Metadata-Version: 1.1
Name: lmfit
Version: 0.7.2
Summary: Least-Squares Minimization with Bounds and Constraints
Home-page: http://cars9.uchicago.edu/software/python/lmfit/
Author: Matthew Newville
Author-email: newville@cars.uchicago.edu
License: BSD
Download-URL: http://newville.github.com/lmfit-py/
Description: A library for least-squares minimization and data fitting in
        Python.  Built on top of scipy.optimize, lmfit provides a Parameter object
        which can be set as fixed or free, can have upper and/or lower bounds, or
        can be written in terms of algebraic constraints of other Parameters.  The
        user writes a function to be minimized as a function of these Parameters,
        and the scipy.optimize methods are used to find the optimal values for the
        Parameters.  The Levenberg-Marquardt (leastsq) is the default minimization
        algorithm, and provides estimated standard errors and correlations between
        varied Parameters.  Other minimization methods, including Nelder-Mead's
        downhill simplex, Powell's method, BFGS, Sequential Least Squares, and
        others are also supported.  Bounds and contraints can be placed on
        Parameters for all of these methods.
        
        In addition, methods for explicitly calculating confidence intervals are
        provided for exploring minmization problems where the approximation of
        estimating Parameter uncertainties from the covariance matrix is
        questionable.
Platform: Windows
Platform: Linux
Platform: Mac OS X
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires: numpy
Requires: scipy
