Local Area graphs

CHANGELOG.md

@@ -10,11 +10,17 @@ Keep it human-readable, your future self will thank you!
 
 ## [Unreleased](https://github.com/ecmwf/anemoi-graphs/compare/0.3.0...HEAD)
 
+### Added
+- New node builder class, CutOutZarrDatasetNodes, to create nodes from 2 datasets. (#30)
+- New class, KNNAreaMaskBuilder, to specify Area of Interest (AOI) based on a set of nodes. (#30)
+- New node builder classes, LimitedAreaXXXXXNodes, to create nodes within an Area of Interest (AOI). (#30)
+- Expanded MultiScaleEdges to support multi-scale connections in limited area graphs. (#30)
+
 ## [0.3.0 Anemoi-graphs, minor release](https://github.com/ecmwf/anemoi-graphs/compare/0.2.1...0.3.0) - 2024-09-03
 
 ### Added
 
-- HEALPixNodes - nodebuilder based on Hierarchical Equal Area isoLatitude Pixelation of a sphere.
+- HEALPixNodes - nodebuilder based on Hierarchical Equal Area isoLatitude Pixelation of a sphere
 
 - Inspection tools: interactive plots, and distribution plots of edge & node attributes.
 

docs/usage/getting_started.rst

@@ -54,7 +54,7 @@ following command:
 
 .. code:: console
 
-   $ anemoi-graphs inspect graph.pt
+   $ anemoi-graphs inspect graph.pt output_plots
 
 This will generate the following graph:
 

src/anemoi/graphs/create.py

@@ -41,10 +41,14 @@ def generate_graph(self) -> HeteroData:
                 graph, nodes_cfg.get("attributes", {})
             )
 
-        for edges_cfg in self.config.edges:
-            graph = instantiate(edges_cfg.edge_builder, edges_cfg.source_name, edges_cfg.target_name).update_graph(
-                graph, edges_cfg.get("attributes", {})
-            )
+        for edges_cfg in self.config.get("edges", {}):
+            graph = instantiate(
+                edges_cfg.edge_builder,
+                edges_cfg.source_name,
+                edges_cfg.target_name,
+                source_mask_attr_name=edges_cfg.get("source_mask_attr_name", None),
+                target_mask_attr_name=edges_cfg.get("target_mask_attr_name", None),
+            ).update_graph(graph, edges_cfg.get("attributes", {}))
 
         return graph
 

src/anemoi/graphs/edges/builder.py

@@ -9,15 +9,18 @@
 import torch
 from anemoi.utils.config import DotDict
 from hydra.utils import instantiate
+from scipy.sparse import coo_matrix
 from sklearn.neighbors import NearestNeighbors
 from torch_geometric.data import HeteroData
 from torch_geometric.data.storage import NodeStorage
 
 from anemoi.graphs import EARTH_RADIUS
-from anemoi.graphs.generate import hexagonal
-from anemoi.graphs.generate import icosahedral
-from anemoi.graphs.nodes.builder import HexNodes
-from anemoi.graphs.nodes.builder import TriNodes
+from anemoi.graphs.generate import hex_icosahedron
+from anemoi.graphs.generate import tri_icosahedron
+from anemoi.graphs.nodes.builders.from_refined_icosahedron import HexNodes
+from anemoi.graphs.nodes.builders.from_refined_icosahedron import LimitedAreaHexNodes
+from anemoi.graphs.nodes.builders.from_refined_icosahedron import LimitedAreaTriNodes
+from anemoi.graphs.nodes.builders.from_refined_icosahedron import TriNodes
 from anemoi.graphs.utils import get_grid_reference_distance
 
 LOGGER = logging.getLogger(__name__)
@@ -26,9 +29,17 @@
 class BaseEdgeBuilder(ABC):
     """Base class for edge builders."""
 
