Gvar

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New features include PDFIntegrator for integrating over PDFs and the
ability to read compressed files in gvar.Dataset. Several bug fixes as well.

- New class PDFIntegrator for evaluating expectation values weighted
by the probability density function (PDF) for arbitrary multi-dimensional
Gaussian distributions. This class uses the vegas module to evaluate
the multi-dimensional integrals, and optimizes the integrands for vegas.
vegas uses adaptive Monte Carlo integration and so can handle fairly
high-dimension integrals (dim=10, 20, 50 ...) efficiently. The vegas
module must be installed separately. Three other modules are provided
in addition: PDF, PDFStatistics and PDFHistogram. See the section
on "Non-Gaussian Expectation Values" in the tutorial for an example.

- gvar.Dataset can now read gzipped and bzipped files. It looks for .gz
and .bz2 at the ends of file names to identify which files are
compressed.

- Made gvar.gvar(a, sd) substantially faster (30%) when a is a large array.
Replaced a Python list by a numpy array -- corrects an oversight from
before. This probably won't have a big effect on most codes since
gvar creation is usually a small part of the cost.

- Bug fixes in gvar.powerseries that make it work better with
coefficients that are numpy arrays.

- Bug fixes in gvar.cspline when spline is used outside of its range,
with extrap_order set to 0, 1 or 2. The array size returned
by the spline function (or derivatives) was not always correct.
Additional test code.

- Small change in BufferDict to help with legacy code issues.

- BufferDict(d) for d=dictionary no longer stores keys in sorted order. This
never made sense but also causes trouble in python3 when keys of mixed type
are used.

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- Fixed error in evalcov(g) and evalcorr(g) when g is a dictionary.
The return values are doubly indexed dictionaries c[i,j] whose
values correspond to g[i] and g[j]. The shape of c[i,j] is
an array whose shape is the sum of the shapes of g[i] and g[j],
where scalars are treated as shape=(1,) objects. Also evalcorr(g) worked
incorrectly when elements g[k] were arrays.

- Added correlate(g, corr) which takes a set of uncorrelated GVars in g
and adds correlations as specified by correlation matrix corr. It is
common for people to publish correlated data as a set of means and
standard deviations, plus a correlation matrix. This routine
facilitates converting such data into GVars.

- Minor corrections to documentation and improved testing for evalcov
and evalcorr.

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- Added gvar.tabulate(g) for tabulating values stored in arrays or
dictionaries of GVars.

- Fixed typos in the tutorial.

- Documentation fix in gvar.BufferDict.

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- fixed default initialization of gvar.ranseed so it doesn't generate
errors due to new restrictions on numpy's random generator.

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Changed to pip + distutils for installation. Fixed inconsistencies in
INSTALLATION.txt.

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Had to replace setuptools with distutils again, because I could not get cython
pxd files to work properly with the eggs, causing build problems for
lsqfit.