Fix networkx export of nodes without links

Fix networkx export of nodes without links. The creation of the DiGraph
object was done by interating over links in the results. This did not
pick up nodes with no inferred links.

Fixes #50.

idtxl/idtxl_io.py

@@ -378,6 +378,7 @@ class for directed graphs (DiGraph). Multiple options for the weight are
     # use 'weights' parameter (string) as networkx edge property name and use
     # adjacency matrix entries as edge property values
     G = nx.DiGraph()
+    G.add_nodes_from(np.arange(adjacency_matrix.n_nodes()))
     G.add_weighted_edges_from(adjacency_matrix.get_edge_list(), weights)
     return G
 

test/test_idtxl_io.py

@@ -11,6 +11,8 @@
 from idtxl.network_comparison import NetworkComparison
 from idtxl.multivariate_te import MultivariateTE
 
+SEED = 0
+
 # Generate data and load network inference results.
 n_nodes = 5
 data_0 = Data()
@@ -42,6 +44,33 @@
 
 def test_export_networkx():
     """Test export to networkx DiGrap() object."""
+    # Test export of graph with unconnected nodes.
+    max_lag = 3
+    data = Data(seed=SEED)
+    data.generate_mute_data(500, 5)
+    settings = {
+        'cmi_estimator': 'JidtKraskovCMI',
+        'noise_level': 0,
+        'n_perm_max_stat': 21,
+        'n_perm_min_stat': 21,
+        'n_perm_max_seq': 21,
+        'n_perm_omnibus': 21,
+        'max_lag_sources': max_lag,
+        'min_lag_sources': 1,
+        'max_lag_target': max_lag}
+    target = 3
+    sources = [0, 4]
+    te = MultivariateTE()
+    results = te.analyse_single_target(
+        settings, data, target=target, sources=sources)
+    weights = 'binary'
+    adj_matrix = results.get_adjacency_matrix(weights=weights, fdr=False)
+    digraph = io.export_networkx_graph(
+        adjacency_matrix=adj_matrix, weights=weights)
+    np.testing.assert_array_equal(
+        np.sort(digraph.nodes), np.arange(data.n_processes),
+        err_msg='Wrong nodes in exported DiGraph.')
+
     # raise AssertionError('Test not yet implemented.')
     # Test export of networx graph for network inference results.
     weights = 'binary'