我正在使用solve_ivp求解一系列积分微分方程。以下代码是更大代码的简化表示。我希望能够提取和打印/打印“随时间推移”的中间值的不断变化的值，例如A_value vs t。
我尝试添加一个计数器，并通过A（t，pools）每次迭代将'A_value'的当前值添加到列表中，这确实可行-但问题出在该模型的完整版本中，变得过大-我遇到了存储空间/大小问题（我要遵循200多个中间计算，每个变量我的存储阵列大小超过一百万）。我试图找出是否有一种方法可以更有效地做到这一点。我想要一些允许最终绘图A_value vs t的东西。
我也尝试了以下解决方案：Is there a way to store intermediate values when using solve_ivp?，但是我真的需要一种方法（由于尺寸大小）内的所有中间变量，而且我不能完全重新创建它们的绘图结果（行得通吗？）。
（其他说明：我知道'time'在我的A（t，pools）方法中没有任何作用，我只是在重新创建更大模型的结构，其中t是该方法中的必需变量。我确定我也错误地使用了'global'，但只是尝试了任何事情...）
任何帮助或指导将不胜感激！如何在不使用计数器的情况下保存数据并绘制A_value vs t。我仍然是python“新手”，并尝试边做边学。预先感谢
“”“
import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
tspan = (0, 10) #set the start and stop time
pools0 = [1, 2] #set the initial values
def A(t, pools):
global A_value
global B_value
global C_value
global D_value
global time
time = t
if (time ==0):
cows = 1.0
C1, C2 = pools #the pools get saved in here
A_value = 2*C1 + 3*C2 - C1**2
B_value = 1*C1 + 4*C2
C_value = 1*C1 + 3*C2 - C2**2
D_value = 2*C1 + 5*C2
print(time, A_value, B_value, C_value, D_value) # essentially, how this prints, I want to be able to save/store this data
return [time, A_value, B_value, C_value, D_value]
time, A_value, B_value, C_value, D_value = A(t, pools)
def model_integ(t, pools):
global A_value
global B_value
global C_value
global D_value
global time
time, A_value, B_value, C_value, D_value = A(t, pools)
dC1 = A_value + B_value
dC2 = C_value + D_value
return [dC1, dC2] # return the differential
pools = solve_ivp(model_integ, tspan, pools0)
#print(A(pools.y[0], pools.y[1]) )
C1, C2 = pools.y
t = pools.t
print(t, C1, C2) # this works fine, pulling the t, C1, C2 values from model_integ() for printing
print(time, A_value, B_value, C_value, D_value) # this only prints out their FINAL values
# C1 pool vs t, storage variables
#---------------------------------------------------------------
plt.scatter(t,C1)
plt.xlabel("t, d") # adding labels and title on the plot
plt.ylabel("C1 pools") # adding labels and title on the plot
#plt.ylim(0.0,2500)
plt.xlim(0,10)
plt.title("C1/C2 change with time") # adding labels and title on the plot
plt.tight_layout() # this takes care of labels overflowing the plot. It's good to use it all the time.
plot_file = "cute_little_plot.png" # output file to save plot
plt.savefig(plot_file) # output file to save plot
plt.show()
plt.close()
# A_value vs t, storage variables
#---------------------------------------------------------------
plt.scatter(t,A_value)
plt.xlabel("t, d") # adding labels and title on the plot
plt.ylabel("A_value") # adding labels and title on the plot
#plt.ylim(0.0,2500)
plt.xlim(0,10)
plt.title("A_value change with time") # adding labels and title on the plot
plt.tight_layout() # this takes care of labels overflowing the plot. It's good to use it all the time.
plot_file = "Avalue_plot.png" # output file to save plot
plt.savefig(plot_file) # output file to save plot
plt.show()
plt.close()
"""