我不能运行你的代码，有错误，无论如何，根据scipy.fsolve的文档，你应该添加一个初始猜测作为第二个输入参数，而不是像你在fsolve(equation, x0, (kappa,tau))中所做的那样添加一个范围。
当然，你也可以在循环中传递它，对数组np.linspace(0.5, 0.5, 25)中的每个值进行循环。虽然我不明白你试图通过改变kappa和tau来实现什么，但如果我认为对于这些给定的参数，你有兴趣寻找根，这里是我如何做的。

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

# Take it as it is
kappa   = 0.1
tau     = 90

def equation(x, parameters):
    kappa,tau = parameters
    return -x + kappa * np.sin(-tau*x)

# Initial guess of x = -0.1
SolutionStack   = []
x0              = -kappa
y               = fsolve(equation, x0, [kappa, tau])
SolutionStack.append(y[0])

y               = fsolve(equation, SolutionStack[-1], [kappa, tau])
SolutionStack.append(y[0])
deltaY = SolutionStack[-1] - SolutionStack[0]

# Define tolerance
tol = 5e-4
while ((SolutionStack[-1] <= kappa) and (deltaY <= tol)):
    y = fsolve(equation, SolutionStack[-1], [kappa, tau])
    SolutionStack.append(y[0])
    deltaY = SolutionStack[-1] - SolutionStack[-2]
    # Obviously a little guesswork is involved here, as it pertains to 0.07
    if deltaY <= tol:
        SolutionStack[-1] = SolutionStack[-1] + 0.07

# Obtaining the roots
Roots = []
Roots.append(SolutionStack[0])
for i in range(len(SolutionStack)-1):
    if (SolutionStack[i+1] - SolutionStack[i]) <= tol:
        continue
    else:
        Roots.append(SolutionStack[i+1]

可能不是最聪明的方法（假设我理解正确），但也许你现在有了主意。