def frank_wolfe_on_I210():
'''
Frank-Wolfe on I210
'''
graph = np.loadtxt('data/I210_attack_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/I210_od.csv', delimiter=',', skiprows=1)
demand[:,2] = 1. * demand[:,2] / 4000
# run solver
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
# display cost
for a,b in zip(cost(f, graph), f*4000): print a,b
# visualization
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
# extract features: 'capacity', 'length', 'fftt'
feat = extract_features('data/I210_attack_Sketch_net.csv')
ratio = cost_ratio(f, graph)
# merge features with the cost ratios
features = np.zeros((feat.shape[0],4))
features[:,:3] = feat
features[:,3] = ratio
# join features with (lat1,lon1,lat2,lon2)
links = process_links(graph, node, features)
color = features[:,3] # we choose the costs
names = ['capacity', 'length', 'fftt', 'tt_over_fftt']
geojson_link(links, names, color)
def frank_wolfe_on_I210():
'''
Frank-Wolfe on I210
'''
graph = np.loadtxt('data/I210_attack_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/I210_od.csv', delimiter=',', skiprows=1)
demand[:, 2] = 1. * demand[:, 2] / 4000
# run solver
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
# display cost
for a, b in zip(cost(f, graph), f * 4000):
print a, b
# visualization
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
# extract features: 'capacity', 'length', 'fftt'
feat = extract_features('data/I210_attack_Sketch_net.csv')
ratio = cost_ratio(f, graph)
# merge features with the cost ratios
features = np.zeros((feat.shape[0], 4))
features[:, :3] = feat
features[:, 3] = ratio
# join features with (lat1,lon1,lat2,lon2)
links = process_links(graph, node, features)
color = features[:, 3] # we choose the costs
names = ['capacity', 'length', 'fftt', 'tt_over_fftt']
geojson_link(links, names, color)
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():
net, demand, node = load_LA()
f = np.loadtxt('data/LA_output.csv', delimiter=',', skiprows=0)
features = np.zeros((f.shape[0], 4))
features[:,:3] = extract_features('data/LA_net.txt')
f = np.divide(f, features[:,0])
features[:,3] = f
links = process_links(net, node, features, in_order=True)
color = 2.0*f + 1.0
geojson_link(links, ['capacity', 'length', 'fftt', 'flow_over_capacity'], color)
def visualize_LA_capacity():
graph, demand, node = load_LA()
features = extract_features('data/LA_net.txt')
links = process_links(graph, node, features, in_order=True)
color = features[:, 0] # we choose to color by the capacities
names = ['capacity', 'length', 'fftt']
# color = 2.1 * features[:,0] / 2000.
color = 2. * (features[:, 0] <= 900.) + 5. * (features[:, 0] > 900.)
weight = (features[:, 0] <= 900.) + 3. * (features[:, 0] > 900.)
geojson_link(links, names, color, weight)
def visualize_LA_capacity():
graph, demand, node = load_LA()
features = extract_features('data/LA_net.txt')
links = process_links(graph, node, features, in_order=True)
color = features[:,0] # we choose to color by the capacities
names = ['capacity', 'length', 'fftt']
# color = 2.1 * features[:,0] / 2000.
color = 2.*(features[:,0] <= 900.) + 5.*(features[:,0] > 900.)
weight = (features[:,0] <= 900.) + 3.*(features[:,0] > 900.)
geojson_link(links, names, color, weight)
def visualize_links_by_city(city):
# visualize the links from a specific city
graph, demand, node, features = load_LA_3()
linkToCity = np.genfromtxt('data/LA/link_to_cities.csv', delimiter=',', \
skiprows=1, dtype='str')
links = process_links(graph, node, features, in_order=True)
names = ['capacity', 'length', 'fftt']
color = 3 * (linkToCity[:, 1] == city)
color = color + 10 * (features[:, 0] > 900.)
weight = (features[:, 0] <= 900.) + 3. * (features[:, 0] > 900.)
