Gvar

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

- Works with python3.3 (and numpy >= 1.17 which is necessary for 3.3).
Fixed minor errors in gvar.BufferDict.__str__ and in some of the unittests
that showed up with python3.3.

- Support for log-normal and "sqrt-normal" prior distributions for fit
function parameters. The idea is to use parameters with names like
"log(a)" instead of "a" in the prior, while expressing the fit
function in terms of "a": so prior["log(a)"] is
specified in the prior but not prior["a"], while the fit
function uses parameter p["a"] but not p["log(a)"]. Parameter
p["a"] has a log-normal distribution because prior["log(a)"] is
a gaussian variable. See the section "Positive Parameters" in
the overview section of the html documentation, for more
information.

- gvar.dataset.Dataset changed to an OrderedDict from a dict. This mostly
doesn't matter. Just about the only non-cosmetic effect concerns what
happens when an svdcut is applied to the output of avg_data --- small
differences arise when rows and columns of the covariance matrix are
interchanged (roundoff error).

- Changed == and != for GVars to allow comparisons with non-GVar types; a GVar
compares as not equal to a non-GVar unless its mean equals the
non-GVar and its standard deviation is zero. Note that >, <, etc are
not defined for GVars since GVars are not unambiguously ordered
--- eg, a number drawn from the distribution 100(99) will be
larger than one from 101(1) almost 50% of the time, even though
100 < 101.

- Had too many pieces in the version number, so moved to 4.3. A
third component, as in 4.3.1, will indicate bug fixes and minor
features. There has been a lot added since 4.2 started (see 4.2.2).

==============================
gvar.fmt_errbudget(...) has new parameter to specify column widths. This
allows for longer names for outputs and inputs.

=============================
Adds a further tweak to the exception handling inside fit functions ---
slightly more robust than what is in 4.2.7.

===========================
Another minor update:

- gvar.raniter and gvar.bootstrap_iter now work with single gvar.GVar's as
arguments (in addition to the more useful cases of arrays and
dictionaries). This makes them more consistent with the other utility
functions.

- Python errors buried inside fit functions now result in slightly more
intelligible error messages. Added two new unittests for such
exception-handling.

===========================
This is a minor update:

- Adds load (and loads) and dump (and dumps) methods to gvar.BufferDict to
facilitate saving serialized BufferDicts in files (or strings) for later
use. This is particularly useful when the BufferDict contains gvar.GVars
since the correlations between the different GVars in the BufferDict are
complicated to retain properly. These are implemented using pickle or,
optionally, json. pickle already worked with BufferDicts. json was added
because pickle is not compatible between python2 and python3. json files
are also readable by non-python code (and by yaml). The json
implementation has some limitations (around the types used for keys in
the BufferDict, as well as types for the values) so pickle may be
preferable except in situations where data must be moved from python2 to
python3.

===========================
This update is to fix a bug. Since version 4.2.2 lsqfit has been able to
deal correctly with statistical correlations between priors and the input
fit data. The code checks automatically for such correlations, and modifies
the definition of chi**2 appropriately if it finds correlations. There was
a bug in part of the code that checks for correlations, causing it to miss
certain situations. That bug is fixed in this update. Also

Other changes:

- Renamed gvar.orthogonal to gvar.uncorrelated, which is more intelligible
(and also now has correct code).

- Fixed bug in gvar.GVar.partialvar (and therefore also
gvar.fmt_errorbudget). The partial variance due to some GVar g should
include the contributions from all other GVars that are statistically
correlated with g. This previous code missed correlated but unreferenced
variables that should have been included automatically.

- gvar.dataset.autocorr() is now done properly (with FFTs) and so can
handle large datasets. It now computes autocorrelations for all
intervals.

- lsqfit now issues deprecation warnings if the old classes GPrior,
CGPrior, or LSQFit are used. These have been superseded in recent
versions (by gvar.BufferDict and lsqfit.nonlinear_fit), and the old names
have been attached to the new constructs, but the correspondence between
old and new is only approximate --- hence the warning.

- Documentation improvements in the Tutorial.