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/).
169 lines
7.7 KiB
Python
169 lines
7.7 KiB
Python
"""Macro context + per-district velocity trend.
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Adds these tables/columns:
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macro_context — single-row mortgage rate, city avg price, etc.
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district_economics + (real_velocity_6mo, real_velocity_prior_6mo, real_trend_ratio,
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sat_factor, trend_factor, price_factor)
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scoring_weights — single-row config (so we can change weights from UI later)
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Source for trend: per-flat register_date (objective_lots).
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Source for mortgage rate: prod cbr_mortgage_series (Sverdl region, latest "ставка ипотечная").
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"""
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import sqlite3, pathlib, psycopg2, datetime as dt, statistics
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DB = pathlib.Path(__file__).parent / "analysis.db"
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EXTRA = """
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CREATE TABLE IF NOT EXISTS macro_context (
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key TEXT PRIMARY KEY,
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value REAL,
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label TEXT,
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period TEXT,
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fetched_at TEXT DEFAULT CURRENT_TIMESTAMP
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);
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ALTER TABLE district_economics ADD COLUMN real_velocity_6mo REAL;
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ALTER TABLE district_economics ADD COLUMN real_velocity_prior_6mo REAL;
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ALTER TABLE district_economics ADD COLUMN real_trend_ratio REAL;
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ALTER TABLE district_economics ADD COLUMN sat_factor REAL;
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ALTER TABLE district_economics ADD COLUMN trend_factor REAL;
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ALTER TABLE district_economics ADD COLUMN price_factor REAL;
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CREATE TABLE IF NOT EXISTS scoring_weights (
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component TEXT PRIMARY KEY,
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weight REAL NOT NULL,
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note TEXT
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);
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"""
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def safe_alter(conn, sql):
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for stmt in sql.strip().split(";"):
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s = stmt.strip()
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if not s: continue
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try: conn.execute(s)
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except sqlite3.OperationalError as e:
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if "duplicate column" not in str(e): raise
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def main():
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local = sqlite3.connect(DB)
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safe_alter(local, EXTRA)
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local.commit()
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# ---- 1. Pull macro from prod ----
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pg = psycopg2.connect(host="127.0.0.1", port=15432, user="gendesign",
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password="2J2SBPMKuS998fiwhtQqDhMI", dbname="gendesign")
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cur = pg.cursor()
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# Mortgage rate (Sverdl, latest weighted average)
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cur.execute("""SELECT period, value FROM cbr_mortgage_series
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WHERE region='sverdl' AND title ILIKE '%ставка%ипотечн%'
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ORDER BY period DESC LIMIT 1""")
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r = cur.fetchone()
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if r:
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local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period)
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VALUES ('mortgage_rate_sverdl', ?,
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'Средневзв. ставка по ипотеке (Свердл, %)', ?)""",
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(float(r[1]), r[0]))
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print(f"Mortgage rate Sverdl ({r[0]}): {r[1]}%")
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# City average POI density per домрф_kn (для prod-стиля POI factor)
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cur.execute("""SELECT count(*)::float / (SELECT count(DISTINCT obj_id) FROM domrf_kn_infrastructure)
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FROM domrf_kn_infrastructure
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WHERE distance_m <= 1000""")
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avg_poi = cur.fetchone()[0]
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local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period)
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VALUES ('city_avg_poi_1km', ?, 'Средний POI/ЖК в 1км (Ekb)', '2026-05')""",
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(avg_poi,))
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print(f"City avg POI 1km: {avg_poi:.1f}")
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pg.close()
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# ---- 2. Velocity trend per district (6mo vs prior 6mo) ----
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today = dt.date.today()
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cut_recent = (today - dt.timedelta(days=180)).isoformat()
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cut_prior = (today - dt.timedelta(days=360)).isoformat()
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rows = local.execute("""
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SELECT district, register_date, project, corpus
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FROM objective_lots
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WHERE district IS NOT NULL AND register_date IS NOT NULL""").fetchall()
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by_d = {}
