我尝试使用scipy解微分方程，但无法通过生成的TypeError。我已经查看过，但不确定如何解决此问题，因为**运算符是用来表示指数的。我该如何解决此问题？
以下是重现错误所需的 Dataframe ：
# Opening the packages
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import CubicSpline
from scipy.integrate import odeint
from math import *
# Here is the data
data = {'day': [1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
83,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93],
'soil_temp': [18.15,
17.5,
19.1,
20.3,
19.75,
17.7,
15.2,
15.45,
14.3,
12.45,
12.75,
14.55,
16.55,
18.3,
19,
19,
18.8,
17.45,
17.15,
17.4,
19.9,
19.85,
21.4,
22.05,
21.75,
19.9,
21.9,
23.45,
24.65,
24.4,
25.1,
24.75,
25.2,
25.45,
25.75,
26.35,
26.5,
24.8,
24.55,
25.95,
26.35,
23.9,
22.2,
21.2,
21.9,
23.4,
25.45,
25.75,
25.25,
25.65,
26.4,
25.7,
25,
26.1,
27,
26.75,
26.95,
26.55,
25.9,
26.2,
27.15,
28.25,
27.95,
27.25,
26.5,
27.45,
27.55,
27.8,
28.4,
28.8,
28.05,
25.05,
25.15,
25.45,
25.3,
22.95,
22.6,
25.1,
25.95,
26.3,
26.55,
26.25,
27.15,
27.75,
28.2,
25.45,
25,
25.1,
25.15,
25.15,
26.05,
26.2,
27.45]}
# Create DataFrame
df = pd.DataFrame(data)下面是生成错误的代码：
# Define parameters
alpha = 52.875
beta = 13.345
gamma = -1.44
delta = 2.29
constant = 60.589
g = 80.64
g2 = 1.04
# Define model
def model(a,t,om):
# Cubic spline
day = df['day'].to_numpy()
temp = df['soil_temp'].to_numpy()
cubT = CubicSpline(day, temp, bc_type='natural',extrapolate=False)
d_cubT = CubicSpline.derivative(cubT)
# Model parameters
bigfrac = (t/(((alpha-(beta*(-delta+(gamma*om))))/constant)*(g -(g2**cubT))))
smallfrac = (t*(alpha-(beta*(-delta+(gamma*om)))/constant))
# Derivative equation
k = (0.5**bigfrac) * log(0.5) * (((bigfrac - smallfrac - (g2**cubT) * log(g2) * d_cubT)) / (bigfrac**2))
dherb_dt = -k*a
return dherb_dt
# Initial condition
a0 = 4.271
# Time, in days, to interpolate over
t = np.linspace(0, 90) # 90 days
om = 0.3
y1 = odeint(model, a0, t, args=(om,))
# Plot
plt.plot(t,y1, 'r-', linewidth = 2, label = 'om = 0.3')
plt.xlabel("xlabel")
plt.ylabel("ylabel")
plt.legend()
plt.show()将显示以下错误消息：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [1], in <cell line: 233>()
230 t = np.linspace(0, 90) # 90 days
232 om = 0.3
--> 233 y1 = odeint(model, a0, t, args=(om,))
235 # Plot
236 plt.plot(t,y1, 'r-', linewidth = 2, label = 'om = 0.3')
File ~\anaconda3\envs\agron893\lib\site-packages\scipy\integrate\_odepack_py.py:241, in odeint(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)
239 t = copy(t)
240 y0 = copy(y0)
--> 241 output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,
242 full_output, rtol, atol, tcrit, h0, hmax, hmin,
243 ixpr, mxstep, mxhnil, mxordn, mxords,
244 int(bool(tfirst)))
245 if output[-1] < 0:
246 warning_msg = _msgs[output[-1]] + " Run with full_output = 1 to get quantitative information."
Input In [1], in model(a, t, om)
215 d_cubT = CubicSpline.derivative(cubT)
217 # Model parameters
--> 218 bigfrac = (t/(((alpha-(beta*(-delta+(gamma*om))))/constant)*(g -(g2**cubT))))
219 smallfrac = (t*(alpha-(beta*(-delta+(gamma*om)))/constant))
221 # Derivative equation
TypeError: unsupported operand type(s) for ** or pow(): 'float' and 'CubicSpline'如何解决此问题？
1条答案
按热度按时间8yoxcaq71#
CubicSpline.derivative(nu)的自变量是导数的阶数，1表示一阶导数，2表示二阶导数，以此类推，你需要对自变量求值，以得到给定输入下导数的数值。