Light1YT
97b19a0b85
Adds site-finder/ subfolder with:
- server.py — FastAPI scoring service v2 (35 endpoints, ~85KB)
- 01_load_sites.py … 12_more_pois.py — data ingest pipeline
- db_init.py — SQLite schema bootstrap
- static/ — Leaflet UI (index.html ~3500 lines + sw.js)
- cache/ — small persistent caches (admin districts, jk polygons,
geocode warm cache, parcel polygons drop-zone with README)
- reports/ — sample generated parcel report (HTML+JSON)
Excluded via .gitignore (regeneratable, too big for git):
- analysis.db (336MB SQLite — rebuild via 01_*..12_*.py)
- cache/objective_raw/ (1.2GB Объектив raw dumps)
- cache/overpass_raw.json, cache/osm_buildings_all.geojson
(regen from Overpass API)
Production deploy: /opt/gendesign/site-finder/ on gendsgn.ru
(container gendesign-site-finder-1, served at /sf/).
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
"""Local SQLite DB for parcel scoring analysis.
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Mirrors the logic the prod gendesign server uses:
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- domrf_kn_objects (subset: under-construction ЖК in Ekb with coords)
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- domrf_kn_infrastructure (POI cache)
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- ekb_districts (geometry, median price)
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- recommend_mix output (per-parcel score)
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We don't need the full ~3.2GB warehouse — only what the scorer touches.
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"""
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import sqlite3, pathlib
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DB = pathlib.Path(__file__).parent / "analysis.db"
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SCHEMA = """
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CREATE TABLE IF NOT EXISTS sites (
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site_id TEXT PRIMARY KEY, -- 'parcel:66:41:0204016:10' or 'obj:NNN'
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kind TEXT NOT NULL, -- 'parcel' | 'jk'
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name TEXT,
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address TEXT,
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district TEXT,
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obj_class TEXT,
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developer TEXT,
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flat_count INTEGER,
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square_living REAL,
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ready_dt TEXT,
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obj_status TEXT,
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lat REAL NOT NULL,
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lon REAL NOT NULL,
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geom_geojson TEXT, -- nullable polygon as raw GeoJSON
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obj_id INTEGER -- domrf_kn obj_id when kind='jk'
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);
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CREATE INDEX IF NOT EXISTS sites_kind_idx ON sites(kind);
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CREATE INDEX IF NOT EXISTS sites_district_idx ON sites(district);
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CREATE TABLE IF NOT EXISTS pois (
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poi_id INTEGER PRIMARY KEY AUTOINCREMENT,
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site_id TEXT NOT NULL REFERENCES sites(site_id),
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category TEXT NOT NULL, -- 'kindergarten','school','shop_supermarket', ...
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osm_type TEXT,
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osm_id TEXT,
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name TEXT,
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lat REAL NOT NULL,
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lon REAL NOT NULL,
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distance_m REAL NOT NULL,
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raw_tags TEXT
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);
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CREATE INDEX IF NOT EXISTS pois_site_cat ON pois(site_id, category);
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CREATE TABLE IF NOT EXISTS features (
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site_id TEXT NOT NULL REFERENCES sites(site_id),
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feature TEXT NOT NULL, -- 'kindergarten_nearest_m','schools_in_1km','transit_500m', ...
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value REAL,
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PRIMARY KEY (site_id, feature)
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);
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CREATE TABLE IF NOT EXISTS scores (
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site_id TEXT NOT NULL REFERENCES sites(site_id),
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component TEXT NOT NULL, -- 'education','retail','health','transit','leisure'
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score_0_100 REAL NOT NULL,
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PRIMARY KEY (site_id, component)
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);
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CREATE TABLE IF NOT EXISTS scores_total (
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site_id TEXT PRIMARY KEY REFERENCES sites(site_id),
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weighted REAL NOT NULL,
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rank_overall INTEGER,
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rank_district INTEGER
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);
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CREATE TABLE IF NOT EXISTS run_log (
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run_id INTEGER PRIMARY KEY AUTOINCREMENT,
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started_at TEXT DEFAULT CURRENT_TIMESTAMP,
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finished_at TEXT,
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notes TEXT
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);
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"""
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if __name__ == "__main__":
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conn = sqlite3.connect(DB)
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conn.executescript(SCHEMA)
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conn.commit()
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print(f"DB ready at {DB}")
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for tbl in ['sites','pois','features','scores','scores_total','run_log']:
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n = conn.execute(f"SELECT count(*) FROM {tbl}").fetchone()[0]
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print(f" {tbl}: {n} rows")
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conn.close()
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