Hi, I'm trying to build a bipartite graph from a MxN array. So far I've been able to do this: # adjacency is the original np.array S = [] E = [] W = [] for x in range(adjacency.shape[0]): for y in np.where(adjacency[x] > 0)[0]: S.append(x) E.append(y + adjacency.shape[0]) # so that node index starts with an offset) W.append(adjacency[x, y]) g = gt.Graph(directed=True) g.add_edge_list(np.hstack([np.array(S)[:, None], np.array(E)[:, None]])) ew = g.new_edge_property("double") ew.a = W g.ep['weight'] = ew recs = [g.ep.weight] rec_types = ['real-exponential'] state = gt.NestedBlockState(g, state_args=dict(recs=recs, rec_types=rec_types)) […] I'm pretty sure this is not the most efficient way to go, but gt.is_bipartite(g) returns True. Can anybody suggest a more straightforward approach? Also, is it necessary to specify a VertexPropertyMap with node type (i.e. M or N)? Anyhow, after I had the graph and the final model, I tried to draw: gt.draw_hierarchy(state, layout='bipartite') but I get this error IndexError Traceback (most recent call last) <ipython-input-67-d7655416181e> in <module> ----> 1 gt.draw_hierarchy(state, layout='bipartite') ~/anaconda3/envs/schist/lib/python3.8/site-packages/graph_tool/draw/cairo_draw.py in draw_hierarchy(state, pos, layout, beta, node_weight, vprops, eprops, hvprops, heprops, subsample_edges, rel_order, deg_size, vsize_scale, hsize_scale, hshortcuts, hide, bip_aspect, empty_branches, **kwargs) 1773 rel_order_leaf=True) 1774 elif layout == "bipartite": -> 1775 tpos = get_bip_hierachy_pos(state, aspect=bip_aspect, 1776 node_weight=node_weight) 1777 tpos = t.own_property(tpos) ~/anaconda3/envs/schist/lib/python3.8/site-packages/graph_tool/draw/cairo_draw.py in get_bip_hierachy_pos(state, aspect, node_weight) 2145 t.add_edge(root, p2) 2146 for p in ps: -> 2147 if bc.a[tb[p]] == 0: 2148 t.add_edge(p2, p) 2149 else: IndexError : index 1 is out of bounds for axis 0 with size 1 Any help is appreciated d