f2062a2fbe
Closes the "no system" corpus variant fully (ΔSAP +1.18 → <1e-4 on all four metrics). The cert lodges §15.0 "Water Heating Code: NON / SapCode 999" and §15.1 "Hot Water Cylinder Present: No". Per RdSAP 10 §10.7 (PDF p.55) "No water heating system" verbatim: "the calculation is done for an electric immersion heater. If the electric meter is dual the immersion heater is also dual, but is a single immersion otherwise... for a cylinder defined by the first row of Table 28 (110 litres) and the first row of Table 29." Table 29 row 1 gives age-band cylinder insulation (age G -> 25 mm foam) and assumes a cylinder thermostat present for immersion-heated DHW. The BRE-approved Elmhurst engine confirms the substitution: the P960 worksheet header lodges "WHS: 903 Electric immersion, Single", a 110 L cylinder, and storage loss (56) = 594.32 kWh/yr, so HW (64) = (45) 1935.37 + 594.32 = 2529.6927. Pre-slice the cascade trusted the lodged "no cylinder" -> added no storage loss and a spurious Table 3a keep-hot combi loss; the wrong HW heat-gains also propagated through §5/§7, over-stating the base MIT by +0.25 K and space fuel by +228 kWh. New `_apply_rdsap_no_water_heating_system_default(epc)` rebinds the epc at the top of cert_to_inputs (the demand cascade delegates here too) when water_heating_code == 999, injecting WHC 903 + electricity fuel + 110 L cylinder + Table 29 insulation + assumed cylinder thermostat. This closes HW fuel AND the downstream space residual in one move. Age bands A-F (12 mm loose jacket) raise UnmappedSapCode — no corpus member exercises that and the Table 2 loss-factor dispatch only has the factory-foam path plumbed. Gate is keyed on code 999, unique to "no system" in the corpus; 40 other variants + 858 section pins + 6 U985 fixtures unchanged. 936 pass; pyright net-zero 32 -> 32. 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 | ||
| 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