da5e7196c4
Cert 0330 (mid-terrace boiler, Summary_000897.pdf) Summary path was at
Δ +0.4667 SAP vs worksheet 61.5993 because Ext1's flat roof fell through
`_ROOF_BY_AGE` (Table 18 column (1), pitched-roof "between joists"
defaults) to 0.40 W/m²K for age D — the spec value is 2.30 W/m²K from
column (3) "Flat roof" (RdSAP 10 spec page 45).
RdSAP 10 §5.11 Table 18 column (3) verbatim:
Age A,B,C,D → 2.30; E → 1.50; F → 0.68; G → 0.40; H,I → 0.35;
J,K → 0.25; L → 0.18; M → 0.15.
Footnote (a): "If the roof insulation is 'none' use U = 2.3 (all roof
types, except for thatched roofs)" — confirms the col-3 entries for
old ages are the uninsulated row, applied because cert 0330's Ext1
lodges "Flat" construction with no measured insulation thickness.
Changes:
- `_FLAT_ROOF_BY_AGE` added in rdsap_uvalues.py
- `u_roof` gains `is_flat_roof: bool = False` parameter
- `heat_transmission_from_cert` detects flat roofs from
`part.roof_construction_type` ("flat" substring) and routes through
the new column.
Effect on baseline:
- cert 0330 Summary chain test: RED Δ+0.4667 → GREEN at 1e-4 (worksheet
total fabric heat loss 237.7549 W/K matches cascade to 4 d.p.)
- cert 001479 Layer 4 chain test: unchanged (Main pitched, no flat
components)
- cohort certs 000477/000516: unchanged (no flat roofs)
- golden cert 0300-2747-7640-2526-2135: SAP residual +1 → 0 (improved),
Ext1 is genuinely flat; pe/co2 residuals re-pinned. The dwelling has
the same Main-pitched + Ext1-flat shape as cert 0330; same fix.
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