380115e244
Table 6d note 2: roof windows / rooflights use Z_L = 1.0 regardless of
the overshading bucket applied to the rest of the dwelling's glazing.
Before this slice the orchestrator approximated rooflights as average
overshading (Z_L=0.83), driving 000516's (67) lighting 0.18 W (0.54%)
high. All wall windows in our 6-fixture corpus were correctly handled;
000516 is the only fixture with a lodged rooflight (the 1.18 m² NE
"window" showing Z=1.0 in the worksheet §6).
fixture | (67) max |err| before | after
--------+----------------------+--------
000516 | 0.1823 W (0.54%) | <0.005 W (<0.02%)
others | <0.0003 W | <0.0003 W
Changes:
- internal_gains_from_cert gains rooflight_total_area_m2 (default 0).
Rooflights summed at g_L=0.80 (Table 6b DG) × FF=0.7 (Table 6c PVC)
× Z_L=1.0 alongside wall windows (which still use the dwelling's
overshading-derived Z_L).
- SECTION_5_ROOFLIGHT_AREAS_M2 added to every fixture (empty tuple
except 000516 which carries (1.18,)).
- Tolerances on the §5 parametrised e2e test tightened from 2e-1 W
on (67) and 3e-1 W on (73) to 5e-3 W on both — every fixture now
closes to display rounding.
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