Deterministic Scripts

Deterministic Script Execution

Overview

HASTE has established scripts and commands for common operations. Agents must use these exact commands instead of generating free-form alternatives. This prevents hallucinated scripts, ensures consistency, and makes operations repeatable.

Key Concepts

Why Deterministic Scripts Matter

• LLMs can "helpfully" generate plausible-looking but incorrect commands
• HASTE has specific build tooling (hatch, conda) that must be used correctly
• Azure Functions deployment has specific prerequisites and ordering
• Geospatial processing requires exact GDAL/rasterio invocations

Patterns & Techniques

Build & Package Operations

| Operation | Exact Command | Notes |

|-----------|--------------|-------|

| Build core wheel | cd hastelib && hatch build -t wheel | Auto-increments version, copies to func apps |

| Run Python tests | cd hastelib && hatch run test:pytest | Uses conda env with GDAL |

| Build UI | cd ui && npm run build | Production build via Vite |

| Lint UI | cd ui && npm run lint | ESLint with React rules |

| Install UI deps | cd ui && npm install | Uses package-lock.json |

| Create conda env | conda env create -f env.yml | Full env with GDAL and dependencies |

| Update conda env | conda env update -f env.yml | Preserves existing packages |

| Install hastelib editable | pip install -e hastelib/ | For local development hot-reload |

Local Development

| Operation | Exact Command | Notes |

|-----------|--------------|-------|

| Start API locally | cd api/hastefuncapi && func host start | Requires .venv or conda env |

| Start UI locally | cd ui && swa start --app-devserver-url http://localhost:5173 --run 'npm run dev' | SWA CLI with Vite dev server |

| Start Azurite | azurite --silent --location ./data --debug ./data/debug.log | Local Azure Storage emulator |

| Start Docker stack | docker-compose -f docker/docker-compose.yml up | Full local stack |

Deployment

| Operation | Exact Command | Notes |

|-----------|--------------|-------|

| Deploy Azure Function | func azure functionapp publish <NAME> --subscription <SUB> --tenant <TENANT> | After hatch build |

| Deploy SWA | cd ui && swa deploy --app-location ./dist --app-name <NAME> | After npm run build |

| Build Docker images | ./build_and_push_images.sh | Builds training + imagery prep images |

Imagery Processing

| Operation | Approach | Notes |

|-----------|----------|-------|

| COG generation | Use rasterio with COG driver profile | Never use raw GDAL CLI unless wrapping in Python |

| Tile generation | Through ImageryPreProcessor | Not manual gdal2tiles |

| Reprojection | rasterio.warp.reproject() | Always specify src_crs and dst_crs |

| Format conversion | Through provider-specific adapter | Not generic gdal_translate |

Decision Framework

| Situation | Do This | NOT This |

|-----------|---------|----------|

| Need to run tests | hatch run test:pytest | pytest (wrong env) |

| Need to build wheel | hatch build -t wheel | python setup.py bdist_wheel |

| Need to start API | func host start | python function_app.py |

| Need Azure storage locally | azurite | Custom mock storage |

| Need to process imagery | Use ImageryPreProcessor | Write new GDAL script |

| Need to deploy | func azure functionapp publish | Manual zip deployment |

Common Pitfalls

• Inventing new build commands — Use the established commands above
• Running pytest directly — Use hatch run test:pytest to get the correct conda env
• Starting the UI with npm start — HASTE uses swa start with Vite dev server
• Using python setup.py — HASTE uses hatch/hatchling, not setuptools
• Generating GDAL scripts from scratch — Use existing processor methods