import matplotlib.pyplot as plt
import networkx as nx

def nn(cluster_no, node_name):
    return str(cluster_no) + '-' + node_name

def build_cluster(n):
    g = nx.Graph()
    nodes = ['ls', 'lu', 'ld', 'm', 'rd', 'ru', 'rs']
    edges = [('ls', 'lu'), ('ls', 'ld'), ('ld', 'lu'),
             ('rs', 'ru'), ('rs', 'rd'), ('rd', 'ru'),
             ('lu', 'm'),  ('ld', 'm'),
             ('ru', 'm'),  ('rd', 'm')]

    g.add_nodes_from([nn(n, x) for x in nodes])
    g.add_edges_from([tuple(nn(n, y) for y in x) for x in edges])

    return g

def analyze_network(N_max):
    i = 0
    g = build_cluster(i)
    stats = []

    throw_dist = 4

    while g.number_of_nodes() < N_max:
        i += 1
        c = build_cluster(i)
        g = nx.union(g, c)

        if i % 2:
            g.add_edge(nn(i, 'lu'), nn(i-1, 'lu'))
            g.add_edge(nn(i, 'rd'), nn(i-1, 'rd'))
        else:
            g.add_edge(nn(i, 'ld'), nn(i-1, 'ld'))
            g.add_edge(nn(i, 'ru'), nn(i-1, 'ru'))

        #if not i % 2:

        #elif i % 5 != 1:
        g.add_edge(nn(i, 'ls'), nn(i-1, 'rs'))
        g.add_edge(nn(i, 'rs'), nn(i-1, 'ls'))

        if i >= 1 * throw_dist:
            g.add_edge(nn(i, 'rs'), nn(i-(1*throw_dist), 'ls'))
            g.add_edge(nn(i, 'ls'), nn(i-(1*throw_dist), 'rs'))

        if i >= 2 * throw_dist:
            g.add_edge(nn(i, 'lu'), nn(i-(2*throw_dist), 'lu'))
        if i >= 3 * throw_dist:
            g.add_edge(nn(i, 'ld'), nn(i-(3*throw_dist), 'ld'))
        if i >= 4 * throw_dist:
            g.add_edge(nn(i, 'ru'), nn(i-(4*throw_dist), 'ru'))
        if i >= 5 * throw_dist:
            g.add_edge(nn(i, 'rd'), nn(i-(5*throw_dist), 'rd'))
        if i >= 7 * throw_dist:
            g.add_edge(nn(i, 'm'), nn(i-(7*throw_dist), 'm'))

        stats.append(analyze(g))
        print(stats[-1])

    return g, stats

def analyze(G: nx.Graph) -> dict:
    N = G.number_of_nodes()
    E = G.number_of_edges()
    degs = G.degree()
    s_vals = [s[1] for s in degs]
    S = max(s_vals)
    D = nx.diameter(G)
    D_average = nx.average_shortest_path_length(G)
    Q = N*D_average/E
    C = N*D*S
    return {
            'N': N,
            'E': E,
            'Ступінь': S,
            'Діаметр': D,
            'Середній діаметр': D_average,
            'Топологічний трафік': Q,
            'Вартість': C,
    }

def main():
    g, r = analyze_network(1000)

    #print(r)
    nx.draw(g, with_labels = True)
    plt.show()

if __name__=="__main__":
    main()