python - 在Python中寻找功能的根源

我正在尝试使用scipy函数fsolve计算函数的根，但是始终出现错误：

TypeError: 'numpy.array' object is not callable

我认为将方程式定义为函数可能更容易，但是我尝试了几次却无济于事。

码：

import scipy
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize

# Constants
wavelength = 0.6328
ncore = 1.462420
nclad = 1.457420
a = 8.335

# Mode Order
l = 0

# Mode parameters
V = (2 * np.pi * a / wavelength) * np.sqrt(ncore**2 - nclad**2)
U = np.arange(0, V, 0.01)
W = np.sqrt(V**2-U**2)

func = U * scipy.special.jv(l+1, U) / scipy.special.jv(l, U) - W * scipy.special.kv(l+1, W) / scipy.special.kv(l, W)

from scipy.optimize import fsolve
x = fsolve(func,0)
print x

堆栈跟踪：

Traceback (most recent call last):

  File "<ipython-input-52-081a9cc9c0ea>", line 1, in <module>
    runfile('/home/luke/Documents/PythonPrograms/ModeSolver_StepIndex/ModeSolver_StepIndex.py', wdir='/home/luke/Documents/PythonPrograms/ModeSolver_StepIndex')

  File "/usr/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 580, in runfile
    execfile(filename, namespace)

  File "/home/luke/Documents/PythonPrograms/ModeSolver_StepIndex/ModeSolver_StepIndex.py", line 52, in <module>
    x = fsolve(func,0)

  File "/usr/lib64/python2.7/site-packages/scipy/optimize/minpack.py", line 140, in fsolve
    res = _root_hybr(func, x0, args, jac=fprime, **options)

  File "/usr/lib64/python2.7/site-packages/scipy/optimize/minpack.py", line 197, in _root_hybr
    shape, dtype = _check_func('fsolve', 'func', func, x0, args, n, (n,))

  File "/usr/lib64/python2.7/site-packages/scipy/optimize/minpack.py", line 20, in _check_func
    res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))

TypeError: 'numpy.ndarray' object is not callable

最佳答案

这是因为fsolve将函数作为参数。
尝试此操作，请注意您仍然会遇到一些运行时错误，您将必须检查func返回的构造是否正确，我将留给您找出答案。

import scipy
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize

# Constants
wavelength = 0.6328
ncore = 1.462420
nclad = 1.457420
a = 8.335

# Mode Order
# l = 0

# Mode parameters
V = (2 * np.pi * a / wavelength) * np.sqrt(ncore**2 - nclad**2)
U = np.arange(0, V, 0.01)
W = np.sqrt(V**2-U**2)

def func(l):
    return U * scipy.special.jv(l+1, U) / scipy.special.jv(l, U) - W * scipy.special.kv(l+1, W) / scipy.special.kv(l, W)

from scipy.optimize import fsolve
x = fsolve(func,0)
print x

关于python - 在Python中寻找功能的根源，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/35857190/