zwitschi
4b3a15ed15
- Introduced multiple architecture documentation files covering building block view, runtime view, deployment view, concepts, architecture decisions, quality requirements, technical risks, glossary, UI and styling, testing, CI, and development setup. - Migrated existing content from `architecture_overview.md` and `implementation_plan.md` into structured documentation. - Created scripts for checking broken links in documentation and formatting Markdown files for consistency. - Updated quickstart guide to provide clearer setup instructions and usage overview. - Removed outdated MVP features and testing strategy documents to streamline documentation.
1.1 KiB
1.1 KiB
04 — Solution Strategy
Status: skeleton
High-level solution strategy describing major approaches, technology choices, and trade-offs.
Monte Carlo engine & persistence
- Monte Carlo engine:
services/simulation.pywill incorporate stochastic sampling (e.g., NumPy, SciPy) to populatesimulation_resultand feed reporting. - Persistence of simulation results: plan to extend
/api/simulations/runto persist iterations tomodels/simulation_resultand provide a retrieval endpoint for historical runs.
Simulation Roadmap
- Implement stochastic sampling in
services/simulation.py(e.g., NumPy random draws based on parameter distributions). - Store iterations in
models/simulation_result.pyvia/api/simulations/run. - Feed persisted results into reporting for downstream analytics and historical comparisons.
Status update (2025-10-21)
- A scaffolded simulation service (
services/simulation.py) and/api/simulations/runroute exist and return in-memory results. Persisting those iterations tomodels/simulation_resultis scheduled for a follow-up change.