# AR Grid Tutor: Mobile AR Digital Twin (MVP Skeleton)

This repository provides a production-minded MVP scaffold for a mobile-first AR platform that leverages vendor-neutral digital twins for DER assets (solar, inverter, wind, storage). The core components include:
- A lightweight digital twin engine that reconciles telemetry with archived models
- An API scaffold (FastAPI) for telemetry ingestion and health checks
- A packaging-ready Python project with packaging metadata and tests
- Offline-friendly architecture notes and forward-looking integration points

Highlights
- Core data contracts: TelemetrySample, AssetModel, and delta reconciliation results
- Minimal but extensible API to ingest telemetry and query health
- Tests validating core reconciliation behavior

Project layout
- pyproject.toml: packaging metadata and build-system configuration
- src/ar_grid_tutor_mobile_ar_digital_twin_for/: core library (TelemetrySample, AssetModel, TwinEngine)
- api/app.py: FastAPI endpoints for telemetry ingestion and health check
- tests/: pytest-based unit tests
- test.sh: test runner that also builds the package
- AGENTS.md: contributor and architecture guide
- README.md: this document
- READY_TO_PUBLISH: (empty file created upon milestone completion)

How to run locally
- Install dependencies (virtualenv recommended):
  pip install -r requirements.txt  # if you add, otherwise install via build tooling
- Run tests: ./test.sh
- Run API (for development): uvicorn api.app:app --reload

Notes
- This MVP intentionally uses in-memory state for simplicity; the architecture supports plugging in a SQLite/PostgreSQL cache and a fuller offline sync layer in future iterations.