如何使用scipy和matplotlib来拟合反向sigmoid函数

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

def inverse_sigmoid(x, a, b, c):
    return a / (1 + np.exp(-b * (x - c)))

x_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y_data = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])

params, params_covariance = curve_fit(inverse_sigmoid, x_data, y_data)

x_range = np.linspace(1, 10, 100)
y_range = inverse_sigmoid(x_range, params[0], params[1], params[2])

plt.scatter(x_data, y_data, label='Data')
plt.plot(x_range, y_range, label='Fit')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()