Exposition To Notebook

Exposition To Notebook

Quick start

1. Pin down constraints (or assume defaults and add a TODO cell).
2. Extract the “computable core” (symbols, inputs/outputs, equations, algorithms).
3. Choose a notebook outline and scaffolding approach.
4. Build the notebook (.ipynb) and keep code importable/testable.
5. Add validation (sanity checks + minimal tests) and rerun top-to-bottom.
6. Polish for handoff (clear narrative, stable outputs, next steps).

Workflow

1) Intake (constraints + deliverables)

Capture:

• Goal: what the notebook should demonstrate/produce.
• Audience: research peer vs. product engineer vs. stakeholder.
• Runtime + environment: CPU/GPU, offline/online, expected Python version.
• Data: provided vs. needs a stub/synthetic generator.

If missing, assume:

• Python 3.10+, CPU-only, no internet, small synthetic data.
• Minimal dependencies (stdlib first; optional numpy, pandas, matplotlib).

Add a top “Open Questions / TODO” markdown cell if anything is unclear.

2) Decompose the exposition into “things to implement”

Produce (in a markdown cell) a compact inventory:

• Definitions table: symbol → meaning → units/shape/type → notes.
• Assumptions: e.g. independence, boundary conditions, ranges.
• Inputs/outputs: what goes in/out of the core functions.
• Algorithm steps: numbered, with edge cases.

For math: identify which equations must be computed vs. just documented.

3) Notebook design (default section order)

Default section order (adjust to repo conventions):

1. Title + TL;DR
2. Goal + success criteria
3. Setup (deps, versions, seeds)
4. Background / exposition (LaTeX + narrative)
5. Implementation (small, reusable functions/classes)
6. Validation (sanity checks + tests)
7. Demo / experiments (plots, tables)
8. Conclusion + next steps
9. Appendix (derivations, references)

Prefer moving reusable code into a module (e.g. src/ or *_utils.py) and importing it, keeping the notebook focused on orchestration and narrative.

4) Scaffold the .ipynb

• Start from scratch: run scripts/scaffold_notebook.py --out <path>.ipynb --title "<title>"
• Start from existing Markdown notes: run scripts/md_to_ipynb.py <notes>.md --out <path>.ipynb --title "<title>" --with-scaffold

5) Implement the computable core

• Convert each computable equation/step into a function with a docstring stating inputs/outputs and assumptions.
• Keep cells idempotent: no hidden state; rerun-from-scratch should match.
• Add small, readable examples next to new functionality.

6) Validate (make it “product-ready”)

Minimum bar:

• Restart kernel + “Run all” completes without errors.
• Deterministic results when possible (seeded randomness).
• At least:
  • shape/range/unit sanity checks, and
  • a few assert-style tests for key invariants.

For deeper guidance, see references/product_ready_checklist.md.

Typical outputs

• A single runnable notebook: notebooks/<slug>.ipynb (or repo-standard location).
• Optional support files when helpful:
  • src/<module>.py for reusable code
  • requirements.txt or pyproject.toml snippet for dependencies
  • synthetic data generator (so the notebook runs without external data)