DM-54482: Integrate PSF-shapelet subtask into calibrateImage as a donut-detector

doc/changes/DM-54482.feature.rst

@@ -0,0 +1 @@
+Added psf star shapelet decomposition parameters and metrics to the ``ObservationSummaryStats`` class.

python/lsst/images/_observation_summary_stats.py

@@ -64,6 +64,69 @@ class ObservationSummaryStats(pydantic.BaseModel, ser_json_inf_nan="constants"):
         default_factory=_default_corners, description="Declination of bounding box corners (degrees)."
     )
 
+    psfAdaptiveThresholdValue: float = pydantic.Field(
+        math.nan,
+        description="Threshold value used in the adaptive threshold detection pass for PSF modelling.",
+    )
+
+    psfAdaptiveIncludeThresholdMultiplier: float = pydantic.Field(
+        math.nan,
+        description="Threshold multiplier used in the adaptive threshold detection pass for PSF modelling.",
+    )
+
+    nShapeletsStar: int = pydantic.Field(
+        0,
+        description="Number of sources used in the shapelet decomposition.",
+    )
+
+    shapeletsOnlyIqScore: float = pydantic.Field(
+        math.nan,
+        description=(
+            "The dimensionless image quality score as determined from the shapelets decomposition "
+            "that includes power only from the non-atmospheric decomposition coefficients. The "
+            "score spans the range [0.0, 1.0] with lower values indicating better image quality."
+        ),
+    )
+
+    shapeletsIqScore: float = pydantic.Field(
+        math.nan,
+        description=(
+            "The dimensionless image quality score as determined from the shapelets decomposition "
+            "that includes power from the median centroid offset between those used in the decomposition "
+            "and those of the centroid slot in addition to non-atmospheric decomposition coefficients. "
+            "The score spans the range [0.0, 1.0] with lower values indicating better image quality."
+        ),
+    )
+
+    shapeletsCoeffs: tuple[float, ...] = pydantic.Field(
+        default_factory=tuple,
+        description="Coefficients from the PSF star shapelet decomposition.",
+    )
+
+    centroidDiffShapeletsVsSlotMedian: float = pydantic.Field(
+        math.nan,
+        description=(
+            "Median centroid difference (sqrt((slot_x - shapelet_x)**2 + (slot_y - shapelet_y)**2)) for "
+            "sources used in the shapelet decomposition (pixels)."
+        ),
+    )
+
+    shapeletsStarEMedian: float = pydantic.Field(
+        math.nan,
+        description=(
+            "Median ellipticity (sqrt(starE1**2.0 + starE2**2.0)) of the sources used in the "
+            "shapelet decomposition."
+        ),
+    )
+
+    shapeletsStarUnNormalizedEMedian: float = pydantic.Field(
+        math.nan,
+        description=(
+            "Median un-normalized ellipticity (sqrt((starXX - starYY)**2.0 + (2.0*starXY)**2.0)) "
+            "of the sources used in the shapelet decomposition (pixel**2)."
+        ),
+    )
+
     astromOffsetMean: float = pydantic.Field(math.nan, description="Astrometry match offset mean.")
 
     astromOffsetStd: float = pydantic.Field(math.nan, description="Astrometry match offset stddev.")
@@ -228,6 +291,8 @@ def __eq__(self, other: object) -> bool:
             a = getattr(self, name)
             b = getattr(other, name)
             if isinstance(a, tuple) and isinstance(b, tuple):
+                if len(a) != len(b):
+                    return False
                 for ai, bi in zip(a, b):
                     if ai != bi and not (math.isnan(ai) and math.isnan(bi)):
                         return False