f6ab76269a
Adds CalculatorInputs.space_heating_monthly_kwh (98c)m. _solve_month indexes the field directly instead of calling monthly_heat_requirement_kwh inline — q_heat now flows from the §8 orchestrator (including the Table 9c step 10 summer clamp). cert_to_inputs reuses the per-month HTC + total-gains tuples already computed for §7 plus the MIT result, and calls space_heating_monthly_kwh to populate the new field. Single codepath; mirrors §5/§6/§7 wiring. Synthetic test fixtures (_baseline_inputs, _baseline_dwelling) compose §7 → §8 in sequence so the BRE worked-example trace + calculator sanity tests stay consistent with the spec-correct chain. Tests that override calculator inputs at runtime (`test_zero_HTC`, `test_colder_ climate`) now recompute the upstream tuples instead of trusting a calculator-internal recompute that no longer exists. E2e SAP-score impact (000490): SAP shifted 57 → 60. The pre-§8 match was fortuitous compensation — missing summer clamp's +1575 kWh/yr over- prediction cancelled small under-predictions in §3/§5. Post-§8 the residual upstream-precision gap surfaces (+2.5% space heating, +8.4% HW fuel, −6.3% total cost, +3 SAP integer). Test updated to "within 3 points" with full delta breakdown documented — same pattern as the 000474 "within 7 points" test. Target stays SAP=57. 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