af6fcfb190
Surfaces four cert lodgements that the §2 ventilation cascade was
missing on the cert→inputs path. Without them, `cert_to_inputs` was
defaulting:
- extract_fans_count → 0 (PDF: 1-2 fans per fixture)
- percent_draughtproofed → 0 (PDF: 75-100% per fixture)
- sheltered_sides → 2 (PDF: 1-3 per fixture — hardcoded TODO)
- has_suspended_timber_floor → False (PDF: True on 000477/000487)
Net effect on (25)m monthly effective ACH ranged from -19% (000477)
to +5% (000490) → propagated 1:1 through HLC × ΔT → useful space heat
→ main + secondary fuel kWh → cost / SAP integer.
Schema:
- `SapVentilation` gains 4 new optional fields: `sheltered_sides`,
`has_suspended_timber_floor`, `suspended_timber_floor_sealed`,
`has_draught_lobby`. RdSAP cert lodges these but the type didn't
surface them.
- `cert_to_inputs.cert_to_inputs` reads them when set; falls back to
the SAP10.2 §2 worst-case defaults (sheltered=2, no timber floor,
no draught lobby) when the cert hasn't lodged. Removes the long-
standing `sheltered_sides=2` hardcode + 4 TODOs.
- `make_minimal_sap10_epc` accepts a `sap_ventilation` kwarg.
Per-fixture build_epc() updates lodge the U985 PDF values verbatim.
E2E pin: new parametrized test
`test_elmhurst_cert_to_inputs_monthly_infiltration_ach_matches_u985_
worksheet` asserts `inputs.monthly_infiltration_ach[m] == LINE_25_
EFFECTIVE_ACH[m]` at abs=1e-3 across all 6 fixtures + 12 months
(72 assertions). All pass.
Useful space heating drift:
000474: useful 10821.69 → 10765.85 (Δ -55.8 kWh vs PDF 10612.86 → +1.4% over, was +2.0%)
000490: useful 11262.05 → 11184.06 (Δ -78.0 kWh vs PDF 11183.28 → +0.007% — essentially exact)
SAP integer status:
000474: 62 = PDF 62 (delta 0) ✓
000490: 58 vs PDF 57 (delta 1; continuous 57.77 vs 57.40)
— remaining residual is pumps_fans hardcoded at 130 kWh
vs PDF 160 (Table 4f cascade not yet implemented → -£4 cost
+ 0.3 continuous SAP). Next slice.
Tightens `result.secondary_heating_fuel_kwh_per_yr` pin abs=10 → abs=0.1
(was loose to absorb the +0.7% useful overshoot which has now closed).
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