0786921357
The cascade's `additional_standing_charges_gbp(main_fuel_code, water_heating_fuel_code, tariff)` function (table_32.py:178) was already producing the right values — for cert 000565 it returns £143 (£120 mains gas standing + £23 10-hour high-rate electricity standing per Table 32 page 95). But the value only landed in `FuelCostResult.additional_standing_charges_gbp` inside `_fuel_cost`, which returns `_ZERO_FUEL_COST_FOR_OFF_PEAK` for non-STANDARD tariff. The calculator then falls back to the inline cost math (scalar fuel-cost × kWh) which had no standing-charge component → £143 was silently dropped from the off-peak cost cascade. New `CalculatorInputs.standing_charges_gbp: float = 0.0` field carries the standing-charge total into the fallback path. The inline cost summation adds it before max-clamp + PV credit. STANDARD-tariff certs route via `fuel_cost.additional_standing_ charges_gbp` (set inside `_fuel_cost`) and the calculator ignores this scalar on that path — no double-count. `cert_to_inputs` populates the new field unconditionally; the value is just zero on standard-tariff certs (Table 12 note (a) gates standing-charge inclusion regardless). Cert 000565 cascade impact: - standing_charges_gbp = £143.00 ✓ (exact match to worksheet line 251) - total_fuel_cost_gbp: Δ −310 → −167 (46% reduction) - sap_score_continuous: Δ +3.61 → +1.91 (47% reduction) - co2_kg_per_yr: Δ unchanged (standing charges don't bill CO2) Cohort regression check: 427 pass + 10 expected 000565 fails. The 14 existing Elmhurst fixtures + JSON fixtures all have meter_type= None → STANDARD → standing routes via FuelCostResult unchanged. Spec source: RdSAP 10 Table 32 page 95 standing-charge column; SAP 10.2 Table 12 note (a) inclusion gating. Pyright net-zero on both files (0 / 34). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
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| __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 | ||
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| test.requirements.txt | ||
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| 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