scipy curve_fit 不拟合数据；没有抛出错误

Question

我正在尝试使用以下函数（高斯与指数的卷积）来拟合我的数据（参见附图）衰变）。然而，无论我最初的猜测是什么，拟合效果都不好。我不知道为什么？有人可以帮忙吗？

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
plt.rcParams["font.size"] = 16
plt.rcParams['figure.figsize'] = (10, 5)

#u is the location (mean) of Gaussian (determines the rise of feature)
#d is FWHM of the Gaussian (determines the width of convolution)

def one_exp_fit_function(t1, d, u, k1):
    d_bar = d/(2*np.sqrt(np.log(2)))
    i = ((d_bar*np.sqrt(2*np.pi))**(-1))*np.exp(-np.log(2)*(((2*(t1-u))/d)**2))
    f = np.exp(-(k1)*t1)
    k = np.convolve(i, f, mode='same')
    return k

p0 = [0.15, 908.1, 0.0029]


popt, pcov = curve_fit(one_exp_fit_function, Time, Intensity, p0)

输出：

>>> popt
array([2.36403281e-01, 9.08178303e+02, 2.68108346e-03])

>>>pcov
array([[ 1.30221168e-03,  7.18606249e-04, -5.22339673e-06],
   [ 7.18606249e-04,  4.48554164e-04, -3.15605374e-06],
   [-5.22339673e-06, -3.15605374e-06,  2.25596318e-08]])

拟合数据和原始数据如下所示：

这是我试图拟合的数据（查看附图的外观） 时间
907.6, 907.61, 907.62, 907.63, 907.64, 907.65, 907.66, 907.67, 907.68, 907.69, 907.7, 907.71, 907.72, 907.73, 907.74, 907.75, 907.76, 907.77, 907.78, 907.79, 907.8, 907.81, 907.82, 907.83, 907.84, 907.85, 907.86, 907.87, 907.88, 907.89, 907.9, 907.91, 907.92, 907.93, 907.94, 907.95, 907.96, 907.97, 907.98, 907.99, 908.0, 908.01, 908.02, 908.03, 908.04, 908.05, 908.06, 908.07, 908.08, 908.09, 908.1, 908.11, 908.12, 908.13, 908.14, 908.15, 908.16, 908.17, 908.18, 908.19, 908.2, 908.21, 908.22, 908.23, 908.24

强度

0.057805967, 0.5065527, -0.3501974, 0.036141705, 0.035738964, 0.23803276, 0.6114219, -0.3501108, 0.07225589, 0.18216568, 0.25470826, 0.20328628, 0.091083646, 0.5306828, 0.87142694, 1.1959167, 1.2584509, 1.5770732, 1.5870125, 1.844547, 1.5854758, 2.0418897, 3.0503955, 2.5744607, 2.6155453, 2.9460716, 3.3898244, 3.5842943, 3.2935236, 3.3901217, 4.2156982, 3.4158673, 4.341926, 4.3702006, 3.9515905, 4.3101573, 4.2822328, 4.8445034, 4.2415953, 4.7157598, 4.856417, 4.842477, 4.6342535, 4.820195, 5.348257, 4.5149198, 4.8444476, 4.681948, 4.8084526, 4.7724895, 4.3835893, 4.362606, 4.529179, 4.8953743, 4.4109926, 4.924057, 5.0648174, 4.793385, 4.6049685, 4.6032906, 5.037164, 5.379293, 4.9395213, 4.7802463, 4.234617