Flavio

This release brings major improvements especially in two sectors: the treatment of numerical likelihoods and plotting functions for 1D and 2D likelihoods. Users of `flavio.plots.band_plot` should read on as there have been some interface changes.

Improved treatment of numerical likelihoods
- Measurements of one- or multi-dimensional experimental constraints can now be specified in numerical form directly in YAML files. An example are the correlated measurements of rare leptonic B decays by CMS and LHCb that are now [included by default](https://github.com/flav-io/flavio/blob/dd313fa1060b1ad0491e44ed94059af7d1349f13/flavio/data/measurements.yml#L2671-L2752). See also [the documentation](https://flav-io.github.io/docs/probability.html#arbitrary-numerical-univariate-constraints).

Improvements to plot functions
- The function `flavio.plots.band_plot`, used for 2D likelihood plots given a log-likelihood function (e.g. from a `FastFit` instance) has been superseded by the new function `flavio.plots.likelihood_contour`. The old name still works but might be removed in the near future. `flavio.plots.likelihood_contour` actually calls two functions after one another: `flavio.plots.likelihood_contour_data` computes the numerical values to plot; `flavio.plots.contour` produces the actual plot.
The advantage of this split is that these functions can also be called separately, e.g. to perform the time-consuming calculation only once and be able to make tweaks to the plot style without the need for recomputation.
- The function `flavio.plots.density_contour`, used for density plots e.g. for Bayesian MCMC analyses, has been rewritten from scratch:
- Data production is also separated into `flavio.plots.density_contour_data` and the same function `flavio.plots.contour` as for the likelihood plots is used for presentation
- For the kernel density estimate, a fast fourier transform is used now. For a large number of points, as is typical for MCMC analyses, this leads to an enormous speed-up. Density contours can now be plotted from samples with several million points in a matter of seconds!
- The function `flavio.plots.smooth_histogram`, the 1D analogue of density contour, has also been rewritten from scratch:
- Instead of just interpolating a histogram, it now also uses a Gaussian KDE based on FFT
- The 1 sigma region can now be displayed as a shaded vertical band
- A new function `flavio.plots.pdf_plot` plots 1D likelihoods, e.g. from `FastFit` instances, with the same style as `smooth_histogram`.

All the old and new plot functions are documented in detail in the [API docs](https://flav-io.github.io/apidoc/flavio/plots/plotfunctions.m.html). Example notebooks will follow in due time.

Physics updates

Finally, there have also been some small physics improvements, in addition to the experimental measurements mentioned above.
- Some parameters [updated](https://github.com/flav-io/flavio/commit/77b16517afc8560743ea16b7ac8ed6dacd8f3c97) to HFAG 2016 values
- Mass-eigenstate rate asymmetry in B_s→ll added (`ADeltaGamma(Bs->mumu)` etc.)
- Angular observable S₆^c in B→K*ll added (e.g. `<S6c>(B0->K*mumu)`)

This is a quick bug fix release necessary due to an incompatible change in numpy v1.12 [released](https://github.com/numpy/numpy/releases/tag/v1.12.0) two weeks ago that made processes depending on B→P form factors stop working (thanks to Stefanie Reichert for reporting).

This first release in 2017 brings lots of new features and some bug fixes.

Several improvements have been made to probability distributions, which are required to quantify both theoretical and experimental uncertainties needed to compute prediction uncertainties and to construct likelihoods for fits:
- A new class [GammaUpperLimit](https://flav-io.github.io/apidoc/flavio/statistics/probability.m.html#flavio.statistics.probability.GammaUpperLimit) appropriate for low-statistics measurements resulting in an upper limit (e.g. on a rare decay branching ratio),
- A new class [MultivariateNumericalDistribution](https://flav-io.github.io/apidoc/flavio/statistics/probability.m.html#flavio.statistics.probability.MultivariateNumericalDistribution) that allows to specify _any_ probability distribution in an arbitrary number of dimensions. This works with all the functions implemented, like computing uncertainties and Bayesian fits.
- Constraints in the form of a fixed range (uniform likelihood within and zero outside) can now be specified in YAML files in the intuitive form `[0.1, 3.5]`.
- A new way of specifying arbitrary probability distributions in YAML files.

There is also a new page in the documentation about [details on the treatment of probability distributions](https://flav-io.github.io/docs/probability.html) and how to define them in YAML. The [API docs](https://flav-io.github.io/apidoc/flavio/statistics/probability.m.html) have been updated as well.

On the physics side, the following processes have been added:
- The forward-backward asymmetry in inclusive B→X_ql⁺l^- decays. Like The branching ratio added in [v0.15](https://github.com/flav-io/flavio/releases/tag/v0.15), this is based on [arXiv:1503.04849](https://arxiv.org/abs/1503.04849) and profited a lot from help by Tobias Huber.
- The branching ratio of B_c→lν.
- Lepton flavour ratios in B→Mlν decays, including experimental measurements of the μ/τ ratios in B→D^(*)lν.

Finally, the following bugs have been fixed:
- In the presence of new physics, the decay B_d→l⁺l^- erroneously used b→s Wilson coefficients. Thanks to jackypheno (Jacky Kumar) for spotting and fixing this.
- Measurements with a Gaussian upper limit constraint returned the wrong likelihood if the confidence level specified was different from 68%.
- A glitch in the log-enhanced QED corrections to B→X_ql⁺l^- was fixed (the change is numerically irrelevant).

The main new feature in this release is the addition of the branching ratio of the inclusive semi-leptonic B decay B→X_clν, with l=e or μ (or their average). Many thanks to Paolo Gambino for useful discussions and for sharing numerical results necessary for implementing and validating this! See [arXiv:1411.6560](https://arxiv.org/abs/1411.6560) and references therein for details on the theory.

An additional, minor improvement since v0.16.1 concerns the treatment of correlated ucertainties in Bayesian fits; see commit a32c80d45e14522dd5c2e11d942512ad9922dfe7 for details.

Finally, the default values of masses and widths have been updated from PDG 2015 to PDG 2016 values. There has not been any numerically relevant change for the currently implemented observables.

This bug fix release fixes
- a regression introduced by the recent internal change in the `Constraints` class in v0.16 that broke the fit functions,
- an inconsistency in the combination of experimental likelihoods in the presence of multiple experiments measuring the same observable when using the `FastFit` class.

Additional unit tests have been added to improve the stability of these fitting functions.

This release focuses on stability improvements with only few new features.

Improvements on the physics side:
- Added lepton flavour ratios for B->Plnu decays, e.g. `<Rtaumu>(B0->Dlnu)`
- Added lepton flavour ratios for B->Xll decays, e.g. `<Remu>(B->Xsll)`
- Added measurements of B->K*mumu "optimised" angular observables (P') by LHCb

Other improvements:
- More consistent behaviour of `flavio.plots.band_plot` when using the `pre_calculated_z` and `interpolation_factor` options (see [API docs](https://flav-io.github.io/apidoc/flavio/plots/plotfunctions.m.html))
- `flavio.plots.q2_plot_exp` now properly handles asymmetric experimental uncertainties

In addition there have been a few under-the-hood changes in preparation for future features.