8cfeba8e2a
Adds an optional `postcode_climate: Optional[PostcodeClimate]` parameter to every cert→inputs section helper that touches climate: - `cert_to_inputs(epc, postcode_climate=...)` - `ventilation_from_cert` (overrides UK-avg wind tuple) - `mean_internal_temperature_section_from_cert` - `space_heating_section_from_cert` - `space_cooling_section_from_cert` - `solar_gains_section_from_cert` - `energy_requirements_section_from_cert` - `fuel_cost_section_from_cert` - `environmental_section_from_cert` `_climate_source(postcode_climate)` returns `int | PostcodeClimate` (region 0 = UK-avg fallback). The four Appendix U lookup functions (`external_temperature_c`, `wind_speed_m_per_s`, `horizontal_solar_ irradiance_w_per_m2`, `_latitude_deg`) now accept the union and dispatch on isinstance — region path is unchanged, postcode path reads directly from `PostcodeClimate`. CalculatorInputs gains `monthly_external_temp_c_override` so the calculator's per-month solve uses the postcode tuple computed in cert_to_inputs instead of looking up `external_temperature_c(region, m)` (which would always be UK-avg). Adds two public helpers: - `local_climate_for_cert(epc)` — postcode lookup with None fallback - `cert_to_demand_inputs(epc)` — convenience: cert_to_inputs with postcode climate from the cert's postcode field Verification (000474 with postcode "bd3 8aq" injected — fixtures currently lodge placeholder "A1 1AA"; real postcodes land in slice 36): Rating main_1_fuel = 11964.8924 (PDF Block 1: 11964.8924 ✓) Demand main_1_fuel = 12288.0014 (PDF Block 2: 12288.0014 ✓ EXACT) Rating ext_temp Jan = 4.3°C (UK-avg) Demand ext_temp Jan = 4.2°C (BD3) 840/840 existing pins still pass — refactor is backward-compatible. 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