matt-graham/mici: v0.1.2

Minor release with sampling efficiency improvements, new facility for tracking the number of model function calls during sampling, improved robustness to use of non-NumPy arrays, and miscellaneous bug fixes.

• In constrained leapfrog integrator some derivatives of quantities are now pre-evaluated before the associated quantity is evaluated as often the quantity itself will be calculated alongside the derivative and cached. The resulting reduction in the number of model function evaluations provides a small gain in sampling efficiency in models where it applies.
• Chain state objects now optionally may be initialised with a _count_calls dictionary object which will be used to record the number of calls to system methods decorated with the cache_in_state and multi_cache_in_state decorators. This can be used to give a hardware independent measure of the operation cost of a sampling run.
• Code has been refactored to remove explicit dependence on the in-place semantics of the augmented assignment operators such as += when acting on NumPy arrays. NumPy array like objects such as JAX DeviceArray instances do not perform updates in place, instead creating a new array object, meaning relying on such operations happening in place was silently failing when using such arrays.
• An non-guarded dependence on a decorator imported from autograd in the autograd_wrapper module has now been fixed meaning the package can be imported without autograd being installed as intended.
• Progress bar styling in Jupyter notebooks has been updated to remove the dependence on CSS variables defined in the ipywidgets stylesheets, instead now using explicit values.