f81e744b02
Wires all §5 leaf functions into a single from_cert orchestrator that
chains (66) → (67) → (68) → (69) → (70) → (71) → (72) → (73) and
returns an InternalGainsResult. The caller provides §4 (65)m heat
gains (the only non-cert input) and overshading defaults to AVERAGE.
Cert derivations:
- Occupancy via Appendix J Table 1b from TFA
- Lighting: RdSAP §12-1 per-lamp-type bulb defaults aggregated to
C_L,fixed + ε_fixed; C_daylight via L2a from sap_windows × Z_L
from Table 6d. L5b + L8c fallbacks when no bulb/window data lodged.
- Pumps/fans: maps central_heating_pump_age_str on the first
MainHeatingDetail to PumpDateCategory. Liquid-fuel / warm-air / PIV
/ MV / HIU branches deferred (reachable via leaf fns; currently
return 0 in the orchestrator for the combi-gas-natural-vent
population that covers all 6 Elmhurst fixtures).
Slice 9 tracer test hand-builds a 000490-lookalike EPC rather than
mutating `_elmhurst_worksheet_000490.build_epc()` — keeps the existing
e2e SAP-score regression test pinned. Slice 10 will extend the fixture
proper and parametrize over ALL_FIXTURES.
Also: extends make_minimal_sap10_epc with low_energy_fixed_lighting_bulbs_count
since the existing builder only exposed CFL/LED/incandescent separately.
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