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)
