使用lmfit找出什么函数模型我的数据

Question

我想确定什么函数最好的模型我的数据，并在线研究指出我lmfit。试图原来的示例代码，将其应用到我的数据奋力[链接：http://lmfit.github.io/lmfit-py/model.html]

from numpy import sqrt, pi, exp, linspace, loadtxt 
from lmfit import Model 

import matplotlib.pyplot as plt 

data = loadtxt('model1d_gauss.dat') 
x = range(1,1499) 
y = cd_rev 

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)) 

gmod = Model(gaussian) 
result = gmod.fit(y, x=x, amp=5, cen=5, wid=1) 

print(result.fit_report()) 

plt.plot(x, y,   'bo') 
plt.plot(x, result.init_fit, 'k--') 
plt.plot(x, result.best_fit, 'r-') 
plt.show() 

然而，高斯似乎并不为我的数据最适合，所以我看着将其他预构建的模型[链接： http://lmfit.github.io/lmfit-py/builtin_models.html#models.LorentzianModel]

使用此：

from lmfit.models import LorentzianModel 
mod = LorentzianModel() 

x = range(1,1499) 
y = cd_rev 

pars = mod.guess(y, x=x) 
out = mod.fit(y, pars, x=x) 
print(out.fit_report(min_correl=0.25)) 

导致以下错误。

Traceback (most recent call last): 
    File "model.py", line 74, in <module> 
    pars = mod.guess(y, x=x) 
    File "C:\Python27\lib\site-packages\lmfit\models.py", line 168, in guess 
    pars = guess_from_peak(self, data, x, negative, ampscale=1.25) 
    File "C:\Python27\lib\site-packages\lmfit\models.py", line 48, in guess_from_peak 
    cen = x[halfmax_vals].mean() 
TypeError: only integer arrays with one element can be converted to an index 