-    def __init__(self, source_name: str, target_name: str):
+    def __init__(
+        self,
+        source_name: str,
+        target_name: str,
+        source_mask_attr_name: str | None = None,
+        target_mask_attr_name: str | None = None,
+    ):
         self.source_name = source_name
         self.target_name = target_name
+        self.source_mask_attr_name = source_mask_attr_name
+        self.target_mask_attr_name = target_mask_attr_name
 
     @property
     def name(self) -> tuple[str, str, str]:
@@ -117,15 +128,48 @@ def update_graph(self, graph: HeteroData, attrs_config: DotDict | None = None) -
         """
         graph = self.register_edges(graph)
 
-        if attrs_config is None:
-            return graph
-
-        graph = self.register_attributes(graph, attrs_config)
+        if attrs_config is not None:
+            graph = self.register_attributes(graph, attrs_config)
 
         return graph
 
 
-class KNNEdges(BaseEdgeBuilder):
+class NodeMaskingMixin:
+    """Mixin class for masking source/target nodes when building edges."""
+
+    def get_node_coordinates(
+        self, source_nodes: NodeStorage, target_nodes: NodeStorage
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Get the node coordinates."""
+        source_coords, target_coords = source_nodes.x.numpy(), target_nodes.x.numpy()
+
+        if self.source_mask_attr_name is not None:
+            source_coords = source_coords[source_nodes[self.source_mask_attr_name].squeeze()]
+
+        if self.target_mask_attr_name is not None:
+            target_coords = target_coords[target_nodes[self.target_mask_attr_name].squeeze()]
+
+        return source_coords, target_coords
+
+    def undo_masking(self, adj_matrix, source_nodes: NodeStorage, target_nodes: NodeStorage):
+        if self.target_mask_attr_name is not None:
+            target_mask = target_nodes[self.target_mask_attr_name].squeeze()
+            target_mapper = dict(zip(list(range(len(adj_matrix.row))), np.where(target_mask)[0]))
+            adj_matrix.row = np.vectorize(target_mapper.get)(adj_matrix.row)
+
+        if self.source_mask_attr_name is not None:
+            source_mask = source_nodes[self.source_mask_attr_name].squeeze()
+            source_mapper = dict(zip(list(range(len(adj_matrix.col))), np.where(source_mask)[0]))
+            adj_matrix.col = np.vectorize(source_mapper.get)(adj_matrix.col)
+
+        if self.source_mask_attr_name is not None or self.target_mask_attr_name is not None:
+            true_shape = target_nodes.x.shape[0], source_nodes.x.shape[0]
+            adj_matrix = coo_matrix((adj_matrix.data, (adj_matrix.row, adj_matrix.col)), shape=true_shape)
+
+        return adj_matrix
+
+
+class KNNEdges(BaseEdgeBuilder, NodeMaskingMixin):
     """Computes KNN based edges and adds them to the graph.
 
     Attributes
@@ -136,6 +180,10 @@ class KNNEdges(BaseEdgeBuilder):
         The name of the target nodes.
     num_nearest_neighbours : int
         Number of nearest neighbours.
+    source_mask_attr_name : str | None
+        The name of the source mask attribute to filter edge connections.
+    target_mask_attr_name : str | None
+        The name of the target mask attribute to filter edge connections.
 
     Methods
     -------
@@ -147,22 +195,30 @@ class KNNEdges(BaseEdgeBuilder):
         Update the graph with the edges.
     """
 
-    def __init__(self, source_name: str, target_name: str, num_nearest_neighbours: int):
-        super().__init__(source_name, target_name)
+    def __init__(
+        self,
+        source_name: str,
+        target_name: str,
+        num_nearest_neighbours: int,
+        source_mask_attr_name: str | None = None,
+        target_mask_attr_name: str | None = None,
+    ):
+        super().__init__(source_name, target_name, source_mask_attr_name, target_mask_attr_name)
         assert isinstance(num_nearest_neighbours, int), "Number of nearest neighbours must be an integer"
         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: NodeStorage, target_nodes: NodeStorage):
         """Compute the adjacency matrix for the KNN method.
 