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for d, r, p, c in rows:
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try: rd = dt.date.fromisoformat(r[:10])
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except: continue
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bucket = "recent" if r >= cut_recent else ("prior" if r >= cut_prior else None)
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if not bucket: continue
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by_d.setdefault(d, {"recent":[], "prior":[], "corpuses":set()})
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by_d[d][bucket].append(rd)
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by_d[d]["corpuses"].add((p, c))
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for d, info in by_d.items():
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n_corp = max(len(info["corpuses"]), 1)
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v_rec = len(info["recent"]) / 6 / n_corp
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v_prior = len(info["prior"]) / 6 / n_corp
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ratio = (v_rec / v_prior) if v_prior > 0 else (None if v_rec == 0 else 2.0)
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# Prod-style clamping
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trend_factor = max(0.7, min(2.0, ratio)) if ratio else 1.0
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# sat_factor: sold_pct in district. >50% = mature market (prod logic +30%
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# multiplier on velocity for mature districts since absorption is proven)
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sold_pct = local.execute(
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"SELECT real_sold_pct FROM district_economics WHERE district=?", (d,)).fetchone()
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sat_factor = 1 + ((sold_pct[0] or 50) - 50) / 100 * 0.30 if sold_pct and sold_pct[0] else 1.0
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local.execute("""UPDATE district_economics
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SET real_velocity_6mo=?, real_velocity_prior_6mo=?,
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real_trend_ratio=?, sat_factor=?, trend_factor=?
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WHERE district=?""",
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(v_rec, v_prior, ratio, sat_factor, trend_factor, d))
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# price_factor = district median / city median
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city_med = local.execute(
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"SELECT real_median_price_m2 FROM district_economics ORDER BY real_n_lots DESC LIMIT 1"
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).fetchone()
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# actually weighted by lots
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weighted_rows = local.execute(
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"SELECT real_median_price_m2, real_n_lots FROM district_economics WHERE real_median_price_m2 IS NOT NULL"
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).fetchall()
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if weighted_rows:
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s = sum((p or 0) * (n or 0) for p, n in weighted_rows)
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wn = sum(n or 0 for _, n in weighted_rows)
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city_med = s / wn if wn else 100
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else:
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city_med = 100
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local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period)
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VALUES ('city_med_price_m2', ?, 'Средневзв. цена м² Ekb (тыс ₽)', '2026-05')""",
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(city_med,))
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print(f"City weighted median price: {city_med:.1f} тыс ₽/м²")
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local.execute("""UPDATE district_economics
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SET price_factor = real_median_price_m2 / ?
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WHERE real_median_price_m2 IS NOT NULL""", (city_med,))
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local.commit()
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# ---- 3. Default weights ----
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weights = [
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("education", 0.18, "Школы, садики, ВУЗы"),
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("health", 0.10, "Аптеки, поликлиники, больницы"),
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("retail", 0.13, "Магазины (большие/средние/малые)"),
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("transit", 0.15, "Метро, трамвай, автобус"),
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("leisure", 0.09, "Парки, площадки, спорт"),
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("economic", 0.30, "Цена, скорость продаж, тренд (Объектив)"),
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("market", 0.05, "Конкурентная плотность, sat/trend факторы"),
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]
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local.execute("DELETE FROM scoring_weights")
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for c, w, n in weights:
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local.execute("INSERT INTO scoring_weights VALUES (?,?,?)", (c, w, n))
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local.commit()
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print("\nDistrict trend (top 10 by recent velocity):")
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print(f"{'район':<22}{'price_f':>8}{'sat':>6}{'trend':>6}{'v_rec':>6}{'v_pr':>6}{'ratio':>6}")
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for r in local.execute("""SELECT district, price_factor, sat_factor, trend_factor,
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real_velocity_6mo, real_velocity_prior_6mo, real_trend_ratio
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FROM district_economics
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WHERE real_velocity_6mo IS NOT NULL
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ORDER BY real_velocity_6mo DESC LIMIT 10""").fetchall():
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d,pf,sf,tf,vr,vp,rr = r
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print(f"{d:<22}{pf or 0:>8.2f}{sf or 0:>6.2f}{tf or 0:>6.2f}{vr or 0:>6.2f}{vp or 0:>6.2f}{rr or 0:>6.2f}")
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local.close()
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if __name__ == "__main__":
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main()
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