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

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Branch: pkgsrc-2019Q1, Version: 0.9.12, Package name: py27-lmfit-0.9.12, 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:
[lang/py-six] [lang/python27] [math/py-asteval] [math/py-numpy]

Master sites:

SHA1: 7c4a15975d9920c9cf12fd98aca7e7706daf18c9
RMD160: dc4bb6835e34fadd8786c1dd760f09df694dba6b
Filesize: 1532.712 KB

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