lmfit
Least-Squares Minimization with Bounds and Constraints
Decision gist · record as of 2026-08-14
Yes. Lmfit is stable, permissively licensed, and widely used for scientific curve fitting. Install friction is low and it has no known vulnerabilities. The 391-day release gap suggests aging maintenance rather than active development, so treat it as a mature tool rather than one receiving frequent feature updates—but that's appropriate for a well-established optimization library. Install if you need flexible, named-parameter fitting with bounds and constraints.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; scipy and numpy must be installed first as core dependencies.
- Low friction: pure Python wheel with five runtime dependencies (numpy, scipy, asteval, uncertainties, dill).
- Last release 391 days ago marks aging maintenance, but the package is stable and widely used.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required.
last release 2025-07-19 (391 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 867,327 downloads/mo, #4,855 on PyPI
Alternatives
Verify before relying
pip install lmfit
import lmfit
params = lmfit.create_params(amp=1.2, cen={'value': 40, 'vary': False}, wid={'value': 4, 'min': 0})
result = lmfit.minimize(objective_func, params, args=(x_data, y_data))- Whether the package actively accepts contributions or is in maintenance-only mode given the 391-day release gap.
- Performance characteristics when fitting models with hundreds or thousands of parameters.
What it is and what it does
Lmfit wraps scipy's optimization routines to make curve fitting and least-squares minimization more accessible and flexible. Instead of passing raw arrays to an optimizer, you define named Parameters with optional bounds, fixed values, and mathematical constraints linking them together. The library handles the bookkeeping of which parameters vary and which are held constant, so your fitting function stays clean and model-focused rather than optimization-focused.
The package ships with a Model class for common curve-fitting workflows, built-in model functions, and tools to explore confidence intervals and estimate parameter uncertainties even for solvers where scipy doesn't provide them natively. It's built on numpy and scipy, supports multiple optimization algorithms (Levenberg-Marquardt by default, plus Nelder-Mead, differential_evolution, basin-hopping, and others), and is designed for interactive data analysis where you may want to change constraints or fix parameters on the fly.
Use it for
- Fit spectral lines or peaks to experimental data with bounds on amplitude, center, and width.
- Constrain model parameters by mathematical expressions (e.g., force FWHM = width × 2.355) without rewriting the objective function.
- Estimate parameter uncertainties and correlations after fitting, or explore confidence intervals.
- Build reusable model functions and combine them for complex multi-peak or multi-component fits.
- Switch between optimization algorithms without changing your model or parameter setup.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Lmfit is stable, permissively licensed, and widely used for scientific curve fitting. Install friction is low and it has no known vulnerabilities. The 391-day release gap suggests aging maintenance rather than active development, so treat it as a mature tool rather than one receiving frequent feature updates—but that's appropriate for a well-established optimization library. Install if you need flexible, named-parameter fitting with bounds and constraints.
Install
lmfit on PyPI
Before you install
Low friction: pure Python wheel with five runtime dependencies (numpy, scipy, asteval, uncertainties, dill). Last release 391 days ago marks aging maintenance, but the package is stable and widely used.
Requires Python 3.9 or later; scipy and numpy must be installed first as core dependencies.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required.
Quickstart
pip install lmfit
import lmfit
params = lmfit.create_params(amp=1.2, cen={'value': 40, 'vary': False}, wid={'value': 4, 'min': 0})
result = lmfit.minimize(objective_func, params, args=(x_data, y_data))Verify before relying
- Whether the package actively accepts contributions or is in maintenance-only mode given the 391-day release gap.
- Performance characteristics when fitting models with hundreds or thousands of parameters.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesastevalnumpyscipyuncertaintiesdill |
| Maintenance | Aging 391 days since the last release |
| First released | |
| Downloads | 867,327 / month, #4,855 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/Engineering |
Evidence: lmfit-1.3.4-py3-none-any.whl
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