Avoid propagating initial PSF, WCS, PhotoCalib on error.

python/lsst/pipe/tasks/calibrateImage.py

@@ -640,12 +640,21 @@ def run(self, *, exposures, id_generator=None, result=None):
 
         result.exposure = self.snap_combine.run(exposures).exposure
         self._recordMaskedPixelFractions(result.exposure)
+        self.log.info("Initial PhotoCalib: %s", result.exposure.getPhotoCalib())
 
         result.background = None
         summary_stat_catalog = None
+        # Some exposure components are set to initial placeholder objects
+        # while we try to bootstrap them.  If we fail before we fit for them,
+        # we want to reset those components to None so the placeholders don't
+        # masquerade as the real thing.
+        have_fit_psf = False
+        have_fit_astrometry = False
+        have_fit_photometry = False
         try:
             result.psf_stars_footprints, result.background, _ = self._compute_psf(result.exposure,
                                                                                   id_generator)
+            have_fit_psf = True
             summary_stat_catalog = result.psf_stars_footprints
             self._measure_aperture_correction(result.exposure, result.psf_stars_footprints)
             result.psf_stars = result.psf_stars_footprints.asAstropy()
@@ -659,13 +668,15 @@ def run(self, *, exposures, id_generator=None, result=None):
             astrometry_matches, astrometry_meta = self._fit_astrometry(
                 result.exposure, result.stars_footprints
             )
+            have_fit_astrometry = True
             self.metadata["astrometry_matches_count"] = len(astrometry_matches)
             if "astrometry_matches" in self.config.optional_outputs:
                 result.astrometry_matches = lsst.meas.astrom.denormalizeMatches(astrometry_matches,
                                                                                 astrometry_meta)
 
             result.stars_footprints, photometry_matches, \
                 photometry_meta, photo_calib = self._fit_photometry(result.exposure, result.stars_footprints)
+            have_fit_photometry = True
             self.metadata["photometry_matches_count"] = len(photometry_matches)
             # fit_photometry returns a new catalog, so we need a new astropy table view.
             result.stars = result.stars_footprints.asAstropy()
@@ -676,13 +687,19 @@ def run(self, *, exposures, id_generator=None, result=None):
             if "photometry_matches" in self.config.optional_outputs:
                 result.photometry_matches = lsst.meas.astrom.denormalizeMatches(photometry_matches,
                                                                                 photometry_meta)
-        # Summary stat calculations can handle missing components gracefully,
-        # but we want to run them as late as possible (but still before we
-        # calibrate pixels, if we do that at all).
-        # So we run them after we succeed or if we get an AlgorithmError.  We
-        # intentionally don't use 'finally' here because we don't want to run
-        # them if we get some other kind of error.
         except pipeBase.AlgorithmError:
+            if not have_fit_psf:
+                result.exposure.setPsf(None)
+            if not have_fit_astrometry:
+                result.exposure.setWcs(None)
+            if not have_fit_photometry:
+                result.exposure.setPhotoCalib(None)
+            # Summary stat calculations can handle missing components gracefully,
+            # but we want to run them as late as possible (but still before we
+            # calibrate pixels, if we do that at all).
+            # So we run them after we succeed or if we get an AlgorithmError.  We
+            # intentionally don't use 'finally' here because we don't want to run
+            # them if we get some other kind of error.
             self._summarize(result.exposure, summary_stat_catalog, result.background)
             raise
         else: