diff --git a/site-finder/.gitignore b/site-finder/.gitignore new file mode 100644 index 00000000..b70c3847 --- /dev/null +++ b/site-finder/.gitignore @@ -0,0 +1,21 @@ +# Heavy generated artefacts — regenerated by the 01_*..12_*.py pipeline +analysis.db +analysis.db-journal +analysis.db-wal +analysis.db-shm + +# Heavy raw caches +cache/objective_raw/ +cache/overpass_raw.json +cache/osm_buildings_all.geojson + +# Local-only +tmp/ +debug.log +*.log +__pycache__/ +*.pyc +.venv/ +venv/ +.env +.env.local diff --git a/site-finder/01_load_sites.py b/site-finder/01_load_sites.py new file mode 100644 index 00000000..946e0a52 --- /dev/null +++ b/site-finder/01_load_sites.py @@ -0,0 +1,75 @@ +"""Load sites into local DB: +- target parcel 66:41:0204016:10 (approximated coords from neighbouring quarters) +- all under-construction ЖК in Ekb from prod (via SSH tunnel) +""" +import sqlite3, psycopg2, pathlib + +DB = pathlib.Path(__file__).parent / "analysis.db" + +PARCEL = { + "site_id": "parcel:66:41:0204016:10", + "kind": "parcel", + "name": "Участок 66:41:0204016:10", + "address": "г. Екатеринбург, ул. Маневровая, 31А (Старая Сортировка, Железнодорожный) · кад.№ 66:41:0204016:10 · 0.28 га · кад.ст. 23.7 млн ₽", + "district": None, + "obj_class": None, + "developer": None, + "flat_count": None, + "square_living": 2788.7, # parcel area m² (Rosreestr polygon) + "ready_dt": None, + "obj_status": "parcel", + "lat": 56.878730, # polygon centroid (rosreestr2coord) + "lon": 60.522677, + "obj_id": None, +} + +def main(): + conn = sqlite3.connect(DB) + pg = psycopg2.connect(host="127.0.0.1", port=15432, user="gendesign", + password="2J2SBPMKuS998fiwhtQqDhMI", dbname="gendesign") + cur = pg.cursor() + + # latest snapshot per obj_id + cur.execute(""" + WITH latest AS ( + SELECT obj_id, MAX(snapshot_date) AS d FROM domrf_kn_objects + WHERE region_cd='66' AND is_ekb=true + GROUP BY obj_id + ) + SELECT o.obj_id, o.comm_name, o.addr, o.district_name, o.obj_class, + o.dev_name, o.flat_count, o.square_living::float, o.ready_dt, + o.obj_status, o.latitude::float, o.longitude::float + FROM domrf_kn_objects o + JOIN latest l USING (obj_id) + WHERE o.snapshot_date = l.d + AND o.latitude IS NOT NULL AND o.longitude IS NOT NULL + AND o.site_status='Строящиеся' + ORDER BY o.obj_id; + """) + rows = cur.fetchall() + print(f"Pulled {len(rows)} ЖК from prod") + + conn.execute("DELETE FROM sites;") + conn.execute("""INSERT INTO sites(site_id,kind,name,address,district,obj_class,developer, + flat_count,square_living,ready_dt,obj_status,lat,lon,obj_id) VALUES ( + :site_id,:kind,:name,:address,:district,:obj_class,:developer, + :flat_count,:square_living,:ready_dt,:obj_status,:lat,:lon,:obj_id)""", PARCEL) + + for r in rows: + (obj_id, name, addr, district, klass, dev, flat_count, sq_living, ready_dt, + obj_status, lat, lon) = r + if not (50 < lat < 65 and 55 < lon < 70): # sanity for Sverdl + continue + conn.execute("""INSERT INTO sites(site_id,kind,name,address,district,obj_class, + developer,flat_count,square_living,ready_dt,obj_status,lat,lon,obj_id) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?)""", + (f"jk:{obj_id}", "jk", name, addr, district, klass, dev, flat_count, + sq_living, str(ready_dt) if ready_dt else None, obj_status, lat, lon, obj_id)) + conn.commit() + n = conn.execute("SELECT count(*) FROM sites").fetchone()[0] + by_kind = dict(conn.execute("SELECT kind, count(*) FROM sites GROUP BY 1").fetchall()) + print(f"Sites in local DB: {n} ({by_kind})") + conn.close(); pg.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/02_fetch_pois.py b/site-finder/02_fetch_pois.py new file mode 100644 index 00000000..e7a4f288 --- /dev/null +++ b/site-finder/02_fetch_pois.py @@ -0,0 +1,145 @@ +"""Fetch POIs from OSM Overpass for all sites in one bulk query. + +POI taxonomy (matches the user's spec): + EDUCATION + kindergarten amenity=kindergarten + school amenity=school + university amenity=university | college + HEALTH + pharmacy amenity=pharmacy + clinic amenity=clinic | doctors + hospital amenity=hospital + RETAIL + shop_big shop=mall | supermarket | department_store | hypermarket + shop_med shop=convenience | grocery | bakery + shop_small shop=kiosk | newsagent + TRANSIT + bus_stop highway=bus_stop | public_transport=platform + tram_stop railway=tram_stop + metro railway=station + station=subway + LEISURE + park leisure=park | garden + playground leisure=playground + sports leisure=sports_centre | fitness_centre | pitch +""" +import sqlite3, pathlib, requests, time, json, math, urllib3 +urllib3.disable_warnings() + +DB = pathlib.Path(__file__).parent / "analysis.db" +CACHE = pathlib.Path(__file__).parent / "cache" / "overpass_raw.json" + +OVERPASS_ENDPOINTS = [ + "https://overpass-api.de/api/interpreter", + "https://overpass.kumi.systems/api/interpreter", + "https://overpass.openstreetmap.ru/api/interpreter", +] + +# (category, overpass filter) +QUERIES = [ + ("kindergarten", '["amenity"="kindergarten"]'), + ("school", '["amenity"="school"]'), + ("university", '["amenity"~"^(university|college)$"]'), + ("pharmacy", '["amenity"="pharmacy"]'), + ("clinic", '["amenity"~"^(clinic|doctors)$"]'), + ("hospital", '["amenity"="hospital"]'), + ("shop_big", '["shop"~"^(mall|supermarket|department_store|hypermarket)$"]'), + ("shop_med", '["shop"~"^(convenience|grocery|bakery)$"]'), + ("shop_small", '["shop"~"^(kiosk|newsagent)$"]'), + ("bus_stop", '["highway"="bus_stop"]'), + ("tram_stop", '["railway"="tram_stop"]'), + ("metro", '["station"="subway"]'), + ("park", '["leisure"~"^(park|garden)$"]'), + ("playground", '["leisure"="playground"]'), + ("sports", '["leisure"~"^(sports_centre|fitness_centre|pitch)$"]'), +] + +def haversine_m(lat1, lon1, lat2, lon2): + R = 6371000 + p1, p2 = math.radians(lat1), math.radians(lat2) + dp = math.radians(lat2-lat1); dl = math.radians(lon2-lon1) + a = math.sin(dp/2)**2 + math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2 * R * math.asin(math.sqrt(a)) + +def bbox(rows, pad_deg=0.05): + lats=[r[0] for r in rows]; lons=[r[1] for r in rows] + return (min(lats)-pad_deg, min(lons)-pad_deg, max(lats)+pad_deg, max(lons)+pad_deg) + +def overpass_query(filt, b): + q = f""" +[out:json][timeout:120]; +( + node{filt}({b[0]},{b[1]},{b[2]},{b[3]}); + way{filt}({b[0]},{b[1]},{b[2]},{b[3]}); +); +out center tags; +""" + last_err = None + for ep in OVERPASS_ENDPOINTS: + try: + r = requests.post(ep, data={"data": q}, timeout=180, verify=False, + headers={"User-Agent":"gendesign-research/1.0"}) + if r.status_code == 200: + return r.json() + last_err = f"HTTP {r.status_code} from {ep}" + time.sleep(2) + except Exception as e: + last_err = f"{ep}: {e}" + time.sleep(2) + raise RuntimeError(f"All Overpass endpoints failed: {last_err}") + +def main(): + conn = sqlite3.connect(DB) + sites = conn.execute("SELECT site_id, lat, lon FROM sites").fetchall() + print(f"Sites: {len(sites)}") + coords = [(s[1], s[2]) for s in sites] + b = bbox(coords) + print(f"BBox (S,W,N,E): {b}") + + cache_data = {} + for cat, filt in QUERIES: + print(f" fetching {cat:<14} ...", end=" ", flush=True) + d = overpass_query(filt, b) + elements = d.get("elements", []) + cache_data[cat] = elements + print(f"{len(elements)} elements") + time.sleep(2) # be kind + + CACHE.parent.mkdir(parents=True, exist_ok=True) + with open(CACHE, 'w') as f: json.dump(cache_data, f, ensure_ascii=False) + print(f"\nCache: {CACHE} ({CACHE.stat().st_size/1024:.0f} KB)") + + # Compute nearest POI per site per category, plus all POIs within 1km + conn.execute("DELETE FROM pois") + n_pois = 0 + for cat, elems in cache_data.items(): + # extract (lat,lon,name,raw) + poi_list = [] + for el in elems: + if el["type"] == "node": + lat, lon = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {} + lat, lon = c.get("lat"), c.get("lon") + if lat is None or lon is None: continue + tags = el.get("tags") or {} + name = tags.get("name") or tags.get("operator") or "" + poi_list.append((lat, lon, name, el["type"], el["id"], json.dumps(tags, ensure_ascii=False))) + # for each site, find within 2 km (we'll bucket by distance later) + for s in sites: + site_id, slat, slon = s + for plat, plon, pname, ptype, pid, ptags in poi_list: + d = haversine_m(slat, slon, plat, plon) + if d <= 2000: + conn.execute("""INSERT INTO pois(site_id,category,osm_type,osm_id,name,lat,lon,distance_m,raw_tags) + VALUES (?,?,?,?,?,?,?,?,?)""", + (site_id, cat, ptype, str(pid), pname, plat, plon, d, ptags)) + n_pois += 1 + conn.commit() + print(f"\nStored {n_pois} POI-site pairs (within 2 km)") + print("Per-category:") + for cat, n in conn.execute("SELECT category,count(*) FROM pois GROUP BY 1 ORDER BY 2 DESC").fetchall(): + print(f" {cat:<14} {n}") + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/03_score.py b/site-finder/03_score.py new file mode 100644 index 00000000..3044fd45 --- /dev/null +++ b/site-finder/03_score.py @@ -0,0 +1,229 @@ +"""Compute features and weighted parcel score. + +Per-site feature vector: + edu_kindergarten_nearest_m, edu_school_nearest_m, edu_university_nearest_m + health_pharmacy_nearest_m, health_clinic_nearest_m, health_hospital_nearest_m + retail_big_nearest_m, retail_med_nearest_m, retail_small_nearest_m + transit_bus_nearest_m, transit_tram_nearest_m, transit_metro_nearest_m + leisure_park_nearest_m, leisure_playground_nearest_m, leisure_sports_nearest_m + + counts within 500m / 1000m for each category + +Component scores 0..100 (higher = better): + education = avg(score(kindergarten,500m), score(school,800m)) + health = avg(score(pharmacy,500m), score(clinic,1000m)) + retail = max(score(shop_big,1000m), 0.7*score(shop_med,500m)) + transit = max(score(metro,1500m), 0.8*score(tram,500m), 0.6*score(bus,300m)) + leisure = avg(score(park,800m), score(playground,400m), score(sports,800m)) + +Distance-to-score: piecewise linear, + 0..ideal_m → 100 + ideal_m..max_m → 100..0 + >max_m → 0 + +Total weighted (sums to 1.0): + education 0.30 (садики/школы — главное для семейных, ядро ЦА девелопера) + health 0.15 + retail 0.20 + transit 0.20 + leisure 0.15 +""" +import sqlite3, pathlib + +DB = pathlib.Path(__file__).parent / "analysis.db" + +# (category, ideal_m, max_m, weight_in_component) +EDU = [("kindergarten", 300, 1000, 1.0), + ("school", 400, 1500, 1.0), + ("university", 1000, 5000, 0.3)] # nice-to-have +HEALTH = [("pharmacy", 300, 1000, 1.0), + ("clinic", 500, 2000, 1.0), + ("hospital", 1500, 5000, 0.5)] +RETAIL_BIG = [("shop_big", 500, 2000, 1.0)] +RETAIL_MED = [("shop_med", 300, 1000, 1.0)] +TRANSIT_MAIN = [("metro", 1000, 3000, 1.0)] +TRANSIT_TRAM = [("tram_stop", 400, 1500, 1.0)] +TRANSIT_BUS = [("bus_stop", 200, 800, 1.0)] +LEISURE = [("park", 500, 2000, 1.0), + ("playground", 200, 700, 1.0), + ("sports", 500, 2000, 0.7)] + +WEIGHTS = { + "education": 0.20, + "health": 0.10, + "retail": 0.15, + "transit": 0.15, + "leisure": 0.10, + "economic": 0.30, +} + +# Economic sub-weights (sum to 1, used inside `economic` component) +ECON_WEIGHTS = {"price": 0.50, "velocity": 0.25, "liquidity": 0.25} + +def dist_score(d_m, ideal, mx): + if d_m is None: return 0.0 + if d_m <= ideal: return 100.0 + if d_m >= mx: return 0.0 + return 100.0 * (mx - d_m) / (mx - ideal) + +def component_score(conn, site_id, cats): + """Average distance-score across the categories listed (with weights).""" + total_w = sum(c[3] for c in cats) + s = 0.0 + for cat, ideal, mx, w in cats: + nearest = conn.execute( + "SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?", + (site_id, cat) + ).fetchone()[0] + s += w * dist_score(nearest, ideal, mx) + return s / total_w if total_w else 0.0 + +def features(conn, site_id): + """Materialize feature vector — nearest distance + counts within 500/1000m.""" + feats = {} + cats = ["kindergarten","school","university","pharmacy","clinic","hospital", + "shop_big","shop_med","shop_small","bus_stop","tram_stop","metro", + "park","playground","sports"] + for cat in cats: + nearest = conn.execute( + "SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?", + (site_id, cat)).fetchone()[0] + c500 = conn.execute( + "SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=500", + (site_id, cat)).fetchone()[0] + c1000 = conn.execute( + "SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=1000", + (site_id, cat)).fetchone()[0] + feats[f"{cat}_nearest_m"] = nearest + feats[f"{cat}_count_500m"] = c500 + feats[f"{cat}_count_1km"] = c1000 + return feats + +def economic_score(conn, site_id, econ_bounds): + """Returns (component_0_100, breakdown dict) using REAL per-flat district aggregates. + + Prefers real_* columns from objective_lots (Поквартирные/Лоты, 303k lots); + falls back to weighted_price_m2 + deals_per_month_avg from corp_sum if missing. + """ + row = conn.execute("""SELECT + COALESCE(de.real_median_price_m2, de.weighted_price_m2) AS price, + COALESCE(de.real_velocity_per_month, de.deals_per_month_avg) AS v, + de.months_to_sellout, de.real_sold_pct + FROM site_district sd + JOIN district_economics de USING (district) + WHERE sd.site_id=?""", (site_id,)).fetchone() + if not row: + return 0.0, {} + price, v, mts, sold_pct = row + pmin, pmax, vmax, mts_cap = econ_bounds + p_score = max(0.0, min(100.0, (price - pmin) * 100.0 / (pmax - pmin))) if price else 0.0 + v_score = max(0.0, min(100.0, (v or 0) * 100.0 / vmax)) + if mts is None: + liq_score = 0.0 + else: + liq_score = max(0.0, 100.0 - min(mts, mts_cap) * 100.0 / mts_cap) + total = (ECON_WEIGHTS["price"] * p_score + + ECON_WEIGHTS["velocity"] * v_score + + ECON_WEIGHTS["liquidity"] * liq_score) + return total, {"price_score": p_score, "velocity_score": v_score, + "liquidity_score": liq_score, "median_price_m2": price, + "velocity": v, "months_to_sellout": mts, + "district_sold_pct": sold_pct} + + +def main(): + conn = sqlite3.connect(DB) + sites = conn.execute("SELECT site_id, kind FROM sites").fetchall() + conn.execute("DELETE FROM features") + conn.execute("DELETE FROM scores") + conn.execute("DELETE FROM scores_total") + + # Compute econ bounds from district_economics — prefer real_* + prices = sorted([r[0] for r in conn.execute( + "SELECT COALESCE(real_median_price_m2, weighted_price_m2) FROM district_economics " + "WHERE COALESCE(real_median_price_m2, weighted_price_m2) IS NOT NULL").fetchall()]) + if not prices: prices = [100, 200] + pmin, pmax = prices[0], prices[-1] + if pmax <= pmin: pmax = pmin + 1 + vels = sorted([r[0] for r in conn.execute( + "SELECT COALESCE(real_velocity_per_month, deals_per_month_avg) FROM district_economics " + "WHERE COALESCE(real_velocity_per_month, deals_per_month_avg) IS NOT NULL").fetchall()]) + vmax = vels[int(len(vels) * 0.9)] if vels else 1.0 + if vmax <= 0: vmax = 1.0 + econ_bounds = (pmin, pmax, vmax, 24.0) + print(f"Econ bounds (real): price [{pmin:.1f}, {pmax:.1f}] тыс, vel_p90={vmax:.2f}, mts_cap=24 мес") + + for site_id, kind in sites: + # features + for k, v in features(conn, site_id).items(): + conn.execute("INSERT INTO features(site_id,feature,value) VALUES (?,?,?)", + (site_id, k, float(v) if v is not None else None)) + + # component scores + edu = component_score(conn, site_id, EDU) + health = component_score(conn, site_id, HEALTH) + # retail = max(big, 0.7*med). med alone is enough for daily needs. + big = component_score(conn, site_id, RETAIL_BIG) + med = component_score(conn, site_id, RETAIL_MED) + retail = max(big, 0.7 * med) + # transit: best of metro / tram / bus + metro = component_score(conn, site_id, TRANSIT_MAIN) + tram = component_score(conn, site_id, TRANSIT_TRAM) + bus = component_score(conn, site_id, TRANSIT_BUS) + transit = max(metro, 0.85 * tram, 0.7 * bus) + leisure = component_score(conn, site_id, LEISURE) + + econ, _ = economic_score(conn, site_id, econ_bounds) + comps = {"education": edu, "health": health, "retail": retail, + "transit": transit, "leisure": leisure, "economic": econ} + for c, v in comps.items(): + conn.execute("INSERT INTO scores(site_id,component,score_0_100) VALUES (?,?,?)", + (site_id, c, v)) + + weighted = sum(WEIGHTS[c] * v for c, v in comps.items()) + conn.execute("INSERT INTO scores_total(site_id,weighted) VALUES (?,?)", + (site_id, weighted)) + + conn.commit() + + # Compute ranks + rows = conn.execute("""SELECT s.site_id, s.kind, s.district, st.weighted + FROM sites s JOIN scores_total st USING (site_id) + ORDER BY st.weighted DESC""").fetchall() + for rank, (sid, kind, _, _) in enumerate(rows, 1): + conn.execute("UPDATE scores_total SET rank_overall=? WHERE site_id=?", (rank, sid)) + # district rank + by_dist = {} + for sid, kind, dist, _ in rows: + by_dist.setdefault(dist or "—", []).append(sid) + for dist, sids in by_dist.items(): + for rank, sid in enumerate(sids, 1): + conn.execute("UPDATE scores_total SET rank_district=? WHERE site_id=?", (rank, sid)) + conn.commit() + + # Summary + parcel_id = "parcel:66:41:0204016:10" + p = conn.execute("SELECT weighted, rank_overall FROM scores_total WHERE site_id=?", + (parcel_id,)).fetchone() + n = conn.execute("SELECT count(*) FROM sites").fetchone()[0] + print(f"\n=== Parcel score ===") + print(f" weighted: {p[0]:.1f}/100 rank: {p[1]}/{n}") + print("\n Components:") + for c, v in conn.execute("SELECT component, score_0_100 FROM scores WHERE site_id=?", + (parcel_id,)).fetchall(): + print(f" {c:<10} {v:.1f}") + print("\n Nearest POIs (m):") + for k, v in conn.execute( + "SELECT feature, value FROM features WHERE site_id=? AND feature LIKE '%_nearest_m'", + (parcel_id,)).fetchall(): + print(f" {k:<35} {('%.0f m'%v) if v is not None else '—'}") + + print("\n=== Top-5 ЖК in Ekb ===") + for r in conn.execute("""SELECT s.name, s.district, st.weighted, st.rank_overall + FROM sites s JOIN scores_total st USING (site_id) + WHERE s.kind='jk' ORDER BY st.weighted DESC LIMIT 5""").fetchall(): + print(f" #{r[3]:>3} {r[2]:>5.1f} [{(r[1] or '—'):<25}] {r[0]}") + + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/04_report.py b/site-finder/04_report.py new file mode 100644 index 00000000..3602e9a4 --- /dev/null +++ b/site-finder/04_report.py @@ -0,0 +1,274 @@ +"""Generate JSON + HTML comparison report.""" +import sqlite3, pathlib, json, statistics + +DB = pathlib.Path(__file__).parent / "analysis.db" +OUT = pathlib.Path(__file__).parent / "reports" +OUT.mkdir(exist_ok=True) + +PARCEL_ID = "parcel:66:41:0204016:10" + +def fetch_economics(conn, site_id): + row = conn.execute("""SELECT sd.district, sd.method, sd.nearest_jk_dist_m, + de.n_projects, de.weighted_price_m2, de.median_price_m2, + de.deals_per_month_avg, de.months_to_sellout, + de.real_n_lots, de.real_n_sold, de.real_sold_pct, + de.real_median_price_m2, de.real_p25_price_m2, de.real_p75_price_m2, + de.real_avg_area_sold, de.real_velocity_per_month, + de.real_avg_readiness_pct + FROM site_district sd + LEFT JOIN district_economics de USING (district) + WHERE sd.site_id=?""", (site_id,)).fetchone() + if not row: return None + cols = ["district","district_method","district_dist_m", + "n_projects","weighted_price_m2_corp_sum","median_price_m2_corp_sum", + "deals_per_month_corp_sum","months_to_sellout", + "n_lots","n_sold","sold_pct", + "median_price_m2","p25_price_m2","p75_price_m2", + "avg_area_sold_m2","velocity_per_month","avg_readiness_pct"] + return dict(zip(cols, row)) + + +def fetch_site_full(conn, site_id): + s = dict(zip( + [c[0] for c in conn.execute("PRAGMA table_info(sites)").fetchall()], + conn.execute("SELECT * FROM sites WHERE site_id=?", (site_id,)).fetchone() + )) if False else None + cur = conn.execute("SELECT * FROM sites WHERE site_id=?", (site_id,)) + cols = [c[0] for c in cur.description] + s = dict(zip(cols, cur.fetchone())) + s["features"] = dict(conn.execute( + "SELECT feature,value FROM features WHERE site_id=?", (site_id,)).fetchall()) + s["scores"] = dict(conn.execute( + "SELECT component,score_0_100 FROM scores WHERE site_id=?", (site_id,)).fetchall()) + tot = conn.execute( + "SELECT weighted,rank_overall,rank_district FROM scores_total WHERE site_id=?", + (site_id,)).fetchone() + s["weighted_total"] = tot[0] + s["rank_overall"] = tot[1] + s["rank_district"] = tot[2] + s["economics"] = fetch_economics(conn, site_id) + s["nearest_pois"] = {} + for cat in ["kindergarten","school","university","pharmacy","clinic","hospital", + "shop_big","shop_med","shop_small","bus_stop","tram_stop","metro", + "park","playground","sports"]: + row = conn.execute( + """SELECT name, distance_m, lat, lon FROM pois + WHERE site_id=? AND category=? ORDER BY distance_m LIMIT 5""", + (site_id, cat)).fetchall() + s["nearest_pois"][cat] = [ + {"name": r[0] or "—", "distance_m": round(r[1], 1), "lat": r[2], "lon": r[3]} + for r in row + ] + return s + +def main(): + conn = sqlite3.connect(DB) + parcel = fetch_site_full(conn, PARCEL_ID) + n_total = conn.execute("SELECT count(*) FROM sites").fetchone()[0] + + # Top-10 best-scoring ЖК + parcel position context + rows = conn.execute("""SELECT s.site_id, s.name, s.district, s.developer, s.obj_class, + s.flat_count, s.lat, s.lon, + st.weighted, st.rank_overall + FROM sites s JOIN scores_total st USING (site_id) + WHERE s.kind='jk' ORDER BY st.weighted DESC""").fetchall() + top10 = [dict(zip(['site_id','name','district','developer','obj_class','flat_count', + 'lat','lon','weighted','rank'], r)) for r in rows[:10]] + + # Distribution stats + all_w = [r[8] for r in rows] + stats = { + "n_jk": len(all_w), + "mean": round(statistics.mean(all_w), 1), + "median": round(statistics.median(all_w), 1), + "p25": round(statistics.quantiles(all_w, n=4)[0], 1), + "p75": round(statistics.quantiles(all_w, n=4)[2], 1), + "min": round(min(all_w), 1), + "max": round(max(all_w), 1), + } + + # Component-wise comparison + parcel_comp = parcel["scores"] + comp_stats = {} + for c in parcel_comp: + vals = [v for (v,) in conn.execute( + "SELECT score_0_100 FROM scores s JOIN sites si USING (site_id) WHERE component=? AND si.kind='jk'", + (c,)).fetchall()] + comp_stats[c] = { + "parcel": round(parcel_comp[c], 1), + "median_jk": round(statistics.median(vals), 1), + "p75_jk": round(statistics.quantiles(vals, n=4)[2], 1), + } + + # Closest comparable ЖК (by geographic proximity to parcel) + plat, plon = parcel["lat"], parcel["lon"] + nearby = [] + for r in conn.execute("""SELECT s.site_id, s.name, s.district, s.developer, s.obj_class, + s.lat, s.lon, st.weighted, st.rank_overall + FROM sites s JOIN scores_total st USING (site_id) + WHERE s.kind='jk'""").fetchall(): + import math + R=6371000; p1,p2=math.radians(plat),math.radians(r[5]) + dp=math.radians(r[5]-plat); dl=math.radians(r[6]-plon) + a=math.sin(dp/2)**2+math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + d=2*R*math.asin(math.sqrt(a)) + nearby.append((d, r)) + nearby.sort() + closest = [ + {"distance_m": round(d, 0), "site_id": r[0], "name": r[1], "district": r[2], + "developer": r[3], "obj_class": r[4], "weighted": round(r[7], 1), "rank": r[8]} + for d, r in nearby[:10] + ] + + out = { + "generated_at": __import__("datetime").datetime.now().isoformat(timespec="seconds"), + "parcel": parcel, + "n_compared_jk": stats["n_jk"], + "weighted_score_distribution": stats, + "component_comparison": comp_stats, + "weights_used": {"education":0.30,"health":0.15,"retail":0.20,"transit":0.20,"leisure":0.15}, + "top10_best_jk_ekb": top10, + "10_closest_jk_to_parcel": closest, + } + + json_path = OUT / "parcel_66_41_0204016_10.json" + with open(json_path, "w") as f: + json.dump(out, f, ensure_ascii=False, indent=2, default=str) + print(f"JSON: {json_path}") + + # Build HTML + html = build_html(out) + html_path = OUT / "parcel_66_41_0204016_10.html" + with open(html_path, "w") as f: + f.write(html) + print(f"HTML: {html_path}") + + conn.close() + +def econ_block(e): + if not e: + return "

