v0.4.4

• minor fixes
• fixed RKDP54 butcher table
• test coverage for Value and Parameter classes for AD framework

v0.4.3

• refactoring of numerical integrators (solvers), separate parent classes for diagonally implicit and explicit runge kutta methods simplifies the specific methods significantly
• performance of runge kutta methods improved >10% by using explicit loops with indexing to construct slope instead of sum comprehension

Enhanced AD and Fixes

• created interface for automatic differentiation framework to numpy ufuncs
• updated testsuite
• general bugfixing regarding implicit solvers (jacobian computation)

AD

Lots of refactors, improvements and new features since the last release including:

• Added rudimentary automatic differentiation (AD) framework in pathsim.diff to make the simulations differentiable w.r.t. predefined parameters
• Refactoring of numerical integrators to be compatible with AD framework for transient simulation
• Added lots of test, initial coverage was above 85%, more coming in the future
• Overall cleanup