fix conflicts

Author: wisskarrou

src/rnaglib/transforms/represent/distograph.py

@@ -69,16 +69,15 @@ def __call__(self, rna_graph, features_dict):
                 distogram_dict = pickle.load(f)
                 distogram = distogram_dict["distogram"]["softmax"]
 
+            nb_bins = distogram.shape[2]
             chain_dict = get_sequences(base_graph)
             sorted_distogram_residues = [item for chain in sorted(chain_dict.keys()) for item in chain_dict[chain][1]]
-            nb_bins = distogram.shape[2]
 
             if self.distogram_edges:
                 dist_tensor = torch.from_numpy(distogram)
                 proba_matrix = dist_tensor[:, :, :self.B].sum(dim=2)
                 proba_matrix.fill_diagonal_(float(0))
                 new_edge_indices = torch.nonzero(proba_matrix > self.tau, as_tuple=False)
-                
                 node_map = {n: i for i, n in enumerate(sorted(base_graph.nodes(), key=lambda x:(x.split('.')[1],int(x.split('.')[2]))))}
                 new_edges = [[node_map[sorted_distogram_residues[u]],node_map[sorted_distogram_residues[v]]] for u, v in new_edge_indices]
                 new_edges = torch.tensor(new_edges, dtype=torch.long).T

src/rnaglib/transforms/represent/graph.py

@@ -155,11 +155,17 @@ def to_pyg(self, graph, features_dict):
             # targets: flatten the nearest neighbor indices
             target = neighbor_indices.flatten()
             edge_index= torch.stack([source, target], dim=0)
+<<<<<<< HEAD
             edge_attrs = torch.zeros(edge_index.shape[1],dtype=int)
 
         elif self.graph_construction == "threshold":
             edges = torch.nonzero(dist_matrix < self.threshold, as_tuple=False)
             edge_attrs = torch.zeros(edge_index.shape[1],dtype=int)
+=======
+
+        elif self.graph_construction == "threshold":
+            edges = torch.nonzero(dist_matrix < self.threshold, as_tuple=False)
+>>>>>>> 70fdf59f1e46542d77c7d48f3930e1d152e223e0
             edge_index = edges.t()
 
         else:
@@ -171,6 +177,10 @@ def to_pyg(self, graph, features_dict):
         if self.distance_edge_features:
             edge_distances = dist_matrix[edge_index[0, :], edge_index[1, :]]
             edge_feats = rbf_expand(dists=edge_distances, num_bins=64, min_distance=2.0, max_distance=22.0)
+<<<<<<< HEAD
+=======
+            edge_attrs = torch.zeros(edge_index.shape[1],dtype=int)
+>>>>>>> 70fdf59f1e46542d77c7d48f3930e1d152e223e0
             return Data(x=x, y=y, edge_attr=edge_attrs, edge_index=edge_index, edge_feats=edge_feats)
 
         return Data(x=x, y=y, edge_attr=edge_attrs, edge_index=edge_index)

src/rnaglib/utils/__init__.py

@@ -17,7 +17,6 @@
 
 from .task_utils import print_statistics
 from .task_utils import DummyResidueModel, DummyGraphModel
-
 from .represent_utils import rbf_expand
 
 from .wrappers import rna_align_wrapper, cdhit_wrapper, locarna_wrapper, US_align_wrapper