Symfit

Bugfix release:

- Global likelihood fitting now works as advertised.
- `CallableNumericalModel`'s now only need the `connectivity_mapping` for non-analytical components.
- Printing of special functions like bessel functions now works.
- Fixed a bug in the printing of matrix multiplication.
- General improvements to the documentation.

The long awaited `symfit 0.5.0` is here. And it was worth the wait, because it has got some very significant improvements over previous versions, including:

- Hessian minimizer trust-constr added, and all our models and objectives support hessian minimizers fully.
- The components of models can now depend on each other, which greatly improves readability, allows for more creative definition of models, and means that expensive to calculate intermediates can be reused by other components.
- MatrixSymbols are now supported, meaning a vast number of Matrix equations can now also be fitted with great ease.
- Improved printing of fit results.
- Better checking if data is provided for all variables.
- BivariateGaussian added as a predefined distribution.
- A whole sway of minor and mayor bug fixes.

A simple but important new version of `symfit`: we have made `symfit` multiprocessing compatible, finally delivering on that long standing promise of scalability.

Most important change is an improvement of the documentation by including examples in the docs, and the new feature `CallableNumericalModel`. With this feature, `symfit` can now also fit arbitrary python callables, while still allowing users access to the convenient `symfit` API.

Proudly presenting `symfit` 0.4.4, the best version of `symfit` to date.

Biggest changes:
- Introduction of the BasinHopping minimizer, a wrapper around scipy's basinhopping.
- Huge performance upgrade by making sure sympy code is translated to scipy compatible code only once per model.
- Fixed an issue with using constraints and bounds at the same time.
- `parameter` convenience method now allows you to set values etc immediately.

Introduces the first global minimizer by wrapping `scipy`'s differential evolution algorithm, and adds the possibility of chaining several minimizers.

Additionally, this fixes an important bug due to an incompatibility with `sympy==1.2` by demanding `1.1.1`.