高斯没有定义

Question

import matplotlib.pyplot as plt
import numpy as np
from lmfit import Model
x=np.array([4698.031, 4698.027, 4698.024, 4698.021, 4698.017, 
4698.014,4698.011, 4698.007, 4698.004, 4698.001, 4697.997, 4697.994, 
4697.991, 4697.987, 4697.984, 4697.981, 4697.977, 4697.974, 4697.971, 
4697.967, 4697.964, 4697.961, 4697.957, 4697.954, 4697.951, 4697.947, 
4697.944, 4697.941, 4697.937, 4697.934, 4697.931, 4697.927, 4697.924, 
4697.921, 4697.917])
y=np.array([0.56565, 0.586575, 0.70335, 0.991245, 1.447545, 4.944375, 
11.97281, 18.22095, 19.7613, 17.13792, 13.35083, 10.26506, 7.898505, 
5.084775, 2.4192, 1.34358, 0.829905, 1.31322, 3.2049, 4.0095, 2.83263, 
1.51605, 0.643275, 0.48972, 0.432675, 0.084375, 0.135345, 0.362145, 
0.34425, 0.307125, 0.469125, 0.297, 0.183255, 0.528855, 0.523125])
gmodel = Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_')
params = gmodel.make_params(p1_amp=0.1, p1_cen=4697.97, p1_wid=0.005, 
p2_amp=0.5, p2_cen=4698.00, p2_wid=0.005)


params['p1_cen'].min = x.min()
params['p1_cen'].max = 4697.98
params['p2_cen'].min = 4697.98
params['p2_cen'].max = x.max()

result = gmodel.fit(y, params, x=x)

我正在运行此程序，消息错误说：

回溯（最近一次调用最后一次）：文件“”，第8行，在gmodel = lt.Model（高斯，前缀='p1_'）+ lt.Model（高斯，前缀='p2_'）NameError：名称'高斯'是没有定义的..

Answer 1

你需要定义gaussian函数：

from numpy import exp

def gaussian(x, amp, cen, wid):
    return amp * exp(-(x-cen)**2 / wid)

或使用内置的GaussianModel：

from lmfit.models import GaussianModel

gmodel = GaussianModel(prefix='p1_') + GaussianModel(prefix='p2_')

Answer 2

高斯是一个你必须定义的函数，所以你可以在模型中使用它。这在docs中得到了很好的解释。

您必须将此添加到您的代码中：

def gaussian(x, amp, cen, wid):
     return amp * exp(-(x-cen)**2 / wid)

Answer 3

你需要定义高斯：

import numpy as np
def gaussian(x, amp, cen, wid):
  return amp * np.exp(-(x-cen)**2 / wid)

请参阅此页面上的模型类部分：https://lmfit.github.io/lmfit-py/model.html#lmfit.model.Model