Using lmfit to find out what function models my data

Question

I am trying to determine what function best models my data, and online research has pointed me to lmfit. Trying the original example code, applying it to my data worked [link: 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()

However, Gaussian does not seem to be the best fit for my data, so I looked into applying the other prebuilt models [link: http://lmfit.github.io/lmfit-py/builtin_models.html#models.LorentzianModel]

Using this:

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))

Lead to the following error.

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

My x values are simply 1 - 1498, and my y values are listed below:

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
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9297795.4
9303542.65
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9317456.27
9325878.77
9335515.1
9345471.94
9356241.76
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9406117.54
9420653.73
9435641.28
9451171.3
9467980.33
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9503648.27
9522796.67
9543117.19
9563609.45
9584674.92
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9723960.1
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9775898.59
9802748.9
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10006167.52
10038678.16
10071958.29
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10245858.43
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10318479.85
10355869.47
10393394.82
10432406.69
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10512080.29
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10635996.2
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11029769.1
11075840.9
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11527162.54
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2455562.72
2456500.53
2457242.73

If anyone has an idea as to what type of modelling method I should be using based off this data, your suggestion is super-welcomed, and otherwise, any tips on how I can avoid this error I am seeing so I can keep testing on my own, that would be splendid.

Thanks in advance!

Answer 1

因此，我不确定为什么它会完全起作用，但是当我使x值等于一个由数字1-> 1498组成的数据帧时，而不是定义的范围（1,1499），它就起作用了。