docs: fix docstrings

src/anemoi/graphs/edges/builder.py

@@ -153,7 +153,7 @@ def __init__(self, source_name: str, target_name: str, num_nearest_neighbours: i
         assert num_nearest_neighbours > 0, "Number of nearest neighbours must be positive"
         self.num_nearest_neighbours = num_nearest_neighbours
 
-    def get_adjacency_matrix(self, source_nodes: np.ndarray, target_nodes: np.ndarray):
+    def get_adjacency_matrix(self, source_nodes: np.ndarray, target_nodes: np.ndarray) -> np.ndarray:
         """Compute the adjacency matrix for the KNN method.
 
         Parameters
@@ -162,6 +162,11 @@ def get_adjacency_matrix(self, source_nodes: np.ndarray, target_nodes: np.ndarra
             The source nodes.
         target_nodes : np.ndarray
             The target nodes.
+
+        Returns
+        -------
+        np.ndarray
+            The adjacency matrix.
         """
         assert self.num_nearest_neighbours is not None, "number of neighbors required for knn encoder"
         LOGGER.info(
@@ -203,13 +208,13 @@ class CutOffEdges(BaseEdgeBuilder):
         Update the graph with the edges.
     """
 
-    def __init__(self, source_name: str, target_name: str, cutoff_factor: float):
+    def __init__(self, source_name: str, target_name: str, cutoff_factor: float) -> None:
         super().__init__(source_name, target_name)
         assert isinstance(cutoff_factor, (int, float)), "Cutoff factor must be a float"
         assert cutoff_factor > 0, "Cutoff factor must be positive"
         self.cutoff_factor = cutoff_factor
 
-    def get_cutoff_radius(self, graph: HeteroData, mask_attr: torch.Tensor | None = None):
+    def get_cutoff_radius(self, graph: HeteroData, mask_attr: torch.Tensor | None = None) -> float:
         """Compute the cut-off radius.
 
         The cut-off radius is computed as the product of the target nodes
@@ -238,7 +243,7 @@ def prepare_node_data(self, graph: HeteroData) -> tuple[NodeStorage, NodeStorage
         self.radius = self.get_cutoff_radius(graph)
         return super().prepare_node_data(graph)
 
-    def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStorage):
+    def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStorage) -> np.ndarray:
         """Get the adjacency matrix for the cut-off method.
 
         Parameters
@@ -247,6 +252,11 @@ def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStor
             The source nodes.
         target_nodes : NodeStorage
             The target nodes.
+
+        Returns
+        -------
+        np.ndarray
+            The adjacency matrix.
         """
         LOGGER.info(
             "Using CutOff-Edges (with radius = %.1f km) between %s and %s.",

src/anemoi/graphs/generate/hexagonal.py

@@ -62,6 +62,11 @@ def add_nodes_for_resolution(
         The H3 refinement level. It can be an integer from 0 to 15.
     area_kwargs: dict
         Additional arguments to pass to the get_nodes_at_resolution function.
+
+    Returns
+    -------
+    graph : networkx.Graph
+        The graph with the added nodes.
     """
 
     nodes = get_nodes_at_resolution(resolution, **area_kwargs)
@@ -173,6 +178,11 @@ def add_edges_to_children(
     depth_children : Optional[int], optional
         The number of resolution levels to consider for the connections of children. Defaults to 1, which includes
         connections up to the next resolution level, by default None
+
+    Returns
+    -------
+    graph : nx.Graph
+        graph with the added edges
     """
     if depth_children is None:
         depth_children = len(refinement_levels)
@@ -215,6 +225,11 @@ def add_edge(
         The H3 index of the tail of the edge.
     target_node_h3_idx : str
         The H3 index of the head of the edge.
+
+    Returns
+    -------
+    graph : networkx.Graph
+        The graph with the added edge.
     """
     if not graph.has_node(source_node_h3_idx) or not graph.has_node(target_node_h3_idx):
         return graph

src/anemoi/graphs/generate/icosahedral.py

@@ -22,8 +22,6 @@ def create_icosahedral_nodes(
     ----------
     resolutions : list[int]
         Levels of mesh resolution to consider.
-    aoi_mask_builder : KNNAreaMaskBuilder
-        KNNAreaMaskBuilder with the cloud of points to limit the mesh area, by default None.
 
     Returns
     -------
@@ -86,10 +84,6 @@ def add_edges_to_nx_graph(
         Levels of mesh refinement levels to consider.
     x_hops : int, optional
         Number of hops between 2 nodes to consider them neighbours, by default 1.
-    aoi_mask_builder : KNNAreaMaskBuilder
-        NearestNeighbors with the cloud of points to limit the mesh area, by default None.
-    margin_radius_km : float, optional
-        Margin radius in km to consider when creating the processor mesh, by default 0.0.
 
     Returns
     -------
@@ -137,9 +131,6 @@ def get_neighbours_within_hops(tri_mesh: trimesh.Trimesh, x_hops: int) -> dict[i
         The mesh to consider.
     x_hops : int
         Number of hops between 2 nodes to consider them neighbours.
-    valid_nodes : list[int], optional
-        List of valid nodes to consider, by default None. It is useful to consider only a subset of the nodes to save
-        computation time.
 
     Returns
     -------
@@ -178,7 +169,7 @@ def add_neigbours_edges(
         A 2D array of shape (num_vertices, 2) with the planar coordinates of the mesh, in radians.
     node_idx : int
         The node considered.
-    neighbours : list[int]
+    neighbour_indices : list[int]
         The neighbours of the node.
     self_loops : bool, optional
         Whether is supported to add self-loops, by default False.

src/anemoi/graphs/nodes/attributes.py

@@ -43,6 +43,10 @@ def compute(self, graph: HeteroData, nodes_name: str, *args, **kwargs) -> torch.
             Graph.
         nodes_name : str
             Name of the nodes.
+        args : tuple
+            Additional arguments.
+        kwargs : dict
+            Additional keyword arguments.
 
         Returns
         -------
@@ -70,6 +74,10 @@ def get_raw_values(self, nodes: NodeStorage, *args, **kwargs) -> np.ndarray:
         ----------
         nodes : NodeStorage
             Nodes of the graph.
+        args : tuple
+            Additional arguments.
+        kwargs : dict
+            Additional keyword arguments.
 
         Returns
         -------
@@ -111,6 +119,10 @@ def get_raw_values(self, nodes: NodeStorage, *args, **kwargs) -> np.ndarray:
         ----------
         nodes : NodeStorage
             Nodes of the graph.
+        args : tuple
+            Additional arguments.
+        kwargs : dict
+            Additional keyword arguments.
 
         Returns
         -------

src/anemoi/graphs/nodes/builder.py

@@ -204,6 +204,8 @@ class IcosahedralNodes(BaseNodeBuilder, ABC):
     ----------
     resolution : list[int] | int
         Refinement level of the mesh.
+    name : str
+        The name of the nodes.
     """
 
     def __init__(