@@ -226,7 +226,8 @@ def cmat(
 # Add node information from specified parcellation scheme
 path, name, ext = split_filename(resolution_network_file)
 if ext == ".pck":
-gp = nx.read_gpickle(resolution_network_file)
+with open(resolution_network_file, 'rb') as f:
+gp = pickle.load(f)
 elif ext == ".graphml":
 gp = nx.read_graphml(resolution_network_file)
 else:
@@ -263,7 +264,7 @@ def cmat(
 )
 intersection_matrix = np.matrix(intersection_matrix)
 I = G.copy()
-H = nx.from_numpy_matrix(np.matrix(intersection_matrix))
+H = nx.from_numpy_array(np.matrix(intersection_matrix))
 H = nx.relabel_nodes(H, lambda x: x + 1) # relabel nodes so they start at 1
 I.add_weighted_edges_from(
 ((u, v, d["weight"]) for u, v, d in H.edges(data=True))
@@ -379,7 +380,8 @@ def cmat(
 fibdev.add_edge(u, v, weight=di["fiber_length_std"])
 
 iflogger.info("Writing network as %s", matrix_name)
-nx.write_gpickle(G, op.abspath(matrix_name))
+with open(op.abspath(matrix_name), 'wb') as f:
+pickle.dump(G, f, pickle.HIGHEST_PROTOCOL)
 
 numfib_mlab = nx.to_numpy_matrix(numfib, dtype=int)
 numfib_dict = {"number_of_fibers": numfib_mlab}
@@ -394,7 +396,8 @@ def cmat(
 path, name, ext = split_filename(matrix_name)
 intersection_matrix_name = op.abspath(name + "_intersections") + ext
 iflogger.info("Writing intersection network as %s", intersection_matrix_name)
-nx.write_gpickle(I, intersection_matrix_name)
+with open(intersection_matrix_name, 'wb') as f:
+pickle.dump(I, f, pickle.HIGHEST_PROTOCOL)
 
 path, name, ext = split_filename(matrix_mat_name)
 if not ext == ".mat":
@@ -1070,7 +1073,8 @@ def create_nodes(roi_file, resolution_network_file, out_filename):
 )
 )
 G.nodes[int(u)]["dn_position"] = tuple([xyz[0], xyz[2], -xyz[1]])
-nx.write_gpickle(G, out_filename)
+with open(out_filename, 'wb') as f:
+pickle.dump(G, f, pickle.HIGHEST_PROTOCOL)
 return out_filename
 
 

@@ -6,6 +6,7 @@
 
 import numpy as np
 import networkx as nx
+import pickle
 
 from ... import logging
 from ..base import (
@@ -149,8 +150,8 @@ def _run_interface(self, runtime):
 pADJ[x, y] = PVAL[idx]
 
 # Create networkx graphs from the adjacency matrix
-nbsgraph = nx.from_numpy_matrix(ADJ)
-nbs_pval_graph = nx.from_numpy_matrix(pADJ)
+nbsgraph = nx.from_numpy_array(ADJ)
+nbs_pval_graph = nx.from_numpy_array(pADJ)
 
 # Relabel nodes because they should not start at zero for our convention
 nbsgraph = nx.relabel_nodes(nbsgraph, lambda x: x + 1)
@@ -172,12 +173,14 @@ def _run_interface(self, runtime):
 
 path = op.abspath("NBS_Result_" + details)
 iflogger.info(path)
-nx.write_gpickle(nbsgraph, path)
+with open(path, 'wb') as f:
+pickle.dump(nbsgraph, f, pickle.HIGHEST_PROTOCOL)
 iflogger.info("Saving output NBS edge network as %s", path)
 
 pval_path = op.abspath("NBS_P_vals_" + details)
 iflogger.info(pval_path)
-nx.write_gpickle(nbs_pval_graph, pval_path)
+with open(pval_path, 'wb') as f:
+pickle.dump(nbs_pval_graph, f, pickle.HIGHEST_PROTOCOL)
 iflogger.info("Saving output p-value network as %s", pval_path)
 return runtime
 

@@ -200,7 +200,8 @@ def average_networks(in_files, ntwk_res_file, group_id):
 
 # Writes the networks and returns the name
 network_name = group_id + "_average.pck"
-nx.write_gpickle(avg_ntwk, op.abspath(network_name))
+with open(op.abspath(network_name), 'wb') as f:
+pickle.dump(avg_ntwk, f, pickle.HIGHEST_PROTOCOL)
 iflogger.info("Saving average network as %s", op.abspath(network_name))
 avg_ntwk = fix_keys_for_gexf(avg_ntwk)
 network_name = group_id + "_average.gexf"
@@ -483,7 +484,8 @@ def _run_interface(self, runtime):
 for key in list(node_measures.keys()):
 newntwk = add_node_data(node_measures[key], ntwk)
 out_file = op.abspath(self._gen_outfilename(key, "pck"))
-nx.write_gpickle(newntwk, out_file)
+with open(out_file, 'wb') as f:
+pickle.dump(newntwk, f, pickle.HIGHEST_PROTOCOL)
 nodentwks.append(out_file)
 if isdefined(self.inputs.out_node_metrics_matlab):
 node_out_file = op.abspath(self.inputs.out_node_metrics_matlab)
@@ -497,7 +499,8 @@ def _run_interface(self, runtime):
 for key in list(edge_measures.keys()):
 newntwk = add_edge_data(edge_measures[key], ntwk)
 out_file = op.abspath(self._gen_outfilename(key, "pck"))
-nx.write_gpickle(newntwk, out_file)
+with open(out_file, 'wb') as f:
+pickle.dump(newntwk, f, pickle.HIGHEST_PROTOCOL)
 edgentwks.append(out_file)
 if isdefined(self.inputs.out_edge_metrics_matlab):
 edge_out_file = op.abspath(self.inputs.out_edge_metrics_matlab)
@@ -521,7 +524,8 @@ def _run_interface(self, runtime):
 out_file = op.abspath(
 self._gen_outfilename(self.inputs.out_k_crust, "pck")
 )
-nx.write_gpickle(ntwk_measures[key], out_file)
+with open(out_file, 'wb') as f:
+pickle.dump(ntwk_measures[key], f, pickle.HIGHEST_PROTOCOL)
 kntwks.append(out_file)
 gpickled.extend(kntwks)
 

@@ -2,6 +2,7 @@
 from ....utils.misc import package_check
 import numpy as np
 import networkx as nx
+import pickle
 import pytest
 
 have_cv = True
@@ -17,10 +18,11 @@ def creating_graphs(tmpdir):
 graphnames = ["name" + str(i) for i in range(6)]
 for idx, name in enumerate(graphnames):
 graph = np.random.rand(10, 10)
-G = nx.from_numpy_matrix(graph)
+G = nx.from_numpy_array(graph)
 out_file = tmpdir.strpath + graphnames[idx] + ".pck"
 # Save as pck file
-nx.write_gpickle(G, out_file)
+with open(out_file, 'wb') as f:
+pickle.dump(G, f, pickle.HIGHEST_PROTOCOL)
 graphlist.append(out_file)
 return graphlist
 

@@ -434,7 +434,7 @@ def _generate_dependency_list(self, graph):
 import networkx as nx
 
 self.procs, _ = topological_sort(graph)
-self.depidx = nx.to_scipy_sparse_matrix(
+self.depidx = nx.to_scipy_sparse_array(
 graph, nodelist=self.procs, format="lil"
 )
 self.refidx = self.depidx.astype(int)