ae7e6a0c42
Replace the flat placeholder scalars (boiler £3000; tune-up £500/£900) with a per-dwelling composite cost, mirroring the ASHP architecture (ADR-0025): a `HeatingRates` table (data, `heating_rates.json`), typed `BoilerCostInputs` / `TuneUpCostInputs`, pure `Products.boiler_bundle_cost` / `tune_up_cost`, and modelling-layer interpreters that read the dwelling into those inputs. The cost mirrors the Simulation Overlay component-for-component, sharing the controls + cylinder pricing across both options: - tune-up (standard) = standard controls + cylinder fixes - tune-up (zone) = zone controls + cylinder fixes - boiler upgrade = £3200 all-in + standard controls (only when the upgrade fired a controls change) + cylinder fixes Standard controls are priced INCREMENTALLY — only the parts missing to reach SAP 2106 (programmer £120 / room thermostat £150 / TRV £35×radiators), read from a Table 4e Group-1 feature map so a dwelling that already has a room thermostat + TRVs is only charged the programmer. Zone controls are a full smart kit (hub £205 + smart TRV £50×radiators) — the smart TRV is itself the room sensor, so there is no separate per-room sensor line. Cylinder fixes: jacket £50 (when under-insulated) + thermostat £150 (when absent). The boiler is a like-for-like wet swap (no radiators/flue/pipework — eligibility already requires an existing wet boiler), so those dead-code extras are not modelled. Figures are research-validated 2025/26 UK installed costs (legacy Costs.py lineage); fully-loaded totals with one contingency on top (Model B, not the legacy VAT/preliminaries engine). Contingency: boiler 0.26; tune-ups 0.10 (was a 0.15 placeholder). ADR-0027 records the design; CONTEXT.md's Heating Eligibility entry updated to cover the partial boiler/tune-up family + composed cost. Products cost pins (delta<=1e-9) + interpreter tests + generator composite-cost assertions. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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