Do not decorrelate the diffim PSF for now.

This is not implemented correctly, and raises occasional errors.
lsst · Nov 5, 2024 · 42483b5 · 42483b5
1 parent d09aa04
commit 42483b5
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/python/lsst/ip/diffim/imageDecorrelation.py b/python/lsst/ip/diffim/imageDecorrelation.py
@@ -274,7 +274,7 @@ def run(self, scienceExposure, templateExposure, subtractedExposure, psfMatching
             corr = self.computeDiffimCorrection(kArr, varianceMean, targetVarianceMean)
 
         correctedImage = self.computeCorrectedImage(corr.corrft, subtractedExposure.image.array)
-        correctedPsf = self.computeCorrectedDiffimPsf(corr.corrft, psfImg.array)
+        # correctedPsf = self.computeCorrectedDiffimPsf(corr.corrft, psfImg.array)
 
         # The subtracted exposure variance plane is already correlated, we cannot propagate
         # it through another convolution; instead we need to use the uncorrelated originals
@@ -300,7 +300,7 @@ def run(self, scienceExposure, templateExposure, subtractedExposure, psfMatching
             correctedVariance /= kSumSq
         subtractedExposure.image.array[...] = correctedImage  # Allow for numpy type casting
         subtractedExposure.variance.array[...] = correctedVariance
-        subtractedExposure.setPsf(correctedPsf)
+        # subtractedExposure.setPsf(correctedPsf)
 
         newVarMean = self.computeVarianceMean(subtractedExposure)
         self.log.info("Variance plane mean of corrected diffim: %.5e", newVarMean)