a04329770d
The API glazing-transmission table mapped only the double-glazing codes [1,2,3,13,14]; single (5/15), secondary (4/11/12) and triple (6/8/9/10) glazing codes returned None from _api_glazing_transmission, so the cascade silently routed them to the u_window all-None default U=2.5 instead of their RdSAP 10 Table 24 (spec p.50) value. Single glazing (U=4.8) was the worst: modelled at half its true heat loss → systematic over-rate (cert 0370-2933, 7 single-glazed windows, +17 SAP). Extended _API_GLAZING_TYPE_TO_TRANSMISSION + the gap-keyed override table with the Table 24 (U, g, frame-factor) rows for every RdSAP-21 glazing code (single 4.8/g0.85; secondary normal-E 2.9 / low-E 2.2 /g0.85; triple pre-2002 2.4/2.1/2.0 by gap, 2002-2022 2.0, all g0.68/0.72; known-data codes 7/8 alias their family default). 94 corpus certs carry an unmapped glazing code (code 5 = 79); they sat at 32% within-0.5 vs 54.9% baseline. Eval: within-0.5 54.90% -> 56.66% (net +16 certs: 22 in, 6 offsetting-error out), within-1.0 70.2 -> 71.9%, mean|err| 1.224 -> 1.203, 909 computed / 0 raises. Spec-applied uniformly per the determinism principle. 7 AAA tests, goldens + gate green, pyright net-zero (38=38). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .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