ar-grid-tutor-mobile-ar-digital-twin-for/README.md

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.