What problem does it solve? Turning the mathematics of a fluid mechanics textbook (definitions, theorems, numbered equations) into correct, testable Python code is error-prone: OCR extraction corrupts symbols, numerical methods must be chosen per equation type, and pitfalls like cancellation, singularities, and stiffness silently produce wrong results. ## Core Features & Use Cases - Transcription discipline: Render the defining book page, transcribe equations into LaTeX, verify symbolically with sympy against the book's stated special cases before writing any numerical code. - Method selection guide: A decision table maps each mathematical form (closed-form, implicit equation, integral, ODE IVP/BVP, eigenvalue problem, PDE, series) to the right scipy/numpy approach with its known failure modes. - Numerical pitfall checklist: Concrete rules for grids, boundary conditions, non-dimensionalisation, units (pint), cancellation, singularities, stiffness, and reproducibility, plus an accumulating lessons-learned log. - Use Case: Implementing the Blasius boundary-layer equation from the textbook: transcribe it, verify the flat-plate special case symbolically, solve it as a BVP with shooting and solve_bvp cross-checks, then validate before later chapters reuse it. ## Quick Start Ask the assistant to implement equation 8.42 from chapter 8 as a tested Python function following the math-to-python workflow.
