2a4d67e396
Close the §6.1 conservatory demand cascade per RdSAP 10 §6.1 + Table 25. Solar gains (§6, solar_gains.py) — Table 25 note (PDF p.51): "The orientation of windows in a conservatory is not recorded, thus solar gains are calculated using the default solar flux (East/West orientation, with 20° pitch for roof windows)." The glazed wall bills onto the (76) East line (vertical, average-overshading Z); the glazed roof onto the (82) roof-window line (20° pitch, Z=1.0), both at Table 25 g=0.76, FF=0.70. TFA-occupancy (mapper) — §6.1: the conservatory floor area is added to the dwelling total floor area. TFA drives occupancy → §5 internal gains + §4 hot-water demand, so the non-separated conservatory's floor area now enters `EpcPropertyData.total_floor_area_m2` (the worksheet's (4) = 95.38 carries it). Separated conservatories (§6.2) stay excluded. Pinned against the case-44 P960 demand cascade at abs=1e-4: (73) internal gains 625.1759, (83) solar gains 495.8655, (95) useful gains 1079.6510, (99) space heating per m² 89.8073 — the full §6.1 chain reproduces EXACTLY. The whole-dwelling SAP (72.9517) / CO2 (3241.8656) are not pinned: the case-44 Summary omits the House-Coal secondary heater (SAP 633) the P960 descriptor carries (cf. case 43), so the cascade computes no secondary — the entire residual (+349.77 kg CO2). A Summary-input defect, independent of §6.1; every conservatory-affected line ref is exact. Worksheet harness stays 47/47 0-raised; corpus unchanged (API path; mirror is the next slice). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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 | ||
| next_claude_prompt.txt | ||
| 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