ad953d49c7
8 slices shipped this session: S0380.96 RIR Unknown insulation (RdSAP 10 §3.10.1) S0380.97 Floor §9 insulation thickness (RdSAP 10 §5.13 Table 20) S0380.98 PCDB Table 322 ETL+parser (PCDF Spec §A.19) S0380.99 PCDB Table 329 ETL+parser (PCDF Spec §A.20) S0380.100 MEV SFPav + (230a) helpers (SAP 10.2 §2.6.4) S0380.101 HP SAP code → cat=4 mapper (SAP 10.2 Table 4a) S0380.102 Wire MEV into pumps_fans (SAP 10.2 Table 4f 230a) S0380.103 MEV-fan cost split (SAP 10.2 Table 12a Grid 2) Cert 000565 at HEAD `e3abe9b2`: sap_score (int) ✓ EXACT pumps_fans_kwh_per_yr ✓ EXACT (was +2.48 over) hot_water_kwh_per_yr ✓ 0 EXACT sap_score_continuous Δ +0.0182 (SH cascade-driven) 7 expected fails (was 9) Next slice candidate: S0380.104 investigate §3-§8 space-heating cascade -27 kWh under-count (cert-000565-specific; cohort certs pass at 1e-4). Alternative: S0380.105 CO2 MEV split (mirror of .103 for Table 12d monthly factors). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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 | ||
| 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