lmfit/lmfit-py

Health v2 · maintenance only

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Adoption not part of the score

1231 stars · 296 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

lmfit is a pure Python library built on SciPy and NumPy for non-linear least-squares minimization and curve fitting. It wraps scipy.optimize with named Parameter objects that support bounds, fixed values, algebraic constraints, and uncertainty estimation.

Use cases

• fit a curve to experimental data with nonlinear least squares
• fit a model with bounded and constrained parameters
• estimate parameter uncertainties and confidence intervals after a fit
• switch fitting algorithms without rewriting the objective function
• fit peak models like Gaussian and Lorentzian to spectra
• constrain one fit parameter as an expression of others

When to choose

• you need curve fitting or nonlinear optimization with named, bounded, or constrained parameters
• you want better confidence interval estimation than raw scipy.optimize provides
• you want a high-level Model API for common fitting shapes

When to avoid

• you need linear regression or simple polynomial fits where numpy.polyfit suffices
• you need general-purpose global optimization unrelated to least squares
• you need machine-learning model training rather than scientific curve fitting

Facets

library · maturity stable

math data-science data-science python cross-platform curve-fitting least-squares optimization scipy parameter-estimation confidence-intervals algorithms scientific-computing

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