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Contains projects associated to the development of modeling products
8e6560d744
Refactors Elmhurst `Renewables` PV detail from four scalar fields
(pv_peak_power_kw / pv_orientation / pv_elevation_deg / pv_overshading
— single-array shape) to `pv_arrays: List[ElmhurstPvArray]`, then
walks the §19.0 PV Panel block in 4-tuples so dwellings with multiple
PV arrays surface every array.
Forced by cert 0350-2968-2650-2796-5255 (Summary_000903.pdf), the
second ASHP cohort cert through the Summary path and first to lodge
multiple PV arrays — the dr87 worksheet pins 2 arrays at 1.50 kWp
each (one SE at 45°, one NW at 45°). Pre-slice the extractor's
hardcoded "break at len(values) == 4" capped output at one array
regardless of how many the PDF lodged.
Three-layer end-to-end change:
1. `datatypes/epc/surveys/elmhurst_site_notes.py` — add
`ElmhurstPvArray` dataclass (kw, orientation, elevation_deg,
overshading); replace four `Renewables.pv_*` scalars with
`pv_arrays: List[ElmhurstPvArray] = field(default_factory=list)`.
2. `backend/documents_parser/elmhurst_extractor.py` — rename
`_extract_pv_array_detail` → `_extract_pv_arrays`; walk values
after the "Photovoltaic panel details" anchor in 4-tuples until a
stop token ("batteries"/"export"/etc.) or a §-header closes the
block. §-header regex tightened to `\d{1,2}\.\d\s+\w` so kWp
values like "1.50" don't trip the close (without the `\s+\w` the
regex matched both "20.0 Wind Turbine" AND "1.50").
3. `datatypes/epc/domain/mapper.py` — `_elmhurst_pv_arrays` iterates
the list and emits one `PhotovoltaicArray` per row; collapses
empty list → None so the cascade keeps its no-PV fallback.
Forcing function: cert 0350 first-attempt Summary SAP closes from
Δ -4.5829 (Slice 8 baseline) to Δ **+0.0458** — within the ±0.07
ASHP-cohort spec-precision floor. PV export credit GBP moves from
158.91 (one array surfaced) to 265.99 (both arrays surfaced) — the
extra ~107 GBP of avoided cost lifts cert 0350's SAP by ~4.6 points.
This validates the structural-debt-amortizes hypothesis: cert 0350
needed only TWO new slices (S0380.8 inheritance + S0380.9 multi-PV)
beyond the cert 0380 closure work, vs cert 0380's 6 slices from
scratch. Subsequent cohort certs should converge similarly fast as
fixture-specific gaps are paid down.
Added two tests:
- `test_summary_0350_surfaces_two_pv_arrays` — unit test pinning
the multi-array contract on the mapper boundary.
- `test_summary_0350_full_chain_sap_within_spec_floor_of_worksheet`
— chain test pinning Δ < ±0.07 (matches cert 0380's chain test).
Cert 0380 (single-array, 3 kWp) continues to pass its chain test +
all 6 unit-level pins — the refactor preserves single-array behaviour.
Pyright net-zero across all four edited files:
datatypes/epc/domain/mapper.py: 32 (baseline)
datatypes/epc/surveys/elmhurst_site_notes.py: 0
backend/documents_parser/elmhurst_extractor.py: 0
backend/documents_parser/tests/test_summary_pdf_mapper_chain.py: 0
Regression suite: 677 pass + 10 fail (= handover baseline 669 + 10
+ 8 new GREEN unit+chain tests across Slices S0380.2..S0380.9).
Fixtures added: `backend/documents_parser/tests/fixtures/Summary_
000903.pdf` (copied from `sap worksheets/Additional data with api/
0350-2968-2650-2796-5255/`).
Spec refs:
- SAP 10.2 Appendix M (PDF p.103) — multiple PV arrays sum to total
electricity generation per Equation M-1 (each array's surface flux
computed independently per Appendix U3.3).
- SAP 10.2 Appendix U3.3 (PDF p.124) — per-array surface flux keyed
on orientation + tilt + overshading.
- Cert 0350 worksheet `dr87-0001-000903.pdf` (29a Main 19.4575 W/K
+ Ext1 1.3025 W/K = 20.7600 ≡ Summary cascade walls_w_per_k; (39)
avg HTC 173.4202 ≡ Summary cascade; (64) HW 2084.66 ÷ (216) HW eff
1.7285 = 1206.04 ≡ Summary cascade hot_water_kwh_per_yr).
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 | ||
| repositories | ||
| 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