Khalim Conn-Kowlessar 0918dd37ec feat(scripts): run_modelling_e2e — inspect recommendations per property_id ... Revives the local recommendation-inspection flow for specific Properties. `scripts/run_modelling_e2e.py` reads each Property's UPRN from the DB (read-only), fetches the latest EPC live from the gov EPC API by UPRN, runs the Modelling stage in memory (all Generators → Optimiser → costed, attributed Plan), and prints a per-Property plan table + writes a Markdown/CSV summary. Persists nothing — purely for inspection. The local DB's Properties have no linked ingested EPC (epc_property.property_id is NULL for all rows; Ingestion's source clients are stubbed, #1136), so the EPC must be fetched inline rather than read back. Builds the connection from the `DB_*` env vars in backend/.env and the EPC token from `EPC_AUTH_TOKEN`. Threads optional solar insights through harness `run_modelling` (so Solar PV Options can fire once coordinates are wired) and adds the `solar_pv` catalogue row. Solar + planning restrictions + DB persistence are noted follow-ups. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>		2026-06-08 14:25:33 +00:00
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__init__.py	feat(modelling): sense-check table for a Plan in the DB-less harness	2026-06-04 08:06:53 +00:00
cohort.py	feat(modelling): turnkey offline cohort script (tables + CSV)	2026-06-04 09:30:53 +00:00
console.py	feat(scripts): run_modelling_e2e — inspect recommendations per property_id	2026-06-08 14:25:33 +00:00
epc_bulk.py	feat(modelling): sample a year from the EPC bulk export, offline-ready	2026-06-04 12:20:57 +00:00
plan_table.py	feat(modelling): wire Valuation Uplift onto the Plan	2026-06-04 08:59:04 +00:00
report.py	feat(modelling): wire the ASHP bundle into the candidate pool	2026-06-06 17:12:07 +00:00
sample_catalogue.json	feat(scripts): run_modelling_e2e — inspect recommendations per property_id	2026-06-08 14:25:33 +00:00