我正在尝试对这些数据进行高斯拟合
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我已经尝试了Python gaussian fit on simulation gaussian noise data和Fitting (a gaussian) with Scipy vs. ROOT et al中给出的示例 ，但没有运气。
我正在寻找这样做，lmfit因为它有几个优点。这个尝试是按照lmfit文档完成的，这里是代码和情节
from numpy import sqrt, pi, exp
from lmfit import Model
import matplotlib.pyplot as plt
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(sqrt(2*pi)*wid)) * exp(-(x-cen)**2 /(2*wid**2))
gmodel = Model(gaussian)
result = gmodel.fit(y, x=x, amp=-0.5, cen=4200, wid=2)
plt.plot(x, y,'ro', ms=6)
plt.plot(x, result.init_fit, 'g--', lw=2)
plt.plot(x, result.best_fit, 'b-', lw=2)
所以绿色是与初始参数的拟合，蓝色是最好的拟合，正如你所看到的，我得到一个从我的点和一条直线偏移的高斯。
此外，我的数据的第三行是 y 轴上的误差。在拟合数据时如何考虑错误lmfit？