geojson_link(links, names, color, weight)
def visualize_links_by_city(city):
# visualize the links from a specific city
graph, demand, node, features = load_LA_3()
linkToCity = np.genfromtxt('data/LA/link_to_cities.csv', delimiter=',', \
skiprows=1, dtype='str')
links = process_links(graph, node, features, in_order=True)
names = ['capacity', 'length', 'fftt']
color = 3*(linkToCity[:,1] == city)
color = color + 10*(features[:,0] > 900.)
weight = (features[:,0] <= 900.) + 3.*(features[:,0] > 900.)
geojson_link(links, names, color, weight)
def visualize_LA_result():
net, demand, node = load_LA()
f = np.loadtxt('data/LA_output.csv', delimiter=',', skiprows=0)
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features('data/LA_net.txt')
f = np.divide(f, features[:, 0])
features[:, 3] = f
links = process_links(net, node, features, in_order=True)
color = 2.0 * f + 1.0
geojson_link(links, ['capacity', 'length', 'fftt', 'flow_over_capacity'],
color)
def chicago_ratio_r_nr():
"""
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
"""
fs = np.loadtxt("data/test_3.csv", delimiter=",", skiprows=0)
ratio = np.divide(fs[:, 0], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_chicago()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features)
color = 5.0 * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ["capacity", "length", "fftt", "r_non_routed"], color)
def chicago_ratio_r_nr():
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
'''
fs = np.loadtxt('data/test_3.csv', delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 0], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_chicago()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features)
color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def visualize_equilibrium_in_chicago():
'''
visualize costs in the network of Chicago
'''
net, demand, node, f = load_chicago()
flow = np.loadtxt('data/Chicago_results_2.csv', delimiter=',')
# flow = np.loadtxt('data/Chicago_results.csv', delimiter=',', skiprows=1)[:,2]
print average_cost(flow/4000., net, demand)
costs = cost_ratio(flow/4000., net)
features = np.zeros((f.shape[0],4))
features[:,:3] = f
features[:,3] = costs
links = process_links(net, node, features)
color = features[:,3] - 1.0 # we choose the costs
geojson_link(links, ['capacity', 'length', 'fftt', 'tt_over_fftt'], color)
def visualize_equilibrium_in_chicago():
"""
visualize costs in the network of Chicago
"""
net, demand, node, f = load_chicago()
flow = np.loadtxt("data/Chicago_results_2.csv", delimiter=",")
# flow = np.loadtxt('data/Chicago_results.csv', delimiter=',', skiprows=1)[:,2]
print average_cost(flow / 4000.0, net)
costs = cost_ratio(flow / 4000.0, net)
features = np.zeros((f.shape[0], 4))
features[:, :3] = f
features[:, 3] = costs
links = process_links(net, node, features)
color = features[:, 3] - 1.0 # we choose the costs
geojson_link(links, ["capacity", "length", "fftt", "tt_over_fftt"], color)
def visualize_LA():
net, demand, node = load_LA()
f = np.loadtxt('data/la/LA_Cython.csv', delimiter=',', skiprows=0)
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features('data/LA_net.txt')
#import pdb; pdb.set_trace()
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
#congestion = f/features[:,0] #determines the congestion levels of links
geojson_link(links, ['capacity', 'length', 'fftt', 'flow_over_capacity'],
color)
def I210_ratio_r_total():
'''
study the test_*.csv files generated by I210_parametric_study()
in particular, visualize the ratio each type of users on each link
'''
fs = np.loadtxt('data/test_50.csv', delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 1], np.maximum(np.sum(fs, axis=1), 1e-8))
net = np.loadtxt('data/I210_net.csv', delimiter=',', skiprows=1)
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
geometry = extract_features('data/I210Sketch_net.csv')
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features)
color = 2 * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_routed'], color)
def visualize_LA_flow_variation(only_local=False):
'''
visualize the variations in link flows
'''
net, demand, node, geom = load_LA_2()
data = np.loadtxt("data/LA/link_variation.csv", delimiter=',', skiprows=1)
links = process_links(data[:,:3], node, data[:,[0,3,4,5,6,19,20,21]], \
in_order=True)
names = ['link_id','capacity','length','fftt','local','max_id','inc','dec']
color = (data[:,19] - 1.) / 2.
