Merge branch 'fix/docs_and_cleanup' of github.com:Loop3D/LoopStructur…

…al into fix/docs_and_cleanup

LoopStructural/datatypes/_bounding_box.py

@@ -288,36 +288,37 @@ def with_buffer(self, buffer: float = 0.2) -> BoundingBox:
     #     xyz = np.array(xyz)
     #     if len(xyz.shape) == 1:
     #         xyz = xyz.reshape((1, -1))
-        
-    #     distances = np.maximum(0, 
+
+    #     distances = np.maximum(0,
     #                         np.maximum(self.global_origin+self.origin - xyz,
     #                                 xyz - self.global_maximum))
     #     distance = np.linalg.norm(distances, axis=1)
     #     distance[self.is_inside(xyz)] = -1
     #     return distance
-        
-    def __call__(self,xyz):
-         # Calculate center and half-extents of the box
-        center = (self.maximum + self.global_origin+self.origin) / 2
-        half_extents = (self.maximum - self.global_origin+self.origin) / 2
-        
+
+    def __call__(self, xyz):
+        # Calculate center and half-extents of the box
+        center = (self.maximum + self.global_origin + self.origin) / 2
+        half_extents = (self.maximum - self.global_origin + self.origin) / 2
+
         # Calculate the distance from point to center
         offset = np.abs(xyz - center) - half_extents
-        
+
         # Inside distance: negative value based on the smallest penetration
-        inside_distance = np.min(half_extents - np.abs(xyz - center),axis=1)
-        
+        inside_distance = np.min(half_extents - np.abs(xyz - center), axis=1)
+
         # Outside distance: length of the positive components of offset
         outside_distance = np.linalg.norm(np.maximum(offset, 0))
-        
+
         # If any component of offset is positive, we're outside
         # Otherwise, we're inside and return the negative penetration distance
         distance = np.zeros(xyz.shape[0])
-        mask = np.any(offset > 0,axis=1)
+        mask = np.any(offset > 0, axis=1)
         distance[mask] = outside_distance
         distance[~mask] = -inside_distance[~mask]
         return distance
         # return outside_distance if np.any(offset > 0) else -inside_distance
+
     def get_value(self, name):
         ix, iy = self.name_map.get(name, (-1, -1))
         if ix == -1 and iy == -1:

LoopStructural/interpolators/supports/_3d_unstructured_tetra.py

@@ -76,7 +76,9 @@ def __init__(
                 aabb_nsteps[aabb_nsteps < 2] = 2
             aabb_nsteps = np.array(aabb_nsteps, dtype=int)
             step_vector = (self.maximum - self.minimum) / (aabb_nsteps - 1)
-            self.aabb_grid = StructuredGrid(self.minimum, nsteps=aabb_nsteps, step_vector=step_vector)
+            self.aabb_grid = StructuredGrid(
+                self.minimum, nsteps=aabb_nsteps, step_vector=step_vector
+            )
         # make a big table to store which tetra are in which element.
         # if this takes up too much memory it could be simplified by using sparse matrices or dict but
         # at the expense of speed