我遇到了lmfit的猜测功能问题。我正在尝试拟合一些实验数据，我想使用不同的lmfit内置模型，但我不能运行内置模块，只有直接定义函数。
以下代码不起作用，但如果我对猜测函数进行评论，则可以正常工作。
P.S。对我来说更有意思的是索引是第一列，因为我将把它放在一个循环中，它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新列放入DataFrame。
# -*- 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
我试过从models.py运行guess_from_peak，我没有遇到导致整数的问题。
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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3.9603174e+000 3.5532330e+002
3.9672165e+000 3.5889478e+002
3.9741156e+000 3.6407483e+002
3.9810147e+000 3.6295535e+002
3.9879138e+000 3.6387720e+002
3.9948130e+000 3.6416183e+002
4.0017118e+000 3.6089911e+002
4.0086112e+000 3.6826599e+002
4.0155101e+000 3.7570581e+002
4.0224090e+000 3.6361679e+002
4.0293083e+000 3.6003177e+002
4.0362072e+000 3.7528265e+002
4.0431066e+000 3.7368362e+002
4.0500054e+000 3.8174683e+002
4.0569048e+000 4.0386084e+002
4.0638037e+000 4.2738324e+002
4.0707026e+000 4.4587668e+002
4.0776019e+000 4.5433987e+002
4.0845008e+000 4.4404083e+002
4.0914001e+000 4.2589066e+002
4.0982990e+000 3.9662262e+002
4.1051979e+000 3.7311325e+002
4.1120973e+000 3.5790594e+002
4.1189961e+000 3.4554794e+002
4.1258955e+000 3.5435367e+002
4.1327944e+000 3.7766489e+002
4.1396937e+000 3.7425708e+002
4.1465926e+000 3.5805182e+002
4.1534915e+000 3.5078519e+002
4.1603909e+000 3.5888739e+002
4.1672897e+000 3.7242688e+002
4.1741891e+000 3.7792575e+002
4.1810880e+000 3.7338031e+002
4.1879873e+000 3.6538324e+002
4.1948862e+000 3.5872525e+002
4.2017851e+000 3.4688391e+002
4.2086844e+000 3.4881918e+002
4.2155833e+000 3.4818274e+002
4.2224827e+000 3.4055273e+002
4.2293816e+000 3.3977536e+002
4.2362804e+000 3.3322891e+002
4.2431798e+000 3.3594962e+002
4.2500787e+000 3.4658536e+002
4.2569780e+000 3.4479083e+002
4.2638769e+000 3.4267456e+002
4.2707763e+000 3.4828876e+002
4.2776752e+000 3.4845041e+002
4.2845740e+000 3.3986469e+002
4.2914734e+000 3.3093433e+002
4.2983723e+000 3.3255331e+002
4.3052716e+000 3.4089511e+002
4.3121705e+000 3.4742932e+002
4.3190699e+000 3.3570422e+002
4.3259687e+000 3.2636673e+002
4.3328676e+000 3.3228806e+002
4.3397670e+000 3.5141977e+002
4.3466659e+000 3.5683167e+002
4.3535652e+000 3.4719943e+002
4.3604641e+000 3.4054718e+002
4.3673630e+000 3.2842471e+002
4.3742623e+000 3.2503146e+002
4.3811612e+000 3.3431540e+002
4.3880606e+000 3.3462808e+002
4.3949594e+000 3.3529224e+002
4.4018588e+000 3.3313510e+002
4.4087577e+000 3.4015598e+002
4.4156566e+000 3.3703552e+002
4.4225559e+000 3.3024448e+002
4.4294548e+000 3.2974786e+002
答案 0 :(得分：0)
此示例适用于我：
#!/usr/bin/env python
from lmfit.models import LorentzianModel
import matplotlib.pyplot as plt
import pandas as pd
dframe = pd.read_csv('peak.csv')
model = LorentzianModel()
params = model.guess(dframe['y'], x=dframe['x'])
result = model.fit(dframe['y'], params, x=dframe['x'])
print(result.fit_report())
result.plot_fit()
plt.show()
，peaks.csv
x,y
0.000000, 0.021654
0.200000, 0.385367
0.400000, 0.193304
0.600000, 0.103481
0.800000, 0.404041
1.000000, 0.212585
1.200000, 0.253212
1.400000, -0.037306
1.600000, 0.271415
1.800000, 0.025614
2.000000, 0.066419
2.200000, -0.034347
2.400000, 0.153702
2.600000, 0.161341
2.800000, -0.097676
3.000000, -0.061880
3.200000, 0.085341
3.400000, 0.083674
3.600000, 0.190944
3.800000, 0.222168
4.000000, 0.214417
4.200000, 0.341221
4.400000, 0.634501
4.600000, 0.302566
4.800000, 0.101096
5.000000, -0.106441
5.200000, 0.567396
5.400000, 0.531899
5.600000, 0.459800
5.800000, 0.646655
6.000000, 0.662228
6.200000, 0.820844
6.400000, 0.947696
6.600000, 1.541353
6.800000, 1.763981
7.000000, 1.846081
7.200000, 2.986333
7.400000, 3.182907
7.600000, 3.786487
7.800000, 4.822287
8.000000, 5.739122
8.200000, 6.744448
8.400000, 7.295213
8.600000, 8.737766
8.800000, 9.693782
9.000000, 9.894218
9.200000, 10.193956
9.400000, 10.091519
9.600000, 9.652392
9.800000, 8.670938
10.000000, 8.004205
10.200000, 6.773599
10.400000, 6.076502
10.600000, 5.127315
10.800000, 4.303762
11.000000, 3.426006
11.200000, 2.416431
11.400000, 2.311363
11.600000, 1.748020
11.800000, 1.135594
12.000000, 0.888514
12.200000, 1.030794
12.400000, 0.543024
12.600000, 0.767751
12.800000, 0.657551
13.000000, 0.495730
13.200000, 0.447520
13.400000, 0.173839
13.600000, 0.256758
13.800000, 0.596106
14.000000, 0.065328
14.200000, 0.197267
14.400000, 0.260038
14.600000, 0.460880
14.800000, 0.335248
15.000000, 0.295977
15.200000, -0.010228
15.400000, 0.138670
15.600000, 0.192113
15.800000, 0.304371
16.000000, 0.442517
16.200000, 0.164944
16.400000, 0.001907
16.600000, 0.207504
16.800000, 0.012640
17.000000, 0.090878
17.200000, -0.222967
17.400000, 0.391717
17.600000, 0.180295
17.800000, 0.206875
18.000000, 0.240595
18.200000, -0.037437
18.400000, 0.139918
18.600000, 0.012560
18.800000, -0.053009
19.000000, 0.226069
19.200000, 0.076879
19.400000, 0.078599
19.600000, 0.016125
19.800000, -0.071217
20.000000, -0.091474
答案 1 :(得分：0)
正如我在上面的评论中所建议的，将熊猫系列强制转换为ndarray将解决问题：
mod = GaussianModel()
ydata = np.array(dataDataframe[0])
xdata = np.array(dataDataframe.index.values)
pars = mod.guess(ydata, x=xdata)
out = mod.fit(ydata, pars, x=xdata)