labfit 0.1.0

Student-friendly least-squares curve fitting for Python

Documentation · Quick start · Gallery

LabFit is a small, student-friendly Python library for least-squares curve fitting, error propagation, and publication-quality plotting - built on NumPy, SciPy, and Matplotlib.

pip install labfit

Quick start

from labfit import fit, plot_fit

result = fit("data.csv", model="exponential")
plot_fit(result)
print(f"χ²/ν = {result.reduced_chi2:.3f}")

Three lines: CSV → fit → plot → goodness-of-fit.

Features

• Built-in models - linear, quadratic, Gaussian, Lorentzian, exponential, power law, logistic, sinc, bimodal Gaussian, damped oscillator, and more - all by name.
• Reduced χ² on every fit result, so you can immediately assess goodness-of-fit.
• Asymmetric & correlated errors - handle sigma_low/sigma_high and full covariance matrices without rewriting propagation code.
• Multi-series fitting - fit and compare many data sets on shared or independent axes.
• Plots - residual panels, multi-fit grids, proper axis labels.
• 100 % Python - no compiled extensions, installs everywhere.

Longer example

import numpy as np
from labfit import quick_fit, plot_fit

rng = np.random.default_rng(42)
x = np.linspace(-5, 5, 200)
y = 3.0 * np.exp(-0.5 * ((x - 0.5) / 1.2) ** 2) + rng.normal(0, 0.05, size=x.size)

result = quick_fit(x, y, model="gaussian")
print(f"amplitude = {result.params['amplitude']:.3f}")
print(f"mean      = {result.params['mean']:.3f}")
print(f"sigma     = {result.params['sigma']:.3f}")
print(f"χ²/ν      = {result.reduced_chi2:.4f}")

plot = plot_fit(result, show_residuals=True)
plot.save("gaussian_fit.png")

Dependencies

• Python ≥ 3.10
• NumPy, SciPy, Matplotlib