Fit with Data in a pandas DataFrame

Simple example demonstrating how to read in the data using pandas and supply the elements of the DataFrame from lmfit.

import matplotlib.pyplot as plt
import pandas as pd

from lmfit.models import LorentzianModel

read the data into a pandas DataFrame, and use the ‘x’ and ‘y’ columns:

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'])

and gives the plot and fitting results below:

result.plot_fit()
plt.show()

print(result.fit_report())

Out:

/Users/Newville/Codes/lmfit-py/examples/example_use_pandas.py:26: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  plt.show()
[[Model]]
    Model(lorentzian)
[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 21
    # data points      = 101
    # variables        = 3
    chi-square         = 13.0737250
    reduced chi-square = 0.13340536
    Akaike info crit   = -200.496119
    Bayesian info crit = -192.650757
[[Variables]]
    sigma:      1.15503893 +/- 0.02603688 (2.25%) (init = 1.3)
    center:     9.22379520 +/- 0.01835869 (0.20%) (init = 9.3)
    amplitude:  39.1530829 +/- 0.62389698 (1.59%) (init = 50.7825)
    fwhm:       2.31007785 +/- 0.05207377 (2.25%) == '2.0000000*sigma'
    height:     10.7899514 +/- 0.17160472 (1.59%) == '0.3183099*amplitude/max(1.e-15, sigma)'
[[Correlations]] (unreported correlations are < 0.100)
    C(sigma, amplitude) =  0.709