e1b7b30c40
Cert 1536 lodged window dimensions including (0.65 × 0.70) × 3
windows. In float arithmetic 0.65 × 0.70 = 0.45499999999999996,
which the `_round_half_up(float, dp)` helper snaps to 0.45 vs the
spec answer 0.46 (Decimal: 0.65 × 0.70 = 0.4550 exact, HALF_UP at
2 d.p. = 0.46). The shortfall of 0.01 m² × 3 windows = 0.03 m²
under-counted as ~0.073 W/K of conduction loss vs the worksheet's
windows_w_per_k = 25.6354 — closing the cert 1536 residual at
+0.00152 to <2e-6.
Same class of bug as the S0380.34/35 living-area / gross-wall /
party-wall closures (Decimal HALF_UP at the 0.005 boundary that
float drops). RdSAP10 §15 (p.66) lists "all element areas (gross)
including window areas: 2 d.p." — Decimal is the only arithmetic
that matches that boundary deterministically.
Three cascade sites now use Decimal HALF_UP for per-window areas:
- heat_transmission.py: `_decimal_round_half_up_product(W, H, 2)`
replaces `_round_half_up(W × H, 2)` at the windows_w_per_k cascade
AND at the per-bp window-area accumulation (the wall-net deduction
branch must agree with the conduction branch for cascade-internal
consistency, per the existing comment at line 575-583).
- internal_gains.py: `_decimal_window_area_2dp(W, H)` replaces the
inline `_round_area_2dp(W × H)` in the daylight factor `g_l`
sum so §5 (66)..(67) sees the same per-window areas as §3 (27).
- solar_gains.py: same Decimal helper replaces `_round_area_2dp` in
`_wall_window_solar_gain_monthly_w` so §6 (74)..(81) area = (27).
The `_round_area_2dp` helpers were inlined per-module in pre-S0380.42
work; this slice deletes them since the Decimal-aware product
replaces all call sites. `_round_half_up` stays in heat_transmission
for non-product per-element area calls (single-value rounds).
Test impact:
- Cohort-2 cert 1536 API path: +0.00152 → -1e-6 (<1e-4 ✓).
Moves from _COHORT_2_API_OPEN to _COHORT_2_API_CLOSED. Cohort
distribution: 37/38 exact (was 34/38 at start of session);
only cert 2102 (-6.30 secondary-heating routing) remains open.
- Cohort-2 cert 0300/9380 unchanged (already <1e-4 after S0380.41).
- Cohort-1 ASHP 9/9 unchanged: <1e-4 on both paths.
- Elmhurst 6-cert worksheet sweep: unchanged (lodges
`window_width=area, window_height=1.0` per the Elmhurst lodging
convention — Decimal(area) × Decimal(1.0) = Decimal(area), no
rounding shift).
Test suite: 750 pass + 0 fail. Pyright net-zero per touched file
(heat_transmission 13/13; internal_gains 4/4 pre-existing; solar_gains
0/0; chain test 0/0).
Spec citation: RdSAP 10 Specification §15 "Rounding of data" p.66 —
"All element areas (gross) including window areas and conservatory
wall area: 2 d.p." Decimal is the float-precision-stable arithmetic
that matches this rule at the .005 boundary.
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