Fix/Ordering Numpy attributes

src/pathpyG/core/temporal_graph.py

@@ -31,24 +31,20 @@ def __init__(self, data: Data, mapping: IndexMap | None = None) -> None:
             print(t)
             ```
         """
-        if not isinstance(data.edge_index, EdgeIndex):
-            data.edge_index = data.edge_index = EdgeIndex(
-                data=data.edge_index.contiguous(), sparse_size=(data.num_nodes, data.num_nodes)
+        self.data = data
+        if not isinstance(self.data.edge_index, EdgeIndex):
+            self.data.edge_index = EdgeIndex(
+                data=self.data.edge_index.contiguous(), sparse_size=(self.data.num_nodes, self.data.num_nodes)
             )
 
         # reorder temporal data
-        # TODO: Fix in PyG
-        if data.num_nodes != data.num_edges:
-            self.data = data.sort_by_time()
-        else:
-            sorted_idx = torch.argsort(data.time)
-            data.time = data.time[sorted_idx]
-            for edge_attr in data.edge_attrs():
-                if edge_attr == "edge_index":
-                    data.edge_index = data.edge_index[:, sorted_idx]
-                else:
-                    data[edge_attr] = data[edge_attr][sorted_idx]
-            self.data = data
+        # Note that we do not use `torch_geometric.self.data.Data.sort_by_time` because it cannot sort numpy arrays`
+        sorted_idx = torch.argsort(self.data.time)
+        for edge_attr in set(self.data.edge_attrs()).union(set(["time"])):
+            if edge_attr == "edge_index":
+                self.data.edge_index = self.data.edge_index[:, sorted_idx]
+            else:
+                data[edge_attr] = data[edge_attr][sorted_idx]
 
         if mapping is not None:
             self.mapping = mapping
@@ -68,6 +64,12 @@ def from_edge_list(edge_list, num_nodes: Optional[int] = None) -> TemporalGraph:
         edge_array = np.array(edge_list)
         ts = edge_array[:, 2].astype(np.number)
 
+        # Convert timestamps to tensor
+        if np.issubdtype(ts.dtype, np.integer):
+            ts = torch.tensor(ts, dtype=torch.long)
+        else:
+            ts = torch.tensor(ts, dtype=torch.float32)
+
         index_map = IndexMap(np.unique(edge_array[:, :2]))
         edge_index = index_map.to_idxs(edge_array[:, :2].T)
 
@@ -77,7 +79,7 @@ def from_edge_list(edge_list, num_nodes: Optional[int] = None) -> TemporalGraph:
         return TemporalGraph(
             data=Data(
                 edge_index=edge_index,
-                time=torch.Tensor(ts),
+                time=ts,
                 num_nodes=num_nodes,
             ),
             mapping=index_map,

src/pathpyG/io/netzschleuder.py

@@ -174,9 +174,7 @@ def read_netzschleuder_graph(
                         if timestamps:
                             g = df_to_temporal_graph(df=edges, is_undirected=not is_directed, num_nodes=num_nodes)
                         else:
-                            g = df_to_graph(df=edges, multiedges=True, num_nodes=num_nodes)
-                            if not is_directed:
-                                g = g.to_undirected()
+                            g = df_to_graph(df=edges, multiedges=multiedges, is_undirected=not is_directed, num_nodes=num_nodes)
 
                         node_attrs = pd.read_csv(
                             f"{temp_dir}/nodes.csv", header=0, sep=",", skip_blank_lines=True, skipinitialspace=True