如何使用Scipy curve_fit对数据进行对数函数拟合

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

x = np.array([1, 2, 3, 4, 5])
y = np.array([0.1, 0.3, 0.7, 1.5, 2.6])

def logarithmic_func(x, a, b):
    return a * np.log(x) + b

params, params_covariance = curve_fit(logarithmic_func, x, y)

a = params[0]
b = params[1]

x_range = np.linspace(1, 5, 100)
y_fit = logarithmic_func(x_range, a, b)

plt.scatter(x, y, label='Original Data')
plt.plot(x_range, y_fit, 'r', label='Fitted Curve')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()