Import py27-lsqfit-4.5.1 as wip/py-lsqfit.

These packages facilitate least-squares fitting of noisy data by
multi-dimensional, nonlinear functions of arbitrarily many
parameters. The central package is :mod:`lsqfit` which provides
the fitting capability. :mod:`lsqfit` makes heavy use of package
:mod:`gvar`, which provides tools for the analysis of error
propagation, and also for the creation of complicated
multi-dimensional gaussian distributions. :mod:`lsqfit` supports
Bayesian priors for the fit parameters, with arbitrarily
complicated multidimensional gaussian distributions. It uses
automatic differentiation to compute gradients, greatly simplifying
the design of fit functions.
Import py27-corrfitter-3.6 as wip/py-corrfitter.

This module contains tools that facilitate least-squares fits, as functions
of time ``t``, of simulation (or other statistical) data for 2-point and
3-point correlators of the form::
Gab(t)    =  <b(t) a(0)>
Gavb(t,T) =  <b(T) V(t) a(0)>
Each correlator is modeled using |Corr2| for 2-point correlators, or
|Corr3| for 3-point correlators in terms of amplitudes for each source
``a``, sink ``b``, and vertex ``V``, and the energies associated with each
intermediate state. The amplitudes and energies are adjusted in the
least-squares fit to reproduce the data; they are specified in a shared prior
(typically a dictionary).

An object of type |CorrFitter| describes a collection of correlators and is
used to fit multiple models to data simultaneously. Any number of
correlators may be described and fit by a single |CorrFitter| object.
|CorrFitter| objects can also be used to to extract the appropriate fit
data from |Dataset| objects.

This module has been used extensively for analyzing results from lattice
QCD simulations