Lsqfit

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- str(x) and repr(x) for GVar x both now return strings using the
'2.31(10)' format rather than the older '2.31 +- 0.1'.
The old format is still supported on input, but it will
no longer appear in (default) printing. Use x.fmt(-1) to obtain the old
format.

- Added gv.evalcorr(g) which calculates the correlation matrix of the
GVars in g.

- gv.chi2 has a new option (fmt=True) that causes it to return a string
(describing the chi**2) rather than the numerical value of chi**2.

- Operators > and < are now defined for gvar.GVars. This allows algorithms
to order GVars, which is occasionally useful. The ordering is based upon
the mean values. Operators >= and <= are still *not* defined, because of
incompatibilities with == and !=, which look not just at mean values but
also at all the dependencies. These incompatibilities suggest that one
shouldn't define > and < either, except that there are times when it is
quite useful to be able to order a numerical data type for algorithmic
reasons. The setup here is a compromise (kludge?).

- Fixed very minor bug in lsqfit.nonlinear_fit.format().

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

- polishing/minor fixes for nonlinear_fit.simulated_fit_iter. Also now has
a bootstrap option.

- copy.copy and copy.deepcopy now work with GVars.

- very minor fix to gvar.uncorrelated

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

- nonlinear_fit.simulated_fit_iter generates fits of new simulated
data that is generated randomly from the original fit data. This
data is useful for testing fits and tuning parameters in them.
Simulated data has the same covariance matrix as the original data but
its mean values fluctuate around values given by the fitting
function evaluated at user-specified parameter values p=pexact.
The values in pexact are the "correct" values that should
be obtained from a fit of the simulated data --- that is, the
results of the fit to simulated data should agree with pexact
to within errors. Knowing the correct answers for the fit
parameters ahead of a fit allows for very realistic testing. See
the documentation in the Tutorial section on Testing Fits with
Simulated Data for more information.

- nonlinear_fit.format() now adds 1 to 5 stars at the end of any
parameter line where the parameter and the prior differ by more
than 1 to 5 (or more) standard deviations, respectively. Stars
are also added when fit data is printed out where fit data
and the fit differ by more than 1 standard deviation. These are
meant to draw attention to potential problems.

- New function: gvar.chi2(g1, g2) computes the chi**2 of g1-g2, where
g1 and g2 are (multi-dimensional) distributions. One of g1 or g2 can
contain numbers instead of GVars (and/or can be missing entries
contained in the other). Also gvar.chi2(diff) where diff = g1 - g2
equals gvar.chi2(g1, g2).

- gvar.dataset.avg_data has new option specified by parameter noerror.
Setting noerror=True causes avg_data to compute averages but not
the errors in those averages.

- gvar.ranseed() called without an argument generates its own random
seed to reinitialize the numpy random number generates. The seed is
returned by the subroutine and can be used to recover the random
number stream in later work. The seed is also stored in gvar.ranseed.seed.
The idea is to use gv.ranseed() at the start of a code and print out
gvar.ranseed.seed so that the seed can, if desired, be used to recreate
the same random numbers in a later run. The key here is the 'if desired';
usually you might not care to recreate a run unless something unusual
happens.

- The tutorial in the documentation has a new section (at the end)
with a pedagogical discussion of simple fit strategies.

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

- gvar.SVD sometimes complains that "SVD failed to converge". This is a
numpy.linalg problem (that might be solved by *not* linking with atlas).
Have introduced a back up routine (numpy.linalg.eigh) that is tried when
this error is encountered.

- lsqfit.wavg now accepts a list of dictionaries (containing GVars or
arrays of GVars), as well as lists of GVars or arrays of GVars.

- Modest optimization for gvar.evalcov. Small optimizaitons for gvar.svec
and gvar.smat.

- Fixed bug in svec.add (where one or other svec is size=0 svec)

- Fixed very minor bug in gvar.gvar() (makes, eg, gvar(array(1.)) work).

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

- Improved syntax for transform_p from lsqfit. The old syntax still works
but the new syntax is simpler: 1) use transform_p(priorkeys,0) instead
of transform(prior,0,'p'); and 2) fit.transformed_p is the same as
fit.p but augmented with the exponentials of any log-normal terms, etc.

- Rules for initial values p0 in nonlinear_fit are more flexible: p0 can
include keys that are not in prior (these will be ignored, unless prior
is None). This makes it more likely that an old p0 will be useful for
priming a new fit.

===========================
This is another minor upgrade.

- Evaluation of logGBF in nonlinear_fit was having problems (in one user's
code, at least) with very large covariance matrices. This is now fixed.