Нет данных Объектива для этого района.

" + f = lambda x, suf="": (f"{x:.1f}{suf}" if isinstance(x,(int,float)) else "—") + method_note = "по совпадению имени ЖК" if e["district_method"]=="name_match" \ + else f"по голосованию 5 ближайших ЖК (расст. до репрезентанта {e['district_dist_m']:.0f} м)" + return f""" + + + + + + + + + + +
Район (Объектив){e["district"]} ({method_note})
Проектов в районе{e["n_projects"]}
Лотов всего / продано{e["n_lots"] or 0:,} / {e["n_sold"] or 0:,} ({f(e["sold_pct"], '%')})
Цена за м² (медиана){f(e["median_price_m2"])} тыс ₽ · P25={f(e["p25_price_m2"])} · P75={f(e["p75_price_m2"])}
Средняя площадь сделки{f(e["avg_area_sold_m2"])} м²
Скорость продаж (real){f(e["velocity_per_month"])} зарег. ДДУ/корпус/мес (по 12 мес)
Средняя готовность{f(e["avg_readiness_pct"], '%')}
Цена corp_sum (взвеш.){f(e["weighted_price_m2_corp_sum"])} тыс ₽/м² · скорость {f(e["deals_per_month_corp_sum"])}
Распродажа стока (corp_sum){f(e["months_to_sellout"], ' мес')}
+

real_* рассчитаны по {e["n_lots"] or 0:,} лотам из Поквартирные/Лоты (303 677 квартир Екб). Это per-flat, основной источник правды.

+""" + +def build_html(d): + p = d["parcel"] + cs = d["component_comparison"] + poi_table_rows = [] + for cat, items in p["nearest_pois"].items(): + nearest = items[0] if items else None + poi_table_rows.append( + f"{cat}" + f"{nearest['name'] if nearest else '—'}" + f"{('%.0f м' % nearest['distance_m']) if nearest else '—'}" + f"{p['features'].get(f'{cat}_count_500m','')}" + f"{p['features'].get(f'{cat}_count_1km','')}" + ) + + comp_rows = [] + for c, v in cs.items(): + delta = v["parcel"] - v["median_jk"] + cls = "g" if delta > 0 else ("r" if delta < 0 else "") + comp_rows.append( + f"{c}{v['parcel']}" + f"{v['median_jk']}{v['p75_jk']}" + f"{'+' if delta>=0 else ''}{delta:.1f}" + ) + + closest_rows = "".join( + f"{x['distance_m']:.0f} м" + f"{x['name'] or '—'}{x['district'] or '—'}" + f"{x['developer'] or ''}{x['obj_class'] or ''}" + f"{x['weighted']}#{x['rank']}" + for x in d["10_closest_jk_to_parcel"] + ) + + top_rows = "".join( + f"#{x['rank']}{x['name'] or '—'}" + f"{x['district'] or '—'}{x['developer'] or ''}" + f"{x['obj_class'] or ''}{x['weighted']:.1f}" + for x in d["top10_best_jk_ekb"] + ) + + return f""" + +Анализ участка {p['name']} + +

Анализ участка 66:41:0204016:10

+
Сгенерировано {d["generated_at"]} · Сравнение с {d["n_compared_jk"]} строящимися ЖК Екатеринбурга
+ +
+ Координаты участка получены из ссылки на NSPD-карту (EPSG:3857 → WGS84): + {p['lat']}, {p['lon']}. Район — центрально-северная часть ЕКБ (Пионерский / Втузгородок). +
+ +
+
Итоговый балл{p["weighted_total"]:.1f}из 100
+
Ранг по ЕКБ#{p["rank_overall"]} / {d["n_compared_jk"]+1}
+
Перцентиль{(1 - (p["rank_overall"]-1)/(d["n_compared_jk"]+1))*100:.0f}%
+
Медиана ЖК{d["weighted_score_distribution"]["median"]}
+
Топ-25% ЖК ≥{d["weighted_score_distribution"]["p75"]}
+
+ +

Компоненты (взвешенные)

+ +{"".join(comp_rows)}
КомпонентУчастокМедиана ЖКP75 ЖКΔ vs медиана
+
Веса: образование 20% · здоровье 10% · ритейл 15% · транспорт 15% · досуг 10% · экономика 30%
+ +

Экономика района (Объектив API, последние 90 дней)

+{econ_block(p.get("economics"))} + +

Ближайшие POI вокруг участка

+ +{"".join(poi_table_rows)}
КатегорияБлижайшийДо негоВ 500 мВ 1 км
+ +

10 ближайших ЖК (для прямого бенчмарка)

+ +{closest_rows}
Расст.НазваниеРайонДевелоперКлассБаллРанг
+ +

Топ-10 ЖК ЕКБ по локационной привлекательности

+ +{top_rows}
РангНазваниеРайонДевелоперКлассБалл
+ +

Методика

+

Источник POI: OpenStreetMap (Overpass API), bbox по всем 381 ЖК Свердл.

+

Логика: для каждой категории — расстояние-в-балл (piecewise linear от ideal_m к max_m), +далее агрегация в 5 компонент с весами (max-pool там, где категории альтернативны: ритейл = max(big, 0.7×med); транспорт = max(metro, 0.85×tram, 0.7×bus)). +Финальный балл — взвешенная сумма компонент.

+

База данных: локальная SQLite analysis.db (sites/pois/features/scores), под-выборка строящихся ЖК ЕКБ из прода domrf_kn_objects.

