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
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