./math/py-lmfit, Least-squares minimization with bounds and constraints

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Branch: pkgsrc-2016Q3, Version: 0.9.5, Package name: py27-lmfit-0.9.5, Maintainer: prlw1

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.


Required to run:
[math/py-scipy]

Master sites:

SHA1: e18d8f5c2fc21c327780a13e50051fcd13401ac4
RMD160: 3e9c32c8fe88987e35211e4d7ed694eff292dc7d
Filesize: 1102.526 KB

Version history: (Expand)