pl.plot(t,
cases,
marker=markers[i + 2],
c=colors[i + 2],
label='data',
mfc='None')
pl.plot(tt, f(tt, *p), c='k', lw=1, label='$Q_I$')
_c = i % (n_col)
_r = i // (n_col)
if _r == n_row - 1:
pl.xlabel('days since Jan. 20th')
if _c == 0 and _r == 0:
pl.ylabel('confirmed cases', )
pl.gca().yaxis.set_label_coords(-0.3, -0.2)
pl.xlim([1, 30])
pl.xscale('log')
pl.yscale('log')
ylim = ax[i].set_ylim([1, 2e3])
ylim = ax[i].get_ylim()
ax[i].plot([12, 12], ylim, ':')
ax[i].text(0.03,
0.97,
"{}".format(roman[i]),
transform=ax[i].transAxes,
ha='left',
va='top',
fontweight='bold',
fontsize=10,
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()
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, 32])
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/all_confirmed_fit.png", dpi=300)
pl.gcf().savefig("model_fit_figures/Fig_03.png", dpi=300)
if not loaded_fits:
with open('fit_parameters/all_provinces.p', 'wb') as f:
pickle.dump(fit_parameters, f)
pl.show()
ax[i].text(0.8,0.03,
r"$P=%4.2f$" %((params['kappa0'].value)/(params['kappa'].value+params['kappa0'].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([1,60])
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/all_confirmed_fit_before_feb_12.png",dpi=300)
if not loaded_fits:
with open('fit_parameters/all_provinces_before_feb_12.p','wb') as f:
pickle.dump(fit_parameters,f)
pl.show()
# ha='right',
# va='bottom',
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
if not LINLIN:
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
if not LINLIN:
pl.ylim([min_ylim, max_ylim])
pl.xlim([1, 60])
else:
pl.xlim([0, 40])
if not LINLIN:
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)
fn = 'all_provinces_after_feb_12.p'
#fn = 'different_initial_conds_after_feb_12.p'
name = 'linlin' if LINLIN else 'loglog'
pl.gcf().savefig(
"model_fit_figures/all_confirmed_fit_after_feb_12_{}.png".format(name),
dpi=300)
# 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])
#if _r < n_row-1:
# [ x.set_visible(False) for x in ax[i].xaxis.get_major_ticks() ]
pl.xlim([0, 35])
pl.xticks([0, 10, 20, 30])
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/linlin_shutdown_model_all_confirmed_fit_after_feb_12.png",
dpi=300)
pl.gcf().savefig("model_fit_figures/SI_Fig_06.png", dpi=300)
if not loaded_fits:
with open('fit_parameters/shutdown_model_all_provinces_after_feb_12.p',
'wb') as f:
pickle.dump(fit_parameters, f)
# transform=ax[i].transAxes,
# ha='right',
# va='bottom',
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
#pl.xscale('log')
#pl.yscale('log')
ylim = pl.gca().set_ylim([1,1.5e3])
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([0,30])
if _r < n_row-1:
ax[i].set_xticklabels('')
# [ x.set_visible(False) for x in ax[i].xaxis.get_major_ticks() ]
ax[i].set_yticks([0,500,1000,1500])
ax[i].set_yticklabels(['0','0.5k','1.0k','1.5k'])
bp.strip_axis(pl.gca())
#bp.humanify_yticks(ax[i],precision=1)
ax[0].text(-0.4,1.1,
'C',
transform=ax[0].transAxes,
ha='left',
va='top',
fontweight='bold',
fontsize=14,
if __name__ == "__main__":
import numpy as np
import bfmplot as bp
fig, ax = pl.subplots(1, 1)
x = np.linspace(1, 10, 100)
mus = np.linspace(1, 4, 4)
for mu in mus:
y = x**mu
pl.plot(x, y, c=bp.brewer_qualitative[0])
#pl.xscale('log')
bp.strip_axis(ax, horizontal='left')
pl.yscale('log')
ax.set_xlabel('x')
ax.set_ylabel('y')
pl.gcf().tight_layout()
pl.xlim([1, 10])
for mu in mus:
label = r'$\mu={:d}$'.format(int(mu))
y = x**mu
add_curve_label(ax, x, y, label, label_pos_rel=0.5 + mu / 50)
print(human_format(112345, precision=2))
pl.show()
import numpy as np
#import matplotlib.pyplot as plt
from bfmplot import pl as plt
from ThredgeCorr.basic_patterns import *
### mean degree plots
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
for iC, (C, res) in enumerate(result_sim.items()):
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')
ax = fig.add_subplot(1, 3, 1)
pl.hist(k,
bins=np.arange(0, kmax, 2),
density=True,
alpha=1,
align='left',
color=bp.brewer_qualitative[1],
label='data',
histtype='step')
p = pk(n, t, rho, kmax, X, W)
pl.plot(np.arange(kmax),
p,
lw=1,
c=bp.brewer_qualitative[2],
label='model fit')
pl.xlim(0, 30)
ax.text(0.04, 0.95, '(a)', va='top', ha='left', transform=ax.transAxes)
bp.strip_axis(ax)
leg = pl.legend()
bp.align_legend_right(leg)
ax.set_ylabel('probability density $p_k$')
ax.set_xlabel('node degree $k$')
#pl.show()
#sys.exit(0)
### collab network
print("loading", 'degree_sequences/cond-mat.degree_sequence', '...')
k = np.loadtxt('degree_sequences/cond-mat.degree_sequence')
k = np.array(k, dtype=int)
kmax = np.max(k) + 1
I0f = p['I0_factor'].value
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 = (
