Update 4.5.1 to 4.5.3:
	Version 4.5.3 - 2013-12-22
===========================

- Fixed bug in gvar._gvarcore that caused problems on win64 systems.

- GVar's __cinit__ has been changed to an __init__, which makes derivation
  from GVar possible. GVar also has new property: g.internaldata.
  This allows simplifies derivation from GVar --- see, for example,
  class WAvg in lsqfit._extras.py. Finally a cython declaration file,
  gvar.pxd, is installed for the benefit of other cython modules:
  cimport gvar gives the module access to the internal definitions of
  cython extension types GVar, svec and smat.

- lsqfit.wavg (weighted averages) now returns a variable
  of type WAvg which is a class derived from GVar (with all of
  its functionality) but with added attributes: chi2,
  dof, and Q which are the chi2, dof, Q from the wavg. In the past these were
  read off the function itself (eg, wavg.Q) but this is nonintuitive.
  Now ans = lsqfit.wavg(list_of_GVars) is a GVar with the extra
  attributes (ans.chi2, ans.dof, ans.Q). lsqfit.wavg still has attributes
  chi2, Q etc to help with legacy code. Also this is useful if the average
  is over a list of arrays or dictionaries (ie, a multidimensional random
  variable). In this case the individual GVars in the result have chi2s, etc
  as described above, while lsqfit.wavg has the chi2 for the entire set (ie,
  the sum of the chi2s for all the components).

Version 4.5.2 - 2013-09-26
==========================

- 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().