ax.plot(TT, RRd, label='Rd')
ax.plot(TT, RRo, label='Ropt')
ax.legend(shadow=True, loc=2)
plt.xlabel('time (days)')
plt.ylabel('recovered count')
plt.title('comparison between methods: number of recovered people')
plt.savefig('mcm2015files/output/R_cmp_%s.png' % TT[-1])
if __name__ == "__main__":
fields = ['S', 'E', 'I', 'R', 'rD', 'rV'
] + ['from%s' % i for i in xrange(1, 11)]
abbr = ['CO', 'FR', 'MO', 'LA', 'KA', 'GR', 'KI', 'MA', 'VA', 'KO']
Y = np.array(
tabular.tbarr('mcm2015files/Pop_Transfer.csv', fields,
{field: 'float'
for field in fields}, {}))
t = 0
dt = .1
final_t = 200
idx = 0
YY = [Y]
YYd = [Y]
YYo = [Y]
TT = [t]
aborted = False
playing = True
playdir = 1
tlen = 1
idx = 0
distributor = drug_distributor(YYd, nD, nV, days, dt)
fig,ax = plt.subplots()
ax.plot(TT,RR,label='R')
ax.plot(TT,RRd,label='Rd')
ax.plot(TT,RRo,label='Ropt')
ax.legend(shadow=True,loc=2)
plt.xlabel('time (days)')
plt.ylabel('recovered count')
plt.title('comparison between methods: number of recovered people')
plt.savefig('mcm2015files/output/R_cmp_%s.png'%TT[-1])
if __name__ == "__main__":
fields = ['S','E','I','R','rD','rV'] + ['from%s'%i for i in xrange(1,11)]
abbr = ['CO','FR','MO','LA','KA','GR','KI','MA','VA','KO']
Y = np.array(tabular.tbarr('mcm2015files/Pop_Transfer.csv',fields,{field:'float' for field in fields},{}))
t = 0
dt = .1
final_t = 200
idx = 0
YY = [Y]
YYd = [Y]
YYo = [Y]
TT = [t]
aborted = False
playing = True
playdir = 1
tlen = 1
idx = 0
distributor = drug_distributor(YYd,nD,nV,days,dt)
distributor_opt = drug_distributor_opt(YYo,nD,nV,days,dt)
S, E, I, R, D, I_c = tuple(Y)
N = sum(Y[0:4])
return np.array([
br * N - beta * S * I / N - mu * S,
beta * S * I / N - (eps + mu) * E, eps * E - (gamma + ddr) * I,
(1. / 10 - ddr) * I - mu * R, ddr * I, eps * E
])
return f
if __name__ == "__main__":
rfields = ['t', 'C']
realdata = np.array(
tabular.tbarr('mcm2015files/betatest.csv', rfields,
{field: 'float'
for field in rfields}, {}))
initState = np.array([22e6, 0, 86, 0, 0, 86])
lobound, hibound = 0.0, 1.0
guess = lobound * 0.5 + hibound * 0.5
T, Y = odeint(ebola_kernel(guess), initState, 0, 317)
ret = Y[-1][5]
print guess, ret
while abs(ret - 22460) > 0.1:
if ret - 22460 < 0:
lobound = guess
else:
hibound = guess
guess = lobound * 0.5 + hibound * 0.5
T, Y = odeint(ebola_kernel(guess), initState, 0, 317)
ret = Y[-1][5]
S,E,I,R,D, I_c = tuple(Y)
N = sum(Y[0:4])
return np.array([
br*N-beta*S*I/N-mu*S,
beta*S*I/N - (eps+mu)*E,
eps*E - (gamma+ddr)*I,
(1./10 -ddr)*I- mu*R,
ddr*I,
eps*E
])
return f
if __name__ == "__main__":
rfields = ['t','C']
realdata = np.array(tabular.tbarr('mcm2015files/betatest.csv',rfields,{field:'float' for field in rfields},{}))
initState = np.array([22e6,0,86,0,0,86])
lobound,hibound = 0.0, 1.0
guess = lobound*0.5+hibound*0.5
T,Y = odeint(ebola_kernel(guess),initState,0,317)
ret = Y[-1][5]
print guess, ret
while abs(ret-22460) > 0.1:
if ret-22460 < 0:
lobound = guess
else:
hibound = guess
guess = lobound*0.5 + hibound*0.5
T,Y = odeint(ebola_kernel(guess),initState,0,317)
ret = Y[-1][5]
print guess, ret
