from execute.optimal_distribution import optimal_distribution
# Choose simulation options
uniform = False
num_steps = 250
# Load data and generate network
_, routes = load_airport_and_route(deep_load=True)
netx = from_edgelist(routes)
N = number_of_nodes(netx)
net = network(N, graph=netx)
budget = balls_per_node * N
print('Data imported and network generated')
# Get optimal distribution
optimal = optimal_distribution(uniform, deep_load=True)
# Initialize opponent distribution
if uniform:
red = array([balls_per_node] * N)
else:
degrees = array(sorted(degree(netx), key=lambda d: d[0]))[:, 1]
max_d_node = argmax(degrees)
red = zeros(N)
red[max_d_node] = budget
# Run optimal strategy
net.set_initial_distribution(black=optimal, red=red)
exposures = run_polya(net, steps=num_steps)
from model.optimize import simple_centrality, metric_names
from execute.optimal_distribution import optimal_distribution
# Red distribution (uniform or single)
uniform = False
airports, routes = load_airport_and_route(deep_load=True) # Import data
N = len(airports) # Initialize N
budget = balls_per_node * N
netx = from_edgelist(routes) # Generate networkx network
net = network(N, graph=netx) # Generate network
print('Data imported and network generated')
degrees = array(sorted(degree(netx), key=lambda d: d[0]))[:, 1]
max_d_node = argmax(degrees) # Get index of max degree
optimal = optimal_distribution(deep_load=True)
if uniform:
red = array([balls_per_node] * N)
else:
red = zeros(N)
red[max_d_node] = budget
exposures = []
# Run basic metrics
for metric_id, _ in enumerate(metric_names):
simple_centrality(net, metric_id, red=red)
exposures.append(run_polya(net))
# Run optimal strategy
from execute.import_data import load_airport_and_route
from execute.optimal_distribution import optimal_distribution
from utilities.plotting import plot_net_w_routes, plot_network
from networkx import from_edgelist
from utilities import filter_degree, re_index
from numpy import max, min, multiply, add
# Define constants
data_path = '../../data/optimal_distribution/f_100_analytical.csv'
path = '../../results/network/optimal.csv'
# Import data and filter
airports, routes = load_airport_and_route(deep_load=True, filter_data=False)
airports, _, routes = filter_degree(airports, routes, d_cut_off=100)
airports, routes = re_index(airports, routes)
# Initialize/calculate plotting values
weights = optimal_distribution(alt_file=data_path)
min_val, max_val = min(weights), max(weights)
delt = max_val - min_val
weights = multiply(add(weights, -min_val), 1 / delt)
# Plot network
netx = from_edgelist(routes)
plot_network(netx,
netx_plot=True,
weights=weights,
file_name=path,
plot_edges=True,
size=(10, 6))