LMFIT Contents Download Develop
Non-Linear Least-Squares Minimization and Curve-Fitting for Python Introduction Parameters Models

Getting LMFIT

Current version: 0.8.0rc4

Download:   PyPI (Python.org)

Install:   pip install lmfit

Development version:

Support and Feedback

  Mailing List
  Issue Tracker

Off-line Documentation

[PDF |EPUB |HTML(zip) ]

Non-Linear Least-Square Minimization and Curve-Fitting for PythonΒΆ

The lmfit python package provides a simple and flexible interface to non-linear optimization and curve fitting problems. Initially designed to extend the the Levenberg-Marquardt algorithm in scipy.optimize.leastsq(), lmfit supports most of the optimization methods from scipy.optimize. It also provides a simple way to apply this extension to curve fitting or data modeling problems.

The key concept in lmfit is the Parameter – the quantity to be optimized in all minimization problems in place of a plain floating point number. A Parameter has a value that can be varied in the fit, fixed, have upper and/or lower bounds. It can even have a value that is constrained by an algebraic expression of other Parameter values. Since Parameters live outside the core optimization routines, they can be used in all optimization routines from scipy.optimize. By using Parameter objects instead of plain variables, the objective function does not have to be modified to reflect every change of what is varied in the fit. This simplifies the writing of models, allowing general models that describe the phenomenon to be written, and gives the user more flexibility in using and testing variations of that model.

Lmfit supports several optimization methods from scipy.optimize. The default and best tested optimization method (and the origin of the name) is the Levenberg-Marquardt algorithm of scipy.optimize.leastsq(). An important feature of this method is that it automatically estimates uncertainties and correlations between fitted variables from the covariance matrix used in the fit. But, because this approach is sometimes questioned (and rightly so), lmfit also supports methods to do a brute force determination of the confidence intervals for a set of parameters.

Lmfit provides high-level curve-fitting or data modeling functionality through its Model class, which extends the capabilities of scipy.optimize.curve_fit(). This allows you to turn a function that models for your data into a python class that helps you parametrize and fit data with that model. Many pre-built models for common lineshapes are included and ready to use.

The lmfit package is an open-source project, and the software and this document are works in progress. If you are interested in participating in this effort please use the lmfit github repository.