8ae978a646
The cascade lumped a dwelling with two main heating systems into one: `space_heating_fuel_monthly_kwh` hard-coded (203)=0 (a documented scope-A placeholder) and the calculator's per-month fuel read only main_1, so the full §8 space-heat demand billed against system 1's efficiency. Simulated case 6 (one oil boiler feeding radiators 51% + underfloor 49%) exposed it: main fuel ≈ demand/eff1 instead of the worksheet's (211)+(213) per-system split. Implements the SAP 10.2 §9a two-main model: (204) = (202) × (1 − (203)) → system 1 share of total heat (205) = (202) × (203) → system 2 share of total heat (211)m = (98c)m × (204) × 100 / (206) (213)m = (98c)m × (205) × 100 / (207) (203) = the second system's lodged `main_heating_fraction`; (207) = its own seasonal efficiency via the new per-detail `_main_heating_detail_ efficiency` (the core of `_main_heating_efficiency`, now reused for system 2). Calculator `_solve_month` aggregates main_1 + main_2 into `main_heating_fuel_kwh`. Cost (§10a 241), CO2 (§12 262) and PE (§13 276) main_2 paths were already wired and now activate. Site-notes gap also fixed: §14.1 Main Heating2 omits the "Fuel Type" cell when the second system shares Main 1's fuel (case 6: one oil boiler, two emitters). `_map_elmhurst_main_heating_2` now inherits Main 1's resolved fuel as a fallback. Blast radius: only dual-main certs. 0240 (2× oil code 130, identical Eq-D1 efficiency) is unchanged — its split collapses to the lumped total. Suite: 2355 passed, 1 skipped. New code: 0 pyright errors. NOTE: case 6 is not yet fully pinnable end-to-end — its two systems have DIFFERENT efficiencies (radiators 55°C → 79%, underfloor 35°C → 84%), a flow-temperature boiler-efficiency adjustment not yet modelled, and its dual-system auxiliary pumps ((230c)+(230d)=356) differ from the cascade. Both are separate follow-on features; this slice is the §9a fuel split. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| 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