Merge pull request #23 from neuroneural/glag2gc

added conversion function from graph lag format to compressd gunfolds…

gunfolds/conversions.py

@@ -845,3 +845,26 @@ def encode_list_sccs(glist, scc_members=None):
     # if there is an edge between SCCs in the produced graph and none in the measured for nonsingleton SCCs - no go
     s += ':- directed(X,Y,U), scc(X,K), scc(Y,L), K != L, sccsize(L,Z), Z > 1, not dag(K,L,N), u(U,N).'
     return s
+
+def Glag2CG(results):
+    """Converts lag graph format to gunfolds graph format,
+   and A and B matrices representing directed and bidirected edges weights.
+
+   Args:
+       results (dict): A dictionary containing:
+           - 'graph': A 3D NumPy array of shape [N, N, 2] representing the graph structure.
+           - 'val_matrix': A NumPy array of shape [N, N, 2] storing edge weights.
+
+   Returns:
+       tuple: (graph_dict, A_matrix, B_matrix)
+   """
+
+    graph_array = results['graph']
+    bidirected_edges = np.where(graph_array == 'o-o', 1, 0).astype(int)
+    directed_edges = np.where(graph_array == '-->', 1, 0).astype(int)
+
+    graph_dict = adjs2graph(np.transpose(directed_edges[:, :, 1]), np.transpose((bidirected_edges[:, :, 0])))
+    A_matrix = results['val_matrix'][:, :, 1]
+    B_matrix = results['val_matrix'][:, :, 0]
+
+    return graph_dict, A_matrix, B_matrix