dpks-lab1/main.py

import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd

nn_total_levels = 0

def nn(cluster_no, node_name):
    if not nn_total_levels:
        return str(cluster_no) + '-' + node_name
    return str(cluster_no % nn_total_levels) + '-' + node_name

def join_nodes(g, a, b):
    if not (g.degree[a] == 6 or g.degree[b] == 6):
        g.add_edge(a, b)

def start_file(p):
    index = ['i', 'N', 'E', 'S', 'D', 'D_avg', 'T', 'C']
    open(p, 'w').write(','.join(index) + '\n')

def write_file(p, data):
    index = ['i', 'N', 'E', 'S', 'D', 'D_avg', 'T', 'C']
    open(p, 'a').write(','.join([str(data[i]) for i in index]) + '\n')

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 build_network(n):
    throw_dist = 4

    g = build_cluster(0)

    global nn_total_levels
    nn_total_levels = n

    for i in range(1, n):
        c = build_cluster(i)
        g = nx.union(g, c)

    for i in range(n):
        if n >= 2:
            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'))

            g.add_edge(nn(i, 'ls'), nn(i-1, 'rs'))
            g.add_edge(nn(i, 'rs'), nn(i-1, 'ls'))

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

    return g

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,
            'S': S,
            'D': D,
            'D_avg': round(D_average, 4),
            'T': round(Q, 4),
            'C': C,
    }

def main():
    i = 0

    start_file('data.csv')
    d = None

    while True:
        i += 1
        g = build_network(i)

        r = analyze(g)
        r.update({"i": i})

        '''
        if d.__class__ == None.__class__:
            d = pd.DataFrame(r, index = ['i', 'N', 'E', 'S', 'D', 'D_avg', 'T', 'C'])
        else:
            d.add(r)
        '''

        print(r)
        write_file('data.csv', r)

        if g.number_of_nodes() > 1000:
            break

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

    #d.to_csv('data.csv')

def test():
    g = build_network(15)
    print(analyze(g))
    print([i for i, v in g.degree if v > 6])
    print(g.adj['14-ld'])
    nx.draw(g, with_labels = True)
    plt.show()

def test2():
    g = build_network(150)
    print(analyze(g))
    print(set([v for i, v in g.degree()]))

if __name__=="__main__":
    main()