def process_degree_distribution(N, Pk, color, Psi, DPsi, symbol, label, count):
report_times = scipy.linspace(0, 30, 3000)
sums = 0 * report_times
for cnt in range(count):
G = generate_network(Pk, N)
t, S, I, R = EoN.fast_SIR(G, tau, gamma, rho=rho)
plt.plot(t, I * 1. / N, '-', color=color, alpha=0.1, linewidth=1)
subsampled_I = EoN.subsample(report_times, t, I)
sums += subsampled_I * 1. / N
ave = sums / count
plt.plot(report_times, ave, color='k')
#Do EBCM
N = G.order(
) #N is arbitrary, but included because our implementation of EBCM assumes N is given.
t, S, I, R = EoN.EBCM_uniform_introduction(N,
Psi,
DPsi,
tau,
gamma,
rho,
tmin=0,
tmax=10,
tcount=41)
plt.plot(t, I / N, symbol, color=color, markeredgecolor='k', label=label)
for cnt in range(3): #do 3 highlighted simulations
G = generate_network(Pk, N)
t, S, I, R = EoN.fast_SIR(G, tau, gamma, rho=rho)
plt.plot(t, I * 1. / N, '-', color='k', linewidth=0.1)
def SIR_process(G, degree_prob, tau, gamma, tmax=10):
N = G.order()
plt.figure(2)
plt.clf()
plt.figure(3)
plt.clf()
plt.figure(5)
plt.clf()
for index, starting_node in enumerate([x * N / 100. for x in range(100)]):
plt.figure(2)
t, S, I, R = EoN.fast_SIR(G,
tau,
gamma,
initial_infecteds=[starting_node])
subt = scipy.linspace(0, t[-1], 101)
subI, subR = EoN.subsample(subt, t, I, R)
plt.plot(subt, subI)
if R[-1] > 500:
plt.figure(3)
shift = EoN.get_time_shift(t, R, threshold)
plt.plot(subt - shift, subI)
plt.figure(5)
plt.plot(subt - shift, subR)
#t, S, I, R = EoN.EBCM(degree_prob, tau, gamma, rho)
rho = 1. / N
def psi(x):
return sum(degree_prob[k] * x**k for k in degree_prob)
def psiPrime(x):
return sum(k * degree_prob[k] * x**(k - 1) for k in degree_prob)
t, S, I, R = EoN.EBCM_uniform_introduction(N,
psi,
psiPrime,
tau,
gamma,
rho,
tmax=tmax)
shift = EoN.get_time_shift(t, R, threshold)
plt.figure(2)
#plt.savefig('sw_SIR_epi_N{}_p{}_k{}_tau{}.pdf'.format(N,p,k,tau))
plt.figure(3)
plt.plot(t - shift, I, '--')
plt.xlabel('$t$', fontsize=18)
plt.ylabel('$I$', fontsize=18)
#plt.set_xtick_labels(fontsize = 15)
xmax = get_xmax(t - shift, I)
plt.axis(xmax=xmax)
plt.savefig('sw_SIR_epi_N{}_p{}_k{}_tau{}_shifted.pdf'.format(
N, p, k, tau))
plt.figure(5)
plt.plot(t - shift, R, '--')