+""" + +if __name__ == "__main__": + main() diff --git a/site-finder/05_objective_pull.py b/site-finder/05_objective_pull.py new file mode 100644 index 00000000..e4a6b1d7 --- /dev/null +++ b/site-finder/05_objective_pull.py @@ -0,0 +1,148 @@ +"""Step 1.1: Pull Objective API and store in local DB. + +We pull "Сводные/Корпуса" (corp_sum) for Ekb covering last 12 calendar months — +this is the primary source of: + - per-corpus monthly: deals (DDU+DKP), volume sold (m²), avg price/m², stock left + - готовность %, старт продаж, планируемая дата ввода + - район (Objective's classification) + +Data lands in two tables: + objective_raw_reports — JSON payload archive (1 row per fetch) + objective_corp_month — flattened rows (one per month × project × corpus × room-type) +""" +import sqlite3, pathlib, requests, json, time, datetime as dt + +DB = pathlib.Path(__file__).parent / "analysis.db" +API = "https://api.objctv.ru" +KEY = "623f6a57-0179-434b-8202-259525bdc77c" + +SCHEMA = """ +CREATE TABLE IF NOT EXISTS objective_raw_reports ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + fetched_at TEXT DEFAULT CURRENT_TIMESTAMP, + report_kind TEXT, + group_name TEXT, + start_date TEXT, + end_date TEXT, + n_rows INTEGER, + payload TEXT +); + +CREATE TABLE IF NOT EXISTS objective_corp_month ( + month TEXT, -- 'YYYY-MM' + project TEXT, + developer TEXT, + district TEXT, + obj_class TEXT, + corpus TEXT, + sales_start TEXT, + plan_date TEXT, + fact_date TEXT, + months_in_sales INTEGER, + rooms_bucket TEXT, -- '1', '2', '3', '4+', 'студия', etc. + lots_pd INTEGER, + area_pd REAL, + deals_total INTEGER, + deals_priced INTEGER, + sold_volume_m2 REAL, + avg_price_m2 REAL, + avg_area_m2 REAL, + stock_lots INTEGER, + stock_m2 REAL, + stock_avg_price_m2 REAL, + PRIMARY KEY (month, project, corpus, rooms_bucket) +); +CREATE INDEX IF NOT EXISTS oc_district ON objective_corp_month(district); +CREATE INDEX IF NOT EXISTS oc_project ON objective_corp_month(project); +""" + +def get_token(): + r = requests.get(f"{API}/Users/User/GetToken", params={"apiKey": KEY}, timeout=30) + r.raise_for_status() + return r.json()["token"] + +def fetch_corp_sum(token, group, sd, ed): + r = requests.get(f"{API}/v2/Report/GetReport", + params={"Page":"Отчеты","ReportSection":"Объединенные данные", + "ReportType":"Сводные","ReportName":"Корпуса", + "GroupName":group,"StartDate":sd,"EndDate":ed, + "UseDdu":"true","UseDkp":"true"}, + headers={"Authorization":f"Bearer {token}","Accept-Encoding":"br"}, + timeout=120) + r.raise_for_status() + return r.json().get("result", []) + +# Map raw RU column names to our schema +COL_MAP = { + "month":"Месяц","project":"Проект","developer":"Девелопер","district":"Район", + "obj_class":"Класс","corpus":"Корпус","sales_start":"Старт продаж", + "plan_date":"Планируемая дата ввода","fact_date":"Фактическая дата ввода", + "months_in_sales":"Месяцев в реализации","rooms_bucket":"Количество комнат (Данные Объектива)", + "lots_pd":"Лотов по ПД, шт.","area_pd":"Площадь по ПД, м2.", + "deals_total":"Количество в сделках (всего), шт.", + "deals_priced":"Количество лотов в сделках (с ценами), шт.", + "sold_volume_m2":"Объем реализации (всего), м2.", + "avg_price_m2":"Средняя цена м2 лота в сделках, тыс.Р/м2", + "avg_area_m2":"Средняя площадь лота в сделках, м2", + "stock_lots":"Объем предложения, шт.","stock_m2":"Объем предложения, м2.", + "stock_avg_price_m2":"Средняя цена м2 лота в продаже, тыс.Р/м2", +} + +RU_MONTHS = {"январь":1,"февраль":2,"март":3,"апрель":4,"май":5,"июнь":6, + "июль":7,"август":8,"сентябрь":9,"октябрь":10,"ноябрь":11,"декабрь":12} + +def normalize_month(s): + # 'апрель-2026' → '2026-04' + if not s: return None + parts = str(s).lower().replace("—","-").split("-") + if len(parts) != 2: return s + m = RU_MONTHS.get(parts[0].strip()) + y = parts[1].strip() + return f"{y}-{m:02d}" if m else s + +def main(): + conn = sqlite3.connect(DB) + conn.executescript(SCHEMA) + + today = dt.date.today() + sd = (today.replace(day=1) - dt.timedelta(days=365)).strftime("%Y.%m.%d") + ed = today.strftime("%Y.%m.%d") + group = "Екатеринбург" + + print(f"Fetching corp_sum {group} {sd}..{ed}") + token = get_token() + rows = fetch_corp_sum(token, group, sd, ed) + print(f" {len(rows)} rows") + + conn.execute("INSERT INTO objective_raw_reports(report_kind,group_name,start_date,end_date,n_rows,payload) VALUES (?,?,?,?,?,?)", + ("corp_sum_v2", group, sd, ed, len(rows), json.dumps(rows, ensure_ascii=False))) + + conn.execute("DELETE FROM objective_corp_month") + inserted = skipped = 0 + for r in rows: + try: + vals = {k: r.get(v) for k, v in COL_MAP.items()} + vals["month"] = normalize_month(vals["month"]) + cols = list(vals.keys()) + placeholders = ",".join(["?"] * len(cols)) + conn.execute(f"INSERT OR REPLACE INTO objective_corp_month({','.join(cols)}) VALUES ({placeholders})", + [vals[c] for c in cols]) + inserted += 1 + except Exception as e: + skipped += 1 + conn.commit() + print(f" inserted: {inserted}, skipped: {skipped}") + + # Sanity + n = conn.execute("SELECT count(*) FROM objective_corp_month").fetchone()[0] + n_proj = conn.execute("SELECT count(DISTINCT project) FROM objective_corp_month").fetchone()[0] + n_dist = conn.execute("SELECT count(DISTINCT district) FROM objective_corp_month").fetchone()[0] + print(f"\nLocal: {n} rows, {n_proj} проекта, {n_dist} районов") + print("\nDistricts (Objective):") + for r in conn.execute("""SELECT district, count(*) c, count(DISTINCT project) np + FROM objective_corp_month GROUP BY 1 ORDER BY 3 DESC""").fetchall(): + print(f" {r[0]:<30} rows={r[1]:>5} projects={r[2]}") + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/06_match_economics.py b/site-finder/06_match_economics.py new file mode 100644 index 00000000..54241259 --- /dev/null +++ b/site-finder/06_match_economics.py @@ -0,0 +1,208 @@ +"""Step 1.2: Match ЖК ↔ Objective projects by name; compute district economics. + +Output tables: + jk_objective_match — site_id ↔ objective project name (best fuzzy match) + district_economics — per Objective-район aggregates over last 90 days + site_district — which Objective-район each site belongs to +""" +import sqlite3, pathlib, re, math +from difflib import SequenceMatcher + +DB = pathlib.Path(__file__).parent / "analysis.db" + +SCHEMA = """ +CREATE TABLE IF NOT EXISTS jk_objective_match ( + site_id TEXT PRIMARY KEY REFERENCES sites(site_id), + project TEXT, + score REAL, + method TEXT +); + +CREATE TABLE IF NOT EXISTS district_economics ( + district TEXT PRIMARY KEY, + n_projects INTEGER, + n_corpuses INTEGER, + median_price_m2 REAL, -- тыс.Р/м² + weighted_price_m2 REAL, -- weighted by sold m² + avg_price_m2_offer REAL, -- prices in stock right now + deals_per_month_avg REAL, -- avg per корпус + sold_volume_m2_90d REAL, -- last 90 days sold m² + stock_m2 REAL, -- current stock + stock_lots INTEGER, + avg_area_sold_m2 REAL, -- average flat size sold + months_to_sellout REAL -- stock_m2 / monthly_velocity +); + +CREATE TABLE IF NOT EXISTS site_district ( + site_id TEXT PRIMARY KEY REFERENCES sites(site_id), + district TEXT, + method TEXT, -- 'name_match' | 'nearest_jk' + nearest_jk_obj_id INTEGER, + nearest_jk_dist_m REAL +); +""" + +def normalize(s): + if not s: return "" + s = s.lower().strip() + s = re.sub(r'^(жк|жилой\s+комплекс|жилой\s+квартал|жилые\s+кварталы)\s+', '', s) + s = re.sub(r'["«»\'`]+', '', s) + s = re.sub(r'\s+', ' ', s) + return s.strip() + +def fuzzy(a, b): + return SequenceMatcher(None, normalize(a), normalize(b)).ratio() + +def hav(la1,lo1,la2,lo2): + R=6371000; p1,p2=math.radians(la1),math.radians(la2) + dp=math.radians(la2-la1); dl=math.radians(lo2-lo1) + a=math.sin(dp/2)**2+math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2*R*math.asin(math.sqrt(a)) + +def main(): + conn = sqlite3.connect(DB) + conn.executescript(SCHEMA) + + # 1. ЖК ↔ Objective project name match + sites = conn.execute("SELECT site_id, name FROM sites WHERE kind='jk' AND name IS NOT NULL").fetchall() + objective_projects = [r[0] for r in conn.execute("SELECT DISTINCT project FROM objective_corp_month").fetchall() if r[0]] + print(f"Sites: {len(sites)}, Objective projects: {len(objective_projects)}") + + conn.execute("DELETE FROM jk_objective_match") + matched = 0 + for site_id, name in sites: + best = (0.0, None) + for proj in objective_projects: + s = fuzzy(name, proj) + if s > best[0]: best = (s, proj) + if best[0] >= 0.80: + conn.execute("INSERT INTO jk_objective_match VALUES (?,?,?,?)", + (site_id, best[1], best[0], "fuzzy")) + matched += 1 + print(f"Name-matched: {matched}/{len(sites)}") + + # 2. district_economics — last 90 days + # Last 3 calendar months from data + last3 = [r[0] for r in conn.execute( + "SELECT DISTINCT month FROM objective_corp_month ORDER BY month DESC LIMIT 3").fetchall()] + print(f"Months window: {last3}") + + conn.execute("DELETE FROM district_economics") + placeholders = ",".join(["?"]*len(last3)) + + # Latest stock per (project, corpus) — taken from the latest month each corpus appears + latest_stock = {} + for r in conn.execute(f""" + WITH ranked AS ( + SELECT project, corpus, district, month, stock_m2, stock_lots, + ROW_NUMBER() OVER (PARTITION BY project, corpus ORDER BY month DESC) rn + FROM objective_corp_month + WHERE month IN ({placeholders}) + ) + SELECT project, corpus, district, stock_m2, stock_lots FROM ranked WHERE rn=1 + """, last3).fetchall(): + latest_stock[(r[0], r[1])] = (r[2], r[3] or 0, r[4] or 0) + + # Per-district aggregates from window + rows = conn.execute(f""" + SELECT district, project, corpus, deals_total, deals_priced, + sold_volume_m2, avg_price_m2, avg_area_m2, stock_avg_price_m2 + FROM objective_corp_month + WHERE month IN ({placeholders}) + """, last3).fetchall() + + by_dist = {} + for r in rows: + d = r[0] + if not d: continue + by_dist.setdefault(d, []).append(r) + + for d, rs in by_dist.items(): + projects = {r[1] for r in rs} + corpuses = {(r[1], r[2]) for r in rs} + # weighted price (by sold m²) + wn = sum((r[6] or 0) * (r[5] or 0) for r in rs) + wd = sum((r[5] or 0) for r in rs) + wp = wn / wd if wd else None + # median price (priced deals only) + prices = sorted([r[6] for r in rs if r[6] and (r[4] or 0) > 0]) + med = prices[len(prices)//2] if prices else None + # offer prices + offers = [r[8] for r in rs if r[8] and r[8] > 0] + avg_off = sum(offers)/len(offers) if offers else None + # velocity (avg deals_total per row, where row = corpus×month) + vels = [r[3] or 0 for r in rs] + avg_v = sum(vels)/len(vels) if vels else None + # sold volume 90d + sv = sum(r[5] or 0 for r in rs) + # stock from latest_stock for this district + stock_m2 = sum(s[1] for k, s in latest_stock.items() if s[0] == d) + stock_lots = sum(s[2] for k, s in latest_stock.items() if s[0] == d) + # avg area + areas = [r[7] for r in rs if r[7] and r[7] > 0] + avg_a = sum(areas)/len(areas) if areas else None + # months to sellout + mts = (stock_m2 / (sv / 3.0)) if sv > 0 and stock_m2 else None + conn.execute("""INSERT INTO district_economics VALUES (?,?,?,?,?,?,?,?,?,?,?,?)""", + (d, len(projects), len(corpuses), med, wp, avg_off, avg_v, sv, + stock_m2 or None, stock_lots or None, avg_a, mts)) + conn.commit() + print("\nDistrict economics (last 90d):") + for r in conn.execute("""SELECT district, n_projects, ROUND(weighted_price_m2,1) wp, + ROUND(deals_per_month_avg,1) v, ROUND(months_to_sellout,1) mts + FROM district_economics ORDER BY wp DESC""").fetchall(): + print(f" {r[0]:<25} projects={r[1]:>3} price={r[2] or '—':>7}тыс vel={r[3]:>5} to_sellout={r[4] or '—'} мес") + + # 3. site_district: name-matched gets Objective district directly; others — nearest matched ЖК + conn.execute("DELETE FROM site_district") + # 3a. matched sites — pull district from Objective directly + rows = conn.execute(""" + SELECT s.site_id, s.lat, s.lon, m.project, + (SELECT district FROM objective_corp_month + WHERE project=m.project ORDER BY month DESC LIMIT 1) AS district + FROM sites s LEFT JOIN jk_objective_match m USING (site_id) + """).fetchall() + matched_with_dist = [(sid, lat, lon, proj, dist) for sid, lat, lon, proj, dist in rows + if dist is not None] + matched_idx = {sid: (lat, lon, dist) for sid, lat, lon, _, dist in matched_with_dist} + + from collections import Counter + for sid, lat, lon, proj, dist in rows: + if dist: + conn.execute("INSERT INTO site_district VALUES (?,?,?,?,?)", + (sid, dist, "name_match", None, 0)) + else: + # vote among k=5 nearest matched ЖК within 2 km + cands = [] + for sid2, (lat2, lon2, dist2) in matched_idx.items(): + if sid2 == sid: continue + d = hav(lat, lon, lat2, lon2) + cands.append((d, sid2, dist2)) + cands.sort() + top = [c for c in cands[:7] if c[0] <= 2500] + if top: + votes = Counter(c[2] for c in top) + # pick the most-voted district; tie-break: closest representative + top_dist, _ = votes.most_common(1)[0] + rep = next(c for c in top if c[2] == top_dist) + obj_id = None + if rep[1] and rep[1].startswith("jk:"): + try: obj_id = int(rep[1][3:]) + except: pass + conn.execute("INSERT INTO site_district VALUES (?,?,?,?,?)", + (sid, top_dist, "knn_vote_5", obj_id, rep[0])) + conn.commit() + + parcel_dist = conn.execute( + "SELECT district, method, nearest_jk_dist_m FROM site_district WHERE site_id='parcel:66:41:0204016:10'" + ).fetchone() + print(f"\nParcel district: {parcel_dist}") + + print("\nSites assigned per district:") + for r in conn.execute("SELECT district, count(*) FROM site_district GROUP BY 1 ORDER BY 2 DESC LIMIT 15").fetchall(): + print(f" {r[0]:<25} {r[1]}") + + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/07_objective_full_pull.py b/site-finder/07_objective_full_pull.py new file mode 100644 index 00000000..afe4826d --- /dev/null +++ b/site-finder/07_objective_full_pull.py @@ -0,0 +1,264 @@ +"""Pull every available Objective report combination for Ekb. + +The API has 4 reports = ReportType × ReportName: + Сводные / Корпуса — already pulled in 05_* + Сводные / Лоты — probe + Поквартирные / Корпуса — probe + Поквартирные / Лоты — full per-flat data (most valuable) + +Earlier server graph said 2 of 4 returned HTTP 500. We probe again, +then bulk-pull whatever returns 200. +""" +import sqlite3, pathlib, requests, time, json, datetime as dt, sys + +DB = pathlib.Path(__file__).parent / "analysis.db" +RAW = pathlib.Path(__file__).parent / "cache" / "objective_raw" +RAW.mkdir(parents=True, exist_ok=True) + +API = "https://api.objctv.ru" +KEY = "623f6a57-0179-434b-8202-259525bdc77c" +GROUP = "Екатеринбург" +TODAY = dt.date.today() +SD = (TODAY.replace(day=1) - dt.timedelta(days=365)).strftime("%Y.%m.%d") +ED = TODAY.strftime("%Y.%m.%d") + +EXTRA_SCHEMA = """ +-- per-flat snapshot (Поквартирные/Лоты) +CREATE TABLE IF NOT EXISTS objective_lots ( + lot_id INTEGER PRIMARY KEY, -- field "Id" + project_id INTEGER, + project TEXT, + developer TEXT, + city TEXT, + district TEXT, + corpus TEXT, + address TEXT, + obj_class TEXT, + sales_start TEXT, + plan_date TEXT, + fact_date TEXT, + readiness_pct REAL, + construction_stage TEXT, + finish_type TEXT, + section TEXT, + floor INTEGER, + lot_num TEXT, + room_kind TEXT, + status TEXT, + sold TEXT, + rooms_dev TEXT, + rooms_pd TEXT, + rooms_obj TEXT, + area_dev REAL, + area_pd REAL, + budget_rub REAL, + price_per_m2 REAL, + price_method TEXT, + price_set_date TEXT, + price_actual_date TEXT, + offer_price REAL, + delta_price_rub REAL, + delta_price_pct REAL, + contract_date TEXT, + register_date TEXT, + deal_type TEXT, + buyer_type TEXT, + register_num TEXT, + encumbrance TEXT, + bank TEXT, + encumbrance_start TEXT, + egrn_actual_date TEXT, + fetched_at TEXT DEFAULT CURRENT_TIMESTAMP +); +CREATE INDEX IF NOT EXISTS lots_proj ON objective_lots(project); +CREATE INDEX IF NOT EXISTS lots_dist ON objective_lots(district); +CREATE INDEX IF NOT EXISTS lots_bank ON objective_lots(bank); +CREATE INDEX IF NOT EXISTS lots_status ON objective_lots(status); +""" + +# Mapping for per-flat (Поквартирные/Лоты) +LOTS_MAP = { + "lot_id":"Id","project_id":"Id проекта","project":"Проект","developer":"Девелопер", + "city":"Город","district":"Район","corpus":"Корпус","address":"Адрес", + "obj_class":"Класс","sales_start":"Старт продаж", + "plan_date":"Планируемая дата ввода","fact_date":"Фактическая дата ввода", + "readiness_pct":"Готовность","construction_stage":"Стадия строительства", + "finish_type":"Отделка по корпусу","section":"Секция","floor":"Этаж", + "lot_num":"Номер лота","room_kind":"Вид помещения","status":"Статус", + "sold":"Продано","rooms_dev":"Количество комнат(Сайт девелопера)", + "rooms_pd":"Количество комнат(ПД)","rooms_obj":"Количество комнат(Данные объектива)", + "area_dev":"Площадь, м2(Сайт девелопера)","area_pd":"Площадь, м2(ПД)", + "budget_rub":"Расчетный бюджет лота, Р","price_per_m2":"Цена за м2, Р", + "price_method":"Способ определения цены","price_set_date":"Дата установки цены", + "price_actual_date":"Дата актуальности цены","offer_price":"Цена предложения, Р", + "delta_price_rub":"Дельта цена, Р","delta_price_pct":"Дельта цена, %", + "contract_date":"Дата договора","register_date":"Дата регистрации", + "deal_type":"Тип сделки","buyer_type":"Тип покупателя","register_num":"Номер регистрации", + "encumbrance":"Тип обременения","bank":"Банк","encumbrance_start":"Дата начала обременения", + "egrn_actual_date":"Дата актуальности данных из ЕГРН", +} + + +def get_token(): + r = requests.get(f"{API}/Users/User/GetToken", params={"apiKey": KEY}, timeout=30) + r.raise_for_status() + return r.json()["token"] + + +def fetch(token, params, attempts=5): + """Fetch with backoff for 429/5xx.""" + last = None + for i in range(attempts): + r = requests.get(f"{API}/v2/Report/GetReport", + params=params, + headers={"Authorization": f"Bearer {token}", "Accept-Encoding": "br"}, + timeout=180) + if r.ok: + return r + last = (r.status_code, r.text[:200]) + # If 401 — refresh token + if r.status_code == 401: + token = get_token() + continue + wait = int(r.headers.get("Retry-After") or (10 * (2 ** i))) + print(f" HTTP {r.status_code} · ждём {wait}с (попытка {i+1}/{attempts})") + time.sleep(min(wait, 120)) + raise RuntimeError(f"failed after {attempts}: {last}") + + +def normalize_pct(s): + if s is None: return None + s = str(s).strip() + if s.endswith("%"): s = s[:-1] + try: return float(s) + except: return None + + +def parse_dec(v): + if v in (None, ""): return None + if isinstance(v, (int, float)): return v + try: return float(str(v).replace(" ","").replace(",",".")) + except: return None + + +def main(): + conn = sqlite3.connect(DB) + conn.executescript(EXTRA_SCHEMA) + + token = get_token() + print(f"Group={GROUP} window={SD}..{ED}") + + # -------- probe all four (without ComplexName, then with) -------- + print("\n=== Probe all four reports ===") + for rt in ("Сводные", "Поквартирные"): + for rn in ("Корпуса", "Лоты"): + base = {"Page":"Отчеты","ReportSection":"Объединенные данные", + "ReportType":rt,"ReportName":rn,"GroupName":GROUP, + "UseDdu":"true","UseDkp":"true"} + # Сводные wants StartDate/EndDate + if rt == "Сводные": + base["StartDate"] = SD; base["EndDate"] = ED + try: + r = fetch(token, base, attempts=2) + rows = r.json().get("result", []) + rows_n = len(rows) if isinstance(rows, list) else "?" + print(f" {rt}/{rn:<7} (whole Ekb): rows={rows_n}, bytes={len(r.content)}") + ts = dt.datetime.now().strftime("%Y%m%dT%H%M%S") + fname = RAW / f"{rt}_{rn}_whole_{ts}.json" + fname.write_text(json.dumps(rows, ensure_ascii=False)) + if isinstance(rows, list) and rows: + print(f" sample keys ({len(rows[0])}): {list(rows[0].keys())[:8]}") + except Exception as e: + print(f" {rt}/{rn} FAIL: {e}") + # try with ComplexName fallback + try: + base2 = dict(base, ComplexName="Парк Культуры") + r = fetch(token, base2, attempts=2) + rows = r.json().get("result", []) + rows_n = len(rows) if isinstance(rows, list) else "?" + print(f" +ComplexName=Парк Культуры → rows={rows_n}") + ts = dt.datetime.now().strftime("%Y%m%dT%H%M%S") + (RAW / f"{rt}_{rn}_pk_{ts}.json").write_text(json.dumps(rows, ensure_ascii=False)) + except Exception as e2: + print(f" +ComplexName FAIL: {e2}") + time.sleep(15) # be nice — Objective rate-limits aggressively + + # -------- bulk-pull Поквартирные/Лоты (the most valuable per-flat data) -------- + # If whole-Ekb works, prefer that. Otherwise iterate over distinct projects from corp_month. + print("\n=== Bulk pull Поквартирные/Лоты ===") + pf_params = {"Page":"Отчеты","ReportSection":"Объединенные данные", + "ReportType":"Поквартирные","ReportName":"Лоты","GroupName":GROUP, + "UseDdu":"true","UseDkp":"true"} + rows = None + try: + r = fetch(token, pf_params, attempts=3) + rows = r.json().get("result", []) + print(f" whole Ekb: {len(rows)} flats, {len(r.content)/1024:.0f} KB") + except Exception as e: + print(f" whole-Ekb fail: {e}; falling back to per-project loop") + + if rows is None: + projects = [p[0] for p in conn.execute( + "SELECT DISTINCT project FROM objective_corp_month WHERE project IS NOT NULL ORDER BY project" + ).fetchall()] + print(f" iterating {len(projects)} projects") + rows = [] + for i, proj in enumerate(projects, 1): + try: + r = fetch(token, dict(pf_params, ComplexName=proj)) + got = r.json().get("result", []) + rows.extend(got) + print(f" [{i}/{len(projects)}] {proj}: +{len(got)}") + except Exception as e: + print(f" [{i}/{len(projects)}] {proj}: FAIL {e}") + time.sleep(10) + + if rows: + ts = dt.datetime.now().strftime("%Y%m%dT%H%M%S") + (RAW / f"Поквартирные_Лоты_{ts}.json").write_text(json.dumps(rows, ensure_ascii=False)) + # Insert + conn.execute("DELETE FROM objective_lots") + ins = 0; skip = 0 + for r in rows: + try: + vals = {k: r.get(v) for k, v in LOTS_MAP.items()} + vals["readiness_pct"] = normalize_pct(vals["readiness_pct"]) + for f in ("area_dev","area_pd","budget_rub","price_per_m2","offer_price", + "delta_price_rub","delta_price_pct","floor","lot_id","project_id"): + vals[f] = parse_dec(vals[f]) + cols = list(vals.keys()) + conn.execute(f"INSERT OR REPLACE INTO objective_lots({','.join(cols)}) VALUES ({','.join(['?']*len(cols))})", + [vals[c] for c in cols]) + ins += 1 + except Exception: + skip += 1 + conn.commit() + print(f" inserted: {ins}, skipped: {skip}") + + # Sanity stats + n = conn.execute("SELECT count(*) FROM objective_lots").fetchone()[0] + print(f"\n Total flats stored: {n}") + for line in conn.execute("""SELECT status, count(*) FROM objective_lots + GROUP BY 1 ORDER BY 2 DESC""").fetchall(): + print(f" status={line[0]:<25} {line[1]}") + + print("\n Top banks (mortgage encumbrance):") + for line in conn.execute("""SELECT bank, count(*) FROM objective_lots + WHERE bank IS NOT NULL AND bank!='' + GROUP BY 1 ORDER BY 2 DESC LIMIT 10""").fetchall(): + print(f" {line[0]:<35} {line[1]}") + + print("\n By district (priced lots):") + for line in conn.execute("""SELECT district, count(*) total, + SUM(CASE WHEN price_per_m2>0 THEN 1 ELSE 0 END) priced, + ROUND(AVG(price_per_m2)/1000,1) avg_kp + FROM objective_lots + WHERE district IS NOT NULL + GROUP BY 1 ORDER BY 2 DESC LIMIT 12""").fetchall(): + print(f" {line[0]:<22} total={line[1]:>5} priced={line[2]:>5} avg={line[3]} тыс/м²") + + conn.close() + print("\nDone.") + +if __name__ == "__main__": + main() diff --git a/site-finder/08_enrich_economics.py b/site-finder/08_enrich_economics.py new file mode 100644 index 00000000..440bae11 --- /dev/null +++ b/site-finder/08_enrich_economics.py @@ -0,0 +1,132 @@ +"""Recompute district economics using per-flat (Поквартирные/Лоты) data. + +Per-flat is way richer than monthly Сводные: + - real velocity: registered DDU per month (last 12 mo) + - real median price: from individual sold lots + - real average area sold + - готовность distribution + - bank diversity (mortgage market attractiveness) + - sold-out ratio (продано / total in projects with sales started) + - sales freshness (median days since contract for last 90d) + +Replaces values in district_economics with `*_real` columns. +""" +import sqlite3, pathlib, datetime as dt +DB = pathlib.Path(__file__).parent / "analysis.db" + +EXTRA = """ +ALTER TABLE district_economics ADD COLUMN real_n_lots INTEGER; +ALTER TABLE district_economics ADD COLUMN real_n_sold INTEGER; +ALTER TABLE district_economics ADD COLUMN real_sold_pct REAL; +ALTER TABLE district_economics ADD COLUMN real_median_price_m2 REAL; +ALTER TABLE district_economics ADD COLUMN real_p25_price_m2 REAL; +ALTER TABLE district_economics ADD COLUMN real_p75_price_m2 REAL; +ALTER TABLE district_economics ADD COLUMN real_avg_area_sold REAL; +ALTER TABLE district_economics ADD COLUMN real_velocity_per_month REAL; +ALTER TABLE district_economics ADD COLUMN real_n_banks INTEGER; +ALTER TABLE district_economics ADD COLUMN real_top_bank TEXT; +ALTER TABLE district_economics ADD COLUMN real_top_bank_share REAL; +ALTER TABLE district_economics ADD COLUMN real_avg_readiness_pct REAL; +""" + +def safe_alter(conn, sql): + for stmt in sql.strip().split(";"): + s = stmt.strip() + if not s: continue + try: conn.execute(s) + except sqlite3.OperationalError as e: + if "duplicate column" not in str(e): raise + +def percentile(vals, p): + if not vals: return None + vals = sorted(vals) + k = (len(vals) - 1) * p + f = int(k); c = min(f+1, len(vals)-1) + return vals[f] + (vals[c]-vals[f]) * (k - f) + +def main(): + conn = sqlite3.connect(DB) + safe_alter(conn, EXTRA) + conn.commit() + + # n_lots, n_sold, prices, areas, banks, readiness — all per district + rows = conn.execute(""" + SELECT district, status, sold, price_per_m2, area_pd, bank, + readiness_pct, register_date + FROM objective_lots + WHERE district IS NOT NULL AND district!