             # the polynomial order, as the constant term is always first,

python/lsst/cp/pipe/ptc.py

@@ -714,6 +714,7 @@ def errFunc(p, x, y):
             dataset.gainErr[ampName] = ptcGainErr
             dataset.noise[ampName] = ptcNoise
             dataset.noiseErr[ampName] = ptcNoiseErr
+            dataset.ptcFitType[ampName] = ptcFitType
 
             # Non-linearity residuals (NL of mean vs time curve): percentage, and fit to a quadratic function
             # In this case, len(parsIniNl) = 3 indicates that we want a quadratic fit