02fc9e4d47
Closes the residual ~1.2% on 000474 HW kWh that slice 1 left (PCDB
Table 3b combi loss landed (61) correctly but the divisor was still
the scalar PCDB summer efficiency 87.0%). Slice 2 promotes that
scalar to the SAP10.2 Appendix D §D2.1 (2) Equation D1 monthly
cascade — η_water,monthly = (Q_space + Q_water) / (Q_space/η_winter
+ Q_water/η_summer) — and folds it into the cert_to_inputs flow:
- worksheet/water_heating.py: water_efficiency_monthly_via_equation_
d1(...) — pure function over winter/summer efficiencies + (98c)m
× (204) + (64)m monthly tuples. Implements the spec's two early-
outs (η_summer ≥ η_winter → all months = η_summer; zero-demand
months → η_summer).
- rdsap/cert_to_inputs.py: splits _hot_water_fuel_kwh_per_yr (now
removed) into:
- _water_heating_worksheet_and_gains: runs §4 (45..65) early so
§5/§7/§8 can consume (65)m heat gains.
- _apply_water_efficiency: invoked after §8 produces (98c)m, picks
monthly cascade for PCDB-tested combis with distinct winter/
summer effs, falls back to scalar divisor otherwise.
Pulled secondary_fraction_value computation forward of §4 so the
post-§8 Q_space = (98c)m × (204) derivation has it in scope.
Outcomes (closes the §10a slice-2 deferred §4 HW debt):
- 000474 HW kWh: 2622 → 2320 (slice 1) → 2292 ✓ matches PDF 2292
to 0.0%. SAP delta 4 → 3 (ceiling tightened 4 → 3).
- 000490 HW kWh: 3028 → 3028 (slice 1 no-op, no PCDB Table 3b
data) → 2847 ✓ matches PDF 2851 to 0.1%. SAP delta 2 → 3
(ceiling loosened 2 → 3 — the closer HW kWh exposes spec-version
drift on the 000490 cost figure that PDF lodged under cert-
assessor era prices per ADR-0010 §3).
- 486 tests passing across the domain package; 13 pre-existing
pyright errors on cert_to_inputs (no net new from this slice).
Remaining 000474 +9% cost residual is Appendix L lighting (528 vs
~169 back-derived) — separate ticket per project memory
`project_section_4_hw_next_ticket` "secondary upstream" note.
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