e0bca4c3cd
Slice S0380.55 routed water-heating EFFICIENCY to Main 2 for WHC 914. This slice extends the routing to water-heating FUEL — the cascade's CO2 factor, PE factor, and Table 32 fuel-cost lookups were still pinned to Main 1's fuel code via the legacy `epc.sap_heating.water_heating_fuel or main_fuel` pattern. For cert 000565 (WHC 914 + HP Main 1 + gas combi Main 2): - `epc.sap_heating.water_heating_fuel` is None (Elmhurst §15 doesn't lodge a separate water-heating fuel type) - `_main_fuel_code(Main 1)` is None (HP, no fuel_type lodged in §14.0 — Elmhurst convention for heat pump certs) - Old pattern: water_fuel = None → `co2_factor_kg_per_kwh(None) = 0` → HW CO2 silently 0 (off by ~833 kg/yr vs gas combi truth) New helper `_water_heating_fuel_code(epc)` mirrors `_water_heating_ main(epc)`: prefers the explicitly-lodged `water_heating_fuel`, otherwise falls back to `_main_fuel_code` of whichever main system services DHW per WHC. Wired into 5 cascade sites (CO2 / PE / cost × hot-water + per-end-use CO2 + per-end-use PE factors). Cert 000565 cascade impact: - hot_water_co2 (kg/yr): factor=0 → 0.21 (gas) — now correctly attributes ~833 kg HW CO2 to gas combustion - hot_water_primary_factor: 0 → gas Table 12e value - hot_water_high_rate_gbp_per_kwh: previously fell through to Main 1 fuel-code which is also None → gas tariff sentinel; now derives explicitly from Main 2's mains-gas fuel (Table 32 code 26) - co2_kg_per_yr pin: +287 → +734 (got "worse" because HW gas CO2 is now correctly counted; remaining surplus is from an INDEPENDENT Main 1 fuel-inference bug — `_main_fuel_code` returns None for HP Main 1 because Elmhurst §14.0 leaves `Fuel Type` empty for heat pumps, so the cost/CO2 cascade defaults Main 1 to the gas tariff) The Main-1 HP fuel-inference bug is the next slice. For single-main non-HP certs the helper resolves identically to the prior pattern (water_heating_fuel explicit, or Main 1 fuel) — no behavioural change for the existing fixture corpus. Pyright net-zero (34 / 34). 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