Python 将lmfit拟合参数制成表格

我正在尝试拟合这些数据：

12,12,12,12,12,12,12,12,12,12,14,18,38,57,100,130,191,212,285,423,653,949,1209,1412,1784,2281,2876,3661,4499,5423,6633,7730,9134,10995,12612,14459,16018,19856,22304,25233,29155,32964,37575,40174,44550,52128,56989,59105,82165,89953,92839,98010,109069,112950,117749,124869,129654,132591,136779,143303

使用

lmfit

和

pandas

数据帧作为：

import datetime
import os
import os.path
import re

import numpy as np
import pandas
from lmfit.models import PolynomialModel

import matplotlib  # isort:skip
matplotlib.use('TkAgg')  # isort:skip
import matplotlib.pyplot as plt  # isort:skip

lastday = 60
datafile = "mwe.dat"
names=["Trial"]

data = (np.genfromtxt(datafile, delimiter=','))
dataset = pandas.DataFrame(data=data.T, columns=names)
model = PolynomialModel(5)
x = dataset.index.values
print('parameter names: {}'.format(model.param_names))
print('independent variables: {}'.format(model.independent_vars))
for name in names:
  print(name)
  color = next(plt.gca()._get_lines.prop_cycler)['color']
  params = model.guess(dataset[name], x=x)
  result = model.fit(dataset[name], params, x=x)
  print(result.params)
  plt.plot(dataset.index.values, dataset[name], 'o-', color=color, label=name)
  plt.plot(dataset.index.values, result.best_fit, '-', color=color)

plt.legend(loc='best')
plt.tight_layout(True)
plt.show()

'print（result.params）给出的结果如下：

Parameters([('c0', <Parameter 'c0', value=3023.2241950739667 +/- 1.92e+03, bounds=[-inf:inf]>), ('c1', <Parameter 'c1', value=-2257.933933498409 +/- 675, bounds=[-inf:inf]>), ('c2', <Parameter 'c2', value=343.722181719893 +/- 72.4, bounds=[-inf:inf]>), ('c3', <Parameter 'c3', value=-19.15755469779014 +/- 3.14, bounds=[-inf:inf]>), ('c4', <Parameter 'c4', value=0.43895322817807797 +/- 0.0589, bounds=[-inf:inf]>), ('c5', <Parameter 'c5', value=-0.0032273077815159245 +/- 0.000397, bounds=[-inf:inf]>)])

---

等等。

好吧，你可以选择一些基本的印刷品：

print(' '.join(['{:>15s}'.format(p.name) for p in result.params.values()]))
print(' '.join(['{:15.6g}'.format(p.value) for p in result.params.values()]))

因为你用的是熊猫，所以你可能更喜欢这样的东西

pd_pars = pandas.DataFrame([(p.name, p.value, p.stderr) for p in result.params.values()], 
                            columns=('name', 'best-fit value', 'standard error'))
print(pd_pars.transpose())

pd_pars = pandas.DataFrame([(p.name, p.value, p.stderr) for p in result.params.values()], 
                            columns=('name', 'best-fit value', 'standard error'))
print(pd_pars.transpose())