add radius graph option

ThermalSolver/graph_datamodule.py

@@ -18,6 +18,8 @@ class GraphDataModule(LightningDataModule):
         test_size: float = 0.1,
         batch_size: int = 32,
         remove_boundary_edges: bool = False,
+        build_radial_graph: bool = False,
+        radius: float = None,
     ):
         super().__init__()
         self.hf_repo = hf_repo
@@ -29,6 +31,8 @@ class GraphDataModule(LightningDataModule):
         self.test_size = test_size
         self.batch_size = batch_size
         self.remove_boundary_edges = remove_boundary_edges
+        self.build_radial_graph = build_radial_graph
+        self.radius = radius
 
     def prepare_data(self):
         dataset = load_dataset(self.hf_repo, name="snapshots")[self.split_name]
@@ -80,9 +84,8 @@ class GraphDataModule(LightningDataModule):
         )
         temperature = torch.tensor(snapshot["temperature"], dtype=torch.float32)
 
-        edge_index = torch.tensor(geometry["edge_index"], dtype=torch.int64).T
-
         pos = torch.tensor(geometry["points"], dtype=torch.float32)[:, :2]
+
         bottom_ids = torch.tensor(
             geometry["bottom_boundary_ids"], dtype=torch.long
         )
@@ -92,11 +95,28 @@ class GraphDataModule(LightningDataModule):
             geometry["right_boundary_ids"], dtype=torch.long
         )
 
+        if self.build_radial_graph:
+            from pina.graph import RadiusGraph
+
+            if self.radius is None:
+                raise ValueError("Radius must be specified for radial graph.")
+            edge_index = RadiusGraph.compute_radius_graph(
+                pos, radius=self.radius
+            )
+            from torch_geometric.utils import remove_self_loops
+
+            edge_index, _ = remove_self_loops(edge_index)
+        else:
+
+            edge_index = torch.tensor(
+                geometry["edge_index"], dtype=torch.int64
+            ).T
             edge_index = to_undirected(edge_index, num_nodes=pos.size(0))
 
         boundary_mask, boundary_values = self._compute_boundary_mask(
             bottom_ids, right_ids, top_ids, left_ids, temperature
         )
+
         if self.remove_boundary_edges:
             boundary_idx = torch.unique(boundary_mask)
             edge_index_mask = ~torch.isin(edge_index[1], boundary_idx)