Python Toolchain
This part collects the reproducible computational material that accompanies the methods developed in the preceding chapters. It first describes the Python environment and the reproducibility workflow, then presents the notebooks in the same conceptual order as the book: optimization, ODEs, linear and nonlinear DAEs, index reduction, numerical DAE methods, and case studies.
Prerequisites: basic Python syntax and the mathematical topic associated with each laboratory.
Objectives: reproduce the book’s numerical experiments, vary parameters systematically, and interpret solver diagnostics rather than relying only on success flags.
Expected outcomes: the reader can create the environment, run and modify a notebook, preserve reproducibility, and report convergence, residual, and stability evidence.
Each notebook is self-contained: it can be read as a worked example, modified interactively, and rerun without hidden dependencies. The notebooks are the place where methods developed throughout the theoretical chapters are turned into working code and reproducible numerical experiments.