Lsqfit

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This is a very minor upgrade.

- Set default svdcut=1e-15 instead of None in nonlinear_fit. This cut is
very small and so usually has negligible impact in cases where an svdcut is
unneeded. It protects against minor roundoff errors that arise relatively
frequently, even in fairly simple problems. It also prevents problems from
exact zero modes in the data or prior. One might argue that it would be
useful to expose these last problems, rather than dealing with them quitely,
but dealing with much more common minor roundoff errors seems more important.

- exp(fit.logGBF) is the probability (density) for generating
the fit data from the input fit model, assuming Gaussian statistics.
It used to be proportional to that probability; the
proportionality factors are now included. This change will have no
impact at all on almost all uses of logGBF. Change made more for the sake of
clarity than utility.

- More documentation, including a tutorial section on chained fits and more
discussion of svd cuts.

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- New function gvar.deriv(f, x) computes df/dx where f and x
are gvar.GVars, and x is independent (ie, x has only one non-zero
element in x.der). A ValueError exception is raised when x
is dependent on other GVars. f can also be an array of GVars
or a dictionary of GVars and/or arrays of GVars. GVars also
have a method which computes the derivative: f.deriv(x).

- Small code improvements to lsqfit.transform_p.

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

- Slight refinements to the support for log-normal, etc
priors. The decorator name is changed (but the old
name is aliased to the new, to support legacy code
(if there is any)).

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

- 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).

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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.