ax[i].text(0.97,0.03,
r"$N_\mathrm{eff}=%d$" %(params['N'].value),
transform=ax[i].transAxes,
ha='right',
va='bottom',
bbox={'facecolor':'w','edgecolor':'w','pad':0}
)
pl.xscale('log')
pl.yscale('log')
ylim = pl.gca().get_ylim()
min_ylim = 10**np.floor(np.log(ylim[0])/np.log(10))
max_ylim = 10**np.ceil(np.log(ylim[1])/np.log(10))
if min_ylim < 1:
min_ylim = 1
pl.ylim([min_ylim, max_ylim])
pl.xlim([1,35])
if _r < n_row-1:
[ x.set_visible(False) for x in ax[i].xaxis.get_major_ticks() ]
bp.strip_axis(pl.gca())
pl.gcf().tight_layout()
pl.gcf().subplots_adjust(wspace=0.34,hspace=0.3)
pl.gcf().savefig("model_fit_figures/quarantine_model_all_confirmed_fit_after_feb_11.png",dpi=300)
pl.gcf().savefig("model_fit_figures/SI_Fig_03.png",dpi=300)
if not loaded_fits:
with open('fit_parameters/quarantined_model_all_provinces_after_feb_12.p','wb') as f:
pickle.dump(fit_parameters,f)
pl.show()
dtype=float), np.array(self.cascade_size, dtype=float) / self.N
if __name__ == "__main__":
from sixdegrees.utils import to_networkx_graph
from sixdegrees import small_world_network, modular_hierarchical_network
import sixdegrees as sixd
from bfmplot import pl
import netwulf
B = 10
L = 3
N = B**L
k = 10
mu = -0.8
thresholds = [0.1] * N
initially_infected = np.arange(10)
#G = to_networkx_graph(*modular_hierarchical_network(B,L,k,mu))
G = to_networkx_graph(*sixd.random_geometric_kleinberg_network(N, k, mu))
Thresh = ThreshModel(G, initially_infected, thresholds)
t, a = Thresh.simulate()
pl.plot(t, a)
pl.ylim([0, 1])
pl.show()
(T_E, I, chi*QT*k0 , T_E, T_I),
(T_E, E, chi*QT*k0 , T_E, T_E),
#(T_E, S, chi*QT*k0 , T_E, Q),
])
model.set_initial_conditions({
S: int(N*(1 - 0.01)),
I: int(N*(0.005)),
E: int(N*(0.005)),
})
print(model.y0, model.t0)
ODE_result = model.integrate(t)
for c, res in ODE_result.items():
pl.plot(t, res,label=c,lw=2,alpha=0.3)
print(model.y0, model.t0)
tt, MF_result = model.simulate(t[-1])
for c, res in MF_result.items():
pl.plot(tt, res,'--',label=c,lw=2,alpha=0.3)
#pl.plot(t,label=c)
pl.legend()
pl.ylim([0,N])
pl.show()
n = 10**5
t = np.linspace(-6, 6, 800)
density = np.array([edges(tt) for tt in t]).astype(float)
k = (n - 1) * density
fig = plt.figure(1, figsize=(8, 4))
ax = fig.add_subplot(1, 2, 1)
ax.plot(t, density)
plt.xlim(-3, 3)
plt.xlabel(r'$t$')
plt.ylabel(r'$1 - \Phi\left( t \right)$')
ax = fig.add_subplot(1, 2, 2)
ax.plot(t, k)
plt.xlim(2.5, 4.5)
plt.ylim(float((n - 1) * edges(4.5) * 0.9), float(1.1 * (n - 1) * edges(2.5)))
ax.set_yscale('log')
plt.xlabel(r'$t$')
plt.ylabel(r'$E \left[ k \right]$')
plt.savefig('figures/edges.pdf')
### variance plot
n = 10**5
NMAX = 20
rho = np.linspace(0, 0.5, 200)
k = 2**np.arange(0, 10, 2)
k = np.array(k, dtype=float)
variances = np.zeros((200, 5))
for i in range(200):
for j in range(5):
pl.plot(t, res, label=C, lw=1.5, c=bp.colors[iC])
model.set_processes([
(S, I, alpha, I, I),
(I, beta, R),
(None, gamma, S),
])
result = model.integrate(t)
for iC, (C, res) in enumerate(result.items()):
pl.plot(t, res, c=bp.colors[iC], ls='-.')
bp.strip_axis(pl.gca())
pl.xlim([0, 150])
pl.ylim([0, 1000])
pl.xlabel('time [days]')
pl.ylabel('incidence')
pl.legend()
pl.gcf().tight_layout()
pl.gcf().savefig('SIR_birth_model_reimports_zoom.png', dpi=300)
pl.xlim([0, max(t)])
pl.ylim([0, max(result_sim['S'])])
pl.gcf().savefig('SIR_birth_model_reimports_all.png', dpi=300)
pl.yscale('log')
pl.ylim([min(result['I']), max(result_sim['S'])])
b = np.logspace(np.log10(1), np.log10(kmax + 10), 12)
pl.hist(k,
bins=np.arange(0, kmax, 5),
density=True,
alpha=1,
align='mid',
color=bp.brewer_qualitative[1],
histtype='step')
#pl.hist(k,bins=b,normed=True,alpha=0.5)
p = pk(n, t, rho, kmax, X, W)
pl.plot(np.arange(kmax), p, lw=1, c=bp.brewer_qualitative[2])
ax.set_yscale('log')
#ax.set_xscale('log')
#pl.xlim(-2,kmax+10)
pl.ylim(10**-6, 0.5)
ax.text(0.8, 0.95, '(b)', va='top', ha='left', transform=ax.transAxes)
ax.set_xlabel('node degree $k$')
ax.set_ylabel('probability density $p_k$')
bp.strip_axis(ax)
### proteins
print("loading", 'degree_sequences/out.reactome.degree_sequence', '...')
k = np.loadtxt('degree_sequences/out.reactome.degree_sequence')
k = np.array(k, dtype=int)
kmax = np.max(k) + 1
n = len(k)
print("fitting...")
t = solve_t(np.mean(k), n)
N = pdata['population'] / 1e6
print(p['kappa0'],p['kappa'])
Q = (k+k0)/(b+k+k0)
P = k0/(k+k0)
R0eff = a / (b+k+k0)
tabledata.append([titlemap[province], N, Q, P, R0eff, k, k0, I0f, ])
ks.append(k)
k0s.append(k0)
for i, (_k0, _ks) in enumerate(zip(k0s, ks)):
pl.plot([_k0],[_ks],marker=markers[i],color=colors[i],markersize=7)
pl.xlim([0,0.14])
pl.ylim([-.05,0.55])
pl.gcf().savefig('model_fit_figures/'+fn+'.png',dpi=300)
pl.show()
headers = [
'Province',
'$N/10^6$','$Q$',
'$P$',
'$R_{0,\mathrm{eff}}$',
'$\kappa$ [$\mathrm{d}^{-1}$]',
'$\kappa_{0}$ [$\mathrm{d}^{-1}$]',
'$I_0/X_0$',
]
floatfmt = (
"",
