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

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Branch: pkgsrc-2017Q4, Version: 0.9.7, Package name: py27-lmfit-0.9.7, 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: 28c7ace0159f1197495235b124ef8c6e78aad2f9
RMD160: 010acd89cd1039bfc8180d3fa32ce23755e42929
Filesize: 1148.105 KB

Version history: (Expand)