def sin_test(n=8,text_position='start'):
"""Test the stuff from the modules"""
from bfmplot import pl
import bfmplot as bp
import numpy as np
#bp.set_color_cycle(bp.cccs_colors)
#pl.figure(figsize=bp.phys_rev_column())
pl.figure(figsize=bp.golden_ratio(5))
x = np.linspace(1,5*np.pi,100)
for i in range(n):
pl.plot(x, 1-np.sin(x[::-1]/np.sqrt(i+1)), marker=bp.markers[i],mfc='w',label='$i=%d$'%i)
bp.strip_axis(pl.gca())
leg = pl.legend()
bp.align_legend_right(leg)
bp.arrow(pl.gca(), r'$i$',
(3, 1.8),
(6, 0.8),
text_position=text_position)
pl.xlabel('hello')
pl.ylabel('hello')
bp.set_n_ticks(pl.gca(), 3, 2)
#pl.xscale('log')
pl.gcf().tight_layout()
def sin_test(n=8,text_position='start'):
pl.figure(figsize=bp.golden_ratio(5))
x = np.linspace(0,5*np.pi,100)
for i in range(n):
pl.plot(x, 1-np.sin(x[::-1]/np.sqrt(i+1)), marker=bp.markers[i],mfc='w',label='$i=%d$'%i)
bp.strip_axis(pl.gca())
leg = pl.legend()
bp.align_legend_right(leg)
bp.arrow(pl.gca(), r'$i$', (14, 0.8), (10, 0.15), text_position=text_position, rad=0.3)
pl.xlabel('this is the x-label')
pl.ylabel('this is the y-label')
pl.gcf().tight_layout()
def sin_test(n=8, text_position='start', with_legend=True):
pl.figure(figsize=bp.golden_ratio(5))
x = np.linspace(0, 5 * np.pi, 100)
for i in range(n):
pl.plot(x,
1 - np.sin(x[::-1] / np.sqrt(i + 1)),
marker=bp.markers[i],
mfc='w',
label='$i=%d$' % i)
bp.strip_axis(pl.gca())
if with_legend:
leg = pl.legend()
bp.align_legend_right(leg)
pl.xlabel('this is the x-label')
pl.ylabel('this is the y-label')
pl.gca().set_title(name)
pl.gcf().tight_layout()
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()
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()
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)
if LINLIN:
pl.gcf().savefig("model_fit_figures/SI_Fig_02.png", dpi=300)
else:
pl.gcf().savefig("model_fit_figures/SI_Fig_01.png", dpi=300)
if not loaded_fits:
with open('fit_parameters/{}'.format(fn), 'wb') as f:
pickle.dump(fit_parameters, f)
ax[0].text(-0.18,
1.03,
'A',
fontsize=14,
fontweight='bold',
transform=ax[0].transAxes,
va='top')
ax[1].text(-0.21,
1.03,
'B',
fontsize=14,
fontweight='bold',
transform=ax[1].transAxes,
va='top')
pl.gcf().tight_layout()
#bp.add_curve_label(ax[0],
# curve_x=curves[0]['I']['x'],
# curve_y=curves[0]['I']['y'],
# label='$I$',
# label_pos_rel=0.2,
# angle=0,
# #color=colors[2],
# fontsize=12
# )
#
#bp.add_curve_label(ax[0],
# curve_x=curves[0]['S']['x'],
# curve_y=curves[0]['S']['y'],
# label='$S$',
#ax[i].text(0.7,0.03,
# titlemap[province],
# transform=ax[i].transAxes,
# ha='right',
# va='bottom',
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
if _r < n_row - 1:
[x.set_visible(False) for x in ax[i].xaxis.get_major_ticks()]
ax[i].set_yticks([10, 100, 1000, 10000])
bp.strip_axis(pl.gca())
#ax[0].text(-0.4,1.1,
# 'C',
# transform=ax[0].transAxes,
# ha='left',
# va='top',
# fontweight='bold',
# fontsize=14,
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
pl.gcf().tight_layout()
pl.gcf().subplots_adjust(wspace=0.4, hspace=0.3)
pl.gcf().savefig("powerlaw_fit_figures/fit_powerlaw_small_hubei_china.png",
dpi=300,
transparent=True)
pl.show()
import numpy as np
from scipy.ndimage import imread
from bfmplot import pl
def rgb2grey(rgb):
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
grey = 0.2989 * r + 0.5870 * g + 0.1140 * b
return grey
A = imread('column.png')
A = rgb2grey(A)
pl.imshow(A)
pl.gcf().savefig('column_cividis.png')
pl.show()
bp.strip_axis(ax[0])
bp.strip_axis(ax[1])
#bp.align_legend_right(leg)
#ax[i].set_yticks([0,10000,20000,30000,40000])
if i == 0:
ax[i].set_yticks([1e2, 1e3, 1e4, 1e5])
else:
ax[i].set_yticks([1e1, 1e2, 1e3, 1e4])
#bp.humanify_yticks(ax[i],precision=0 if i == 0 else 1)
ax[i].set_xlabel('days since Jan. 20th')
#ax[0].text(-0.18,1.03,'A',fontsize=14,fontweight='bold',transform=ax[0].transAxes,va='top')
#ax[1].text(-0.21,1.03,'B',fontsize=14,fontweight='bold',transform=ax[1].transAxes,va='top')
pl.gcf().tight_layout()
#bp.add_curve_label(ax[0],
