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()
k1 = []
#np_edges = T.get_n_edge_lists(500)
for meas in range(N_meas):
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',
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)) pl.show()
# I (kappa)-> Q
(I, quarantine_rate, Q),
])
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)
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
from ThredgeCorr.basic_patterns import *
from ThredgeCorr.degree_dist import *
import numpy as np
import networkx as nx
import bfmplot as bp
from bfmplot import pl
import sys
fig = pl.figure(1, figsize=(8, 2.5))
### high school network
print("loading", 'degree_sequences/comm50.degree_sequence', '...')
k = np.loadtxt('degree_sequences/comm50.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)
rho = solve_rho(np.mean(k), np.mean(k**2), n)
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)
