def LA_metrics_attacks_all(beta, thres): net, d, node, features = load_LA_3() # import pdb; pdb.set_trace() d[:,2] = d[:,2] / 4000. # modify all small capacity links links_affected = (features[:,0] < thres) net2 = modify_capacity(net, links_affected, beta) return net2, d, node, features
def LA_metrics_attacks_all(beta, thres): net, d, node, features = load_LA_3() # import pdb; pdb.set_trace() d[:, 2] = d[:, 2] / 4000. # modify all small capacity links links_affected = (features[:, 0] < thres) net2 = modify_capacity(net, links_affected, beta) return net2, d, node, features
def LA_metrics_attacks_city(beta, thres, city): net, d, node, features = load_LA_3() # import pdb; pdb.set_trace() d[:,2] = d[:,2] / 4000. # extract the mapping from links to cities linkToCity = np.genfromtxt('data/LA/link_to_cities.csv', delimiter=',', \ skiprows=1, dtype='str') print linkToCity links_affected = np.logical_and(linkToCity[:,1] == city, features[:,0] < thres) print np.sum(links_affected) # modify all small capacity links in GLendale net2 = modify_capacity(net, links_affected, beta)
def LA_metrics_attacks_city(beta, thres, city): net, d, node, features = load_LA_3() # import pdb; pdb.set_trace() d[:, 2] = d[:, 2] / 4000. # extract the mapping from links to cities linkToCity = np.genfromtxt('data/LA/link_to_cities.csv', delimiter=',', \ skiprows=1, dtype='str') print linkToCity links_affected = np.logical_and(linkToCity[:, 1] == city, features[:, 0] < thres) print np.sum(links_affected) # modify all small capacity links in GLendale net2 = modify_capacity(net, links_affected, beta)