='' + """).fetchall() + by_d = {} + for d, st, sold, price, area, bank, ready, reg in rows: + by_d.setdefault(d, []).append((st, sold, price, area, bank, ready, reg)) + + today = dt.date.today() + cutoff_12mo = today - dt.timedelta(days=365) + + for d, items in by_d.items(): + n_lots = len(items) + sold_items = [it for it in items if (it[1] or '').strip().lower() == 'да'] + n_sold = len(sold_items) + sold_pct = 100.0 * n_sold / n_lots if n_lots else None + + prices = [it[2] for it in sold_items if it[2] and it[2] > 0] + med = percentile(prices, 0.5) / 1000 if prices else None # → тыс ₽/м² + p25 = percentile(prices, 0.25) / 1000 if prices else None + p75 = percentile(prices, 0.75) / 1000 if prices else None + + areas = [it[3] for it in sold_items if it[3] and it[3] > 0] + avg_area = sum(areas)/len(areas) if areas else None + + # velocity: registered deals in last 12 mo / 12 / n_corpuses_in_district + reg_dates = [] + for it in sold_items: + r = it[6] + if not r: continue + try: + rd = dt.date.fromisoformat(r[:10]) + if rd >= cutoff_12mo: reg_dates.append(rd) + except: pass + # Distinct corpuses in district (from per-flat data) + n_corp = len({(it[0],) for it in items if it[0]}) # crude — but we want corpuses + n_corp_real = len({(it,) for it in conn.execute( + "SELECT DISTINCT project, corpus FROM objective_lots WHERE district=?", (d,)).fetchall()}) or 1 + velocity = len(reg_dates) / 12.0 / n_corp_real if reg_dates else 0 + + banks = [it[4] for it in items if it[4] and it[4].strip()] + unique_banks = set(banks) + top_bank, top_share = None, None + if banks: + from collections import Counter + c = Counter(banks) + top_bank, top_n = c.most_common(1)[0] + top_share = top_n / len(banks) + + ready_vals = [it[5] for it in items if it[5] is not None] + avg_ready = sum(ready_vals)/len(ready_vals) if ready_vals else None + + conn.execute("""UPDATE district_economics SET + real_n_lots=?, real_n_sold=?, real_sold_pct=?, + real_median_price_m2=?, real_p25_price_m2=?, real_p75_price_m2=?, + real_avg_area_sold=?, real_velocity_per_month=?, + real_n_banks=?, real_top_bank=?, real_top_bank_share=?, + real_avg_readiness_pct=? + WHERE district=?""", + (n_lots, n_sold, sold_pct, med, p25, p75, avg_area, velocity, + len(unique_banks), top_bank, top_share, avg_ready, d)) + conn.commit() + + print(f"{'район':<22}{'лот':>6}{'прод':>7}{'sold%':>7}{'медцена':>9}{'площ':>6}{'vel':>6}{'банки':>6}{'top_bank':>22}{'%':>5}{'готовн':>7}") + for r in conn.execute("""SELECT district, real_n_lots, real_n_sold, real_sold_pct, + real_median_price_m2, real_avg_area_sold, + real_velocity_per_month, real_n_banks, + real_top_bank, real_top_bank_share, real_avg_readiness_pct + FROM district_economics + WHERE real_n_lots>0 + ORDER BY real_median_price_m2 DESC NULLS LAST""").fetchall(): + d, nl, ns, sp, mp, aa, v, nb, tb, ts, rd = r + print(f"{d:<22}{nl:>6}{ns:>7}{sp or 0:>6.1f}%{mp or 0:>9.1f}{aa or 0:>6.1f}{v:>6.2f}{nb or 0:>6}{(tb or '—')[:20]:>22}{(ts or 0)*100:>4.0f}%{rd or 0:>6.0f}%") + + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/09_macro_and_trend.py b/site-finder/09_macro_and_trend.py new file mode 100644 index 00000000..d16b6962 --- /dev/null +++ b/site-finder/09_macro_and_trend.py @@ -0,0 +1,169 @@ +"""Macro context + per-district velocity trend. + +Adds these tables/columns: + macro_context — single-row mortgage rate, city avg price, etc. + district_economics + (real_velocity_6mo, real_velocity_prior_6mo, real_trend_ratio, + sat_factor, trend_factor, price_factor) + scoring_weights — single-row config (so we can change weights from UI later) + +Source for trend: per-flat register_date (objective_lots). +Source for mortgage rate: prod cbr_mortgage_series (Sverdl region, latest "ставка ипотечная"). +""" +import sqlite3, pathlib, psycopg2, datetime as dt, statistics +DB = pathlib.Path(__file__).parent / "analysis.db" + +EXTRA = """ +CREATE TABLE IF NOT EXISTS macro_context ( + key TEXT PRIMARY KEY, + value REAL, + label TEXT, + period TEXT, + fetched_at TEXT DEFAULT CURRENT_TIMESTAMP +); + +ALTER TABLE district_economics ADD COLUMN real_velocity_6mo REAL; +ALTER TABLE district_economics ADD COLUMN real_velocity_prior_6mo REAL; +ALTER TABLE district_economics ADD COLUMN real_trend_ratio REAL; +ALTER TABLE district_economics ADD COLUMN sat_factor REAL; +ALTER TABLE district_economics ADD COLUMN trend_factor REAL; +ALTER TABLE district_economics ADD COLUMN price_factor REAL; + +CREATE TABLE IF NOT EXISTS scoring_weights ( + component TEXT PRIMARY KEY, + weight REAL NOT NULL, + note TEXT +); +""" + +def safe_alter(conn, sql): + for stmt in sql.strip().split(";"): + s = stmt.strip() + if not s: continue + try: conn.execute(s) + except sqlite3.OperationalError as e: + if "duplicate column" not in str(e): raise + +def main(): + local = sqlite3.connect(DB) + safe_alter(local, EXTRA) + local.commit() + + # ---- 1. Pull macro from prod ---- + pg = psycopg2.connect(host="127.0.0.1", port=15432, user="gendesign", + password="2J2SBPMKuS998fiwhtQqDhMI", dbname="gendesign") + cur = pg.cursor() + + # Mortgage rate (Sverdl, latest weighted average) + cur.execute("""SELECT period, value FROM cbr_mortgage_series + WHERE region='sverdl' AND title ILIKE '%ставка%ипотечн%' + ORDER BY period DESC LIMIT 1""") + r = cur.fetchone() + if r: + local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period) + VALUES ('mortgage_rate_sverdl', ?, + 'Средневзв. ставка по ипотеке (Свердл, %)', ?)""", + (float(r[1]), r[0])) + print(f"Mortgage rate Sverdl ({r[0]}): {r[1]}%") + + # City average POI density per домрф_kn (для prod-стиля POI factor) + cur.execute("""SELECT count(*)::float / (SELECT count(DISTINCT obj_id) FROM domrf_kn_infrastructure) + FROM domrf_kn_infrastructure + WHERE distance_m <= 1000""") + avg_poi = cur.fetchone()[0] + local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period) + VALUES ('city_avg_poi_1km', ?, 'Средний POI/ЖК в 1км (Ekb)', '2026-05')""", + (avg_poi,)) + print(f"City avg POI 1km: {avg_poi:.1f}") + + pg.close() + + # ---- 2. Velocity trend per district (6mo vs prior 6mo) ---- + today = dt.date.today() + cut_recent = (today - dt.timedelta(days=180)).isoformat() + cut_prior = (today - dt.timedelta(days=360)).isoformat() + + rows = local.execute(""" + SELECT district, register_date, project, corpus + FROM objective_lots + WHERE district IS NOT NULL AND register_date IS NOT NULL""").fetchall() + by_d = {} + for d, r, p, c in rows: + try: rd = dt.date.fromisoformat(r[:10]) + except: continue + bucket = "recent" if r >= cut_recent else ("prior" if r >= cut_prior else None) + if not bucket: continue + by_d.setdefault(d, {"recent":[], "prior":[], "corpuses":set()}) + by_d[d][bucket].append(rd) + by_d[d]["corpuses"].add((p, c)) + + for d, info in by_d.items(): + n_corp = max(len(info["corpuses"]), 1) + v_rec = len(info["recent"]) / 6 / n_corp + v_prior = len(info["prior"]) / 6 / n_corp + ratio = (v_rec / v_prior) if v_prior > 0 else (None if v_rec == 0 else 2.0) + # Prod-style clamping + trend_factor = max(0.7, min(2.0, ratio)) if ratio else 1.0 + # sat_factor: sold_pct in district. >50% = mature market (prod logic +30% + # multiplier on velocity for mature districts since absorption is proven) + sold_pct = local.execute( + "SELECT real_sold_pct FROM district_economics WHERE district=?", (d,)).fetchone() + sat_factor = 1 + ((sold_pct[0] or 50) - 50) / 100 * 0.30 if sold_pct and sold_pct[0] else 1.0 + local.execute("""UPDATE district_economics + SET real_velocity_6mo=?, real_velocity_prior_6mo=?, + real_trend_ratio=?, sat_factor=?, trend_factor=? + WHERE district=?""", + (v_rec, v_prior, ratio, sat_factor, trend_factor, d)) + + # price_factor = district median / city median + city_med = local.execute( + "SELECT real_median_price_m2 FROM district_economics ORDER BY real_n_lots DESC LIMIT 1" + ).fetchone() + # actually weighted by lots + weighted_rows = local.execute( + "SELECT real_median_price_m2, real_n_lots FROM district_economics WHERE real_median_price_m2 IS NOT NULL" + ).fetchall() + if weighted_rows: + s = sum((p or 0) * (n or 0) for p, n in weighted_rows) + wn = sum(n or 0 for _, n in weighted_rows) + city_med = s / wn if wn else 100 + else: + city_med = 100 + local.execute("""INSERT OR REPLACE INTO macro_context(key,value,label,period) + VALUES ('city_med_price_m2', ?, 'Средневзв. цена м² Ekb (тыс ₽)', '2026-05')""", + (city_med,)) + print(f"City weighted median price: {city_med:.1f} тыс ₽/м²") + + local.execute("""UPDATE district_economics + SET price_factor = real_median_price_m2 / ? + WHERE real_median_price_m2 IS NOT NULL""", (city_med,)) + local.commit() + + # ---- 3. Default weights ---- + weights = [ + ("education", 0.18, "Школы, садики, ВУЗы"), + ("health", 0.10, "Аптеки, поликлиники, больницы"), + ("retail", 0.13, "Магазины (большие/средние/малые)"), + ("transit", 0.15, "Метро, трамвай, автобус"), + ("leisure", 0.09, "Парки, площадки, спорт"), + ("economic", 0.30, "Цена, скорость продаж, тренд (Объектив)"), + ("market", 0.05, "Конкурентная плотность, sat/trend факторы"), + ] + local.execute("DELETE FROM scoring_weights") + for c, w, n in weights: + local.execute("INSERT INTO scoring_weights VALUES (?,?,?)", (c, w, n)) + local.commit() + + print("\nDistrict trend (top 10 by recent velocity):") + print(f"{'район':<22}{'price_f':>8}{'sat':>6}{'trend':>6}{'v_rec':>6}{'v_pr':>6}{'ratio':>6}") + for r in local.execute("""SELECT district, price_factor, sat_factor, trend_factor, + real_velocity_6mo, real_velocity_prior_6mo, real_trend_ratio + FROM district_economics + WHERE real_velocity_6mo IS NOT NULL + ORDER BY real_velocity_6mo DESC LIMIT 10""").fetchall(): + d,pf,sf,tf,vr,vp,rr = r + 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}") + + local.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/10_score_v2.py b/site-finder/10_score_v2.py new file mode 100644 index 00000000..5fb39c8d --- /dev/null +++ b/site-finder/10_score_v2.py @@ -0,0 +1,191 @@ +"""Scoring v2 — uses prod-style macro factors + per-flat metrics from Objective. + +Components (all 0..100): + education 20% школы (×1) + садики (×1) + ВУЗы (×0.3) — distance score + health 10% аптеки (×1) + поликлиники (×1) + больницы (×0.5) + retail 13% max(big, 0.7×med) — distance score + transit 15% max(metro, 0.85×tram, 0.7×bus) — distance score + leisure 9% парки + площадки + спорт + economic 30% real_*: price_position 50% + velocity_real 25% + liquidity 25% + velocity adjusted by trend_factor (clamp 0.7..2.0, prod-style) + market 3% competitive density (jk_count_1km) + saturation factor + (informational; small weight) + +Macro context shown alongside but not in score: + mortgage_rate_sverdl, city_avg_poi_1km, city_med_price_m2. +""" +import sqlite3, pathlib, math +DB = pathlib.Path(__file__).parent / "analysis.db" + +EDU = [("kindergarten", 300, 1000, 1.0), + ("school", 400, 1500, 1.0), + ("university", 1000, 5000, 0.3)] +HEALTH = [("pharmacy", 300, 1000, 1.0), + ("clinic", 500, 2000, 1.0), + ("hospital", 1500, 5000, 0.5)] +RETAIL_BIG = [("shop_big", 500, 2000, 1.0)] +RETAIL_MED = [("shop_med", 300, 1000, 1.0)] +TRANSIT_M = [("metro", 1000, 3000, 1.0)] +TRANSIT_T = [("tram_stop", 400, 1500, 1.0)] +TRANSIT_B = [("bus_stop", 200, 800, 1.0)] +LEISURE = [("park", 500, 2000, 1.0), + ("playground", 200, 700, 1.0), + ("sports", 500, 2000, 0.7)] + + +def dist_score(d_m, ideal, mx): + if d_m is None: return 0.0 + if d_m <= ideal: return 100.0 + if d_m >= mx: return 0.0 + return 100.0 * (mx - d_m) / (mx - ideal) + + +def comp(conn, sid, cats): + tw = sum(c[3] for c in cats); s = 0 + for cat, ideal, mx, w in cats: + d = conn.execute("SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?", + (sid, cat)).fetchone()[0] + s += w * dist_score(d, ideal, mx) + return s / tw if tw else 0 + + +def hav(la1, lo1, la2, lo2): + R = 6371000; p1, p2 = math.radians(la1), math.radians(la2) + dp = math.radians(la2-la1); dl = math.radians(lo2-lo1) + a = math.sin(dp/2)**2 + math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2*R*math.asin(math.sqrt(a)) + + +def main(): + conn = sqlite3.connect(DB) + weights = {r[0]: r[1] for r in conn.execute("SELECT component, weight FROM scoring_weights").fetchall()} + if not weights: + weights = {"education":0.18,"health":0.10,"retail":0.13,"transit":0.15, + "leisure":0.09,"economic":0.30,"market":0.05} + print("Weights:", weights) + + sites = conn.execute("SELECT site_id, lat, lon FROM sites").fetchall() + site_coords = {sid: (la, lo) for sid, la, lo in sites} + + # Bounds for normalization (computed once) + prices = sorted([r[0] for r in conn.execute( + "SELECT real_median_price_m2 FROM district_economics WHERE real_median_price_m2 IS NOT NULL").fetchall()]) + pmin, pmax = (prices[0], prices[-1]) if prices else (100, 200) + if pmax <= pmin: pmax = pmin + 1 + + vels = sorted([r[0] for r in conn.execute( + "SELECT real_velocity_6mo FROM district_economics WHERE real_velocity_6mo IS NOT NULL").fetchall()]) + vmax = vels[int(len(vels) * 0.9)] if vels else 8 + + # Citywide median jk_count_1km — for market component + counts = sorted([r[0] for r in conn.execute( + "SELECT count(*) FROM sites s2 JOIN sites s ON s2.site_id != s.site_id " + "WHERE 1=0 GROUP BY s.site_id").fetchall()]) # noop, computed below + + conn.execute("DELETE FROM features") + conn.execute("DELETE FROM scores") + conn.execute("DELETE FROM scores_total") + + for sid, lat, lon in sites: + # POI features + for cat in ["kindergarten","school","university","pharmacy","clinic","hospital", + "shop_big","shop_med","shop_small","bus_stop","tram_stop","metro", + "park","playground","sports"]: + n = conn.execute("SELECT MIN(distance_m) FROM pois WHERE site_id=? AND category=?", + (sid, cat)).fetchone()[0] + c500 = conn.execute("SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=500", + (sid, cat)).fetchone()[0] + c1k = conn.execute("SELECT count(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=1000", + (sid, cat)).fetchone()[0] + for k, v in (("nearest_m", n), ("count_500m", c500), ("count_1km", c1k)): + conn.execute("INSERT INTO features(site_id,feature,value) VALUES (?,?,?)", + (sid, f"{cat}_{k}", float(v) if v is not None else None)) + + # Locational components + edu = comp(conn, sid, EDU) + health = comp(conn, sid, HEALTH) + retail = max(comp(conn, sid, RETAIL_BIG), 0.7 * comp(conn, sid, RETAIL_MED)) + transit = max(comp(conn, sid, TRANSIT_M), + 0.85 * comp(conn, sid, TRANSIT_T), + 0.7 * comp(conn, sid, TRANSIT_B)) + leisure = comp(conn, sid, LEISURE) + + # Economic + market + econ_row = conn.execute("""SELECT de.real_median_price_m2, de.real_velocity_6mo, + de.real_trend_ratio, de.months_to_sellout, + de.sat_factor, de.real_sold_pct, de.real_n_lots + FROM site_district sd + JOIN district_economics de USING (district) + WHERE sd.site_id=?""", (sid,)).fetchone() + if econ_row: + price, v_rec, trend, mts, sat, sold_pct, n_lots = econ_row + # price_score: linear by district median price percentile + p_score = max(0, min(100, ((price or 0) - pmin) * 100 / (pmax - pmin))) + # velocity_score: real recent (6mo), capped at p90 + v_score = max(0, min(100, (v_rec or 0) * 100 / vmax)) if vmax else 0 + # trend modifier (prod-style): clamp 0.7..2.0 → 0.5..1.0 multiplier on velocity + tf = max(0.7, min(2.0, trend or 1.0)) + v_score = v_score * (0.5 + 0.5 * (tf / 2.0)) # 0.7→0.675, 1.0→0.75, 2.0→1.0 + # liquidity_score from months_to_sellout + liq_score = max(0, 100 - min(mts or 24, 24) * 100 / 24) if mts else 50 + economic = 0.50 * p_score + 0.25 * v_score + 0.25 * liq_score + else: + economic = 0; trend = None; sat = None; sold_pct = None; n_lots = None + + # Competitive density: number of OTHER ЖК within 1km + n_jk_1km = sum( + 1 for sid2, (la2, lo2) in site_coords.items() + if sid2 != sid and sid2.startswith("jk:") and hav(lat, lon, la2, lo2) <= 1000 + ) + # market component: 50/50 saturation × density penalty + # density: 0..15 jks → 100..0 (linearly capped) + density_score = max(0, 100 - n_jk_1km * 100 / 15) + # saturation: optimal 50% sold (proven absorption, room to grow). 0% raw, 100% over-saturated + sat_score = 100 - abs((sold_pct or 50) - 50) * 2 if sold_pct is not None else 50 + market = 0.5 * density_score + 0.5 * sat_score + conn.execute("INSERT INTO features(site_id,feature,value) VALUES (?,?,?)", + (sid, "jk_count_1km", n_jk_1km)) + + comps = {"education": edu, "health": health, "retail": retail, + "transit": transit, "leisure": leisure, "economic": economic, + "market": market} + for c, val in comps.items(): + conn.execute("INSERT INTO scores(site_id,component,score_0_100) VALUES (?,?,?)", + (sid, c, val)) + weighted = sum(weights.get(k, 0) * v for k, v in comps.items()) + conn.execute("INSERT INTO scores_total(site_id,weighted) VALUES (?,?)", (sid, weighted)) + + conn.commit() + + # Ranks + rows = conn.execute("""SELECT s.site_id, st.weighted FROM sites s + JOIN scores_total st USING (site_id) + ORDER BY st.weighted DESC""").fetchall() + for rank, (sid, _) in enumerate(rows, 1): + conn.execute("UPDATE scores_total SET rank_overall=? WHERE site_id=?", (rank, sid)) + # district rank + by_dist = {} + for sid, _ in rows: + d = conn.execute("SELECT district FROM site_district WHERE site_id=?", (sid,)).fetchone() + by_dist.setdefault(d[0] if d else "—", []).append(sid) + for d, sids in by_dist.items(): + for rank, sid in enumerate(sids, 1): + conn.execute("UPDATE scores_total SET rank_district=? WHERE site_id=?", (rank, sid)) + conn.commit() + + # Print parcel summary + pid = "parcel:66:41:0204016:10" + p = conn.execute("SELECT weighted, rank_overall, rank_district FROM scores_total WHERE site_id=?", + (pid,)).fetchone() + print(f"\n=== Parcel ===") + print(f" total: {p[0]:.2f}/100 overall #{p[1]} district #{p[2]}") + print(f" components:") + for r in conn.execute("SELECT component, score_0_100 FROM scores WHERE site_id=? ORDER BY component", (pid,)).fetchall(): + wt = weights.get(r[0], 0) + print(f" {r[0]:<10} {r[1]:>6.1f} × {wt*100:>4.0f}% = {r[1]*wt:>5.2f}") + n_jk = conn.execute("SELECT count(*) FROM sites WHERE kind='jk'").fetchone()[0] + print(f" vs {n_jk} строящихся ЖК Ekb") + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/11_refetch_pois_extended.py b/site-finder/11_refetch_pois_extended.py new file mode 100644 index 00000000..70b75b1a --- /dev/null +++ b/site-finder/11_refetch_pois_extended.py @@ -0,0 +1,114 @@ +"""Refetch OSM POIs adding social/lifestyle categories. + +New categories: + cafe amenity=cafe + restaurant amenity=restaurant | fast_food + fuel amenity=fuel + atm amenity=atm + post amenity=post_office + worship amenity=place_of_worship + trolley_stop highway=bus_stop + bus=yes; OR amenity=bus_station + police amenity=police + fire amenity=fire_station + library amenity=library + +Existing 15 stay; only fetch new and append. +Recompute distances + features. +""" +import sqlite3, pathlib, requests, time, json, math, urllib3 +urllib3.disable_warnings() + +DB = pathlib.Path(__file__).parent / "analysis.db" +CACHE = pathlib.Path(__file__).parent / "cache" / "overpass_raw.json" + +NEW_QUERIES = [ + ("cafe", '["amenity"="cafe"]'), + ("restaurant", '["amenity"~"^(restaurant|fast_food)$"]'), + ("fuel", '["amenity"="fuel"]'), + ("atm", '["amenity"="atm"]'), + ("post", '["amenity"="post_office"]'), + ("worship", '["amenity"="place_of_worship"]'), + ("library", '["amenity"="library"]'), + ("police", '["amenity"="police"]'), + ("fire", '["amenity"="fire_station"]'), + ("bank", '["amenity"="bank"]'), +] +ENDPOINTS = [ + "https://overpass-api.de/api/interpreter", + "https://overpass.kumi.systems/api/interpreter", + "https://overpass.openstreetmap.ru/api/interpreter", +] + +def hav(la1,lo1,la2,lo2): + R=6371000;p1,p2=math.radians(la1),math.radians(la2) + dp=math.radians(la2-la1);dl=math.radians(lo2-lo1) + a=math.sin(dp/2)**2+math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2*R*math.asin(math.sqrt(a)) + +def overpass(filt, b): + q = f"[out:json][timeout:120];(node{filt}({b[0]},{b[1]},{b[2]},{b[3]});way{filt}({b[0]},{b[1]},{b[2]},{b[3]}););out center tags;" + last = None + for ep in ENDPOINTS: + try: + r = requests.post(ep, data={"data": q}, timeout=180, verify=False, + headers={"User-Agent":"gendesign/0.5"}) + if r.ok: return r.json().get("elements", []) + last = f"{ep} {r.status_code}" + time.sleep(2) + except Exception as e: + last = f"{ep} {e}"; time.sleep(2) + raise RuntimeError(last) + +def main(): + cache = json.loads(CACHE.read_text()) if CACHE.exists() else {} + conn = sqlite3.connect(DB) + sites = conn.execute("SELECT site_id, lat, lon FROM sites").fetchall() + lats=[s[1] for s in sites]; lons=[s[2] for s in sites] + b = (min(lats)-0.05, min(lons)-0.05, max(lats)+0.05, max(lons)+0.05) + print(f"BBox: {b}") + + for cat, filt in NEW_QUERIES: + if cat in cache: + print(f" {cat:<12} cached: {len(cache[cat])}") + continue + print(f" {cat:<12} fetching ...", end=" ", flush=True) + try: + elems = overpass(filt, b) + cache[cat] = elems + print(f"{len(elems)}") + except Exception as e: + print(f"FAIL {e}") + cache[cat] = [] + time.sleep(2) + + CACHE.write_text(json.dumps(cache, ensure_ascii=False)) + print(f"\nCache saved: {CACHE.stat().st_size/1024:.0f} KB ({len(cache)} categories)") + + # Recompute pois table from full cache + conn.execute("DELETE FROM pois") + n=0 + for cat, elems in cache.items(): + for el in elems: + if el["type"]=="node": + la,lo=el.get("lat"),el.get("lon") + else: + c=el.get("center") or {}; la,lo=c.get("lat"),c.get("lon") + if la is None: continue + tags=el.get("tags") or {} + name = tags.get("name") or tags.get("operator") or "" + for sid,sla,slo in sites: + d = hav(sla,slo,la,lo) + if d <= 2000: + conn.execute("""INSERT INTO pois(site_id,category,osm_type,osm_id,name,lat,lon,distance_m,raw_tags) + VALUES (?,?,?,?,?,?,?,?,?)""", + (sid,cat,el["type"],str(el["id"]),name,la,lo,d,json.dumps(tags,ensure_ascii=False))) + n+=1 + conn.commit() + print(f"\nrecomputed {n} site-poi pairs") + print("\nPOI counts per category:") + for cat,c in conn.execute("SELECT category,count(*) FROM pois GROUP BY 1 ORDER BY 2 DESC").fetchall(): + print(f" {cat:<14} {c}") + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/12_more_pois.py b/site-finder/12_more_pois.py new file mode 100644 index 00000000..8e6b9ca2 --- /dev/null +++ b/site-finder/12_more_pois.py @@ -0,0 +1,102 @@ +"""Add more POI categories: parking, gym/fitness, theater, taxi, marketplaces, etc.""" +import sqlite3, pathlib, requests, time, json, math, urllib3 +urllib3.disable_warnings() + +DB = pathlib.Path(__file__).parent / "analysis.db" +CACHE = pathlib.Path(__file__).parent / "cache" / "overpass_raw.json" + +NEW_QUERIES = [ + ("parking", '["amenity"="parking"]'), + ("gym", '["leisure"="fitness_centre"]'), + ("theater", '["amenity"="theatre"]'), + ("cinema", '["amenity"="cinema"]'), + ("marketplace", '["amenity"="marketplace"]'), + ("museum", '["tourism"="museum"]'), + ("hotel", '["tourism"~"^(hotel|hostel|apartment)$"]'), + ("nightclub", '["amenity"~"^(nightclub|bar|pub)$"]'), + ("car_wash", '["amenity"="car_wash"]'), + ("car_rental", '["amenity"~"^(car_rental|taxi)$"]'), + ("vet", '["amenity"="veterinary"]'), + ("courier", '["amenity"~"^(parcel_locker|post_box)$"]'), + ("kindergarten_priv", '["amenity"="kindergarten"]["fee"="yes"]'), # platnyye + ("dentist", '["amenity"="dentist"]'), + ("childcare", '["amenity"="childcare"]'), +] +ENDPOINTS = [ + "https://overpass-api.de/api/interpreter", + "https://overpass.kumi.systems/api/interpreter", +] + +def hav(la1,lo1,la2,lo2): + R=6371000;p1,p2=math.radians(la1),math.radians(la2) + dp=math.radians(la2-la1);dl=math.radians(lo2-lo1) + a=math.sin(dp/2)**2+math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2*R*math.asin(math.sqrt(a)) + +def overpass(filt, b): + q = f'[out:json][timeout:120];(node{filt}({b[0]},{b[1]},{b[2]},{b[3]});way{filt}({b[0]},{b[1]},{b[2]},{b[3]}););out center tags;' + last = None + for ep in ENDPOINTS: + try: + r = requests.post(ep, data={"data": q}, timeout=180, verify=False, + headers={"User-Agent":"gendesign/0.7"}) + if r.ok: return r.json().get("elements", []) + last = f"{ep} {r.status_code}" + time.sleep(2) + except Exception as e: + last = f"{ep} {e}"; time.sleep(2) + raise RuntimeError(last) + +def main(): + cache = json.loads(CACHE.read_text()) if CACHE.exists() else {} + conn = sqlite3.connect(DB) + sites = conn.execute("SELECT site_id, lat, lon FROM sites").fetchall() + lats=[s[1] for s in sites]; lons=[s[2] for s in sites] + b = (min(lats)-0.005, min(lons)-0.005, max(lats)+0.005, max(lons)+0.005) + + for cat, filt in NEW_QUERIES: + if cat in cache: + print(f" {cat:<20} cached: {len(cache[cat])}") + continue + print(f" {cat:<20} fetching...", end=" ", flush=True) + try: + elems = overpass(filt, b) + cache[cat] = elems + print(f"{len(elems)}") + except Exception as e: + print(f"FAIL {e}") + cache[cat] = [] + time.sleep(2) + + CACHE.write_text(json.dumps(cache, ensure_ascii=False)) + print(f"\nCache: {len(cache)} categories, {CACHE.stat().st_size/1024:.0f} KB") + + # rebuild pois table + conn.execute("DELETE FROM pois") + n = 0 + for cat, elems in cache.items(): + for el in elems: + if el["type"] == "node": + la, lo = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {} + la, lo = c.get("lat"), c.get("lon") + if la is None: continue + tags = el.get("tags") or {} + name = tags.get("name") or tags.get("operator") or "" + for sid, sla, slo in sites: + d = hav(sla, slo, la, lo) + if d <= 2000: + conn.execute("""INSERT INTO pois(site_id,category,osm_type,osm_id,name,lat,lon,distance_m,raw_tags) + VALUES (?,?,?,?,?,?,?,?,?)""", + (sid, cat, el["type"], str(el["id"]), name, la, lo, d, json.dumps(tags, ensure_ascii=False))) + n += 1 + conn.commit() + print(f"recomputed {n} site-poi pairs") + print("\nTop categories:") + for c, n in conn.execute("SELECT category, count(*) FROM pois GROUP BY 1 ORDER BY 2 DESC LIMIT 25").fetchall(): + print(f" {c:<20} {n}") + conn.close() + +if __name__ == "__main__": + main() diff --git a/site-finder/cache/ekb_admin_districts.geojson b/site-finder/cache/ekb_admin_districts.geojson new file mode 100644 index 00000000..f3dc2069 --- /dev/null +++ b/site-finder/cache/ekb_admin_districts.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {"name": "Верх-Исетский"}, "geometry": {"type": "MultiPolygon", "coordinates": [[[[60.6034442, 56.8383183], [60.5948514, 56.8373725], [60.5947656, 56.8375895], [60.5942033, 56.8375374], [60.5872823, 56.8368374], [60.5818978, 56.8362896], [60.5819774, 56.8360833], [60.5843569, 56.8299117], [60.5871894, 56.8214705], [60.5879831, 56.8189513], [60.5880215, 56.8185682], [60.5881719, 56.8181096], [60.5886761, 56.8158616], [60.588377, 56.8158353], 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"externalKey": "66:41:0204016:10", + "interactionId": 103594786, + "label": "66:41:0204016:10", + "options": { + "area": null, + "cad_num": "66:41:0204016:10", + "cost_application_date": "2023-01-01", + "cost_approvement_date": "", + "cost_determination_date": "2022-01-01", + "cost_index": 8497.03, + "cost_registration_date": "2023-01-03", + "cost_value": 23706713.7, + "declared_area": null, + "determination_couse": "Акт об утверждении результатов определения кадастровой стоимости, полученных в соответствии с положениями Закона 237-ФЗ\n\n\n\n", + "land_record_area": 2790, + "land_record_category_type": "Земли населенных пунктов", + "land_record_reg_date": "2006-03-06", + "land_record_subtype": "Землепользование", + "land_record_type": "Земельный участок", + "ownership_type": "Частная", + "permitted_use_established_by_document": "магазины (общей площадью до 5000 кв.м), общественное питание, бытовое обслуживание, деловое управление", + "previously_posted": "Ранее учтенный", + "quarter_cad_number": "66:41:0204016", + "readable_address": "Российская Федерация, Свердловская область, муниципальное образование \"город Екатеринбург\", город Екатеринбург, улица Билимбаевская, 3", + "right_type": "Собственность", + "specified_area": 2790, + "status": "Ранее учтенный" + }, + "subcategory": 5, + "systemInfo": { + "inserted": "2023-11-07T18:40:05.722318", + "insertedBy": "7EF5CD0B-AC08-4097-AF2B-07CD85992A79", + "updated": "2026-02-03T04:59:16.064005", + "updatedBy": "7EF5CD0B-AC08-4097-AF2B-07CD85992A79" + } + } + } + ], + "_centroid": { + "lat": 56.878729832837834, + "lon": 60.5226773243924 + }, + "_area_m2": 2788.7, + "_source": "rosreestr2coord", + "_props": { + "area": null, + "cad_num": "66:41:0204016:10", + "cost_application_date": "2023-01-01", + "cost_approvement_date": "", + "cost_determination_date": "2022-01-01", + "cost_index": 8497.03, + "cost_registration_date": "2023-01-03", + "cost_value": 23706713.7, + "declared_area": null, + "determination_couse": "Акт об утверждении результатов определения кадастровой стоимости, полученных в соответствии с положениями Закона 237-ФЗ\n\n\n\n", + "land_record_area": 2790, + "land_record_category_type": "Земли населенных пунктов", + "land_record_reg_date": "2006-03-06", + "land_record_subtype": "Землепользование", + "land_record_type": "Земельный участок", + "ownership_type": "Частная", + "permitted_use_established_by_document": "магазины (общей площадью до 5000 кв.м), общественное питание, бытовое обслуживание, деловое управление", + "previously_posted": "Ранее учтенный", + "quarter_cad_number": "66:41:0204016", + "readable_address": "Российская Федерация, Свердловская область, муниципальное образование \"город Екатеринбург\", город Екатеринбург, улица Билимбаевская, 3", + "right_type": "Собственность", + "specified_area": 2790, + "status": "Ранее учтенный" + } +} \ No newline at end of file diff --git a/site-finder/cache/parcel_polygons/README.md b/site-finder/cache/parcel_polygons/README.md new file mode 100644 index 00000000..f7a3d974 --- /dev/null +++ b/site-finder/cache/parcel_polygons/README.md @@ -0,0 +1,11 @@ +# Parcel polygons cache + +Drop `.geojson` here when you have NSPD polygon for a parcel +(e.g., manually exported from nspd.gov.ru after trusting the Минцифры cert). + +Format: standard GeoJSON Feature or FeatureCollection in EPSG:4326 (WGS84). +The pipeline (`01_load_sites.py`) will pick it up next run, store the +geometry, and use polygon centroid as the parcel point. + +If a polygon is loaded, the JSON report adds a `geometry` field with +the polygon for map rendering. diff --git a/site-finder/db_init.py b/site-finder/db_init.py new file mode 100644 index 00000000..0b65c295 --- /dev/null +++ b/site-finder/db_init.py @@ -0,0 +1,87 @@ +"""Local SQLite DB for parcel scoring analysis. + +Mirrors the logic the prod gendesign server uses: +- domrf_kn_objects (subset: under-construction ЖК in Ekb with coords) +- domrf_kn_infrastructure (POI cache) +- ekb_districts (geometry, median price) +- recommend_mix output (per-parcel score) + +We don't need the full ~3.2GB warehouse — only what the scorer touches. +""" +import sqlite3, pathlib + +DB = pathlib.Path(__file__).parent / "analysis.db" + +SCHEMA = """ +CREATE TABLE IF NOT EXISTS sites ( + site_id TEXT PRIMARY KEY, -- 'parcel:66:41:0204016:10' or 'obj:NNN' + kind TEXT NOT NULL, -- 'parcel' | 'jk' + name TEXT, + address TEXT, + district TEXT, + obj_class TEXT, + developer TEXT, + flat_count INTEGER, + square_living REAL, + ready_dt TEXT, + obj_status TEXT, + lat REAL NOT NULL, + lon REAL NOT NULL, + geom_geojson TEXT, -- nullable polygon as raw GeoJSON + obj_id INTEGER -- domrf_kn obj_id when kind='jk' +); +CREATE INDEX IF NOT EXISTS sites_kind_idx ON sites(kind); +CREATE INDEX IF NOT EXISTS sites_district_idx ON sites(district); + +CREATE TABLE IF NOT EXISTS pois ( + poi_id INTEGER PRIMARY KEY AUTOINCREMENT, + site_id TEXT NOT NULL REFERENCES sites(site_id), + category TEXT NOT NULL, -- 'kindergarten','school','shop_supermarket', ... + osm_type TEXT, + osm_id TEXT, + name TEXT, + lat REAL NOT NULL, + lon REAL NOT NULL, + distance_m REAL NOT NULL, + raw_tags TEXT +); +CREATE INDEX IF NOT EXISTS pois_site_cat ON pois(site_id, category); + +CREATE TABLE IF NOT EXISTS features ( + site_id TEXT NOT NULL REFERENCES sites(site_id), + feature TEXT NOT NULL, -- 'kindergarten_nearest_m','schools_in_1km','transit_500m', ... + value REAL, + PRIMARY KEY (site_id, feature) +); + +CREATE TABLE IF NOT EXISTS scores ( + site_id TEXT NOT NULL REFERENCES sites(site_id), + component TEXT NOT NULL, -- 'education','retail','health','transit','leisure' + score_0_100 REAL NOT NULL, + PRIMARY KEY (site_id, component) +); + +CREATE TABLE IF NOT EXISTS scores_total ( + site_id TEXT PRIMARY KEY REFERENCES sites(site_id), + weighted REAL NOT NULL, + rank_overall INTEGER, + rank_district INTEGER +); + +CREATE TABLE IF NOT EXISTS run_log ( + run_id INTEGER PRIMARY KEY AUTOINCREMENT, + started_at TEXT DEFAULT CURRENT_TIMESTAMP, + finished_at TEXT, + notes TEXT +); +""" + +if __name__ == "__main__": + conn = sqlite3.connect(DB) + conn.executescript(SCHEMA) + conn.commit() + print(f"DB ready at {DB}") + for tbl in ['sites','pois','features','scores','scores_total','run_log']: + n = conn.execute(f"SELECT count(*) FROM {tbl}").fetchone()[0] + print(f" {tbl}: {n} rows") + conn.close() diff --git a/site-finder/reports/parcel_66_41_0204016_10.html b/site-finder/reports/parcel_66_41_0204016_10.html new file mode 100644 index 00000000..62fab43e --- /dev/null +++ b/site-finder/reports/parcel_66_41_0204016_10.html @@ -0,0 +1,77 @@ + + +Анализ участка Участок 66:41:0204016:10 + +

