def visualize_result_ratio_study(fileName, ratio, name, mode):
net, demand, node, features = load_network_data(name)
#Loading the flow per link resulting from frank-wolfe
f = np.loadtxt(fileName, delimiter=',', skiprows=0)
#Location of the features
featureLocation = 'data/' + name + '_net.txt'
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features(featureLocation)
#Multiply the flow obtained by 4000 since we initially divided by 4000 before frank-wolfs
f = np.divide(f * 4000, features[:, 0])
features[:, 3] = f
links = process_links(net, node, features, in_order=True)
#creates color array used to visulized the links
#values useful in differenciating links based of flow on links
color = 2.0 * f + 1.0
#Keeping track of the percentage of congestion
links_congested = len(color[np.where(color >= 3)])
percentage_of_congestion = float(links_congested) / float(len(color))
print("congestion is at %3f " % percentage_of_congestion)
geojson_link_Scenario_Study(
ratio, links, ['capacity', 'length', 'fftt', 'flow_over_capacity'],
color, name, mode)
def visualize_LA_result_Scenario_Study(fileName, ratio):
net, demand, node = load_LA()
f = np.loadtxt(fileName, delimiter=',', skiprows=0)
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features('data/LA_net.txt')
#features[:,:3] = extract_features('data/ChicagoRegional_net.txt')
f = np.divide(f * 4000, features[:, 0])
features[:, 3] = f
links = process_links(net, node, features, in_order=True)
#creates color array used to visulized the links
#values useful in differenciating links based of flow on links
color = 2.0 * f + 1.0
#Keeping track of the percentage of congestion
links_congested = len(color[np.where(color >= 3)])
percentage_of_congestion = float(links_congested) / float(len(color))
print("congestion is at %3f " % percentage_of_congestion)
geojson_link_Scenario_Study(
ratio, links, ['capacity', 'length', 'fftt', 'flow_over_capacity'],
color)