a97ff60b01
`_CYLINDER_SIZE_CODE_TO_LITRES` held only codes 2/3/4 (Normal/Medium/Large → 110/160/210 L); codes 5 (Inaccessible) and 6 (Exact) fell through to None, so the Table-13 high-rate fraction AND the cylinder storage loss were skipped for those certs (20 code-6 certs in the API sample). Per RdSAP 10 Specification (10-06-2025) §10.5 Table 28 (PDF p.55): - Code 6 "Exact": use the lodged measured volume. The gov API carries it in `cylinder_size_measured` (e.g. 150 L) — now plumbed through the 21.0.0/21.0.1 schema → mapper → `SapHeating.cylinder_volume_measured_l`. - Code 5 "Inaccessible": 210 L if off-peak electric dual immersion, 160 L from a solid-fuel boiler, otherwise 110 L (n=0 in the current sample, but spec-complete). New `_cylinder_volume_l_from_code` centralises Table 28 resolution and replaces the three raw-dict call sites (`_hot_water_cylinder_volume_l`, the cylinder storage-loss path, and the PCDB performance check) so all three honour codes 5/6 identically. `_cylinder_inaccessible_volume_l` applies the code-5 context rule via the existing immersion/off-peak-meter/solid-fuel-boiler detectors. Worksheet harness UNAFFECTED (47/47, 0 divergers): the Summary path lodges neither code 5/6 nor a measured volume. API gauge: within-0.5 64.4% -> 65.1% (mean|err| 1.085 -> 1.075) — the 20 code-6 certs now size their cylinder from the measured volume. 4 AAA tests (code 6 measured; code 5 solid-fuel/default/ off-peak-dual-immersion). pyright net-zero. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| 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_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