Анализ участка 66:41:0204016:10

+
Сгенерировано 2026-05-08T00:34:04 · Сравнение с 380 строящимися ЖК Екатеринбурга
+ +
+ Координаты участка получены из ссылки на NSPD-карту (EPSG:3857 → WGS84): + 56.878913, 60.522615. Район — центрально-северная часть ЕКБ (Пионерский / Втузгородок). +
+ +
+
Итоговый балл72.1из 100
+
Ранг по ЕКБ#183 / 381
+
Перцентиль52%
+
Медиана ЖК70.9
+
Топ-25% ЖК ≥81.7
+
+ +

Компоненты (взвешенные)

+ +
КомпонентУчастокМедиана ЖКP75 ЖКΔ vs медиана
economic40.247.259.5-7.0
education79.575.994.7+3.6
health88.783.298.3+5.5
leisure98.486.196.9+12.3
retail100.0100.0100.0+0.0
transit69.770.085.0-0.3
+
Веса: образование 20% · здоровье 10% · ритейл 15% · транспорт 15% · досуг 10% · экономика 30%
+ +

Экономика района (Объектив API, последние 90 дней)

+ + + + + + + + + + + +
Район (Объектив)Старая Сортировка (по голосованию 5 ближайших ЖК (расст. до репрезентанта 492 м))
Проектов в районе6
Лотов всего / продано8,356 / 4,078 (48.8%)
Цена за м² (медиана)114.9 тыс ₽ · P25=96.1 · P75=132.7
Средняя площадь сделки49.0 м²
Скорость продаж (real)2.7 зарег. ДДУ/корпус/мес (по 12 мес)
Средняя готовность75.8%
Цена corp_sum (взвеш.)128.3 тыс ₽/м² · скорость 0.5
Распродажа стока (corp_sum)15.9 мес
+

real_* рассчитаны по 8,356 лотам из Поквартирные/Лоты (303 677 квартир Екб). Это per-flat, основной источник правды.

