tc.gillespie_node_based_SIS(fwP_el, SIS)
end = time.time()
print("node-based simulation on edge_lists took", end - start, "seconds")
pl.plot(SIS.time, SIS.I)
start = time.time()
SIS = tc.SIS(N,
t_run_total * 2,
infection_rate,
recovery_rate,
N // 2,
seed=seed,
verbose=True)
tc.gillespie_SIS(fwP_ec, SIS, verbose=True)
end = time.time()
print("edge-based simulation on edge_changes took", end - start, "seconds")
pl.plot(SIS.time, SIS.I)
start = time.time()
SIS = tc.SIS(N,
t_run_total * 2,
infection_rate,
recovery_rate,
N // 2,
seed=seed)
tc.gillespie_SIS(fwP_el, SIS)
end = time.time()
print(mean_k)
eta = R0 * rho / mean_k
i_sample = np.zeros_like(t_sample)
successful = 0
for meas in range(N_meas):
sis = tc.SIS(N,
t_simulation,
eta,
rho,
number_of_initially_infected=10)
tc.gillespie_SIS(tn, sis)
t = np.array(sis.time)
i = np.array(sis.I, dtype=float) / N
this_sample = tc.sample_a_function(t, i, t_sample)
if this_sample[-1] > 0.0:
successful += 1
i_sample += this_sample
ax[2].plot(t_sample, this_sample, c=line.get_color(), alpha=0.1)
ax[1].plot(t_sample, i_sample / successful)
pl.show()
tmax = 10.0
seed = 7925
R0 = 1.2
recovery_rate = 1.0
infection_rate = R0 / (N - 1) * recovery_rate
complete_graph = tc.complete_graph(N)
start = time.time()
SIS = tc.SIS(N,
t_run_total * 2,
infection_rate,
recovery_rate,
N // 2,
seed=seed)
tc.gillespie_SIS(complete_graph, SIS)
end = time.time()
print("simulation on edge_lists took", end - start, "seconds")
pl.plot(SIS.time, SIS.I)
mv_SIS = tc.MARKOV_SIS(N,
t_run_total * 2,
infection_rate,
recovery_rate,
0.01,
N // 2,
seed=seed)
start = time.time()
P = [ 0.5 ]
rewiring_rate = [ (0.0,1.0) ]
t_run_total = 500.0
tmax = 1000.0
seed = 7925
infection_rate = 1.0
recovery_rate = 0.1
E = flockwork_P_equilibrium_configuration(N,P[0])
fwP_ec = tc.flockwork_P_varying_rates(E,N,P,t_run_total,rewiring_rate,tmax,seed=seed)
fwP_el = tc.convert(fwP_ec)
start = time.time()
SIS = tc.SIS(N,t_run_total*2,infection_rate,recovery_rate,N//2,seed = seed)
tc.gillespie_SIS(fwP_ec,SIS)
end = time.time()
print("simulation on edge_changes took", end-start,"seconds")
pl.plot(SIS.time, SIS.I)
start = time.time()
SIS = tc.SIS(N,t_run_total*2,infection_rate,recovery_rate,N//2,seed = seed)
tc.gillespie_SIS(fwP_el,SIS)
end = time.time()
print("simulation on edge_lists took", end-start,"seconds")
pl.plot(SIS.time, SIS.I)
t = np.array(SIS.time)
I = np.array(SIS.I)
this_pl, = pl.plot(t, I, 's', ms=2, alpha=0.5, mfc='None')
mean_I_1 = tc.time_average(t, I, tmax=t_run_total)
SIS = _tc.SIS(N,
t_run_total,
infection_rate,
recovery_rate,
number_of_initially_infected=N,
sampling_dt=1)
tn = _tc.activity_model(N, k / (N - 1.), omega, t_run_total)
tc.gillespie_SIS(tn, SIS)
t = np.array(SIS.time)
I = np.array(SIS.I)
mean_I_2 = tc.time_average(t, I, tmax=t_run_total)
curve_1.append(mean_I_1)
curve_2.append(mean_I_2)
this_pl, = pl.plot(t,
I,
'-',
lw=1,
alpha=0.5,
c=this_pl.get_color())