32 lines
1.6 KiB
Markdown
32 lines
1.6 KiB
Markdown
# AR Grid Tutor: Mobile AR Digital Twin (MVP Skeleton)
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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:
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- A lightweight digital twin engine that reconciles telemetry with archived models
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- An API scaffold (FastAPI) for telemetry ingestion and health checks
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- A packaging-ready Python project with packaging metadata and tests
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- Offline-friendly architecture notes and forward-looking integration points
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Highlights
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- Core data contracts: TelemetrySample, AssetModel, and delta reconciliation results
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- Minimal but extensible API to ingest telemetry and query health
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- Tests validating core reconciliation behavior
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Project layout
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- pyproject.toml: packaging metadata and build-system configuration
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- src/ar_grid_tutor_mobile_ar_digital_twin_for/: core library (TelemetrySample, AssetModel, TwinEngine)
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- api/app.py: FastAPI endpoints for telemetry ingestion and health check
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- tests/: pytest-based unit tests
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- test.sh: test runner that also builds the package
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- AGENTS.md: contributor and architecture guide
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- README.md: this document
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- READY_TO_PUBLISH: (empty file created upon milestone completion)
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How to run locally
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- Install dependencies (virtualenv recommended):
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pip install -r requirements.txt # if you add, otherwise install via build tooling
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- Run tests: ./test.sh
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- Run API (for development): uvicorn api.app:app --reload
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Notes
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- 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.
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