mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-07-12 13:29:04 +00:00
c0a1bcac95
One-time utility: resolves every corpus cert's uprn -> WGS84 lon/lat from the
OS Open-UPRN parquet (DATA_BUCKET/spatial/) via boto3, grouping UPRNs by their
covering partition so each ~1.7MB partition is read at most once (the efficient
batch lookup we intend to add to GeospatialRepository). Caches {uprn:[lon,lat]}
locally for the validation harness. Resolved 2609/2683 corpus UPRNs (97%).
Signal pre-check result (does intra-postcode proximity predict components?):
intra-postcode distances are non-trivial (median 44m, p90 138m, max ~1km),
and nearer neighbours match the target markedly better on age band (0.63 at
<20m -> 0.16 at >300m), wall, glazing and floor construction. Roof shows no
decay. => geo-proximity is worth building, per-component (strongest for age,
the weakest fabric component).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
|
||
|---|---|---|
| .. | ||
| eon | ||
| analyse_api_sap_clusters.py | ||
| build_epc_prediction_fixture.py | ||
| decompose_api_cost_error.py | ||
| download_cotality_evidence.py | ||
| elmhurst_input_sheet.py | ||
| eval_api_sap_accuracy.py | ||
| fetch_2026_epc_sample.py | ||
| fetch_cohort2_api_jsons.py | ||
| fetch_corpus_coordinates.py | ||
| fetch_epc_bulk_sample.py | ||
| fetch_epc_dump.py | ||
| fetch_epc_prediction_corpus.py | ||
| historic_epc_demo.py | ||
| init_db.py | ||
| profile_api_error.py | ||
| rename_sharepoint_files.py | ||
| run_audit_generator_local.py | ||
| run_modelling_cohort.py | ||
| run_modelling_e2e.py | ||
| run_property_report.py | ||
| sero_address_list.csv | ||
| validate_epc_prediction.py | ||