0a04448217
Wires slice 1-5 primitives into a deployable splitter:
- orchestration/postcode_splitter_orchestrator.py: PostcodeSplitterOrchestrator
loads addresses via UserAddressRepository, groups by postcode via
iter_postcode_grouped_batches, persists each batch under
ara_postcode_splitter_batches/{task_id}/{subtask_id}/, creates a WAITING
child SubTask, and publishes an address2UPRN SQS message per batch.
- applications/postcode_splitter/: Lambda entrypoint. handler.py is decorated
with @subtask_handler() so the parent SubTask lifecycle is decorator-owned;
PostcodeSplitterTriggerBody validates the body. Dockerfile is the
python:3.11 Lambda base with the DDD-shaped source layers and no pandas.
- tests/orchestration/test_postcode_splitter_orchestrator.py: integration
test using moto S3 + moto SQS + in-memory SQLite that exercises the full
wiring against a fixture CSV spanning three postcode groups (one
oversize) and asserts child count, persisted inputs, queue bodies, and
dispatch order.
backend/postcode_splitter/ and .github/workflows/deploy_terraform.yml are
intentionally unchanged: the dockerfile_path flip is deferred until the
companion backend/address2UPRN/ migration is also ready.
|
||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| test.requirements.txt | ||
| tox.ini | ||
| UBIQUITOUS_LANGUAGE.md | ||
Model Repository
This repository contains the code pertaining to the development of the data science and machine learning products being utilised by Hestia.
The different folders in this repository relate to services that can be used independently, or can be imported and used as part of a larger application
Getting Started
Prerequisites
Dev Container Setup
This repo uses a Docker Compose-based dev container. The model-backend service joins a shared-dev Docker network so it can communicate with other local services (e.g. a frontend container) running on your machine.
VS Code users: The initializeCommand in devcontainer.json creates the shared-dev network automatically before the container starts. No manual step required — just open the repo and select Reopen in Container.
Non-VS Code / CI workflows: Run the following once before starting the container:
make dev-setup
This is idempotent and safe to re-run if the network already exists.
Folders
backend/
This folder contains the code for the fastapi backend service, which provides an interface to much of the functionality in this repository, for the frontend
model_data/
This folder contains related to the reading and preparation of assessment model data, including pulling out epc attributes
Testing
All tests can be run, against the configuration in pytest.ini running
pytest
This will run the complete panel of tests and report on coverage in the locations specified by the pytest.ini file.
To run tests in a specific service, e.g. inside of model_data, simply run
pytest --cov-config=model_data/.coveragerc --cov=model_data
This will produce the test results and coverage reports