8c1f895b2d
PCDF Spec Rev 6b §A.19 (May 2021) Format 427 — Decentralised MEV Systems Table. Pcdb10.dat carries the per-fan-configuration block in Format 428 (header `$322,428,72,...`), which drops the spec's per- group "Fan speed setting" string. Each group is a 3-field triplet: (config_code, flow_l_per_s, sfp_w_per_l_per_s). Per the spec § field 14, the 6 fan configurations are: 1 = In-room fan, kitchen 2 = In-room fan, other wet room 3 = In-duct fan, kitchen 4 = In-duct fan, other wet room 5 = Through-wall fan, kitchen 6 = Through-wall fan, other wet room Some configurations may be blank per spec Note 1 — these are not valid SAP selections and are excluded from the SFPav summation downstream. This slice lands the foundation only — typed parser, ETL promotion to typed write, and a runtime lookup `decentralised_mev_record(pcdb_ id)`. No cascade integration yet → no behavioural change on any cert; full test suite + cert 000565 expected fails unchanged. Subsequent slices in the arc: - S0380.99: PCDB Table 329 (In-Use Factors) ETL + lookup - S0380.100: SAP 10.2 §2.6.4 SFPav cascade helper - S0380.101: HP SAP code 211-227 / 521-527 → main_heating_category=4 - S0380.102: wire MEV cascade into pumps_fans Cert 000565 lodges `MV PCDF Reference Number = 500755` (Titon Ultimate dMEV), resolving via this lookup to: config 1 (in-room kitchen): flow=13.0, SFP=0.15 W/(l/s) config 2 (in-room other wet): flow=8.0, SFP=0.15 config 3 (in-duct kitchen): not tested config 4 (in-duct other wet): not tested config 5 (thru-wall kitchen): flow=13.0, SFP=0.11 config 6 (thru-wall other wet): flow=8.0, SFP=0.14 48 Table 322 records ingested. Pyright net-zero per touched file. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| .github/workflows | ||
| .idea | ||
| .vscode | ||
| 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 | ||
| .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