99e5c2cd44
Adds SECTION_5_BULB_COUNT_LEL, SECTION_5_WINDOW_AREAS_M2, SECTION_5_PUMP_AGE_STR and LINE_66..LINE_73 expected outputs to every Elmhurst fixture (000474, 000477, 000480, 000487, 000490, 000516). Constants extracted from the U985-0001-NNNNNN worksheets supplied 2026-05-20. All six fixtures share the same shape: all-LEL bulb lighting, gas combi pump with unknown install date, average overshading. Adds an ALL_FIXTURES-parametrized test in test_internal_gains.py that composes a §5 EPC from the fixture's constants and drives internal_gains_from_cert. Tolerances: ≤1e-3 W on the linear-in-N rows (66/69/71), ≤2e-1 W on (67) lighting (worksheet-rounded N + rooflight Z_L=1.0 approximated by AVERAGE Z_L=0.83), ≤5e-2 W on (68) appliances, ≤3e-1 W on (73) sum. Result: 26 tests pass; six fixtures conform to ≤0.6% lighting bias end-to-end. The fixture's base build_epc() is unchanged — §5 EPC composition lives in a test helper so the existing e2e SAP-score regression (000490, 000474) remains pinned for the upcoming calc.py wiring slice. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_backlog.sh | ||
| 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