This package facilitates least-squares fitting of noisy data by multi-dimensional, nonlinear functions of arbitrarily many parameters. lsqfit supports Bayesian priors for the fit parameters, with arbitrarily complicated multidimensional Gaussian distributions. A tutorial on fitting is included in the documentation; documentation is in the doc/ subdirectory: see doc/html/index.html or <https://lsqfit.readthedocs.io>.

The fitter uses automatic differentiation to compute gradients of the fit function. This greatly simplifies coding of the fit function since only the function itself need be coded. Coding is also simplified by using dictionaries (instead of arrays) for representing fit data and fit priors.

lsqfit makes heavy use of Python package gvar, which simplifies the analysis of error propagation and the creation of multi-dimensional Gaussian distributions (for fit priors).

This code has been used on a laptop to fit functions of tens-to-thousands of parameters to tens-to-thousands of pieces of data. lsqfit uses the GNU Scientific Library (GSL) and/or scipy to do the fitting, numpy for efficient array arithmetic, and cython to compile efficient code that interfaces between Python and the C-based GSL.

Information on how to install the components is in the INSTALLATION file.

To test the libraries try make tests. Some examples are give in the examples/ subdirectory.

Version numbers: Incompatible changes are signaled by incrementing the major version number, where version numbers have the form major.minor.patch. The minor number signals new features, and the patch number bug fixes.