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- 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).