make_graph_weighted.py

import argparse
import numpy as np
from graph_helpers import load_graph_by_name
from graph_tool.stats import remove_self_loops


def main():
    parser = argparse.ArgumentParser(description='')
    parser.add_argument('-g', '--graph', help='graph name')
    parser.add_argument('-d', '--to_directed',
                        action='store_true',
                        help='if make directed or not')
    parser.add_argument('--p_min', default=0.0, type=float,
                        help='lower bound for edge weight')
    parser.add_argument('--p_max', default=1.0, type=float,
                        help='upper bound for edge weight')
    parser.add_argument('-o', '--output')
    
    args = parser.parse_args()
    g = load_graph_by_name(args.graph)
    remove_self_loops(g)

    if args.to_directed:
        g.set_directed(True)
        edges_iter = list(g.edges())
        for e in edges_iter:
            g.add_edge(e.target(), e.source())

    weights = g.new_edge_property('float')
    weights.a = np.random.random(g.num_edges()) * (args.p_max - args.p_min) + args.p_min

    g.edge_properties["weights"] = weights

    g.graph_properties['p_min'] = g.new_graph_property("float", args.p_min)
    g.graph_properties['p_max'] = g.new_graph_property("float", args.p_max)
    print(g.graph_properties['p_min'], args.p_min)
    print(g.graph_properties['p_max'], args.p_max)
    print('g.num_edges()', g.num_edges())

    output_path = args.output  # 'data/{}/graph_weighted.gt'.format(args.graph)
    g.save(output_path)
    
    print('dumped to {}'.format(output_path))

if __name__ == '__main__':
    main()