Network model export

A network generated with Pop2net can be exported as a networkx graph object using model.export_bipartite_network() and model.export_network().

First, let us create a example network:

import random

import networkx as nx

import pop2net as p2n

N_RINGS = 5
N_ACTORS = 25
env = p2n.Environment()
creator = p2n.Creator(env)
inspector = p2n.NetworkInspector(env)


class MyActor(p2n.Actor):
    def __init__(self):
        super().__init__()
        self.age = random.randint(20, 80)


class Ring(p2n.LocationDesigner):
    n_locations = N_RINGS
    nxgraph = nx.cycle_graph(int(N_ACTORS / N_RINGS))

    def weight(self, actor):
        return 10


class Bridge(p2n.LocationDesigner):
    def mutate(self):
        return {"focal_ring_id": list(range(N_RINGS))}

    def filter(self, actor):
        return actor.Ring_id in [self.focal_ring_id, self.focal_ring_id + 1]

    def split(self, actor):
        return actor.Ring_position


_ = creator.create_actors(n=N_ACTORS, actor_class=MyActor)
_ = creator.create_locations(location_designers=[Ring, Bridge])

Let’s have look on the network before we export it:

inspector.plot_networks()

In order to export the bipartite network as a networkx graph object, use model.export_bipartite_network(). Using the arguments location_attrs and agent_attrs, location attributes and agent attributes will be saved as node attributes.

bipartite_graph = env.export_bipartite_network(actor_attrs=["age"])
bipartite_graph

<networkx.classes.graph.Graph at 0x7347d2a33500>

To transform the network into a agent-level network and save it as a networkx graph object, use model.export_actor_network().

actor_graph = env.export_actor_network(node_attrs=["age"])
actor_graph

<networkx.classes.graph.Graph at 0x7347d22407a0>