         Parameters
         ----------
-        source_nodes : np.ndarray
+        source_nodes : NodeStorage
             The source nodes.
-        target_nodes : np.ndarray
+        target_nodes : NodeStorage
             The target nodes.
         """
+        source_coords, target_coords = self.get_node_coordinates(source_nodes, target_nodes)
         assert self.num_nearest_neighbours is not None, "number of neighbors required for knn encoder"
         LOGGER.info(
             "Using KNN-Edges (with %d nearest neighbours) between %s and %s.",
@@ -172,16 +228,20 @@ def get_adjacency_matrix(self, source_nodes: np.ndarray, target_nodes: np.ndarra
         )
 
         nearest_neighbour = NearestNeighbors(metric="haversine", n_jobs=4)
-        nearest_neighbour.fit(source_nodes.x.numpy())
+        nearest_neighbour.fit(source_coords)
         adj_matrix = nearest_neighbour.kneighbors_graph(
-            target_nodes.x.numpy(),
+            target_coords,
             n_neighbors=self.num_nearest_neighbours,
             mode="distance",
         ).tocoo()
+
+        # Post-process the adjacency matrix. Add masked nodes.
+        adj_matrix = self.undo_masking(adj_matrix, source_nodes, target_nodes)
+
         return adj_matrix
 
 
-class CutOffEdges(BaseEdgeBuilder):
+class CutOffEdges(BaseEdgeBuilder, NodeMaskingMixin):
     """Computes cut-off based edges and adds them to the graph.
 
     Attributes
@@ -192,6 +252,10 @@ class CutOffEdges(BaseEdgeBuilder):
         The name of the target nodes.
     cutoff_factor : float
         Factor to multiply the grid reference distance to get the cut-off radius.
+    source_mask_attr_name : str | None
+        The name of the source mask attribute to filter edge connections.
+    target_mask_attr_name : str | None
+        The name of the target mask attribute to filter edge connections.
 
     Methods
     -------
@@ -203,8 +267,15 @@ class CutOffEdges(BaseEdgeBuilder):
         Update the graph with the edges.
     """
 
-    def __init__(self, source_name: str, target_name: str, cutoff_factor: float):
-        super().__init__(source_name, target_name)
+    def __init__(
+        self,
+        source_name: str,
+        target_name: str,
+        cutoff_factor: float,
+        source_mask_attr_name: str | None = None,
+        target_mask_attr_name: str | None = None,
+    ):
+        super().__init__(source_name, target_name, source_mask_attr_name, target_mask_attr_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
@@ -248,6 +319,7 @@ def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStor
         target_nodes : NodeStorage
             The target nodes.
         """
+        source_coords, target_coords = self.get_node_coordinates(source_nodes, target_nodes)
         LOGGER.info(
             "Using CutOff-Edges (with radius = %.1f km) between %s and %s.",
             self.radius * EARTH_RADIUS,
@@ -256,8 +328,12 @@ def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStor
         )
 
         nearest_neighbour = NearestNeighbors(metric="haversine", n_jobs=4)
-        nearest_neighbour.fit(source_nodes.x)
-        adj_matrix = nearest_neighbour.radius_neighbors_graph(target_nodes.x, radius=self.radius).tocoo()
+        nearest_neighbour.fit(source_coords)
+        adj_matrix = nearest_neighbour.radius_neighbors_graph(target_coords, radius=self.radius).tocoo()
+
+        # Post-process the adjacency matrix. Add masked nodes.
+        adj_matrix = self.undo_masking(adj_matrix, source_nodes, target_nodes)
+
         return adj_matrix
 
 
@@ -284,61 +360,52 @@ class MultiScaleEdges(BaseEdgeBuilder):
         Update the graph with the edges.
     """
 
-    def __init__(self, source_name: str, target_name: str, x_hops: int):
+    VALID_NODES = [TriNodes, HexNodes, LimitedAreaTriNodes, LimitedAreaHexNodes]
+
+    def __init__(self, source_name: str, target_name: str, x_hops: int, **kwargs):
         super().__init__(source_name, target_name)
         assert source_name == target_name, f"{self.__class__.__name__} requires source and target nodes to be the same."
         assert isinstance(x_hops, int), "Number of x_hops must be an integer"
         assert x_hops > 0, "Number of x_hops must be positive"
         self.x_hops = x_hops
+        self.node_type = None
 
