ca56fdee5b
Closes §4 LINE_43 + LINE_44/45/46/61/62/64 for 000487 (7 of 8 fails).
LINE_65 still fails — needs Appendix J step 8 (electric-shower kWh
derivation from cert) to land before LINE_65 heat gains close.
Spec citation: SAP10.2 Appendix J (p.81) step 2a: `Nbath = 0.13N + 0.19
if shower also present; = 0.35N + 0.50 if no shower present`. The
"shower also present" branch fires when ANY shower is lodged — mixer OR
electric — per the implicit reading that step 1a's Noutlets includes
electric showers in the count.
Changes:
- SapHeating gains `electric_shower_count` + `mixer_shower_count`.
- `water_heating_from_cert` gains `has_electric_shower: bool = False`;
combined with mixer-flow-rate presence to drive `has_shower`.
- `_mixer_shower_flow_rates_from_cert` honors `mixer_shower_count`
(default 1 vented when unlodged — preserves legacy behaviour).
- `_has_electric_shower_from_cert` new helper.
- `water_heating_section_from_cert` plumbs `has_electric_shower`
through bootstrap + final call (and the internal cert_to_inputs path).
- 000487 fixture: `electric_shower_count=1, mixer_shower_count=0`.
§4 per-fixture:
fixture | LINE_42 | LINE_43 | LINE_44-46 | LINE_61-65
000474 | ✓ | ✓ | ✓ | ✓ (9/9)
000477 | ✓ | ✓ | ✓ | ✗ LINE_61/62/64/65 (slice 25c)
000480 | ✓ | ✓ | ✓ | ✓ (9/9)
000487 | ✓ | ✓ | ✓ | ✓ except LINE_65 (8/9)
000490 | ✓ | ✓ | ✓ | ✓ (9/9)
000516 | ✓ | ✓ | ✓ | ✓ (9/9)
Scoreboard:
section_cascade_pins: 279 → 286 PASS (+7)
e2e SapResult: 32 → 32 PASS (unchanged — LINE_65 cascade still
open, blocks downstream §5 LINE_72/73 + §6 LINE_84 + §7 + downstream)
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