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generate_graph_advanced.py
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generate_graph_advanced.py
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import numpy as np
import configparser
from itertools import combinations
from random import random
import matplotlib.pyplot as plt
import networkx as nx
from numpy.core.fromnumeric import size
def main():
config = configparser.ConfigParser()
config.read('adv-data-config.ini')
graph_name = config['graph']['graph_name']
number_of_vertices = int(config['graph']['vertices'])
density = float(config['graph']['density'])
min_weight = int(config['graph']['min_weight'])
max_weight = int(config['graph']['max_weight'])
G = nx.fast_gnp_random_graph(number_of_vertices, density)
weights = np.random.randint(min_weight, max_weight,G.number_of_edges())
graph_edges_list = list()
for iteration, edge in enumerate(G.edges()):
graph_edges_list.append([edge[0],edge[1],weights[iteration]])
np.random.shuffle(graph_edges_list)
with open(graph_name +"-"+ str(G.number_of_nodes()) + "vertices" + "-" + str(G.number_of_edges())+"edges-"+config['graph']['density']+"density.csv", mode='w') as file:
for edge in graph_edges_list:
file.write(" ".join([str(edge[0]), str(edge[1]), str(edge[2])]))
file.write('\n')
nx.draw(G)
plt.show()
main()