I have a graph in graph-tool with parallel edges. All parallel edges are assigned an `EdgePropertyMap` representing their weight with a double. I would like to return a graph where the edges are condensed into a single edge where a custom reduce operation on these weights is performed. For example: H = gt.Graph(directed=True) H.add_edge(0,1) # edge 0 H.add_edge(0,1) # edge 1 H.add_edge(0,1) # edge 2 H.add_edge(1,0) # edge 3 H.add_edge(0,2) # edge 4 ew = H.new_edge_property("double") ew[list(H.edges())[0]]=1.2 ew[list(H.edges())[1]]=2.3 ew[list(H.edges())[2]]=-4.2 ew[list(H.edges())[3]]=5.8 ew[list(H.edges())[3]]=1.0 H.ep['weights'] = ew In this case the edge (0,1), with a reduce function "sum", should have total weight 1.2+2.3-4.2= -0.7 while the remaining edges should keep the same weight (i.e. edge 3 should keep weight 5.8 and edge 4 should maintain weight 1.0). Using `gt.condensation_graph` seems to do the trick, but only sum operation can be performed. To do this we need to use `H.vertex_index` as vertex property: Hcond, prop, vcount, ecount, v_avg, edge_avg = gt.condensation_graph(H,prop=H.vertex_index, aeprops=[H.ep['weights']]) Unfortunately this function only allows sum operation to be performed. What about using a different "reduce" function, like for example max? What about operations on non-numeric types of edges? It would greatly help carrying complex operations directly in Graph-tool, even if the value type of edges property is different than numeric. More generally, what about introducing a split-apply-reduce framework to vertices and edges, or is it already possible with some modifications to `gt.condensation_graph`?