94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
import matplotlib.pyplot as plt
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import networkx as nx
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def nn(cluster_no, node_name):
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return str(cluster_no) + '-' + node_name
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def build_cluster(n):
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g = nx.Graph()
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nodes = ['ls', 'lu', 'ld', 'm', 'rd', 'ru', 'rs']
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edges = [('ls', 'lu'), ('ls', 'ld'), ('ld', 'lu'),
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('rs', 'ru'), ('rs', 'rd'), ('rd', 'ru'),
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('lu', 'm'), ('ld', 'm'),
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('ru', 'm'), ('rd', 'm')]
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g.add_nodes_from([nn(n, x) for x in nodes])
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g.add_edges_from([tuple(nn(n, y) for y in x) for x in edges])
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return g
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def analyze_network(N_max):
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i = 0
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g = build_cluster(i)
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stats = []
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throw_dist = 4
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while g.number_of_nodes() < N_max:
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i += 1
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c = build_cluster(i)
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g = nx.union(g, c)
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if i % 2:
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g.add_edge(nn(i, 'lu'), nn(i-1, 'lu'))
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g.add_edge(nn(i, 'rd'), nn(i-1, 'rd'))
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else:
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g.add_edge(nn(i, 'ld'), nn(i-1, 'ld'))
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g.add_edge(nn(i, 'ru'), nn(i-1, 'ru'))
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#if not i % 2:
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#elif i % 5 != 1:
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g.add_edge(nn(i, 'ls'), nn(i-1, 'rs'))
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g.add_edge(nn(i, 'rs'), nn(i-1, 'ls'))
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if i >= 1 * throw_dist:
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g.add_edge(nn(i, 'rs'), nn(i-(1*throw_dist), 'ls'))
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g.add_edge(nn(i, 'ls'), nn(i-(1*throw_dist), 'rs'))
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if i >= 2 * throw_dist:
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g.add_edge(nn(i, 'lu'), nn(i-(2*throw_dist), 'lu'))
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if i >= 3 * throw_dist:
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g.add_edge(nn(i, 'ld'), nn(i-(3*throw_dist), 'ld'))
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if i >= 4 * throw_dist:
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g.add_edge(nn(i, 'ru'), nn(i-(4*throw_dist), 'ru'))
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if i >= 5 * throw_dist:
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g.add_edge(nn(i, 'rd'), nn(i-(5*throw_dist), 'rd'))
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if i >= 7 * throw_dist:
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g.add_edge(nn(i, 'm'), nn(i-(7*throw_dist), 'm'))
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stats.append(analyze(g))
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print(stats[-1])
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return g, stats
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def analyze(G: nx.Graph) -> dict:
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N = G.number_of_nodes()
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E = G.number_of_edges()
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degs = G.degree()
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s_vals = [s[1] for s in degs]
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S = max(s_vals)
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D = nx.diameter(G)
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D_average = nx.average_shortest_path_length(G)
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Q = N*D_average/E
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C = N*D*S
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return {
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'N': N,
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'E': E,
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'Ступінь': S,
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'Діаметр': D,
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'Середній діаметр': D_average,
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'Топологічний трафік': Q,
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'Вартість': C,
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}
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def main():
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g, r = analyze_network(1000)
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#print(r)
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nx.draw(g, with_labels = True)
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plt.show()
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if __name__=="__main__":
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main()
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