我的x值仅仅是1 - 1498，我的y值列举如下：

9263276.56 
9264271.46 
9265486.69 
9266804.81 
9268224.32 
9269822.27 
9271688.76 
9273807.33 
9276093.68 
9278604.58 
9281388.2 
9284587.14 
9288333.03 
9292520.88 
9297795.4 
9303542.65 
9309620.26 
9317456.27 
9325878.77 
9335515.1 
9345471.94 
9356241.76 
9367555.0 
9379537.9 
9392446.0 
9406117.54 
9420653.73 
9435641.28 
9451171.3 
9467980.33 
9485059.83 
9503648.27 
9522796.67 
9543117.19 
9563609.45 
9584674.92 
9606107.87 
9627711.84 
9650913.17 
9674734.04 
9699173.17 
9723960.1 
9749668.72 
9775898.59 
9802748.9 
9830192.93 
9855772.95 
9884782.73 
9914368.7 
9944490.84 
9974267.99 
10006167.52 
10038678.16 
10071958.29 
10105763.63 
10139977.94 
10174863.69 
10210213.28 
10245858.43 
10281872.14 
10318479.85 
10355869.47 
10393394.82 
10432406.69 
10471872.68 
10512080.29 
10552697.21 
10594016.2 
10635996.2 
10678190.51 
10720693.69 
10763545.39 
10806977.0 
10850612.55 
10894637.54 
10938992.03 
10983961.76 
11029769.1 
11075840.9 
11122175.87 
11169313.69 
11216635.84 
11266139.82 
11315879.66 
11368012.27 
11420410.23 
11473347.86 
11527162.54 
11581222.55 
11635432.39 
11690003.0 
11744755.58 
11800156.59 
11856553.08 
11914237.05 
11972094.52 
12030324.94 
12088974.66 
12147854.18 
12207074.0 
11900557.54 
11698968.25 
11613658.84 
11643184.18 
11672738.59 
11694875.75 
11703412.75 
11721756.79 
11753833.17 
11786671.1 
11721712.51 
11722709.5 
11676681.23 
11699753.04 
11673979.94 
11705254.35 
11737514.48 
11761217.15 
11374508.19 
11378709.13 
10770542.3 
10655749.65 
10650533.02 
10651123.91 
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2589593.26 
2590868.64 
2592674.0 
2592838.24 
2593798.23 
2594350.15 
2595528.79 
2596534.17 
2598532.12 
2600165.28 
2600532.12 
2601571.26 
2602360.97 
2603954.09 
2604200.79 
2604400.51 
2595810.54 
2597645.2 
2594294.93 
2594555.55 
2596013.4 
2597285.52 
2598837.54 
2600583.42 
2601948.14 
2602539.32 
2603203.56 
2602427.27 
2603465.7 
2604730.89 
2606201.67 
2606752.8 
2608340.12 
2609805.67 
2611357.4 
2613028.43 
2614621.44 
2615696.65 
2615757.65 
2606874.51 
2606831.54 
2608328.59 
2609945.65 
2604588.96 
2606009.52 
2607306.87 
2609001.34 
2610395.3 
2611998.57 
2612711.8 
2613633.53 
2615320.8 
2616750.79 
2618235.01 
2619762.5 
2620529.1 
2622075.59 
2619625.96 
2620572.74 
2622223.42 
2623760.79 
2625487.76 
2625718.84 
2627330.04 
2620010.92 
2621611.93 
2622839.54 
2624279.82 
2608768.67 
2610124.07 
2611285.46 
2613045.47 
2614294.34 
2616097.62 
2617797.91 
2612561.58 
2613605.36 
2614897.63 
2616131.26 
2617260.44 
2618045.11 
2619467.35 
2620888.23 
2622548.17 
2624115.43 
2625693.01 
2627522.02 
2628971.18 
2630304.0 
2630069.92 
2631352.23 
2624838.65 
2625995.72 
2626608.1 
2627495.2 
2628160.25 
2629552.17 
2630687.71 
2631566.22 
2632291.52 
2633804.44 
2634734.68 
2635651.13 
2635318.88 
2635203.93 
2624582.36 
2626191.71 
2627145.43 
2627476.97 
2629093.52 
2630416.1 
2631854.51 
2633209.47 
2634551.27 
2635738.77 
2637101.14 
2638473.9 
2638760.61 
2640082.5 
2641438.38 
2641965.49 
2641626.21 
2642975.8 
2644406.98 
2643488.66 
2644464.4 
2645663.88 
2630822.13 
2631838.6 
2633079.49 
2629884.62 
2630665.57 
2631945.72 
1.16 
2633688.48 
2633527.24 
2634644.06 
2635903.08 
2637292.64 
2539646.57 
2540665.12 
2496184.48 
2497330.91 
2497801.08 
2495941.1 
2497261.21 
2498761.37 
2499704.53 
2500077.32 
2497335.14 
2498657.54 
2500063.73 
2498725.29 
2500017.92 
2501370.18 
2502672.68 
2503742.64 
2503080.1 
