d4d2b222fc
Wire the non-separated conservatory into the §3 heat-transmission +
§1 dimensions cascade per RdSAP 10 §6.1 (PDF p.49) + Table 25 (p.51):
"The floor area and volume of a non-separated conservatory are added to
the total floor area and volume of the dwelling. Its roof area is taken
as its floor area divided by cos(20°), and wall area is taken as the
product of its exposed perimeter and its height. ... The conservatory
walls and roof are taken as fully glazed ... Glazed walls are taken as
windows, glazed roof as rooflight."
New `worksheet/conservatory.py` derives the geometry:
- height from the equivalent storey count (§6.1: 1 storey → ground-floor
room height; 1½ → ground + 0.25 + 0.5×first; etc.);
- glazed WALL → window (27) at Table 25 U (double 3.1 / single 4.8) with
the §3.2 curtain resistance (R=0.04) → U_eff 2.758;
- glazed ROOF → rooflight (27a) at Table 25 roof U (double 3.4 / single
5.3) + curtain → U_eff 2.993;
- FLOOR → (28a) via BS EN ISO 13370 as an uninsulated SOLID ground floor
with 300 mm walls (§5.12, spec p.43), exposed perimeter = glazed
perimeter → U 0.89;
- glazed wall + roof + floor areas join (31)/(36); the fully-glazed
structure walls/roof add nothing (the glazing IS the window/rooflight).
`dimensions_from_cert` adds the conservatory floor area to TFA (4) and
floor area × height to volume (5) (feeds ventilation (8)), without making
it a storey (avg storey height for §2 infiltration is unchanged).
Pinned against the simulated case-44 P960 §3 at abs=1e-4 — every line ref
EXACT: (4) 95.3800, (5) 257.1630, (27) 96.1169, (27a) 38.2201, (28a)
21.4164, (29a) 35.5852, (30) 7.4688, (31) 294.2900, (33) 207.3274,
(36) 23.5432. The remaining whole-dwelling SAP/CO2 gap is the §6 solar
gains, closed in the next slice. Worksheet harness stays 47/47 0-raised.
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