weight = (data[:,6] == 1.) + 3.*(data[:,6] == 0.)
if only_local:
links = links[weight==1.0, :]
color = color[weight==1.0]
weight = weight[weight==1.0]
geojson_link(links, names, color, weight)
def I210_ratio_r_total():
'''
study the test_*.csv files generated by I210_parametric_study()
in particular, visualize the ratio each type of users on each link
'''
fs = np.loadtxt('data/test_50.csv', delimiter=',', skiprows=0)
ratio = np.divide(fs[:,1], np.maximum(np.sum(fs, axis=1), 1e-8))
net = np.loadtxt('data/I210_net.csv', delimiter=',', skiprows=1)
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
geometry = extract_features('data/I210Sketch_net.csv')
features = np.zeros((fs.shape[0], 4))
features[:,:3] = geometry
features[:,3] = ratio
links = process_links(net, node, features)
color = 2 * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_routed'], color)
def capacities_of_chicago():
"""
visualize capacities in the network of Chicago
"""
net, demand, node, features = load_chicago()
links = process_links(net, node, features)
color = features[:, 0] / 2000.0 # we choose the capacity
new_links = []
new_color = []
# remove the high capacity links
for row in range(links.shape[0]):
if features[row, 0] < 49500.0:
new_links.append(links[row, :].tolist())
new_color.append(color[row])
color = np.array(new_color)
weight = (color <= 1.0) + 4.0 * (color > 1.0)
geojson_link(np.array(new_links), ["capacity", "length", "fftt"], color, weight)
def I210_ratio_r_nr(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
#ratio of non-routed over total for each link
'''
fs = np.loadtxt('data/I210/test_{}.csv'.format(int(alpha*100)), delimiter=',', skiprows=0)
ratio = np.divide(fs[:,1], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:,:3] = geometry
features[:,3] = ratio
links = process_links(net, node, features, in_order=True)
color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def capacities_of_chicago():
'''
visualize capacities in the network of Chicago
'''
net, demand, node, features = load_chicago()
links = process_links(net, node, features)
color = features[:, 0] / 2000. # we choose the capacity
new_links = []
new_color = []
# remove the high capacity links
for row in range(links.shape[0]):
if features[row, 0] < 49500.:
new_links.append(links[row, :].tolist())
new_color.append(color[row])
color = np.array(new_color)
weight = (color <= 1.0) + 4. * (color > 1.0)
geojson_link(np.array(new_links), ['capacity', 'length', 'fftt'],
color, weight)
def chicago_flow_over_capacity():
"""
study the test_*.csv files generated by chicago_parametric_study()
visualize flow/calacity for each edge
"""
net, demand, node, geometry = load_chicago()
# f = np.loadtxt('data/test_1.csv', delimiter=',', skiprows=0)
fs = np.loadtxt("data/test_2.csv", delimiter=",", skiprows=0)
# fs = np.loadtxt('data/test_3.csv', delimiter=',', skiprows=0)
# fs = np.loadtxt('data/test_4.csv', delimiter=',', skiprows=0)
# f = np.loadtxt('data/test_5.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
features = np.zeros((f.shape[0], 4))
features[:, :3] = geometry
f = np.divide(f, features[:, 0] / 4000.0)
features[:, 3] = f
links = process_links(net, node, features)
color = 2.0 * f + 1.0
geojson_link(links, ["capacity", "length", "fftt", "flow_over_capacity"], color)
def chicago_flow_over_capacity():
'''
study the test_*.csv files generated by chicago_parametric_study()
visualize flow/calacity for each edge
'''
net, demand, node, geometry = load_chicago()
# f = np.loadtxt('data/test_1.csv', delimiter=',', skiprows=0)
fs = np.loadtxt('data/test_2.csv', delimiter=',', skiprows=0)
# fs = np.loadtxt('data/test_3.csv', delimiter=',', skiprows=0)
# fs = np.loadtxt('data/test_4.csv', delimiter=',', skiprows=0)
# f = np.loadtxt('data/test_5.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
features = np.zeros((f.shape[0], 4))
features[:,:3] = geometry
f = np.divide(f, features[:,0]/4000.0)
features[:,3] = f
links = process_links(net, node, features)
