我对 lmfit 的猜测功能有疑问。我正在尝试拟合一些实验数据，并且我想使用 lmfit 的不同内置模型，但我无法运行内置模块，除非我直接定义函数。
下面的代码不起作用，但如果我注释了guess 函数，它就起作用了。
PS对我来说，索引是第一列会更有趣，因为我会将它放在一个循环中，该循环将使用所有相同的第一列数据，因此我可以将每个新的第二列数据作为一个新列在数据帧中。
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from lmfit import Model
from lmfit.models import GaussianModel
minvalue = 3.25
maxvalue = 3.45
rawdata = pd.read_csv('datafile.txt', delim_whitespace = True, names=['XX','YY'])
#Section the data
data = rawdata[(rawdata['XX']>minvalue) & (rawdata['XX'] < maxvalue)]
#Create a DataFrame with the data
dataDataframe = pd.DataFrame()
dataDataframe[0] = data['YY']
dataDataframe = dataDataframe.set_index(data['XX'])
# Gaussian curve
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(np.sqrt(2*np.pi)*wid)) * np.exp(-(x-cen)**2 /(2*wid**2))
result_gaussian = Model(gaussian).fit(dataDataframe[0], x=dataDataframe.index.values, amp=5, cen=5, wid=1)
mod = GaussianModel()
pars = mod.guess(dataDataframe[0], x = np.float32(dataDataframe.index.values))
out = mod.fit(dataDataframe[0], pars , x = np.float32(dataDataframe.index.values))
plt.plot(dataDataframe.index.values, dataDataframe[0],'bo')
plt.plot(dataDataframe.index.values, result_gaussian.best_fit, 'r-', label = 'Gaussian')
plt.plot(dataDataframe.index.values, out.best_fit, 'b-', label = 'Gaussian2')
plt.legend()
plt.show()
如果我取消注释内置模块，我会收到错误消息：
File "/Users/johndoe/anaconda2/lib/python2.7/site-packages/lmfit/models.py", line 52, in guess_from_peak
cen = x[imaxy]
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
我试图从models.py 运行guess_from_peak，但我没有遇到问题，它导致了一个整数。
原始数据：
1.1661899e+000 7.3414581e+002
1.1730889e+000 7.4060590e+002
1.1799880e+000 7.3778076e+002
1.1868871e+000 7.2950366e+002
1.1937861e+000 7.0154932e+002
1.2006853e+000 7.0399518e+002
1.2075844e+000 7.3814081e+002
1.2144834e+000 7.5750049e+002
1.2213825e+000 7.6613043e+002
1.2282816e+000 7.4348322e+002
1.2351807e+000 7.2836584e+002
1.2420797e+000 7.0964618e+002
1.2489789e+000 7.1938611e+002
1.2558780e+000 7.0620062e+002
1.2627770e+000 7.2354883e+002
1.2696761e+000 7.1347961e+002
1.2765752e+000 7.1027679e+002
1.2834742e+000 7.4422925e+002
1.2903733e+000 7.5596112e+002
1.2972724e+000 7.2770599e+002
1.3041714e+000 7.2000342e+002
1.3110706e+000 7.4451556e+002
1.3179697e+000 7.4411346e+002
1.3248687e+000 6.9408307e+002
1.3317678e+000 6.8662170e+002
1.3386669e+000 7.0951758e+002
1.3455659e+000 6.7616663e+002
1.3524650e+000 6.7230786e+002
1.3593642e+000 7.1053870e+002
1.3662632e+000 7.2593860e+002
1.3731623e+000 7.1484381e+002
1.3800614e+000 7.3073920e+002
1.3869605e+000 7.2766406e+002
1.3938595e+000 7.1958862e+002
1.4007586e+000 7.0147577e+002
1.4076577e+000 6.9747528e+002
1.4145567e+000 6.9634515e+002
1.4214559e+000 6.6082648e+002
1.4283550e+000 6.4877466e+002
1.4352540e+000 6.6942896e+002
1.4421531e+000 6.8172211e+002
1.4490522e+000 6.5540350e+002
1.4559512e+000 6.4846545e+002
1.4628503e+000 6.6383038e+002
1.4697495e+000 6.4449670e+002
1.4766484e+000 6.3950043e+002
1.4835476e+000 6.4479529e+002
1.4904467e+000 6.4849249e+002
1.4973457e+000 6.4100800e+002
1.5042448e+000 6.6731049e+002
1.5111439e+000 6.8118671e+002
1.5180429e+000 6.5618878e+002
1.5249420e+000 6.3446680e+002
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1.5387402e+000 6.5466571e+002
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1.6560246e+000 5.8637256e+002
1.6629237e+000 6.0021320e+002
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1.6767218e+000 6.1003906e+002
1.6836209e+000 5.9548285e+002
1.6905199e+000 5.8961163e+002
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