Inconsistencies with non-parameter model function arguments

[schtandard]

Description

I have a model function that contains a keyword argument that is not part of the fit parameters (but not a compulsory independent variable either). My expectation was that I can just pass it as a keyword argument to the model's fit() method when I need to set it. Here's an example with s being the argument in question.

import lmfit
import numpy as np
from matplotlib import pyplot as plt

# Create a model.
def foo(x, a=3, b=5, s=1):
    return a + s * b * x**2
mdl = lmfit.Model(foo, param_names=['a', 'b'])
mdl.set_param_hint('a', value=3)
mdl.set_param_hint('b', value=5)

# Synthesize some noisy data.
x = np.linspace(-5, 5, 101)
s = 1 + 0.7 * np.sin(x)
y = foo(x, 2.3, 1.7, s) + np.random.normal(0, 1, x.shape)

# Fit the data and report the result
fitres = mdl.fit(y, x=x, s=s)
print(fitres.fit_report())
fitres.plot()
plt.show()

# Manually check the result.
plt.plot(x, y, '.', label='data')
plt.plot(x, fitres.eval(x=x), label='fit result eval()')
plt.plot(x, foo(x, a=fitres.params['a'], b=fitres.params['b'], s=s), label='manual check')
plt.legend()
plt.show()

Passing s to fit() like this results a warning.

C:\Users\wilde\miniconda3\lib\site-packages\lmfit\model.py:1053: UserWarning: The keyword argument s does not match any arguments of the model function. It will be ignored.

However, the fit still seems to perform as expected, i.e. the argument is passed on to the fitting function, the resulting parameters are correct. Here's the fit report (a = 2.3 and b = 1.7 were used during data synthesis).

[[Model]]
    Model(foo)
[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 7
    # data points      = 101
    # variables        = 2
    chi-square         = 109.127855
    reduced chi-square = 1.10230157
    Akaike info crit   = 11.8173660
    Bayesian info crit = 17.0476071
    R-squared          = 0.99636556
[[Variables]]
    a:  2.13258073 +/- 0.13627980 (6.39%) (init = 3)
    b:  1.69611547 +/- 0.01029550 (0.61%) (init = 5)
[[Correlations]] (unreported correlations are < 0.100)
    C(a, b) = -0.6421

Adding to the confusion, the fit result's plot() function draws the correct residuals and data but an incorrect best fit, i.e. it really does ignore s here.

The eval() method on the other hand seems to respect s.

Version information

Python: 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:40:31) [MSC v.1929 64 bit (AMD64)]

lmfit: 1.2.2, scipy: 1.11.3, numpy: 1.26.0,asteval: 0.9.31, uncertainties: 3.1.7

[reneeotten]

The plotting feature is a very rudimentary convenience function. You presumably need to pass the independent variable to the plot function as well. I can take a closer look at this, but it doesn’t necessarily appear as a real GitHub Issue. If you read the instructions you would have realized that, and at least provided all the information requested in the template.