color = 2.0*f + 1.0
geojson_link(links, ['capacity', 'length', 'fftt', 'flow_over_capacity'], color)
def chicago_tt_over_fftt():
"""
study the test_*.csv files generated by chicago_parametric_study()
visualize tt/fftt for each edge
"""
net, demand, node, geometry = load_chicago()
g_nr, small_capacity = add_cognitive_cost(net, geometry, 2000.0, 1000.0)
# f = np.loadtxt('data/test_0.csv', delimiter=',', skiprows=0)
alpha = 0.01
fs = np.loadtxt("data/test_{}.csv".format(int(100.0 * alpha)), delimiter=",", skiprows=0)
# f = np.loadtxt('data/test_100.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
costs = cost_ratio(f, net)
features = np.zeros((f.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = costs
links = process_links(net, node, features)
color = (costs - 1.0) * 2.0 + 1.0
geojson_link(links, ["capacity", "length", "fftt", "tt_over_fftt"], color)
def chicago_tt_over_fftt():
'''
study the test_*.csv files generated by chicago_parametric_study()
visualize tt/fftt for each edge
'''
net, demand, node, geometry = load_chicago()
g_nr, small_capacity = add_cognitive_cost(net, geometry, 2000., 1000.)
# f = np.loadtxt('data/test_0.csv', delimiter=',', skiprows=0)
alpha = 0.01
fs = np.loadtxt('data/test_{}.csv'.format(int(100.*alpha)), delimiter=',', skiprows=0)
# f = np.loadtxt('data/test_100.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
costs = cost_ratio(f, net)
features = np.zeros((f.shape[0], 4))
features[:,:3] = geometry
features[:,3] = costs
links = process_links(net, node, features)
color = (costs - 1.0) * 2.0 + 1.0
geojson_link(links, ['capacity', 'length', 'fftt', 'tt_over_fftt'], color)
def I210_ratio_r_nr(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
ratio of non-routed over total for each link
'''
fs = np.loadtxt(
'data/I210/test_{}.csv'.format(int(alpha * 100)),
delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 1], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features, in_order=True)
color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def visualize_LA_flow_variation(only_local=False):
'''
visualize the variations in link flows
'''
net, demand, node, geom = load_LA_2()
data = np.loadtxt("data/LA/link_variation.csv", delimiter=',', skiprows=1)
links = process_links(data[:,:3], node, data[:,[0,3,4,5,6,19,20,21]], \
in_order=True)
names = [
'link_id', 'capacity', 'length', 'fftt', 'local', 'max_id', 'inc',
'dec'
]
color = (data[:, 19] - 1.) / 2.
weight = (data[:, 6] == 1.) + 3. * (data[:, 6] == 0.)
if only_local:
links = links[weight == 1.0, :]
color = color[weight == 1.0]
weight = weight[weight == 1.0]
geojson_link(links, names, color, weight)
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)
def visualize_LA_total_flows(alpha, only_local=False):
'''
visualize total flow in L.A. using total_link_flows.csv as input
'''
net, demand, node, geom = load_LA_2()
f = np.loadtxt('data/LA/total_link_flows.csv', delimiter=',', skiprows=1)
names = ['link_id', 'capacity', 'length', 'fftt', 'local', 'flow']
features = np.zeros((f.shape[0], 6))
features[:, 0] = net[:, 0]
features[:, 1:4] = geom
features[:, 4] = f[:, 6]
features[:, 5] = f[:, 7 + int(alpha / 10.)]
links = process_links(net, node, features, in_order=True)
color = features[:, 5] * 10.
color = color + (color > 0.0)
weight = (features[:, 1] <= 900.) + 2. * (features[:, 1] > 900.)
if only_local:
links = links[weight == 1.0, :]
color = color[weight == 1.0]
weight = weight[weight == 1.0]
geojson_link(links, names, color, weight)
def visualize_LA_total_flows(alpha, only_local=False):
'''
visualize total flow in L.A. using total_link_flows.csv as input
'''
net, demand, node, geom = load_LA_2()
f = np.loadtxt('data/LA/total_link_flows.csv', delimiter=',', \
skiprows=1)
names = ['link_id', 'capacity', 'length', 'fftt', 'local', 'flow']
features = np.zeros((f.shape[0], 6))
features[:,0] = net[:,0]
features[:,1:4] = geom
features[:,4] = f[:,6]
features[:,5] = f[:,7+int(alpha/10.)]
