Gvar

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- 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.

==========================
This is a very minor upgrade.

- Set default svdcut=1e-15 instead of None in nonlinear_fit. This cut is
very small and so usually has negligible impact in cases where an svdcut is
unneeded. It protects against minor roundoff errors that arise relatively
frequently, even in fairly simple problems. It also prevents problems from
exact zero modes in the data or prior. One might argue that it would be
useful to expose these last problems, rather than dealing with them quitely,
but dealing with much more common minor roundoff errors seems more important.

- exp(fit.logGBF) is the probability (density) for generating
the fit data from the input fit model, assuming Gaussian statistics.
It used to be proportional to that probability; the
proportionality factors are now included. This change will have no
impact at all on almost all uses of logGBF. Change made more for the sake of
clarity than utility.

- More documentation, including a tutorial section on chained fits and more
discussion of svd cuts.

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

- New function gvar.deriv(f, x) computes df/dx where f and x
are gvar.GVars, and x is independent (ie, x has only one non-zero
element in x.der). A ValueError exception is raised when x
is dependent on other GVars. f can also be an array of GVars
or a dictionary of GVars and/or arrays of GVars. GVars also
have a method which computes the derivative: f.deriv(x).

- Small code improvements to lsqfit.transform_p.

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

- Slight refinements to the support for log-normal, etc
priors. The decorator name is changed (but the old
name is aliased to the new, to support legacy code
(if there is any)).