5317175dd3
The mixer-shower hot-water demand (worksheet 42a) divided N_shower by the count of MIXER outlets only. But SAP 10.2 Appendix J step 1a is explicit: "Establish how many shower outlets are present in the dwelling, Noutlets (including in the count any instantaneous electric showers)" — and the electric-shower step (64a) uses that same Noutlets from step 1a. So a dwelling with both a mixer and an electric shower assigned the FULL N_shower to the mixer system AND billed the electric shower on top of it, double- counting shower demand → over-counted main HW → under-rated the dwelling. Fix: thread the electric-shower count into the mixer demand so the denominator is the total outlet count (mixer + electric), iterating the warm-water draw over the mixer outlets only (per step 1e). shower_types=1,2 cohort: -0.37 median -> +0.28 (crossed zero); API gauge 68.4% -> 69.0% within-0.5. Golden cert 0300-2747 (1 mixer + 1 electric) re-pinned: PE +0.93 -> -0.10, CO2 +0.25 -> +0.15 (both toward zero, confirming the double-count). Worksheet harness 47/47, 0 divergers (the Elmhurst fixtures have no electric showers). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .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 | ||
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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 | ||
| next_claude_prompt.txt | ||
| 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