links = process_links(net, node, features, in_order=True)
color = features[:,5] * 10.
color = color + (color > 0.0)
weight = (features[:,1] <= 900.) + 2.*(features[:,1] > 900.)
if only_local:
links = links[weight==1.0, :]
color = color[weight==1.0]
weight = weight[weight==1.0]
geojson_link(links, names, color, weight)
def visual(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
#ratio of non-routed over total for each link
'''
fs = np.loadtxt('data/I210/test_{}.csv'.format(int(alpha * 100)),
delimiter=',',
skiprows=0)
f_total = 4000 * np.sum(fs, axis=1)
color = np.zeros(len(f_total))
v_max = 30000
v_thres = 6000
for i in range(len(f_total)):
if f_total[i] <= v_thres:
color[i] = 1
# elif f_total[i]<=2*v_thres:
# color[i]=2
# elif f_total[i]<=3*v_thres:
# color[i]=3
# elif f_total[i]<=4*v_thres:
# color[i]=4
else:
color[i] = 5
print(np.mean(color))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = f_total
links = process_links(net, node, features, in_order=True)
#color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def visual(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
#ratio of non-routed over total for each link
'''
fs = np.loadtxt('data/I210/test_{}.csv'.format(int(alpha*100)), delimiter=',', skiprows=0)
f_total = 4000*np.sum(fs, axis=1)
color = np.zeros(len(f_total))
v_max= 30000
v_thres = 6000
for i in range(len(f_total)):
if f_total[i]<=v_thres:
color[i]=1
# elif f_total[i]<=2*v_thres:
# color[i]=2
# elif f_total[i]<=3*v_thres:
# color[i]=3
# elif f_total[i]<=4*v_thres:
# color[i]=4
else:
color[i]=5
print(np.mean(color))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:,:3] = geometry
features[:,3] = f_total
links = process_links(net, node, features, in_order=True)
#color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
'{}/{}_net_od_2_alpha_0.{:02d}.txt'.format(dash_data, prefix, 50),
'r') as infile:
run = pickle.loads(infile.read())
fs = run['f']
f = np.array([l[0] + l[1] for l in fs])
if net == 'la':
node = np.loadtxt('data/LA_node.csv', delimiter=',')
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features('data/LA_net.txt')
elif net == 'chicago':
node = np.loadtxt('data/Chicago_node.csv', delimiter=',', skiprows=1)
features = np.zeros((f.shape[0], 4))
features[:, :3] = extract_features('data/ChicagoSketch_net.txt')
f = np.divide(f * 4000, features[:, 0])
features[:, 3] = f
links = process_links(graph, node, features, in_order=True)
link2coord = {}
for i in range(len(links)):
link2coord[i] = {
'origin': [links[i, 0], links[i, 1]],
'destination': [links[i, 2], links[i, 3]],
}
with open('{}/link_coord.json'.format(out_data), 'w') as f:
json.dump(link2coord, f, indent=0)
nds = []
index = 0
path2id = {}
id2path = {}
for alpha in range(0, 100):
def I210_capacities():
net, demand, node, features = load_I210()
links = process_links(net, node, features)
color = 2.*(features[:,0] <= 3000.) + 5.*(features[:,0] > 3000.)
weight = (features[:,0] <= 3000.) + 2.*(features[:,0] > 3000.)
geojson_link(links, ['capacity', 'length', 'fftt'], color, 2.*weight)
def I210_capacities():
net, demand, node, features = load_I210()
links = process_links(net, node, features)
color = 2. * (features[:, 0] <= 3000.) + 5. * (features[:, 0] > 3000.)
weight = (features[:, 0] <= 3000.) + 2. * (features[:, 0] > 3000.)
geojson_link(links, ['capacity', 'length', 'fftt'], color, 2. * weight)