this is a blende operator for scattering vegetation. acress a terrain mesh. I ant to make it more eficient by using proxies or "display as bounds" in the vieport

# Operator for Scattering
def execute(self, context):
    try:
        obj = get_terrain_object()
        if not obj:
            print("Cannot find valid terrain object")
            return {'CANCELLED'}
        scene_props = obj.TF_scatter_data
        if not scene_props.layers:
            self.report({'WARNING'}, "No layers to scatter.")
            return {'CANCELLED'}

        for layer in scene_props.layers:
            print(f"Scattering Layer: {layer.name}")
            self.scatter_layer(context, layer)

        self.report({'INFO'}, "Scattering completed.")
        return {'FINISHED'}
    except Exception as e:
        self.report({'ERROR'}, f"Error during scattering: {e}")
        print(f"Error during scattering: {e}")
        return {'CANCELLED'}

def scatter_layer(self, context, layer):
    veg_directory = context.scene.TF_vegetation_path
    model_path = os.path.join(veg_directory, layer.model)
    imported_object = self.import_fbx(model_path)
    if not imported_object:
        print(f"Failed to import model: {layer.model}")
        return

    target_object = get_terrain_object()
    
    if not target_object or target_object.type != 'MESH':
        print("No valid target object selected for scattering.")
        return

    # Ensure we have up-to-date mesh data
    depsgraph = context.evaluated_depsgraph_get()
    eval_obj = target_object.evaluated_get(depsgraph)
    mesh = eval_obj.to_mesh()

    # Get face and vertex data
    faces = np.array([face.vertices for face in mesh.polygons])
    verts = np.array([vert.co for vert in mesh.vertices])
    normals = np.array([face.normal for face in mesh.polygons])
    face_areas = np.array([face.area for face in mesh.polygons])

    num_points = layer.density

    # Sample points on the mesh surface
    sampled_points, sampled_normals = self.sample_points_on_mesh(verts, faces, normals, face_areas, num_points)

    # Adjust points based on terrain characteristics
    weights = self.calculate_weights(sampled_points, sampled_normals, layer)
    accepted_indices = self.filter_points(weights)
    filtered_points = sampled_points[accepted_indices]

    # Enforce minimum spacing (resource competition)
    final_points, final_normals = self.enforce_min_spacing(filtered_points, sampled_normals, layer.min_spacing)

    print(f"Placing {len(final_points)} instances for layer '{layer.name}'")

    # Scatter the objects
    for loc in final_points:
        obj = self.create_instance(imported_object, loc, final_normals, layer)
        obj.TF_SID = layer.TF_SID
        context.collection.objects.link(obj)

    # Clean up
    eval_obj.to_mesh_clear()

def import_fbx(self, filepath):
    try:
        bpy.ops.import_scene.fbx(filepath=filepath)
        imported_objects = bpy.context.selected_objects
        print(f"Imported model: {filepath}")
        return imported_objects
    except Exception as e:
        print(f"Failed to import FBX: {e}")
        return None

def create_instance(self, source_obj, location, normal, layer):
    obj_copy = source_obj.copy()
    obj_copy.data = source_obj.data.copy()
    obj_copy.location = location
    scale_rand = ((np.random.rand() * layer.scale_rand) + (1 - (np.random.rand() * layer.scale_rand)))
    obj_copy.scale *= (layer.scale * scale_rand)
    rotation_rand = ((2 * np.pi) - ((layer.rot_rand * np.random.rand()) * 2 * np.pi)) + (layer.rot * 2 * np.pi)
    obj_copy.rotation_euler.rotate_axis('Z', rotation_rand)
    self.align_object(obj_copy, normal, layer)
    print(f"Created instance at {location}")
    return obj_copy

def align_object(self, obj, normal, layer):
        if layer.align_to_normal:
            # Align object to the normal
            up = Vector((0, 0, 1))
            direction = normal.normalized()
            rotation = up.rotation_difference(direction).to_euler()
            obj.rotation_euler = rotation
        else:
            # Default orientation
            pass

def sample_points_on_mesh(self, verts, faces, normals, face_areas, num_points):
    # Normalize face areas to probabilities
    face_probs = face_areas / face_areas.sum()
    # Randomly select faces based on area
    chosen_faces = np.random.choice(len(faces), size=num_points, p=face_probs)
    # Generate random barycentric coordinates
    us = np.random.rand(num_points, 1)
    vs = np.random.rand(num_points, 1)
    ws = 1 - us - vs
    mask = ws.flatten() < 0
    us[mask], vs[mask] = vs[mask], us[mask]
    ws = 1 - us - vs
    # Get vertices of the chosen faces
    v0 = verts[faces[chosen_faces, 0]]
    v1 = verts[faces[chosen_faces, 1]]
    v2 = verts[faces[chosen_faces, 2]]
    # Calculate sampled points
    sampled_points = us * v0 + vs * v1 + ws * v2
    sampled_normals = normals[chosen_faces]
    return sampled_points, sampled_normals

def calculate_weights(self, points, normals, layer):
    # Calculate slope based on normal vectors
    slopes = 1 - np.abs(normals[:, 2])  # Assuming Z-up axis
    slope_influence = layer.slope_influence
    # Calculate altitude
    altitudes = points[:, 2]
    altitude_influence = layer.altitude_influence
    # Normalize altitudes
    altitudes_norm = (altitudes - altitudes.min()) / (altitudes.max() - altitudes.min() + 1e-6)
    # Calculate weights
    weights = np.ones(len(points))
    weights *= np.exp(-slope_influence * slopes)
    weights *= np.exp(-altitude_influence * altitudes_norm)
    return weights

def filter_points(self, weights):
    # Normalize weights to probabilities
    probabilities = weights / weights.max()
    # Accept or reject points
    random_values = np.random.rand(len(weights))
    accepted_indices = np.where(random_values < probabilities)[0]
    return accepted_indices

def enforce_min_spacing(self, points, normals, min_spacing):
        if len(points) == 0:
            return points, normals
        tree = kdtree(len(points))
        for i, co in enumerate(points):
            tree.insert(co, i)
        tree.balance()

        accepted_indices = []
        rejected = set()
        for i, co in enumerate(points):
            if i in rejected:
                continue
            accepted_indices.append(i)
            # Find points within min_spacing
            nearby = tree.find_range(co, min_spacing)
            for (co_found, index, dist) in nearby:
                if index > i:
                    rejected.add(index)
        accepted_points = points[accepted_indices]
        accepted_normals = normals[accepted_indices]
        print(f"After enforcing min spacing, {len(accepted_points)} points remain.")
        return accepted_points, accepted_normals