f77229e4b4
Implements Table 5a row-by-row leaf functions:
central_heating_pump_w pump install-date bucket (3/7/10 W)
liquid_fuel_boiler_pump_w 10 W when oil-fuel pump inside dwelling
liquid_fuel_warm_air_pump_w 10 W for liquid-fuel warm-air systems
warm_air_heating_fan_w SFP × 0.04 × V (heating-season)
piv_fan_w IUF × SFP × 0.12 × V (year-round)
balanced_mv_no_hr_fan_w IUF × SFP × 0.06 × V (year-round)
heat_interface_unit_w PCDB kWh/day × 1000 / 24 (year-round)
Plus pumps_fans_monthly_w(heating_season_w, year_round_w) which applies
the Table 5a footnote-a seasonal mask (Jun-Sep = 0 W heating-season
contribution per Elmhurst worksheet convention).
PumpDateCategory enum maps from EpcPropertyData.central_heating_pump_age_str
("Pre 2013" / "Post 2013" / "Unknown" / etc.) at the orchestrator layer.
MVHR and MEV systems intentionally have no leaf fn — gains are zero per
Table 5a notes (MVHR effect is in MVHR efficiency; MEV simply omitted).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .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