def main():
n_networks = 100
ps = np.linspace(0, 1, 25, endpoint=False)
rs = np.linspace(0, 1, 25, endpoint=False)
results_p = []
results_r = []
results_s = []
results_l = []
for r in rs:
for p in ps:
print(r, p)
samples_l = []
samples_s = []
for i in range(n_networks):
#print('-- NEW EPI --')
g = bi_planted_partition(1000, 8, 0)
epi = SIS(g, p=p, r=r)
#epi = SI(g, p=p)
epi.infect_random_node()
epi.simulate()
samples_l.append(epi.length)
samples_s.append(epi.size)
results_r.append(r)
results_p.append(p)
results_s.append(np.average(samples_s))
results_l.append(np.average(samples_l))
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = results_r, results_p
Z = results_l
ax.scatter(X, Y, Z)
ax.set_xlabel('Recovery Probability')
ax.set_ylabel('Transmission Probability')
ax.set_zlabel('Epidemic Length')
plt.show()
# TODO: Figure out the right way to save these plots
plt.clf()
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = results_r, results_p
Z = results_s
ax.scatter(X, Y, Z)
ax.set_xlabel('Recovery Probability')
ax.set_ylabel('Transmission Probability')
ax.set_zlabel('Epidemic Size')
plt.show()
# TODO: Figure out the right way to save these plots
plt.clf()
def main():
# example
n_networks = 500
x = np.linspace(0, 1, 50, endpoint=False)
results_b_length = []
results_b_size = []
for p in x:
print(p)
samples_length = []
samples_size = []
for i in range(n_networks):
#print('-- NEW EPI --')
g = bi_planted_partition(1000, 8, 0)
epi = SIS(g, p=p, r=0.04)
#epi = SI(g, p=p)
epi.infect_random_node()
epi.simulate()
samples_length.append(epi.length)
samples_size.append(epi.size)
results_b_length.append((p, np.average(samples_length)))
results_b_size.append((p, np.average(samples_size)))
fig = plt.figure()
ax = plt.gca()
ax.scatter(*zip(*results_b_length))
plt.xlabel('Transmission probability')
plt.ylabel('Epidemic length')
plt.axhline(y=np.log(1000), linewidth=1, color='red')
plt.show()
#plt.savefig('length.png')
plt.clf()
fig = plt.figure()
ax = plt.gca()
ax.scatter(*zip(*results_b_size))
plt.xlabel('Transmission probability')
plt.ylabel('Epidemic size')
plt.show()