a4580b208a
Session-end handover docs for the cert 000565 wacky-stress-test investigation. Three documents covering: - **HANDOVER_POST_S0380_73_APPENDIX_H_BLOCKED.md** — full state of the cohort closure work (S0380.70-.73) plus the Appendix H Solar HW investigation findings. Cumulative ASHP cluster compression −3.10 → −0.06 PE kWh/m² over 4 slices. Cert 000565 HW pin blocked at +272 kWh/yr on a 1.81× formula over-count. - **BRIEF_APPENDIX_H_EN_15316_RESEARCH.md** — self-contained brief for a research agent or human looking up BS EN 15316-4-3 Method 2 to identify the missing clamp / useful-gain rule / validity envelope behind the over-count. Includes the cert 000565 diagnostic (per-month ratio 1.5-1.7× summer, 3-4× shoulder), seven specific questions ranked by hypothesis likelihood, and the 36-data-point empirical-fit setup. - **NEXT_AGENT_PROMPT_POST_S0380_73.md** — directive for the next agent. Awaits 3 user-generated solar-HW cert worksheets (A baseline / B high-Y / C low-Y) to empirically test whether the 1.81× ratio is systematic or cert-specific. Decision point: ship an empirical correction (if 36-point fit closes all 3 certs + cert 000565) or hold for the EN standard. Also resolves the long-standing H3=4.0 / H4=0.01 default mystery: sub-agent located the source in RdSAP 10 Specification §10.11 Table 29 row "Solar panel" page 58. RdSAP overrides the input set; the calculator is still SAP 10.2 Appendix H. So the defaults aren't the source of the over-count. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| backend | ||
| backlog | ||
| datatypes | ||
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| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
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| survey_report | ||
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| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
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| MEMORY.md | ||
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| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
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| test.requirements.txt | ||
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| 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