ff5d8c70c1
Adds SECTION_7_LIVING_AREA_FRACTION, SECTION_7_CONTROL_TYPE,
SECTION_7_RESPONSIVENESS, SECTION_7_THERMAL_MASS_PARAMETER_KJ_PER_M2_K
plus LINE_85..LINE_94 expected outputs across all 6 _elmhurst_worksheet_*
fixtures, and an ALL_FIXTURES-parametrised end-to-end test.
The test sources its inputs from §1-§6 fixture pins:
(84) monthly total gains = LINE_73 + LINE_83
(39) monthly HTC = LINE_37 + 0.33·V·LINE_25_M
external temp = Appendix U Table U1 region 0 (UK-avg, SAP rating pass)
Asserts every per-zone line ref to abs=5e-3 °C / unitless:
(85) T_h1 × 6 = 6
(86) η_living monthly × 12 × 6 = 72
(87) MIT living monthly × 12 × 6 = 72
(88) T_h2 monthly × 12 × 6 = 72
(89) η_elsewhere monthly × 12 × 6 = 72
(90) MIT elsewhere monthly × 12 × 6 = 72
(91) f_LA × 6 = 6
(92) blended MIT monthly × 12 × 6 = 72
(93) adjusted MIT monthly × 12 × 6 = 72
(94) η_whole monthly × 12 × 6 = 72
total = 588 GREEN assertions
All 6 fixtures land at default scalars (control_type=2 gas combi w/
programmer+RT, R=1.0 Table 4d gas radiators, TMP=250 SAP mass-medium
default, Table 4e adj=0). Per-fixture f_LA reflects habitable_rooms_count.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .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