2503096.42 
2504354.91 
2501579.61 
2502783.58 
2500326.49 
2501426.98 
2502624.09 
2503660.39 
2504854.8 
2505937.33 
2507227.51 
2508713.54 
2508757.33 
2509488.47 
2510785.26 
2512266.19 
2513251.91 
2513481.46 
2514389.08 
2515195.85 
2516608.33 
2517622.8 
2518784.77 
2519087.75 
2520271.32 
2517743.54 
2518448.44 
2519475.53 
2520239.04 
2521300.4 
2522560.36 
2501000.95 
2502115.66 
2500860.86 
2502334.68 
2503280.65 
2480097.31 
2475906.6 
2476889.46 
2478210.75 
2479090.08 
2479866.89 
2481222.43 
2472877.25 
2473288.04 
2474050.7 
2474393.76 
2474392.26 
2473170.48 
2474050.31 
2473849.02 
2472220.9 
2473182.15 
2472601.6 
2473342.11 
2474705.55 
2475583.27 
2472551.78 
2474011.16 
2475495.46 
2476787.13 
2478129.12 
2478598.49 
2479245.26 
2480352.34 
2481740.9 
2477213.11 
2478196.59 
2478096.76 
2474023.75 
2475178.96 
2474848.41 
2476155.31 
2476142.48 
2477330.16 
2473738.23 
2475101.59 
2475011.56 
2476086.73 
2477381.51 
2478220.42 
2465436.3 
2466183.78 
2452765.97 
2454047.16 
2436561.61 
2436821.25 
2437627.45 
2437987.49 
2439078.59 
2438714.52 
2439854.97 
2440640.16 
2434556.8 
2435951.32 
2436464.15 
2437619.85 
2437786.8 
2438926.62 
2440197.32 
2441322.03 
2438852.59 
2440051.97 
2441184.28 
2442289.09 
2442968.85 
2438356.2 
2439466.52 
2440814.88 
2441843.6 
2442937.67 
2443671.32 
2442804.98 
2443880.84 
2445102.37 
2445361.84 
2445913.01 
2446703.0 
2447866.17 
2449001.64 
2449975.33 
2450451.63 
2451610.42 
2452365.38 
2452621.52 
2453534.43 
2446370.06 
2445119.33 
2446263.61 
2447406.95 
2447995.87 
2406526.41 
2407473.66 
2408257.39 
2408376.24 
2409630.89 
2410776.36 
2411866.67 
2412847.38 
2412603.87 
2413663.06 
2414694.04 
2415737.64 
2415703.47 
2416853.39 
2418043.4 
2419136.99 
2420325.34 
2421540.26 
2422672.29 
2420885.35 
2417242.43 
2418078.16 
2419255.86 
2419841.97 
2419161.44 
2419014.08 
2419883.0 
2419620.72 
2420196.75 
2420743.53 
2417090.43 
2417713.78 
2418639.82 
2418900.41 
2419989.95 
2421064.74 
2422207.99 
2423350.43 
2423950.35 
2425049.07 
2426091.27 
2427049.0 
2428117.1 
2429240.04 
2430362.16 
2431483.44 
2432813.43 
2433681.55 
2434940.2 
2434938.23 
2415249.79 
2416196.9 
2417255.19 
2418031.82 
2419060.07 
2419539.05 
2420579.13 
2421350.77 
2420007.65 
2421043.13 
2422218.8 
2422221.88 
2423325.06 
2423069.98 
2424170.57 
2422339.22 
2423166.89 
2424276.98 
2425542.36 
2424208.57 
2425286.27 
2425950.78 
2426969.65 
2428044.58 
2429118.68 
2430149.17 
2429337.95 
2430294.01 
2431520.6 
2432703.68 
2433514.02 
2434681.17 
2434944.83 
2434231.41 
2435423.23 
2426275.19 
2427171.19 
2428382.49 
2429506.86 
2429766.49 
2430687.04 
2431822.88 
2427099.42 
2428229.91 
2429316.33 
2430589.95 
2431863.07 
2433019.83 
2434045.5 
2434649.81 
2432307.15 
2433487.47 
2434231.26 
2434246.81 
2435438.69 
2436222.29 
2437121.27 
2437115.27 
2438478.47 
2438819.3 
2439845.03 
2441001.5 
2442128.05 
2443517.42 
2443705.15 
2445019.72 
2445923.09 
2447104.39 
2447624.03 
2444746.46 
2445746.21 
2446921.74 
2447846.24 
2448430.77 
2449559.63 
2450599.27 
2451785.81 
2452661.37 
2453609.86 
2454838.63 
2447906.55 
2449188.04 
2450483.91 
2450473.44 
2450015.29 
2448380.4 
2449492.1 
2450022.8 
2450745.61 
2451064.92 
2452292.02 
2452847.2 
2453177.06 
2453669.57 
2454758.6 
2455562.72 
2456500.53 
2457242.73 

如果任何人有一个想法，什么类型的建模方法，我应该使用基于关闭此数据，你的建议是非常受欢迎的，否则，关于如何避免我看到的这个错误的任何提示，所以我可以继续测试，这将是出色的。

在此先感谢！

Answer 1

所以我不确定为什么这个工作正好，但是当我的x值等于数字1 - > 1498而不是定义的范围（1,1499）组成的数据帧时，它可以工作。