729229ed61
Adds Table 12e (p.195) monthly PE factors for electricity to `tables/table_12.py` + `pe_monthly_factors_kwh_per_kwh(fuel_code)` helper. Mirrors slice 32's CO2 cascade — same spec text, same shape: electricity end-uses use Σ(kWh_m × PE_m); non-electricity fuels keep the annual Table 12 / RdSAP10 Table 32 (p.95) factor. Calculator now consumes per-end-use PE factors on `CalculatorInputs` (`secondary_heating_primary_factor`, `pumps_fans_primary_factor`, `lighting_primary_factor`, `electric_shower_primary_factor`). Defaults to None → fall back to the global `space_heating_primary_factor` / `other_primary_factor` (synthetic path). Fixes the stale 1.969 default to RdSAP10 Table 32 standard-electricity PE = 1.501. `_effective_monthly_factor(monthly_kwh, monthly_factors)` generalises the slice-32 weighting helper; `_effective_monthly_co2_factor` and the new `_effective_monthly_pe_factor` are thin wrappers over it. Includes the electric-shower kWh in the PE total — closes the audit loop opened by slice 30 (electric shower had fuel cost + CO2 but no PE contribution). §13a cascade pins NOT added — §13a appears only in the Demand-SAP block (postcode climate); our cascade pins live against the Rating-SAP block (UK-average climate). The Demand-SAP postcode cascade is a separate scope, intentionally deferred. The calculator's existing `primary_energy_kwh_per_yr` SapResult output now uses the spec-correct PE factors but stays UK-average climate. Verification (000474): pumps_fans effective PE factor = 1.5128 (PDF: 1.5128 ✓) lighting effective PE factor = 1.5338 (PDF: 1.5338 ✓) pumps_fans PE = 242.0480 kWh (PDF: 242.0480 ✓) lighting PE = 214.6527 kWh (PDF: 214.6527 ✓) Wider regression: 1490/1490 PASS — zero failures. 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