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A mobile-first augmented reality platform that uses vendor-neutral digital twins of solar panels, inverters, wind turbines, and storage assets to guide technicians on-site. It overlays real-time health indicators, maintenance steps, and safety guidan
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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.