Merge pull request #404 from PyAutoLabs/claude/g-heart-green-fable-opus-uuiaqf

fix: guard weighted image-mesh weight map against a blank adapt image

Author: Jammy2211

autoarray/inversion/mesh/image_mesh/abstract_weighted.py

@@ -57,7 +57,19 @@ def weight_map_from(self, adapt_data: np.ndarray):
         The weight map which is used to adapt the Delaunay pixels in the image-plane to components in the data.
         """
 
-        weight_map = np.abs(adapt_data) / np.max(adapt_data)
+        max_value = np.max(adapt_data)
+
+        if max_value <= 0.0:
+            # A blank adapt image (all-zero, or with no positive signal) carries
+            # no structure to adapt the mesh to. Normalising by ``np.max`` would
+            # divide by zero (or a non-positive peak), producing NaN/negative
+            # weights that propagate into NaN mesh coordinates and crash the
+            # downstream Delaunay/Voronoi triangulation ("Points cannot contain
+            # NaN"). Fall back to a uniform weight map so the mesh degrades to
+            # uniform sampling instead of failing.
+            return np.ones_like(adapt_data, dtype=float)
+
+        weight_map = np.abs(adapt_data) / max_value
         weight_map = weight_map**self.weight_power
 
         weight_map[weight_map < self.weight_floor] = self.weight_floor

test_autoarray/inversion/pixelization/image_mesh/test_abstract_weighted.py

@@ -24,3 +24,24 @@ def test__weight_map_from():
     weight_map = pixelization.weight_map_from(adapt_data=adapt_data)
 
     assert weight_map == pytest.approx([1.0, 1.0, 1.0], 1.0e-4)
+
+
+def test__weight_map_from__blank_adapt_image_returns_uniform_not_nan():
+    # A blank (all-zero) adapt image has ``np.max == 0``; the normalisation must
+    # not divide by zero and produce NaN weights, which would propagate into NaN
+    # mesh coordinates and crash the downstream Delaunay triangulation.
+    pixelization = aa.image_mesh.Hilbert(
+        pixels=5, weight_floor=0.01, weight_power=3.5
+    )
+
+    weight_map = pixelization.weight_map_from(adapt_data=np.zeros(4))
+
+    assert np.all(np.isfinite(weight_map))
+    assert weight_map == pytest.approx([1.0, 1.0, 1.0, 1.0], 1.0e-4)
+
+    # An adapt image with no positive signal (non-positive peak) is equally
+    # degenerate and must also fall back to a finite, uniform weight map.
+    weight_map = pixelization.weight_map_from(adapt_data=np.array([-2.0, -1.0, -3.0]))
+
+    assert np.all(np.isfinite(weight_map))
+    assert weight_map == pytest.approx([1.0, 1.0, 1.0], 1.0e-4)