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Bug fixes and a little bit of new functionality.
- A new utility gvar.dataset.svd_diagnosis() analyzes a dataset
containing Monte Carlo results for multiple variables to determine
whether or not an SVD cut is needed for the correlation matrix
describing these variables. The smallest eigenvalues of a correlation
matrix can be badly underestimated when the number of
Monte Carlo samples is insufficiently large. This makes the correlation
matrix more singular than it should be, which can lead to problems,
for example, when trying to fit the correlated data. This utility
estimates the size of the svd cut to use on the correlation matrix.
A new case study illustrates its use.
- Fix plotting by PDFHistogram.make_plot to account for (incompatible)
change introduced in matplotlib-2.0.0's pyplot.bar.
- gvar.dataset.bin_data(xx) returns a gvar.dataset.Dataset ordered
dictionary if xx is a dictionary.
- pickle now works for (the mostly hidden classes) gvar.svec and gvar.smat
and therefore for x.internaldata where x is a GVar. Tricky to use
internaldata, however.