CFit (named after optimization algorithm 'scipy.curve_fit') is a curve fitting tool in Python, based on the method of least squares. It is equipped with some standard functions commonly used in physics, and plotting functionality, with a graphical user interface built using NiceGUI. It can be used out-of-the-box for simple datasets through its "smart" parameter guesses. Try it for yourself at CFit v2.0!

The easiest way to use the code is the web app linked above. However, it can also be used in code without the GUI. A minimal working example is:

import numpy as np
import matplotlib.pyplot as plt
from cfit import dataset, function, fitting

# Load and fit the data
data = dataset.Dataset('path/to/data.csv')
function = function.functions_dict["Gaussian"]
fit = fitting.Fit(data, function)

# Plot data
plt.plot(data.x, data.y, 'o', label='data')
plt.plot(data.x, fit.function(data.x, *fit.fit_params), label='fit')

If you find any bugs, run into errors or have suggestions on functions you would like to see added, feel free to open a GitHub issue or email me directly at jitendhandha[a]gmail[dot]com. I will be more than happy to take a look at it!