Corrfitter

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This release adds a new procedure for testing fits: simulated data. Simulated
data is created from real data by adjusting the mean values to correspond
to fluctuations around correlators computed with know parameters, p=pexact.
This means that fits to simulated data should behave quite similarly to the
original fits, but for simulated data we know what the correct answer is
for every parameter. This provides a very flexible tool for assessing
the reliability of a fit and for testing variations on the original fits.
See CorrFitter.simulated_data_iter. Note that this procedure
seems superficially similar to bootstrap analysis but it is really quite
different, very much faster and much more useful.

- Fixed p0 in CorrFitter.chained_fit.

- Minor documentation change (for changes in lsqfit's format function).

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- Minor bug fix in CorrFitter.chained_lsqfit. More tests (designed
to catch bugs like this in the future).

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CorrFitter.chained_lsqfit continues to evolve in this release. It is
still somewhat experimental but continues to perform well in a wide
variety of real-life applications. Experience shows that it can be
10-100 times faster than CorrFitter.lsqfit for very large fits (eg,
90+ correlators consisting of 1000+ pieces of correlated data).

- Parameter aux_param in CorrFitter is gone. It is no longer needed since
any parameter specified in the prior is included in the fit, whether
or not the correlator models use the parameter explicitly. Setting
parameter fast=True in CorrFitter.lsqfit or CorrFitter.chained_lsqfit
causes the fitter to delete parameters from the prior that are not used
explicitly --- this is the old behavior, which can be faster but loses
information in cases where the prior containes strong correlations.

- Made major changes to CorrFitter.chained_lsqfit. Setting parameter
parallel=True causes fits to be done in parallel, rather than chained.
Correlators are still fit one at a time in a parallel fit, but nothing is
passed from fit to fit --- each fit uses the input prior. Parallel fits are
appropriate when the different models to be fit share few or no parameters.
chained_lsqfit also works with structured lists of models
(eg, [m1, m2, [m3a,mb3b], m4]) that cause the fitter to alternate
between chained and parallel fits at different levels in the
nested list of models.

- Fixed p0 conventions in CorrFitter.lsqfit to be consistent with lsqfit
(and therefore more flexible than before).

Verion 3.4.2 - 2013-04-06
==========================

- Minor tweaks to makefiles and other build files.

- Repackaged examples file with much smaller data files (to reduce the
size of the distribution) and more informative file names.

- Minor optimizations to Corr2 and Corr3.

- Small fix to chained_lsqfit --- add time to fit output.

- Improved documentation, including more on chained_lsqfit.

- Tweaks relating to use of lsqfit.transform_p.

- Doesn't really work with python2.6 any more. The main thing missing
from 2.6 is OrderedDict. Does work for both python2.7 and python3.3.

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- Fix small, mostly harmless bug in CorrFitter.chained_lsqfit()

- More documentation including a complete annotated example.

=========================
This version adds a completely new algorithm for fitting multiple
models: chained fits. These reduce a single multi-correlator fit to a
(correlated) series of single-correlator fits that are generally
faster and much more robust than a standard simultaneous
multi-correlator fit. The need for svd cuts, for example, is
significantly reduced, and fits rarely get stuck. To use
a chained fit replace ``fitter.lsqfit(...)`` by
``fitter.chained_lsqfit(...)``; all else should be the same.

Several of the examples in the examples directory now come
in two forms, one using standard fits and the other (with -chd
in its name) using chained fits. It is instructive to compare
the .out files for corresponding fits.


Other changes:

- Tutorial documentation (see doc/html/index.html) was
extended and rearranged somewhat.

- Unittests for the chained fitting.

- Python 2.6 less and less viable for corrfitter.

=========================

- Added python3.3 compatibility. This required internal tweaking of
CorrFitter. It also requires numpy 1.7 or later.

- Simplified and extended support for non-gaussian priors
(eg, log-normal) for fit parameters. This version requires
version 4.3.1 of lsqfit.