6d82f8842d
Cert 000565 lodges §14.1 Main Heating2 as PCDB 15100 (Vaillant Ecotec
plus 415, 88%, mains gas, 0% space heat) — this is the system that
services DHW via `Water Heating SapCode 914` ("from second main
system"). The previous extractor / mapper shape supported only ONE
main heating system, dropping Main 2 entirely.
New shape:
- `MainHeating2` dataclass (slim §14.1-shaped: PCDB ref, fuel type,
flue type, fan_assisted_flue, percentage_of_heat, SAP code)
- `MainHeating.main_heating_2: Optional[MainHeating2]` — None when
§14.1 is absent OR lodges only placeholder zeros (the PCDB-only
convention; the two JSON fixtures + 14 existing Summary fixtures
all lodge "0 / 0" for an absent Main 2)
- `_extract_main_heating_2` parses §14.1; returns None when neither
PCDB ref nor SAP code identifies Main 2
- `_map_elmhurst_main_heating_2` builds `MainHeatingDetail` from the
Main 2 lodgement with `main_heating_number=2` and `main_heating_
fraction=percentage_of_heat`; strict-raises `UnmappedElmhurstLabel`
(mirroring Slice S0380.53's Main 1 raise) when Main 2 has neither
identifier — surfaces coverage gaps at extraction time
Per RdSAP convention "0%" is lodged without a space (vs Main 1's
"100 %" with a space) — robust percentage parse via `rstrip("%")` so
both forms thread through.
Cohort impact:
- 14 existing Summary PDF fixtures + 2 JSON fixtures: Main 2 returns
None (placeholder zeros) → no 2nd MainHeatingDetail produced → no
cascade behaviour change (regression-tested: 415 pass + 10 expected
000565 fails, identical to S0380.53 baseline)
- Cert 000565: 2nd MainHeatingDetail now lodged with sap_code=None,
pcdb=15100 (Table 105 gas-boiler 88% efficiency), category=2,
fuel=26 (mains gas), fraction=0
Cascade still uses Main 1 for water-heating efficiency in the WHC
914 branch — that routing fix is the next slice. This commit is
the plumbing-only half; the SAP-result pin residuals are unchanged
at HEAD because the cascade hasn't been wired to read Main 2 yet.
Pyright net-zero on all 3 touched files.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| 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