我使用Sci Py zombie代码作为示例。
ODEint的问题是我整合了很长时间(在这种情况下从0到500,但对于其他项目长达数千年)并且它一次完成这种集成。我想实现一个长输出时间刻度,但是一个小的积分时间刻度并将生成的样本数量更改为1,但不断更新时间刻度,以便我可以为每个循环写一个新的文件点。即我不希望它一次性从0到500集成,而是在不断变化的时间尺度上循环。这可以实现吗?
# zombie apocalypse modeling
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
plt.ion()
plt.rcParams['figure.figsize'] = 10, 8
P = 0 # birth rate
d = 0.0001 # natural death percent (per day)
B = 0.0095 # transmission percent (per day)
G = 0.0001 # resurect percent (per day)
A = 0.0001 # destroy percent (per day)
# solve the system dy/dt = f(y, t)
def f(y, t):
Si = y[0]
Zi = y[1]
Ri = y[2]
# the model equations (see Munz et al. 2009)
f0 = P - B*Si*Zi - d*Si
f1 = B*Si*Zi + G*Ri - A*Si*Zi
f2 = d*Si + A*Si*Zi - G*Ri
return [f0, f1, f2]
# initial conditions
S0 = 500. # initial population
Z0 = 0 # initial zombie population
R0 = 0 # initial death population
y0 = [S0, Z0, R0] # initial condition vector
t = np.linspace(0, 50, 1000) # time grid
# solve the DEs
soln = odeint(f, y0, t)
S = soln[:, 0]
Z = soln[:, 1]
R = soln[:, 2]
答案 0 :(得分:0)
为什么不这样做?
for k in range(50):
t = np.linspace(k, k+1, 20)
sol = odeint(f, y0, t)
y0 = sol[-1] #initial value for next segment
S,Z,R = sol.T
# output to file, other post-processing