+ + +

Ближайшие POI вокруг участка

+ +
КатегорияБлижайшийДо негоВ 500 мВ 1 км
kindergartenДетский сад № 357380 м1.04.0
schoolШкола № 122792 м0.03.0
universityЖелезнодорожный техникум906 м0.01.0
pharmacyВита Экспресс471 м1.08.0
clinicИнвитро450 м1.06.0
hospitalДорожная больница станции Екатеринбург-Пассажирский1761 м0.00.0
shop_bigМагнит20 м3.010.0
shop_medКировский450 м1.012.0
shop_small——0.00.0
bus_stopБилимбаевская320 м4.020.0
tram_stopРасточная улица598 м0.08.0
metro——0.00.0
park—566 м0.03.0
playground—163 м12.022.0
sports—243 м7.016.0
+ +

10 ближайших ЖК (для прямого бенчмарка)

+ +
Расст.НазваниеРайонДевелоперКлассБаллРанг
492 мКвартал "Траектория"ЖелезнодорожныйБрусника66.6#223
994 мКвартал ДепоЖелезнодорожныйБрусника69.5#206
994 мКвартал ДепоЖелезнодорожныйБрусника73.3#176
1036 мАстон. ДвижениеЖелезнодорожныйАстон64.3#240
1211 мЖилой Комплекс "Раута" ЖелезнодорожныйЭталон72.0#186
1312 мЖилой Комплекс "Раута" ЖелезнодорожныйЭталон74.4#168
3065 мЖК Белая БашняОрджоникидзевскийРивьера Инвест Екатеринбург81.2#106
3596 мЖК "ПАЙЕР"ЖелезнодорожныйСамолет58.8#267
3612 мЖК ТЕМПОрджоникидзевскийПрактика81.4#104
4021 мЖК "Парк Победы"ОрджоникидзевскийPRINZIP80.3#118
+ +

Топ-10 ЖК ЕКБ по локационной привлекательности

+ +
РангНазваниеРайонДевелоперКлассБалл
#1—ЖелезнодорожныйТЭН91.2
#2Квартал «Лайв»ЛенинскийАтомстройкомплекс91.0
#3Клубный дом инженера ЯтесаЖелезнодорожныйТЭН91.0
#4Клубный дом инженера ЯтесаЖелезнодорожныйТЭН90.7
#5Квартал "На Некрасова"ЖелезнодорожныйБрусника90.6
#6ЖК "Азина 16"ЖелезнодорожныйЛСР90.4
#7Жилой дом "ТЕТРО"ЖелезнодорожныйДевелоперская компания "Люди"90.2
#8Жилой квартал «Тихий центр» 1 оч. 1 этапЖелезнодорожныйСинара-Девелопмент90.1
#9ЖК "Кайдзен"Верх-ИсетскийАстра90.0
#10ЖК "Азина 16"ЖелезнодорожныйЛСР89.7
+ +

Методика

+

Источник POI: OpenStreetMap (Overpass API), bbox по всем 381 ЖК Свердл.

+

Логика: для каждой категории — расстояние-в-балл (piecewise linear от ideal_m к max_m), +далее агрегация в 5 компонент с весами (max-pool там, где категории альтернативны: ритейл = max(big, 0.7×med); транспорт = max(metro, 0.85×tram, 0.7×bus)). +Финальный балл — взвешенная сумма компонент.

+

База данных: локальная SQLite analysis.db (sites/pois/features/scores), под-выборка строящихся ЖК ЕКБ из прода domrf_kn_objects.

