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 set_n_ticks(ax=None, nx=3, ny=2):
"""Set the number of major ticks for each axis"""
if ax is None:
ax = pl.gca()
old_ax = pl.gca()
pl.sca(ax)
if ny is not None:
pl.locator_params(axis='y', nbins=ny)
if nx is not None:
pl.locator_params(axis='x', nbins=nx)
pl.sca(old_ax)
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()
edges = get_fast_edge_list(N, covariance, t)
ks = get_degrees_from_edge_list(N, edges).tolist()
k1.extend(ks)
k1 = np.array(k1, dtype=int)
k1pos = k1[k1 >= 1]
import powerlaw
results = powerlaw.Fit(k1pos, discrete=True, xmin=1)
fig = pl.figure()
powerlaw.plot_ccdf(k1pos)
#powerlaw.plot_pdf(k1)
#pl.hist(k1,bins=np.arange(1,max(k1)+1),histtype='step',density=True)
x = np.arange(1, max(k1pos))
results.lognormal.plot_ccdf(ax=pl.gca())
#results.lognormal.plot_pdf(ax=pl.gca())
pl.xscale('log')
pl.yscale('log')
fig = pl.figure()
pl.hist(
k1,
bins=np.arange(max(k1) + 1),
histtype='step',
density=True,
label=r'10 networks, $N=10^4,\rho=0.5,\left\langle k\right\rangle = 2$')
x = np.arange(1, max(k1pos))
a0 = 2.30753
})
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',
expected_counts[model.get_compartment_id(B)] = N_measurements * rateB / (rateB + rateE + rateA) expected_counts[model.get_compartment_id(E)] = N_measurements * rateE / (rateB + rateE + rateA) expected_counts[model.get_compartment_id(C0)] = N_measurements * ((N-1)*rateA / (rateB + rateE + rateA) * probAC0) expected_counts[model.get_compartment_id(C1)] = N_measurements * ((N-1)*rateA / (rateB + rateE + rateA) * probAC1) expected_counts[model.get_compartment_id(D)] = N_measurements * ((N-1)*rateB / (rateB + rateE + rateA) * probBD) expected_counts[model.get_compartment_id(F)] = (N-1) * expected_counts[model.get_compartment_id(E)] expected_counts[model.get_compartment_id(S)] = N_measurements * N - expected_counts.sum() pl.bar(x+width/2, expected_counts, width) pl.xticks(x) pl.gca().set_xticklabels(model.compartments) pl.figure() ndx = np.where(expected_counts==0) counts[ndx] = 1 expected_counts[ndx] = 1 pl.plot(x, np.abs(1-counts/expected_counts)) from scipy.stats import entropy _counts = np.delete(counts,1) _exp_counts = np.delete(expected_counts,1) print(entropy(_counts, _exp_counts))
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, 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)
# va='bottom',
# bbox={'facecolor':'w','edgecolor':'w','pad':0}
# )
#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,
va=max_dates_va[i],
color=colors[0],
fontsize=9,
bbox={'facecolor':'w','edgecolor':'w','pad':0}
)
ax[i].plot([22.5,22.5], [0,cases[-1]],':',c=colors[0],lw=1.5)
#pl.plot(tt, S,label='model')
_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.title(titlemap[province])
ax[i].text(0.03,0.97,
"{}".format(roman[i]),
transform=ax[i].transAxes,
ha='left',
va='top',
fontweight='bold',
fontsize=10,
bbox={'facecolor':'w','edgecolor':'w','pad':0}
)
ax[i].text(0.03,0.8,
titlemap[province],
transform=ax[i].transAxes,
ha='left',
va='top',
import numpy as np
from bfmplot import pl as plt
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})
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()