-    def adjacency_from_tri_nodes(self, source_nodes: NodeStorage):
-        source_nodes["_nx_graph"] = icosahedral.add_edges_to_nx_graph(
-            source_nodes["_nx_graph"],
-            resolutions=source_nodes["_resolutions"],
+    def add_edges_from_tri_nodes(self, nodes: NodeStorage) -> NodeStorage:
+        nodes["_nx_graph"] = tri_icosahedron.add_edges_to_nx_graph(
+            nodes["_nx_graph"],
+            resolutions=nodes["_resolutions"],
             x_hops=self.x_hops,
-        )  # HeteroData refuses to accept None
-
-        adjmat = nx.to_scipy_sparse_array(
-            source_nodes["_nx_graph"], nodelist=list(range(len(source_nodes["_nx_graph"]))), format="coo"
+            area_mask_builder=nodes.get("_area_mask_builder", None),
         )
-        return adjmat
 
-    def adjacency_from_hex_nodes(self, source_nodes: NodeStorage):
+        return nodes
 
-        source_nodes["_nx_graph"] = hexagonal.add_edges_to_nx_graph(
-            source_nodes["_nx_graph"],
-            resolutions=source_nodes["_resolutions"],
+    def add_edges_from_hex_nodes(self, nodes: NodeStorage) -> NodeStorage:
+        nodes["_nx_graph"] = hex_icosahedron.add_edges_to_nx_graph(
+            nodes["_nx_graph"],
+            resolutions=nodes["_resolutions"],
             x_hops=self.x_hops,
         )
 
-        adjmat = nx.to_scipy_sparse_array(source_nodes["_nx_graph"], format="coo")
-        return adjmat
+        return nodes
 
     def get_adjacency_matrix(self, source_nodes: NodeStorage, target_nodes: NodeStorage):
-        if self.node_type == TriNodes.__name__:
-            adjmat = self.adjacency_from_tri_nodes(source_nodes)
-        elif self.node_type == HexNodes.__name__:
-            adjmat = self.adjacency_from_hex_nodes(source_nodes)
+        if self.node_type in [TriNodes.__name__, LimitedAreaTriNodes.__name__]:
+            source_nodes = self.add_edges_from_tri_nodes(source_nodes)
+        elif self.node_type in [HexNodes.__name__, LimitedAreaHexNodes.__name__]:
+            source_nodes = self.add_edges_from_hex_nodes(source_nodes)
         else:
             raise ValueError(f"Invalid node type {self.node_type}")
 
-        adjmat = self.post_process_adjmat(source_nodes, adjmat)
-
-        return adjmat
+        adjmat = nx.to_scipy_sparse_array(source_nodes["_nx_graph"], format="coo")
 
-    def post_process_adjmat(self, nodes: NodeStorage, adjmat):
-        graph_sorted = {node_pos: i for i, node_pos in enumerate(nodes["_node_ordering"])}
-        sort_func = np.vectorize(graph_sorted.get)
-        adjmat.row = sort_func(adjmat.row)
-        adjmat.col = sort_func(adjmat.col)
         return adjmat
 
     def update_graph(self, graph: HeteroData, attrs_config: DotDict | None = None) -> HeteroData:
-        assert (
-            graph[self.source_name].node_type == TriNodes.__name__
-            or graph[self.source_name].node_type == HexNodes.__name__
-        ), f"{self.__class__.__name__} requires {TriNodes.__name__} or {HexNodes.__name__}."
-
         self.node_type = graph[self.source_name].node_type
+        valid_node_names = [n.__name__ for n in self.VALID_NODES]
+        assert (
+            self.node_type in valid_node_names
+        ), f"{self.__class__.__name__} requires {','.join(valid_node_names)} nodes."
 
         return super().update_graph(graph, attrs_config)