+ \ No newline at end of file diff --git a/site-finder/reports/parcel_66_41_0204016_10.json b/site-finder/reports/parcel_66_41_0204016_10.json new file mode 100644 index 00000000..5ea600c4 --- /dev/null +++ b/site-finder/reports/parcel_66_41_0204016_10.json @@ -0,0 +1,748 @@ +{ + "generated_at": "2026-05-08T00:34:04", + "parcel": { + "site_id": "parcel:66:41:0204016:10", + "kind": "parcel", + "name": "Участок 66:41:0204016:10", + "address": "г. Екатеринбург, кад.№ 66:41:0204016:10 (NSPD coordinate_x=6737346.694, coordinate_y=7735409.767, EPSG:3857)", + "district": null, + "obj_class": null, + "developer": null, + "flat_count": null, + "square_living": null, + "ready_dt": null, + "obj_status": "parcel", + "lat": 56.878913, + "lon": 60.522615, + "obj_id": null, + "geom_geojson": null, + "features": { + "bus_stop_count_1km": 20.0, + "bus_stop_count_500m": 4.0, + "bus_stop_nearest_m": 319.8521044035619, + "clinic_count_1km": 6.0, + "clinic_count_500m": 1.0, + "clinic_nearest_m": 449.74705727658056, + "hospital_count_1km": 0.0, + "hospital_count_500m": 0.0, + "hospital_nearest_m": 1760.9567033538603, + "kindergarten_count_1km": 4.0, + "kindergarten_count_500m": 1.0, + "kindergarten_nearest_m": 379.95653221253724, + "metro_count_1km": 0.0, + "metro_count_500m": 0.0, + "metro_nearest_m": null, + "park_count_1km": 3.0, + "park_count_500m": 0.0, + "park_nearest_m": 565.4776423972781, + "pharmacy_count_1km": 8.0, + "pharmacy_count_500m": 1.0, + "pharmacy_nearest_m": 471.4293930876333, + "playground_count_1km": 22.0, + "playground_count_500m": 12.0, + "playground_nearest_m": 162.85791533155333, + "school_count_1km": 3.0, + "school_count_500m": 0.0, + "school_nearest_m": 792.4938391173765, + "shop_big_count_1km": 10.0, + "shop_big_count_500m": 3.0, + "shop_big_nearest_m": 20.462910702745955, + "shop_med_count_1km": 12.0, + "shop_med_count_500m": 1.0, + "shop_med_nearest_m": 449.68619544132474, + "shop_small_count_1km": 0.0, + "shop_small_count_500m": 0.0, + "shop_small_nearest_m": null, + "sports_count_1km": 16.0, + "sports_count_500m": 7.0, + "sports_nearest_m": 243.3529907625155, + "tram_stop_count_1km": 8.0, + "tram_stop_count_500m": 0.0, + "tram_stop_nearest_m": 598.2477400253116, + "university_count_1km": 1.0, + "university_count_500m": 0.0, + "university_nearest_m": 906.362284697722 + }, + "scores": { + "economic": 40.20396169545286, + "education": 79.52016528424876, + "health": 88.71285351868461, + "leisure": 98.38326808895609, + "retail": 100.0, + "transit": 69.68085645258955 + }, + "weighted_total": 72.12696219413812, + "rank_overall": 183, + "rank_district": 1, + "economics": { + "district": "Старая Сортировка", + "district_method": "knn_vote_5", + "district_dist_m": 491.9035185643666, + "n_projects": 6, + "weighted_price_m2_corp_sum": 128.29226026752744, + "median_price_m2_corp_sum": 132.71, + "deals_per_month_corp_sum": 0.4748201438848921, + "months_to_sellout": 15.931335954809471, + "n_lots": 8356, + "n_sold": 4078, + "sold_pct": 48.803255146002876, + "median_price_m2": 114.88988332384746, + "p25_price_m2": 96.10536852426868, + "p75_price_m2": 132.70871691061976, + "avg_area_sold_m2": 48.96138303089735, + "velocity_per_month": 2.663333333333333, + "avg_readiness_pct": 75.83018190521781 + }, + "nearest_pois": { + "kindergarten": [ + { + "name": "Детский сад № 357", + "distance_m": 380.0, + "lat": 56.8755087, + "lon": 60.5220757 + }, + { + "name": "Детский сад № 132", + "distance_m": 579.1, + "lat": 56.8838508, + "lon": 60.5195843 + }, + { + "name": "Детский сад № 133", + "distance_m": 846.8, + "lat": 56.8805097, + "lon": 60.508987 + }, + { + "name": "Детский сад № 131", + "distance_m": 867.4, + "lat": 56.8746007, + "lon": 60.5107185 + }, + { + "name": "Детский сад № 131 АО \"РЖД\"", + "distance_m": 1116.5, + "lat": 56.8709899, + "lon": 60.5339025 + } + ], + "school": [ + { + "name": "Школа № 122", + "distance_m": 792.5, + "lat": 56.8851571, + "lon": 60.5163261 + }, + { + "name": "Средняя школа № 172", + "distance_m": 857.4, + "lat": 56.8721029, + "lon": 60.5292316 + }, + { + "name": "Средняя общеобразовательная школа № 129", + "distance_m": 871.0, + "lat": 56.8799875, + "lon": 60.508415 + }, + { + "name": "Детская музыкальная школа № 7 им. С. В. Рахманинова", + "distance_m": 1038.8, + "lat": 56.8697152, + "lon": 60.5256114 + }, + { + "name": "Средняя школа № 83", + "distance_m": 1147.0, + "lat": 56.8701653, + "lon": 60.5326188 + } + ], + "university": [ + { + "name": "Железнодорожный техникум", + "distance_m": 906.4, + "lat": 56.8848017, + "lon": 60.5122997 + }, + { + "name": "Екатеринбургский техникум «Автоматика»", + "distance_m": 1445.6, + "lat": 56.8720305, + "lon": 60.5427979 + } + ], + "pharmacy": [ + { + "name": "Вита Экспресс", + "distance_m": 471.4, + "lat": 56.8812191, + "lon": 60.5161039 + }, + { + "name": "Фармленд", + "distance_m": 656.4, + "lat": 56.8808007, + "lon": 60.5123788 + }, + { + "name": "Фармленд", + "distance_m": 733.6, + "lat": 56.8724683, + "lon": 60.5251955 + }, + { + "name": "Аптека апрель", + "distance_m": 796.5, + "lat": 56.871807, + "lon": 60.5242641 + }, + { + "name": "Планета здоровья", + "distance_m": 798.6, + "lat": 56.8717854, + "lon": 60.520997 + } + ], + "clinic": [ + { + "name": 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+ "distance_m": 867.1, + "lat": 56.8711885, + "lon": 60.5206587 + }, + { + "name": "—", + "distance_m": 885.7, + "lat": 56.8713878, + "lon": 60.5273909 + }, + { + "name": "Парк «Семь ключей»", + "distance_m": 1487.0, + "lat": 56.881542, + "lon": 60.4986181 + } + ], + "playground": [ + { + "name": "—", + "distance_m": 162.9, + "lat": 56.8788457, + "lon": 60.5199374 + }, + { + "name": "—", + "distance_m": 170.0, + "lat": 56.8775972, + "lon": 60.5240409 + }, + { + "name": "—", + "distance_m": 173.7, + "lat": 56.8796266, + "lon": 60.5200727 + }, + { + "name": "—", + "distance_m": 216.8, + "lat": 56.8771629, + "lon": 60.5241882 + }, + { + "name": "—", + "distance_m": 220.4, + "lat": 56.8808941, + "lon": 60.5227257 + } + ], + "sports": [ + { + "name": "—", + "distance_m": 243.4, + "lat": 56.8768551, + "lon": 60.523978 + }, + { + "name": "—", + "distance_m": 302.1, + "lat": 56.8792764, + "lon": 60.5176882 + }, + { + "name": "—", + "distance_m": 313.3, + "lat": 56.8780706, + "lon": 60.5176941 + }, + { + "name": "—", + "distance_m": 339.3, + "lat": 56.8793803, + "lon": 60.5170968 + }, + { + "name": "—", + "distance_m": 389.8, + "lat": 56.8791157, + "lon": 60.5162107 + } + ] + } + }, + "n_compared_jk": 380, + "weighted_score_distribution": { + "n_jk": 380, + "mean": 63.9, + "median": 70.9, + "p25": 41.6, + "p75": 81.7, + "min": 15.9, + "max": 91.2 + }, + "component_comparison": { + "economic": { + "parcel": 40.2, + "median_jk": 47.2, + "p75_jk": 59.5 + }, + "education": { + "parcel": 79.5, + "median_jk": 75.9, + "p75_jk": 94.7 + }, + "health": { + "parcel": 88.7, + "median_jk": 83.2, + "p75_jk": 98.3 + }, + "leisure": { + "parcel": 98.4, + "median_jk": 86.1, + "p75_jk": 96.9 + }, + "retail": { + "parcel": 100.0, + "median_jk": 100.0, + "p75_jk": 100.0 + }, + "transit": { + "parcel": 69.7, + "median_jk": 70.0, + "p75_jk": 85.0 + } + }, + "weights_used": { + "education": 0.3, + "health": 0.15, + "retail": 0.2, + "transit": 0.2, + "leisure": 0.15 + }, + "top10_best_jk_ekb": [ + { + "site_id": "jk:70303", + "name": null, + "district": "Железнодорожный", + "developer": "ТЭН", + "obj_class": null, + "flat_count": 0, + "lat": 56.8514, + "lon": 60.6117, + "weighted": 91.20179727537686, + "rank": 1 + }, + { + "site_id": "jk:66704", + "name": "Квартал «Лайв»", + "district": "Ленинский", + "developer": "Атомстройкомплекс", + "obj_class": null, + "flat_count": 569, + "lat": 56.8036, + "lon": 60.5965, + "weighted": 91.04574249916632, + "rank": 2 + }, + { + "site_id": "jk:64409", + "name": "Клубный дом инженера Ятеса", + "district": "Железнодорожный", + "developer": "ТЭН", + "obj_class": null, + "flat_count": 0, + "lat": 56.851, + "lon": 60.6108, + "weighted": 90.96238151414164, + "rank": 3 + }, + { + "site_id": "jk:50617", + "name": "Клубный дом инженера Ятеса", + "district": "Железнодорожный", + "developer": "ТЭН", + "obj_class": null, + "flat_count": 80, + "lat": 56.8506, + "lon": 60.6108, + "weighted": 90.6835128392893, + "rank": 4 + }, + { + "site_id": "jk:60057", + "name": "Квартал \"На Некрасова\"", + "district": "Железнодорожный", + "developer": "Брусника", + "obj_class": null, + "flat_count": 193, + "lat": 56.8526, + "lon": 60.5886, + "weighted": 90.6443625125751, + "rank": 5 + }, + { + "site_id": "jk:67389", + "name": "ЖК \"Азина 16\"", + "district": "Железнодорожный", + "developer": "ЛСР", + "obj_class": null, + "flat_count": 326, + "lat": 56.8555, + "lon": 60.613, + "weighted": 90.36056856326462, + "rank": 6 + }, + { + "site_id": "jk:69469", + "name": "Жилой дом \"ТЕТРО\"", + "district": "Железнодорожный", + "developer": "Девелоперская компания \"Люди\"", + "obj_class": null, + "flat_count": 358, + "lat": 56.8549, + "lon": 60.5824, + "weighted": 90.19036465994816, + "rank": 7 + }, + { + "site_id": "jk:67548", + "name": "Жилой квартал «Тихий центр» 1 оч. 1 этап", + "district": "Железнодорожный", + "developer": "Синара-Девелопмент", + "obj_class": null, + "flat_count": 231, + "lat": 56.8539, + "lon": 60.595, + "weighted": 90.08874176451533, + "rank": 8 + }, + { + "site_id": "jk:69263", + "name": "ЖК \"Кайдзен\"", + "district": "Верх-Исетский", + "developer": "Астра", + "obj_class": null, + "flat_count": 196, + "lat": 56.8214, + "lon": 60.5721, + "weighted": 89.98009185666463, + "rank": 9 + }, + { + "site_id": "jk:53537", + "name": "ЖК \"Азина 16\"", + "district": "Железнодорожный", + "developer": "ЛСР", + "obj_class": null, + "flat_count": 871, + "lat": 56.8567, + "lon": 60.6132, + "weighted": 89.66063320010375, + "rank": 10 + } + ], + "10_closest_jk_to_parcel": [ + { + "distance_m": 492.0, + "site_id": "jk:68027", + "name": "Квартал \"Траектория\"", + "district": "Железнодорожный", + "developer": "Брусника", + "obj_class": null, + "weighted": 66.6, + "rank": 223 + }, + { + "distance_m": 994.0, + "site_id": "jk:63282", + "name": "Квартал Депо", + "district": "Железнодорожный", + "developer": "Брусника", + "obj_class": null, + "weighted": 69.5, + "rank": 206 + }, + { + "distance_m": 994.0, + "site_id": "jk:63122", + "name": "Квартал Депо", + "district": "Железнодорожный", + "developer": "Брусника", + "obj_class": null, + "weighted": 73.3, + "rank": 176 + }, + { + "distance_m": 1036.0, + "site_id": "jk:59643", + "name": "Астон. Движение", + "district": "Железнодорожный", + "developer": "Астон", + "obj_class": null, + "weighted": 64.3, + "rank": 240 + }, + { + "distance_m": 1211.0, + "site_id": "jk:61215", + "name": "Жилой Комплекс \"Раута\" ", + "district": "Железнодорожный", + "developer": "Эталон", + "obj_class": null, + "weighted": 72.0, + "rank": 186 + }, + { + "distance_m": 1312.0, + "site_id": "jk:65011", + "name": "Жилой Комплекс \"Раута\" ", + "district": "Железнодорожный", + "developer": "Эталон", + "obj_class": null, + "weighted": 74.4, + "rank": 168 + }, + { + "distance_m": 3065.0, + "site_id": "jk:66867", + "name": "ЖК Белая Башня", + "district": "Орджоникидзевский", + "developer": "Ривьера Инвест Екатеринбург", + "obj_class": null, + "weighted": 81.2, + "rank": 106 + }, + { + "distance_m": 3596.0, + "site_id": "jk:65542", + "name": "ЖК \"ПАЙЕР\"", + "district": "Железнодорожный", + "developer": "Самолет", + "obj_class": null, + "weighted": 58.8, + "rank": 267 + }, + { + "distance_m": 3612.0, + "site_id": "jk:63123", + "name": "ЖК ТЕМП", + "district": "Орджоникидзевский", + "developer": "Практика", + "obj_class": null, + "weighted": 81.4, + "rank": 104 + }, + { + "distance_m": 4021.0, + "site_id": "jk:60160", + "name": "ЖК \"Парк Победы\"", + "district": "Орджоникидзевский", + "developer": "PRINZIP", + "obj_class": null, + "weighted": 80.3, + "rank": 118 + } + ] +} \ No newline at end of file diff --git a/site-finder/server.py b/site-finder/server.py new file mode 100644 index 00000000..4b437103 --- /dev/null +++ b/site-finder/server.py @@ -0,0 +1,1801 @@ +"""FastAPI scoring service v2 — fast in-memory POI cache + audience profiles + heatmap. + +Endpoints: + GET / — Leaflet UI + GET /healthz + GET /api/sites — all sites (parcels + ЖК) + GET /api/site/{id} — full record + GET /api/districts — district economics + admin polygons + GET /api/district-polygons — admin districts as GeoJSON FeatureCollection + GET /api/macro — mortgage rate, city avg POI, city median price + GET /api/audiences — preset weight profiles + GET /api/district-velocity-trend/{district} — 12-month series + POST /api/analyze — score arbitrary point + +Key speedup: when point falls inside Ekb bbox, reuse 9479 cached OSM POIs +instead of hitting Overpass (~30s → <100ms). +""" +from __future__ import annotations +import sqlite3, json, math, pathlib, sys, time +from typing import Optional +from fastapi import FastAPI, HTTPException +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse +from pydantic import BaseModel + +ROOT = pathlib.Path(__file__).parent +DB = ROOT / "analysis.db" +STATIC = ROOT / "static" +CACHE_OSM = ROOT / "cache" / "overpass_raw.json" +ADMIN_DISTRICTS = ROOT / "cache" / "ekb_admin_districts.geojson" + +app = FastAPI(title="GenDesign Parcel Scoring v2", version="0.2") + +# ---------- Geometry helpers ---------- + +def hav(la1, lo1, la2, lo2): + R = 6371000 + p1, p2 = math.radians(la1), math.radians(la2) + dp = math.radians(la2 - la1); dl = math.radians(lo2 - lo1) + a = math.sin(dp/2)**2 + math.cos(p1)*math.cos(p2)*math.sin(dl/2)**2 + return 2 * R * math.asin(math.sqrt(a)) + + +def point_in_polygon(lat, lon, ring): + """Ray casting. ring is list of [lon, lat].""" + inside = False + n = len(ring) + j = n - 1 + for i in range(n): + xi, yi = ring[i][0], ring[i][1] + xj, yj = ring[j][0], ring[j][1] + if ((yi > lat) != (yj > lat)) and (lon < (xj - xi) * (lat - yi) / (yj - yi + 1e-12) + xi): + inside = not inside + j = i + return inside + + +def point_in_geometry(lat, lon, geom): + if geom["type"] == "Polygon": + rings = geom["coordinates"] + if not point_in_polygon(lat, lon, rings[0]): return False + for hole in rings[1:]: + if point_in_polygon(lat, lon, hole): return False + return True + if geom["type"] == "MultiPolygon": + return any(point_in_geometry(lat, lon, {"type":"Polygon","coordinates":poly}) + for poly in geom["coordinates"]) + return False + +# ---------- Pre-load OSM cache + bbox ---------- + +print("loading osm cache…") +_t = time.time() +_osm_data = json.loads(CACHE_OSM.read_text()) if CACHE_OSM.exists() else {} +# Flatten to list of (cat, lat, lon, name, tags_json) +_OSM_INDEX = [] # global flat list +for cat, elems in _osm_data.items(): + for el in elems: + if el["type"] == "node": + la, lo = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {}; la, lo = c.get("lat"), c.get("lon") + if la is None: continue + tags = el.get("tags") or {} + _OSM_INDEX.append((cat, la, lo, tags.get("name") or tags.get("operator") or "", + el["type"], el["id"])) +print(f" loaded {len(_OSM_INDEX)} POIs in {time.time()-_t:.2f}s") + +# Bbox of cache (for routing decisions) +_lats = [r[1] for r in _OSM_INDEX] +_lons = [r[2] for r in _OSM_INDEX] +EKB_BBOX = (min(_lats), min(_lons), max(_lats), max(_lons)) if _lats else (0,0,0,0) +print(f" bbox: {EKB_BBOX}") + +# Admin districts polygons +ADMIN_GEOM = json.loads(ADMIN_DISTRICTS.read_text()) if ADMIN_DISTRICTS.exists() else {"features":[]} +print(f" admin districts: {len(ADMIN_GEOM.get('features',[]))}") + + +def find_admin_district(lat, lon): + for f in ADMIN_GEOM.get("features", []): + if point_in_geometry(lat, lon, f["geometry"]): + return f["properties"]["name"] + return None + +# ---------- DB ---------- + +def _conn(): + c = sqlite3.connect(DB); c.row_factory = sqlite3.Row; return c + +# ---------- Audience profiles ---------- + +AUDIENCES = { + "balanced": {"label": "Сбалансированный", + "education": 0.18, "health": 0.10, "retail": 0.13, + "transit": 0.15, "leisure": 0.09, "economic": 0.30, "market": 0.05}, + "family": {"label": "Семейный (садики, школы, парки)", + "education": 0.30, "health": 0.12, "retail": 0.10, + "transit": 0.10, "leisure": 0.13, "economic": 0.20, "market": 0.05}, + "premium": {"label": "Премиум (цена, топ-локация)", + "education": 0.10, "health": 0.10, "retail": 0.10, + "transit": 0.10, "leisure": 0.15, "economic": 0.40, "market": 0.05}, + "economy": {"label": "Эконом (транспорт, базовое)", + "education": 0.15, "health": 0.10, "retail": 0.20, + "transit": 0.25, "leisure": 0.05, "economic": 0.20, "market": 0.05}, + "investor": {"label": "Инвестор (скорость, ликвидность)", + "education": 0.10, "health": 0.08, "retail": 0.08, + "transit": 0.12, "leisure": 0.05, "economic": 0.45, "market": 0.12}, +} + +# ---------- Scoring ---------- + +EDU = [("kindergarten", 300, 1000, 1.0), ("school", 400, 1500, 1.0), ("university", 1000, 5000, 0.3)] +HEALTH = [("pharmacy", 300, 1000, 1.0), ("clinic", 500, 2000, 1.0), ("hospital", 1500, 5000, 0.5)] +TRANSIT_M = [("metro", 1000, 3000, 1.0)] +TRANSIT_T = [("tram_stop", 400, 1500, 1.0)] +TRANSIT_B = [("bus_stop", 200, 800, 1.0)] +LEISURE = [("park", 500, 2000, 1.0), ("playground", 200, 700, 1.0), ("sports", 500, 2000, 0.7)] + + +def dist_score(d_m, ideal, mx): + if d_m is None: return 0.0 + if d_m <= ideal: return 100.0 + if d_m >= mx: return 0.0 + return 100.0 * (mx - d_m) / (mx - ideal) + + +def comp_score(distances_by_cat, cats): + tw = sum(c[3] for c in cats); s = 0 + for cat, ideal, mx, w in cats: + d = distances_by_cat.get(cat) + s += w * dist_score(d, ideal, mx) + return s / tw if tw else 0 + + +def in_bbox(lat, lon, padding=0.005): + return (EKB_BBOX[0] - padding <= lat <= EKB_BBOX[2] + padding and + EKB_BBOX[1] - padding <= lon <= EKB_BBOX[3] + padding) + + +def fetch_pois_local(lat, lon, radius_m=2500): + """Fast: scan in-memory OSM cache, return dict cat -> list of (dist, lat, lon, name).""" + by_cat = {} + for cat, la, lo, name, *_ in _OSM_INDEX: + d = hav(lat, lon, la, lo) + if d <= radius_m: + by_cat.setdefault(cat, []).append((d, la, lo, name)) + for cat in by_cat: + by_cat[cat].sort() + return by_cat + + +def fetch_pois_overpass(lat, lon, radius_m=2500): + """Fallback: live Overpass query (slow, used outside Ekb bbox).""" + import requests + POI_QUERIES = [ + ("kindergarten", '["amenity"="kindergarten"]'), + ("school", '["amenity"="school"]'), + ("university", '["amenity"~"^(university|college)$"]'), + ("pharmacy", '["amenity"="pharmacy"]'), + ("clinic", '["amenity"~"^(clinic|doctors)$"]'), + ("hospital", '["amenity"="hospital"]'), + ("shop_big", '["shop"~"^(mall|supermarket|department_store|hypermarket)$"]'), + ("shop_med", '["shop"~"^(convenience|grocery|bakery)$"]'), + ("shop_small", '["shop"~"^(kiosk|newsagent)$"]'), + ("bus_stop", '["highway"="bus_stop"]'), + ("tram_stop", '["railway"="tram_stop"]'), + ("metro", '["station"="subway"]'), + ("park", '["leisure"~"^(park|garden)$"]'), + ("playground", '["leisure"="playground"]'), + ("sports", '["leisure"~"^(sports_centre|fitness_centre|pitch)$"]'), + ("cafe", '["amenity"="cafe"]'), + ("restaurant", '["amenity"~"^(restaurant|fast_food)$"]'), + ("fuel", '["amenity"="fuel"]'), + ("atm", '["amenity"="atm"]'), + ("post", '["amenity"="post_office"]'), + ("worship", '["amenity"="place_of_worship"]'), + ("library", '["amenity"="library"]'), + ("police", '["amenity"="police"]'), + ("fire", '["amenity"="fire_station"]'), + ("bank", '["amenity"="bank"]'), + ] + parts = [] + for _, filt in POI_QUERIES: + parts.append(f"node{filt}(around:{radius_m},{lat},{lon});") + parts.append(f"way{filt}(around:{radius_m},{lat},{lon});") + q = f"[out:json][timeout:60];({''.join(parts)});out center tags;" + last = None + by_cat = {} + cat_lookup = {filt: cat for cat, filt in POI_QUERIES} + for ep in ("https://overpass-api.de/api/interpreter", + "https://overpass.kumi.systems/api/interpreter"): + try: + r = requests.post(ep, data={"data": q}, timeout=120, + headers={"User-Agent":"gendesign/1.0"}) + if not r.ok: + last = f"{ep}: {r.status_code}"; continue + for el in r.json().get("elements", []): + tags = el.get("tags") or {} + cat = _categorize_osm_tags(tags) + if not cat: continue + if el["type"] == "node": + la, lo = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {}; la, lo = c.get("lat"), c.get("lon") + if la is None: continue + d = hav(lat, lon, la, lo) + by_cat.setdefault(cat, []).append((d, la, lo, tags.get("name") or "")) + for c in by_cat: by_cat[c].sort() + return by_cat + except Exception as e: + last = f"{ep}: {e}" + raise HTTPException(502, f"Overpass failed: {last}") + + +def _categorize_osm_tags(tags): + a = tags.get("amenity"); s = tags.get("shop"); l = tags.get("leisure") + h = tags.get("highway"); r = tags.get("railway"); st = tags.get("station") + if a == "kindergarten": return "kindergarten" + if a == "school": return "school" + if a in ("university", "college"): return "university" + if a == "pharmacy": return "pharmacy" + if a in ("clinic","doctors"): return "clinic" + if a == "hospital": return "hospital" + if a == "cafe": return "cafe" + if a in ("restaurant","fast_food"): return "restaurant" + if a == "fuel": return "fuel" + if a == "atm": return "atm" + if a == "bank": return "bank" + if a == "post_office": return "post" + if a == "place_of_worship": return "worship" + if a == "library": return "library" + if a == "police": return "police" + if a == "fire_station": return "fire" + if a == "parking": return "parking" + if l == "fitness_centre": return "gym" + if a == "theatre": return "theater" + if a == "cinema": return "cinema" + if a == "marketplace": return "marketplace" + if tags.get("tourism") == "museum": return "museum" + if tags.get("tourism") in ("hotel","hostel","apartment"): return "hotel" + if a in ("nightclub","bar","pub"): return "nightclub" + if a == "car_wash": return "car_wash" + if a in ("car_rental","taxi"): return "car_rental" + if a == "veterinary": return "vet" + if a in ("parcel_locker","post_box"): return "courier" + if a == "dentist": return "dentist" + if a == "childcare": return "childcare" + if s in ("mall","supermarket","department_store","hypermarket"): return "shop_big" + if s in ("convenience","grocery","bakery"): return "shop_med" + if s in ("kiosk","newsagent"): return "shop_small" + if h == "bus_stop": return "bus_stop" + if r == "tram_stop": return "tram_stop" + if st == "subway": return "metro" + if l in ("park","garden"): return "park" + if l == "playground": return "playground" + if l in ("sports_centre","fitness_centre","pitch"): return "sports" + return None + + +def assign_district(lat, lon, conn): + """Two-tier: admin (PostGIS polygon) + objective-микрорайон (kNN voted).""" + admin = find_admin_district(lat, lon) + matched = conn.execute(""" + SELECT s.lat, s.lon, sd.district FROM sites s + JOIN site_district sd USING (site_id) + JOIN jk_objective_match m USING (site_id)""").fetchall() + cands = sorted([(hav(lat, lon, m["lat"], m["lon"]), m["district"]) for m in matched]) + top = [c[1] for c in cands[:7] if c[0] <= 2500] + from collections import Counter + obj_district = Counter(top).most_common(1)[0][0] if top else None + return admin, obj_district + + +def score_point(lat, lon, weights, what_if=None, remove_cats=None): + """Run full scoring for a point. Returns dict ready for API. + + what_if: optional list of {cat, lat, lon, name?