74bfac049a
Adds `fuel_cost_section_from_cert(epc)` (delegates to `cert_to_inputs` which already wires `_fuel_cost` with full upstream context). Pins (240a)..(255) — 32 line refs × 6 fixtures = 192 cascade pins, all PASS. Three calculator changes needed for closure: 1. Electric shower (247a) — for 000487 the cert lodges 1 electric shower and the PDF reports (247a) = 79.3036 GBP (= (64a)m × std electricity price). The §4 cascade already computes electric-shower kWh via App J step 8 (slice 25d); now exposed on `WaterHeatingResult` as `electric_shower_kwh_per_yr` and plumbed into `_fuel_cost`. The instant-shower input was previously hardcoded to 0. 2. (241a/241b) main 2 + (242a/242b) secondary fractions — when a row's kWh is zero the PDF reports BOTH high/low fractions as 0 (not 1/0). `_split` in fuel_cost now zeros both fractions when kwh_per_yr <= 0. Cost columns already collapse via multiplication, so this is presentation-only. 3. (242a/242b) secondary fractions for 000474 — same pattern: when no secondary system is lodged, both fractions = 0. Adds §10a LINE_ constants to all 6 fixtures. Extracted from `sap worksheets/U985-0001-NNNNNN.txt` PDF blocks. Cascade scoreboard: 468/468 → 660/660 (§7..§10a closed). e2e SapResult: 6 remaining failures (all `co2_kg_per_yr`, await §12). 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