builds fixes

Author: Jammy2211

autoarray/dataset/plot/imaging_plotters.py

@@ -92,15 +92,16 @@ def figures_2d(
             )
 
         if psf:
-            self.mat_plot_2d.plot_array(
-                array=self.dataset.psf.kernel,
-                visuals_2d=self.visuals_2d,
-                auto_labels=AutoLabels(
-                    title=title_str or f"Point Spread Function",
-                    filename="psf",
-                    cb_unit="",
-                ),
-            )
+            if self.dataset.psf is not None:
+                self.mat_plot_2d.plot_array(
+                    array=self.dataset.psf.kernel,
+                    visuals_2d=self.visuals_2d,
+                    auto_labels=AutoLabels(
+                        title=title_str or f"Point Spread Function",
+                        filename="psf",
+                        cb_unit="",
+                    ),
+                )
 
         if signal_to_noise_map:
             self.mat_plot_2d.plot_array(

autoarray/inversion/mesh/interpolator/rectangular.py

@@ -296,7 +296,7 @@ def _mappings_sizes_weights(self):
             adaptive_rectangular_mappings_weights_via_interpolation_from(
                 source_grid_size=self.mesh.shape[0],
                 data_grid=self.data_grid.array,
-                data_grid_over_sampled=self.data_grid.over_sampled,
+                data_grid_over_sampled=self.data_grid.over_sampled.array,
                 mesh_weight_map=self.mesh_weight_map,
                 xp=self._xp,
             )

autoarray/inversion/plot/inversion_plotters.py

@@ -175,6 +175,13 @@ def figures_2d_of_pixelization(
                 mapper_plotter.mapper
             ]
 
+            from autoarray.structures.visibilities import Visibilities
+
+            if isinstance(array, Visibilities):
+                array = self.inversion.mapped_reconstructed_data_dict[
+                    mapper_plotter.mapper
+                ]
+
             self.mat_plot_2d.plot_array(
                 array=array,
                 visuals_2d=self.visuals_2d,

autoarray/mask/mask_2d_util.py

@@ -460,7 +460,10 @@ def min_false_distance_to_edge(mask: np.ndarray) -> Tuple[int, int]:
     # Return the minimum distance to both edges
     return min(top_dist, bottom_dist), min(left_dist, right_dist)
 
-def blurring_mask_2d_from(mask_2d: np.ndarray, kernel_shape_native: Tuple[int, int]) -> np.ndarray:
+
+def blurring_mask_2d_from(
+    mask_2d: np.ndarray, kernel_shape_native: Tuple[int, int]
+) -> np.ndarray:
     """
     Return the blurring mask for a 2D mask and kernel footprint.
 
@@ -474,7 +477,9 @@ def blurring_mask_2d_from(mask_2d: np.ndarray, kernel_shape_native: Tuple[int, i
 
     ky, kx = kernel_shape_native
     if ky <= 0 or kx <= 0:
-        raise ValueError(f"kernel_shape_native must be positive, got {kernel_shape_native}.")
+        raise ValueError(
+            f"kernel_shape_native must be positive, got {kernel_shape_native}."
+        )
 
     # Keep your existing guard (optional)
     y_distance, x_distance = min_false_distance_to_edge(mask_2d)
@@ -510,8 +515,8 @@ def blurring_mask_2d_from(mask_2d: np.ndarray, kernel_shape_native: Tuple[int, i
     blurring_region = mask_2d & near_unmasked  # True on the ring (in region-space)
 
     # Convert region -> mask semantics: ring should be unmasked (False)
-    blurring_mask = np.ones_like(mask_2d, dtype=bool)   # start fully masked
-    blurring_mask[blurring_region] = False              # unmask the ring
+    blurring_mask = np.ones_like(mask_2d, dtype=bool)  # start fully masked
+    blurring_mask[blurring_region] = False  # unmask the ring
 
     return blurring_mask