# curve_x=curves[0]['I']['x'],
# curve_y=curves[0]['I']['y'],
# label='$I$',
# label_pos_rel=0.2,
# angle=0,
# #color=colors[2],
# fontsize=12
# )
#
#bp.add_curve_label(ax[0],
# curve_x=curves[0]['S']['x'],
# curve_y=curves[0]['S']['y'],
# label='$S$',
import bfmplot as bp
t = np.linspace(0, 100, 1000)
result = model.integrate(t)
plt.figure()
for compartment, incidence in result.items():
plt.plot(t, incidence, label=compartment)
plt.xlabel('time [days]')
plt.ylabel('incidence')
plt.legend()
bp.strip_axis(plt.gca())
plt.gcf().tight_layout()
plt.savefig('SEIRQ.png', dpi=300)
N = 1000
I0 = 100
model = epk.EpiModel([S, E, I, R, Q], initial_population_size=N)
model.set_processes([
(S, I, infection_rate, E, I),
(E, symptomatic_rate, I),
(I, recovery_rate, R),
(I, quarantine_rate, Q),
])
model.set_initial_conditions({S: N - I0, I: I0})
t, result = model.simulate(100)
import bfmplot as bp
data = np.array([
(0.0, 2.00),
(0.75, 2.68),
(1.5, 3.00),
(2.25, 2.78),
(3.0, 2.14),
(3.75, 1.43),
(4.5, 1.02),
(5.25, 1.14),
(6.0, 1.72),
])
times, rates = data[:, 0], data[:, 1]
x2 = np.linspace(times[0], times[-1], 1000)
interp_modes = ['zero', 'linear', 'nearest', 'quadratic']
pl.figure()
pl.plot(times, rates, 's', label='data')
for kind in interp_modes:
f = interp1d(times, rates, kind=kind)
pl.plot(x2, f(x2), label=kind)
pl.legend(frameon=False)
bp.strip_axis(pl.gca())
pl.xlabel('time')
pl.ylabel('value')
pl.gcf().tight_layout()
pl.gcf().savefig('interp1d.png', dpi=300)
pl.show()
for colors, name in zip(
[
bp.mpl_default_colors,
bp.new_colors,
bp.brewer_qualitative,
bp.cccs_colors,
bp.get_cividis_colors(8),
bp.wong,
],
[
'bfmplot.mpl_default_colors',
'bfmplot.new_colors',
'bfmplot.brewer_qualitative',
'bfmplot.cccs_colors',
'bfmplot.get_cividis_colors(8)',
'bfmplot.wong',
],
):
bp.set_color_cycle(colors)
sin_test(n=8, text_position='start', with_legend=False)
bp.set_n_ticks(pl.gca(), nx=4, ny=4)
name = name.replace('(', '_')
name = name.replace(')', '_')
pl.gcf().savefig(name + ".png", dpi=100)
pl.show()
model.set_processes(quadratic_processes + linear_processes)
initial_conditions = {(node, "S"): 1.0 for node in nodes}
initial_conditions[(nodes[0], "S")] = 0.8
initial_conditions[(nodes[0], "I")] = 0.2
model.set_initial_conditions(initial_conditions,
allow_nonzero_column_sums=True)
# set compartments for which you want to obtain the
# result
plot_compartments = [(node, "I") for node in nodes]
# integrate
import numpy as np
t = np.linspace(0, 12, 1000)
result = model.integrate(t, return_compartments=plot_compartments)
# plot result
from bfmplot import pl as plt
plt.figure()
for (node, _), concentration in result.items():
plt.plot(t, concentration, label='node: ' + str(node))
plt.xlabel("time")
plt.ylabel("I")
plt.legend()
plt.gcf().tight_layout()
plt.gcf().savefig("chain_I.png", dpi=300)
plt.show()
(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'])])
pl.gcf().savefig('SIR_birth_model_reimports_all_log.png', dpi=300)
pl.show()
bbox={
'facecolor': 'w',
'edgecolor': 'w',
'pad': 0
})
if _r < n_row - 1:
[x.set_visible(False) for x in ax[i].xaxis.get_major_ticks()]
ax[i].set_yticks([1, 10, 100, 1000])
bp.strip_axis(pl.gca())
ax[0].text(-0.4,
1.1,
'C',
transform=ax[0].transAxes,
ha='left',
va='top',
fontweight='bold',
fontsize=14,
bbox={
'facecolor': 'w',
'edgecolor': 'w',
'pad': 0
})
pl.gcf().tight_layout()
pl.gcf().subplots_adjust(wspace=0.3, hspace=0.3)
pl.gcf().savefig("powerlaw_fit_figures/fit_powerlaw_500.png", dpi=300)
pl.show()
# 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)
pl.show()
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,
bbox={'facecolor':'w','edgecolor':'w','pad':0}
)
pl.gcf().tight_layout()
pl.gcf().subplots_adjust(wspace=0.3,hspace=0.3)
pl.gcf().savefig("model_fit_figures/model_fit_confirmed_500.png",dpi=300)
if not loaded_fits:
with open('fit_parameters/confirmed_cases_500.p','wb') as f:
pickle.dump(fit_parameters,f)
pl.show()
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 = (
"",
".1f",
