|Non-Linear Least-Squares Minimization and Curve-Fitting for Python||Introduction||Parameters||Models|
Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on Levenberg-Marquardt algorithm of scipy.optimize.leastsq(), but also supports most of the optimization methods from scipy.optimize. It has a number of useful enhancements, including:
- Using Parameter objects instead of plain floats as variables. 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.
- Ease of changing fitting algorithms. Once a fitting model is set up, one can change the fitting algorithm without changing the objective function.
- Improved estimation of confidence intervals. While scipy.optimize.leastsq() will automatically calculate uncertainties and correlations from the covariance matrix, lmfit also has functions to explicitly explore parameter space to determine confidence levels even for the most difficult cases.
- Improved curve-fitting with the Model class. This which extends the capabilities of scipy.optimize.curve_fit(), allowing 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 Free software, using an MIT license. The software and this document are works in progress. If you are interested in participating in this effort please use the lmfit github repository.