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()
#pl.plot(x, 1/(N-1)*np.sqrt((1-covariance)/covariance) * np.exp(-t**2/2/covariance+np.sqrt(2)*t*a0)* np.exp(-np.sqrt(2)*fac*t*s))
#pl.plot(x, 1/(N-1)*np.sqrt((1-covariance)/covariance) * np.exp(-t**2/2/covariance)*\
# np.exp(-fac*np.sqrt(2*np.log((N-1)/x)))
# )
rho = covariance
prefac = 1 / (N - 1) * np.sqrt((1 - rho) / rho) * np.exp(-t**2 / 2 / rho)
pl.plot(x, pk(x, N, t, covariance), label='asymptotic solution Eq. (29)')
pl.plot(x, 1 / x**(np.sqrt(2)) * 0.1, label='$1/k^{\sqrt{2}}$')
#pl.plot(x, prefac*np.exp(np.sqrt(2)*t*1/(x/(N-1))**0.5/3.25),label='stretched exponential')
pl.plot(x,
prefac * np.exp(np.sqrt(2) * t * np.sqrt(2 * np.log(
(N - 1) / x))) / 15,
label='mu log')
pl.legend()
pl.xscale('log')
pl.yscale('log')
pl.xlabel('degree $k$')
pl.ylabel('probability')
fig.tight_layout()
fig.savefig('degree_asymptotic.png', dpi=200)
pl.show()
#print(np.array(k).mean())
#print("Transitivity from networks =", np.array(C1).mean())
#print("Transitivity from 3-cliques =", A.estimate_transitivity(int(binom(N,3))*50))
'facecolor': 'w',
'edgecolor': 'w',
'pad': 0
})
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,
params['I0_factor'],
)
X = result[2,:]*N
I = result[1,:]*N
#S = result[0,:]*N
pl.plot(t, cases,marker=markers[i],c=colors[i],label='data',mfc='None')
pl.plot(tt, X,c='k',label='$Q_I$ (detected and quarantined)')
pl.plot(tt, I,'--',c=colors[2],lw=1.5,label='$I$ (undected infected)')
#pl.plot(tt, S,label='model')
_c = i % n_col
_r = i // n_col
if _r == n_row-1:
pl.xlabel('days since Jan. 21st')
if _c == 0:
pl.ylabel('confirmed')
#pl.title(titlemap[province])
ax[i].text(0.03,0.97,
"{}.{}".format(letter[_r], roman[_c]),
transform=ax[i].transAxes,
ha='left',
va='top',
fontweight='bold',
fontsize=12,
bbox={'facecolor':'w','edgecolor':'w','pad':0}
)
ax[i].text(0.03,0.8,
titlemap[province],
transform=ax[i].transAxes,
### variance plot
n = 10**5
NMAX = 20
rho = np.linspace(0.0, 0.5, 200)
k = 2**np.arange(0, 10, 2)
k = np.array(k, dtype=float)
if not data_exists:
variances = np.zeros((len(rho), len(k)))
for i in range(len(rho)):
for j in range(len(k)):
t = solve_t(k[j], n)
variances[i, j] = variance(t, rho[i], n, NMAX)
fig, ax = pl.subplots(1, 1, figsize=(4, 2.5))
pl.xlabel(r'local edge weight correlation $\rho$')
pl.ylabel(r'node degree variance $\mathrm{Var}(k)$')
for i in range(len(k)):
pl.plot(rho, variances[:, i], c=bp.brewer_qualitative[1])
bp.strip_axis(ax)
ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_xlim([0, 0.5])
fig.tight_layout()
for i in range(len(k)):
bp.add_curve_label(ax,
rho,
variances[:, i],
I0 = 0.01
model.set_initial_conditions({S: 1 - I0, I: I0})
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),
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()
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
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()
N_meas = 10000
for meas in tqdm(range(N_meas)):
tau_con.append(conservative_method(t0, y0))
tau_new.append(new_method(t0, y0))
from bfmplot import pl
from scipy.integrate import cumtrapz
pl.hist(tau_con, bins=200, density=True, histtype='step')
pl.hist(tau_new, bins=200, density=True, histtype='step')
t = np.linspace(0, 120, 1000)
lamb = np.array([get_event_rates(_t, y0).sum() for _t in t])
integral = cumtrapz(lamb, x=t, initial=0)
p = lamb * np.exp(-integral)
pl.plot(t, p)
pl.xlabel('tau')
pl.ylabel('pdf')
#pl.yscale('log')
pl.gcf().tight_layout()
pl.figure()
pl.plot(*cum_hist(tau_con))
pl.plot(*cum_hist(tau_new))
pl.plot(t, np.exp(-integral))
pl.ylabel('P(t>tau)')
pl.xlabel('tau')
#pl.yscale('log')
pl.gcf().tight_layout()
tau_con = []