} — hypothetical POIs added + before scoring (e.g., "what if there was a metro 500m away?") + remove_cats: optional list of categories to suppress (proxy for "no metro") + """ + t0 = time.time() + if in_bbox(lat, lon): + by_cat = fetch_pois_local(lat, lon) + poi_source = "cache" + else: + by_cat = fetch_pois_overpass(lat, lon) + poi_source = "overpass_live" + # apply what-if mutations + for w in (what_if or []): + cat = w.get("cat") + if not cat: continue + d = hav(lat, lon, w["lat"], w["lon"]) + by_cat.setdefault(cat, []).append((d, w["lat"], w["lon"], w.get("name") or "Hypothetical")) + by_cat[cat].sort() + for cat in (remove_cats or []): + by_cat[cat] = [] + t_poi = time.time() - t0 + + # 2. Build features + feats = {} + cats = ["kindergarten","school","university","pharmacy","clinic","hospital", + "shop_big","shop_med","shop_small","bus_stop","tram_stop","metro", + "park","playground","sports", + # Extended (informational; not in scoring weights) + "cafe","restaurant","fuel","atm","bank","post","worship","library","police","fire", + "parking","gym","theater","cinema","marketplace","museum","hotel", + "nightclub","car_wash","car_rental","vet","courier","dentist","childcare"] + nearest = {} + for cat in cats: + items = by_cat.get(cat, []) + if items: + nearest[cat] = items[0][0] + feats[f"{cat}_nearest_m"] = items[0][0] + feats[f"{cat}_count_500m"] = sum(1 for it in items if it[0] <= 500) + feats[f"{cat}_count_1km"] = sum(1 for it in items if it[0] <= 1000) + feats[f"{cat}_top5"] = [ + {"name": it[3], "distance_m": round(it[0],1), "lat": it[1], "lon": it[2]} + for it in items[:5] + ] + else: + feats[f"{cat}_nearest_m"] = None + feats[f"{cat}_count_500m"] = 0 + feats[f"{cat}_count_1km"] = 0 + feats[f"{cat}_top5"] = [] + + edu = comp_score(nearest, EDU) + health = comp_score(nearest, HEALTH) + big = comp_score(nearest, [("shop_big", 500, 2000, 1.0)]) + med = comp_score(nearest, [("shop_med", 300, 1000, 1.0)]) + retail = max(big, 0.7 * med) + tr_m = comp_score(nearest, TRANSIT_M) + tr_t = comp_score(nearest, TRANSIT_T) + tr_b = comp_score(nearest, TRANSIT_B) + transit = max(tr_m, 0.85 * tr_t, 0.7 * tr_b) + leisure = comp_score(nearest, LEISURE) + + # 3. District + economics + with _conn() as c: + admin, obj_district = assign_district(lat, lon, c) + economics = None + trend_factor = sat_factor = 1.0 + if obj_district: + row = c.execute("SELECT * FROM district_economics WHERE district=?", + (obj_district,)).fetchone() + economics = dict(row) if row else None + if economics: + trend_factor = economics.get("trend_factor") or 1.0 + sat_factor = economics.get("sat_factor") or 1.0 + + # bounds for normalization + prices = sorted([r[0] for r in c.execute( + "SELECT real_median_price_m2 FROM district_economics " + "WHERE real_median_price_m2 IS NOT NULL").fetchall()]) + pmin, pmax = (prices[0], prices[-1]) if prices else (100, 200) + if pmax <= pmin: pmax = pmin + 1 + vels = sorted([r[0] for r in c.execute( + "SELECT real_velocity_6mo FROM district_economics " + "WHERE real_velocity_6mo IS NOT NULL").fetchall()]) + vmax = vels[int(len(vels) * 0.9)] if vels else 8 + + all_jk = c.execute("SELECT lat, lon FROM sites WHERE kind='jk'").fetchall() + n_jk_1km = sum(1 for r in all_jk if hav(lat, lon, r[0], r[1]) <= 1000) + feats["jk_count_1km"] = n_jk_1km + + # 3b. Walkability — sum of POI weights × decay 1/(1+d/200) within 1.5km + walk_w = { + # daily-need POIs weighted higher + "shop_med": 1.5, "shop_small": 1.0, "shop_big": 0.6, + "kindergarten": 1.5, "school": 1.5, + "pharmacy": 1.0, "clinic": 0.8, "hospital": 0.4, + "cafe": 0.6, "restaurant": 0.4, + "park": 1.2, "playground": 1.0, "sports": 0.5, + "bus_stop": 0.8, "tram_stop": 1.0, "metro": 1.5, + "atm": 0.4, "bank": 0.3, "post": 0.4, "library": 0.3, + } + walk_score_raw = 0 + for cat, w in walk_w.items(): + for d, *_ in by_cat.get(cat, [])[:10]: + if d <= 1500: + walk_score_raw += w / (1 + d/200) + # normalize: 30 = "average" → 50, 60+ = "excellent" → 100 + walkability = min(100, walk_score_raw * 100 / 60) + feats["walkability_score"] = round(walkability, 1) + + # 4. Economic score + if economics: + price = economics.get("real_median_price_m2") + v_rec = economics.get("real_velocity_6mo") + mts = economics.get("months_to_sellout") + p_score = max(0, min(100, ((price or 0) - pmin) * 100 / (pmax - pmin))) if price else 0 + v_score_raw = max(0, min(100, (v_rec or 0) * 100 / vmax)) if v_rec else 0 + tf_clamped = max(0.7, min(2.0, trend_factor)) + v_score = v_score_raw * (0.5 + 0.5 * (tf_clamped / 2.0)) + liq = max(0, 100 - min(mts or 100, 24) * 100 / 24) if mts else 50 + econ = 0.50 * p_score + 0.25 * v_score + 0.25 * liq + else: + econ = 0; price = v_rec = None + + # 5. Market score — FIXED: don't penalize high sold_pct, treat as proven absorption + density_score = max(0, 100 - n_jk_1km * 100 / 15) + sold_pct = (economics or {}).get("real_sold_pct") or 50 + # Sold% 0..70 = linear up (proven absorption). 70..100 = plateau (saturated; no penalty needed). + sat_score = min(100, sold_pct * 100 / 70) + market = 0.5 * density_score + 0.5 * sat_score + + # 6. Aggregate + comps = {"education": edu, "health": health, "retail": retail, + "transit": transit, "leisure": leisure, "economic": econ, "market": market} + weighted = sum(weights.get(k, 0) * v for k, v in comps.items()) + + # 7. Rank vs all ЖК (using same weights) + with _conn() as c: + rows = c.execute("""SELECT s.site_id, + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='education'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='health'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='retail'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='transit'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='leisure'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='economic'), + (SELECT score_0_100 FROM scores sc WHERE sc.site_id=s.site_id AND sc.component='market') + FROM sites s WHERE s.kind='jk'""").fetchall() + weights_ord = ["education","health","retail","transit","leisure","economic","market"] + all_w = [] + for r in rows: + w = sum(weights.get(weights_ord[i], 0) * (r[i+1] or 0) for i in range(7)) + all_w.append(w) + all_w.sort(reverse=True) + rank = sum(1 for x in all_w if x > weighted) + 1 + n_jk = len(all_w) + + return { + "scores": comps, + "weighted": round(weighted, 2), + "rank_overall": rank, + "n_jk_compared": n_jk, + "percentile": round(100 * (1 - (rank - 1) / (n_jk + 1)), 1), + "weights": weights, + "admin_district": admin, + "district": obj_district, + "economics": economics, + "macro_factors": {"trend_factor": trend_factor, "sat_factor": sat_factor, + "n_jk_1km": n_jk_1km, "city_pmin": pmin, "city_pmax": pmax, + "city_vmax": vmax}, + "features": feats, + "_perf": {"poi_source": poi_source, "poi_ms": round(t_poi*1000, 1)}, + } + +# ---------- HTTP ---------- + +@app.get("/healthz") +def healthz(): + with _conn() as c: + n_sites = c.execute("SELECT count(*) FROM sites").fetchone()[0] + n_pois = c.execute("SELECT count(*) FROM pois").fetchone()[0] + n_obj = c.execute("SELECT count(*) FROM objective_corp_month").fetchone()[0] + n_lots = c.execute("SELECT count(*) FROM objective_lots").fetchone()[0] + return {"status": "ok", "sites": n_sites, "pois_cached": n_pois, + "objective_corp_month": n_obj, "objective_lots": n_lots, + "osm_cache_pois": len(_OSM_INDEX), "ekb_bbox": EKB_BBOX, + "admin_districts": len(ADMIN_GEOM.get("features", []))} + + +@app.get("/api/sites") +def sites(): + with _conn() as c: + rows = c.execute(""" + SELECT s.site_id, s.kind, s.name, s.address, s.lat, s.lon, + s.obj_class, s.developer, s.flat_count, s.district, + sd.district AS obj_district, + st.weighted, st.rank_overall + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN scores_total st USING (site_id)""").fetchall() + return [dict(r) for r in rows] + + +@app.get("/api/jk-full/{site_id:path}") +def jk_full(site_id: str): + """Full ЖК dossier: site row, scores, features, district economics, + POI counts/nearest, monthly velocity, per-flat sample, prod photos URL, + prod sale_graph data (if available via SSH tunnel). + """ + with _conn() as c: + s = c.execute("SELECT * FROM sites WHERE site_id=?", (site_id,)).fetchone() + if not s: raise HTTPException(404, "site not found") + s = dict(s) + s["scores"] = {r["component"]: r["score_0_100"] for r in + c.execute("SELECT * FROM scores WHERE site_id=?", (site_id,)).fetchall()} + st = c.execute("SELECT weighted,rank_overall,rank_district FROM scores_total WHERE site_id=?", + (site_id,)).fetchone() + if st: s.update({"weighted": st["weighted"], "rank_overall": st["rank_overall"], + "rank_district": st["rank_district"]}) + + # POIs near this site (15 categories with top-3 each) + s["pois"] = {} + for cat in ["kindergarten","school","university","pharmacy","clinic","hospital", + "shop_big","shop_med","shop_small","bus_stop","tram_stop","metro", + "park","playground","sports", + "cafe","restaurant","fuel","atm","bank","post","worship", + "library","police","fire", + "parking","gym","theater","cinema","marketplace","museum","hotel", + "nightclub","car_wash","car_rental","vet","courier","dentist","childcare"]: + rows = c.execute("""SELECT name, distance_m, lat, lon FROM pois + WHERE site_id=? AND category=? + ORDER BY distance_m LIMIT 3""", (site_id, cat)).fetchall() + nearest = c.execute("""SELECT MIN(distance_m), COUNT(CASE WHEN distance_m<=500 THEN 1 END), + COUNT(CASE WHEN distance_m<=1000 THEN 1 END) + FROM pois WHERE site_id=? AND category=?""", + (site_id, cat)).fetchone() + s["pois"][cat] = { + "nearest_m": nearest[0], + "count_500m": nearest[1], + "count_1km": nearest[2], + "top3": [{"name": r["name"] or "—", "distance_m": round(r["distance_m"],1), + "lat": r["lat"], "lon": r["lon"]} for r in rows] + } + + # District economics + e = c.execute("""SELECT sd.district, sd.method, de.* FROM site_district sd + LEFT JOIN district_economics de USING (district) + WHERE sd.site_id=?""", (site_id,)).fetchone() + s["economics"] = dict(e) if e else None + + # Match Objective project + m = c.execute("SELECT project, score FROM jk_objective_match WHERE site_id=?", + (site_id,)).fetchone() + s["objective_match"] = dict(m) if m else None + project = m["project"] if m else None + + # Monthly registrations for this project (12 mo) + sample lots + s["monthly_velocity"] = [] + s["lots_sample"] = [] + s["lots_summary"] = None + if project: + import datetime as dt + today = dt.date.today().replace(day=1) + months = [] + y, mn = today.year, today.month + for _ in range(12): + months.append(f"{y:04d}-{mn:02d}") + mn -= 1 + if mn == 0: mn = 12; y -= 1 + months.reverse() + rows = c.execute("""SELECT substr(register_date,1,7) AS m, COUNT(*) n + FROM objective_lots + WHERE project=? AND register_date IS NOT NULL + AND register_date >= ? + GROUP BY 1""", (project, months[0])).fetchall() + series = {r["m"]: r["n"] for r in rows} + s["monthly_velocity"] = [{"month": m, "n": series.get(m, 0)} for m in months] + + # 10 currently in-sale lots with prices + lots = c.execute("""SELECT lot_id, corpus, section, floor, lot_num, room_kind, + rooms_obj, area_pd, price_per_m2, status, sold, + finish_type, plan_date, readiness_pct + FROM objective_lots + WHERE project=? + AND status IN ('в продаже','резерв') + AND price_per_m2 > 0 + ORDER BY price_per_m2 ASC + LIMIT 10""", (project,)).fetchall() + s["lots_sample"] = [dict(r) for r in lots] + + # Aggregate sample + agg = c.execute("""SELECT COUNT(*) total, + SUM(CASE WHEN sold='да' THEN 1 ELSE 0 END) sold_n, + ROUND(AVG(CASE WHEN price_per_m2>0 THEN price_per_m2 END)/1000,1) avg_price_kp, + ROUND(AVG(CASE WHEN area_pd>0 THEN area_pd END),1) avg_area, + ROUND(MIN(CASE WHEN price_per_m2>0 THEN price_per_m2 END)/1000,1) min_price_kp, + ROUND(MAX(CASE WHEN price_per_m2>0 THEN price_per_m2 END)/1000,1) max_price_kp, + COUNT(DISTINCT corpus) n_corpus, + COUNT(DISTINCT rooms_obj) n_room_types + FROM objective_lots WHERE project=?""", (project,)).fetchone() + s["lots_summary"] = dict(agg) if agg else None + + # OSM building polygons attached to this ЖК + try: + jk_fc = _load_jk_polygons() + s["building_polygons"] = [ + f for f in jk_fc.get("features", []) + if f["properties"].get("site_id") == site_id + ] + except Exception: + s["building_polygons"] = [] + + # Optional: photos + sale_graph from prod via SSH tunnel + s["prod_extras"] = {"photos": [], "sale_graph": [], "sales_agg": []} + obj_id = s.get("obj_id") + if obj_id: + try: + import psycopg2 + pg = psycopg2.connect(host="127.0.0.1", port=15432, user="gendesign", + password="2J2SBPMKuS998fiwhtQqDhMI", + dbname="gendesign", connect_timeout=2) + pcur = pg.cursor() + pcur.execute("""SELECT photo_url, photo_dttm, period_dt, photo_name, ready_desc, thumb_path, hidden + FROM domrf_kn_photos + WHERE obj_id=%s AND COALESCE(hidden,false)=false + ORDER BY period_dt DESC NULLS LAST LIMIT 16""", (obj_id,)) + s["prod_extras"]["photos"] = [ + {"url": r[0], "photo_dttm": str(r[1]) if r[1] else None, + "period_dt": str(r[2]) if r[2] else None, + "name": r[3], "ready_desc": r[4], "thumb_path": r[5]} + for r in pcur.fetchall() + ] + pcur.execute("""SELECT report_month, type, realised, contracted, area_sq, price_avg + FROM domrf_kn_sale_graph WHERE obj_id=%s + ORDER BY report_month""", (obj_id,)) + s["prod_extras"]["sale_graph"] = [ + {"report_month": str(r[0]) if r[0] else None, + "type": r[1], "realised": r[2], "contracted": r[3], + "area_sq": float(r[4]) if r[4] is not None else None, + "price_avg": float(r[5]) if r[5] is not None else None} + for r in pcur.fetchall() + ] + pcur.execute("""SELECT type, name, total, realised, perc + FROM domrf_kn_sales_agg WHERE obj_id=%s + AND snapshot_date=(SELECT MAX(snapshot_date) FROM domrf_kn_sales_agg WHERE obj_id=%s)""", + (obj_id, obj_id)) + s["prod_extras"]["sales_agg"] = [ + {"type": r[0], "name": r[1], "total": r[2], "realised": r[3], + "perc": float(r[4]) if r[4] is not None else None} + for r in pcur.fetchall() + ] + pg.close() + except Exception as e: + s["prod_extras"]["error"] = str(e) + + return s + + +@app.get("/api/site/{site_id:path}") +def site_detail(site_id: str): + with _conn() as c: + s = c.execute("SELECT * FROM sites WHERE site_id=?", (site_id,)).fetchone() + if not s: raise HTTPException(404, "site not found") + s = dict(s) + s["scores"] = {r["component"]: r["score_0_100"] for r in + c.execute("SELECT * FROM scores WHERE site_id=?", (site_id,)).fetchall()} + st = c.execute("SELECT weighted,rank_overall,rank_district FROM scores_total WHERE site_id=?", + (site_id,)).fetchone() + if st: s.update({"weighted": st["weighted"], "rank_overall": st["rank_overall"], + "rank_district": st["rank_district"]}) + s["features"] = {r["feature"]: r["value"] + for r in c.execute("SELECT * FROM features WHERE site_id=?", + (site_id,)).fetchall()} + d = c.execute("""SELECT sd.district, de.* FROM site_district sd + LEFT JOIN district_economics de USING (district) + WHERE sd.site_id=?""", (site_id,)).fetchone() + s["economics"] = dict(d) if d else None + return s + + +@app.get("/api/districts") +def districts(): + with _conn() as c: + rows = c.execute("SELECT * FROM district_economics ORDER BY real_median_price_m2 DESC").fetchall() + return [dict(r) for r in rows] + + +@app.get("/api/district-polygons") +def district_polygons(): + """Return admin districts (8) as GeoJSON FeatureCollection. + Properties enriched with ЖК count + average score per district.""" + with _conn() as c: + rows = c.execute("""SELECT s.district, COUNT(*) n, + ROUND(AVG(st.weighted), 1) avg_score + FROM sites s + LEFT JOIN scores_total st USING (site_id) + WHERE s.kind='jk' AND s.district IS NOT NULL + GROUP BY 1""").fetchall() + enrich = {r["district"]: dict(r) for r in rows} + fc = json.loads(ADMIN_DISTRICTS.read_text()) + for f in fc["features"]: + nm = f["properties"]["name"] + e = enrich.get(nm, {}) + f["properties"].update({"jk_count": e.get("n", 0), "avg_score": e.get("avg_score")}) + return fc + + +@app.get("/api/macro") +def macro(): + with _conn() as c: + rows = c.execute("SELECT key,value,label,period FROM macro_context").fetchall() + out = [] + for r in rows: + d = dict(r) + d["unit"] = "%" if d["key"] == "mortgage_rate_sverdl" else \ + " тыс/м²" if d["key"] == "city_med_price_m2" else "" + out.append(d) + return out + + +@app.get("/api/audiences") +def audiences(): + return AUDIENCES + + +@app.get("/api/reverse-geocode") +def reverse_geocode(lat: float, lon: float): + """Resolve human-readable address via Nominatim (rate-limited; cache locally).""" + import requests + cache_path = ROOT / "cache" / "geocode_cache.json" + cache = {} + if cache_path.exists(): + try: cache = json.loads(cache_path.read_text()) + except: cache = {} + key = f"{lat:.5f},{lon:.5f}" + if key in cache: + return cache[key] + try: + r = requests.get("https://nominatim.openstreetmap.org/reverse", + params={"lat":lat,"lon":lon,"format":"json","accept-language":"ru","zoom":18}, + headers={"User-Agent":"gendesign-research/0.2"}, timeout=10) + r.raise_for_status() + d = r.json() + addr = d.get("address", {}) or {} + out = { + "display_name": d.get("display_name"), + "road": addr.get("road"), + "house_number": addr.get("house_number"), + "suburb": addr.get("suburb") or addr.get("neighbourhood"), + "city_district": addr.get("city_district"), + "city": addr.get("city") or addr.get("town"), + } + except Exception as e: + out = {"error": str(e), "display_name": None} + cache[key] = out + # Cache is best-effort — the volume may be mounted read-only (prod docker setup), + # in which case we just skip persistence and rely on the per-process in-memory dict. + try: + cache_path.parent.mkdir(parents=True, exist_ok=True) + cache_path.write_text(json.dumps(cache, ensure_ascii=False)) + except OSError: + pass + return out + + +@app.get("/api/district-heatmap") +def district_heatmap(metric: str = "score"): + """Admin districts (8) coloured by chosen metric. + + metric ∈ {'score','price','velocity','sold_pct','jk_count'} + """ + fc = json.loads(ADMIN_DISTRICTS.read_text()) + with _conn() as c: + # Map admin district → aggregated metric + rows = c.execute(""" + SELECT s.district AS admin, COUNT(*) AS jk_count, + AVG(st.weighted) AS avg_score + FROM sites s LEFT JOIN scores_total st USING (site_id) + WHERE s.kind='jk' AND s.district IS NOT NULL + GROUP BY 1""").fetchall() + admin_score = {r["admin"]: r["avg_score"] for r in rows} + admin_count = {r["admin"]: r["jk_count"] for r in rows} + # Objective metrics aggregated by admin via site_district mapping + eco = c.execute(""" + SELECT s.district AS admin, + AVG(de.real_median_price_m2) AS price, + AVG(de.real_velocity_6mo) AS vel, + AVG(de.real_sold_pct) AS sold + FROM sites s + JOIN site_district sd USING (site_id) + JOIN district_economics de USING (district) + WHERE s.kind='jk' AND s.district IS NOT NULL + GROUP BY 1""").fetchall() + admin_eco = {r["admin"]: dict(r) for r in eco} + + metric_map = { + "score": lambda nm: admin_score.get(nm), + "price": lambda nm: (admin_eco.get(nm) or {}).get("price"), + "velocity": lambda nm: (admin_eco.get(nm) or {}).get("vel"), + "sold_pct": lambda nm: (admin_eco.get(nm) or {}).get("sold"), + "jk_count": lambda nm: admin_count.get(nm), + } + fn = metric_map.get(metric, metric_map["score"]) + for f in fc["features"]: + nm = f["properties"]["name"] + f["properties"]["value"] = fn(nm) + f["properties"]["jk_count"] = admin_count.get(nm, 0) + f["properties"]["avg_score"] = admin_score.get(nm) + return fc + + +@app.get("/api/developer/{developer_name:path}") +def developer_track_record(developer_name: str): + """Aggregate developer's portfolio: ЖК count, avg score, avg price, total flats, + velocity, sellout history. Pulled from local DB via name match. + """ + with _conn() as c: + rows = c.execute("""SELECT s.site_id, s.name, s.flat_count, s.obj_class, + sd.district AS obj_district, + st.weighted, st.rank_overall, + m.project + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN scores_total st USING (site_id) + LEFT JOIN jk_objective_match m USING (site_id) + WHERE s.kind='jk' AND s.developer = ?""", (developer_name,)).fetchall() + ojects = [dict(r) for r in rows] + if not ojects: + raise HTTPException(404, "developer not found") + + # Aggregate per-project lots stats + projects = [o["project"] for o in ojects if o["project"]] + if projects: + placeholders = ",".join(["?"]*len(projects)) + agg = c.execute(f"""SELECT project, COUNT(*) total, + SUM(CASE WHEN sold='да' THEN 1 ELSE 0 END) sold_n, + AVG(CASE WHEN price_per_m2>0 THEN price_per_m2/1000 END) avg_price, + AVG(CASE WHEN area_pd>0 THEN area_pd END) avg_area, + MIN(register_date) first_deal, + MAX(register_date) last_deal, + COUNT(CASE WHEN register_date >= date('now','-6 months') THEN 1 END)/6.0 vel_6mo + FROM objective_lots WHERE project IN ({placeholders}) + GROUP BY 1""", projects).fetchall() + proj_data = {r["project"]: dict(r) for r in agg} + else: + proj_data = {} + + # roll up + n_jk = len(ojects) + avg_score = sum(o["weighted"] or 0 for o in ojects if o["weighted"]) / max(sum(1 for o in ojects if o["weighted"]), 1) + total_flats = sum(o["flat_count"] or 0 for o in ojects) + matched = [o for o in ojects if o["project"] and proj_data.get(o["project"])] + total_lots = sum(proj_data[o["project"]]["total"] for o in matched) + sold_lots = sum(proj_data[o["project"]]["sold_n"] for o in matched) + avg_price = (sum(proj_data[o["project"]]["avg_price"] or 0 for o in matched) / len(matched)) if matched else None + total_vel = sum(proj_data[o["project"]]["vel_6mo"] or 0 for o in matched) + + portfolio = [] + for o in ojects: + p = proj_data.get(o["project"]) if o["project"] else None + portfolio.append({**o, "project_stats": p}) + portfolio.sort(key=lambda x: -(x["weighted"] or 0)) + + return { + "developer": developer_name, + "summary": { + "n_projects": n_jk, + "n_matched_objective": len(matched), + "avg_score": round(avg_score, 1), + "best_score": max((o["weighted"] or 0) for o in ojects), + "worst_score": min((o["weighted"] or 0) for o in ojects if o["weighted"]), + "total_flats": total_flats, + "total_lots_objective": total_lots, + "sold_lots_objective": sold_lots, + "sold_pct": round(100*sold_lots/total_lots, 1) if total_lots else None, + "avg_price_m2_kr": round(avg_price, 1) if avg_price else None, + "total_velocity_6mo": round(total_vel, 2), + "districts": list({o["obj_district"] for o in ojects if o["obj_district"]}), + }, + "portfolio": portfolio, + } + + +class TEPInput(BaseModel): + parcel_area_m2: float + obj_class: Optional[str] = None # 'комфорт' | 'бизнес' | 'эконом' | 'премиум' + far: float = 1.5 # floor-area ratio (общая GBA / parcel area) + saleable_share: float = 0.65 # доля квартир в общей GBA (typical 0.55-0.75) + avg_unit_area_m2: float = 45 # среднее по сделке + district: Optional[str] = None # для подстановки price/velocity + construction_cost_per_m2: int = 75000 # ₽/м² GBA + soft_cost_pct: float = 0.10 # проектные/маркетинг как доля от выручки + discount_rate: float = 0.18 # годовая для NPV/IRR + months_to_complete: int = 30 + + +@app.post("/api/tep-calc") +def tep_calc(payload: TEPInput): + """Простой generative-TЭП от параметров участка. + + Берёт (по района) реальную median price/m² и velocity. Считает выручку, + себестоимость, NPV/IRR в простой модели «равномерные продажи всё время стройки». + """ + with _conn() as c: + district = payload.district + if district: + row = c.execute("""SELECT real_median_price_m2, real_velocity_6mo + FROM district_economics WHERE district=?""", + (district,)).fetchone() + else: + row = None + if row: + price_kr = row["real_median_price_m2"] or 130 + velocity_per_corp = row["real_velocity_6mo"] or 2.0 + else: + price_kr = 130 + velocity_per_corp = 2.0 + + gba = payload.parcel_area_m2 * payload.far # total built m² + saleable_m2 = gba * payload.saleable_share + n_units = round(saleable_m2 / payload.avg_unit_area_m2) + price_per_m2 = price_kr * 1000 + revenue = saleable_m2 * price_per_m2 + construction = gba * payload.construction_cost_per_m2 + soft = revenue * payload.soft_cost_pct + profit = revenue - construction - soft + + # Sales velocity — single corpus assumption; cap at saleable + months_to_sellout = n_units / max(velocity_per_corp, 0.1) + cashflow_months = max(payload.months_to_complete, months_to_sellout) + + # NPV: monthly equal cashflows + monthly_revenue = revenue / cashflow_months if cashflow_months else 0 + monthly_cost = construction / payload.months_to_complete + r_m = (1 + payload.discount_rate) ** (1/12) - 1 + npv = -construction * 0.20 # initial 20% upfront + for i in range(1, int(cashflow_months) + 1): + cost_i = monthly_cost * (1 if i <= payload.months_to_complete else 0) + rev_i = monthly_revenue + npv += (rev_i - cost_i) / ((1 + r_m) ** i) + # crude IRR via bisection on monthly + def npv_at(r): + s = -construction * 0.20 + rmm = (1+r)**(1/12) - 1 + for i in range(1, int(cashflow_months)+1): + ci = monthly_cost * (1 if i <= payload.months_to_complete else 0) + s += (monthly_revenue - ci) / ((1+rmm)**i) + return s + lo, hi = 0.0, 5.0 + irr = None + if npv_at(lo) > 0 and npv_at(hi) < 0: + for _ in range(40): + mid = (lo+hi)/2 + v = npv_at(mid) + if v > 0: lo = mid + else: hi = mid + irr = (lo+hi)/2 + + return { + "input": payload.dict(), + "district_used": district, + "assumptions": {"price_kr_per_m2": price_kr, "velocity_per_corp_6mo": velocity_per_corp}, + "outputs": { + "gba_m2": round(gba, 1), + "saleable_m2": round(saleable_m2, 1), + "n_units": n_units, + "revenue_total": round(revenue), + "revenue_mln": round(revenue/1e6, 1), + "construction_cost_total": round(construction), + "construction_mln": round(construction/1e6, 1), + "soft_cost_total": round(soft), + "profit_total": round(profit), + "profit_mln": round(profit/1e6, 1), + "profit_margin_pct": round(100*profit/revenue, 1) if revenue else None, + "months_to_sellout": round(months_to_sellout, 1), + "cashflow_months": round(cashflow_months, 1), + "npv": round(npv), + "npv_mln": round(npv/1e6, 1), + "irr_annual": round(irr*100, 1) if irr is not None else None, + } + } + + +@app.get("/api/price-distribution/{district}") +def price_distribution(district: str): + """Boxplot data per ЖК class for a district.""" + with _conn() as c: + rows = c.execute(""" + SELECT s.obj_class AS class_name, ol.price_per_m2/1000.0 AS price + FROM objective_lots ol + JOIN jk_objective_match m ON m.project = ol.project + JOIN sites s USING (site_id) + WHERE ol.district = ? AND ol.price_per_m2 > 0 AND s.obj_class IS NOT NULL""", + (district,)).fetchall() + by_class = {} + for r in rows: + by_class.setdefault(r["class_name"], []).append(r["price"]) + out = {} + for k, vals in by_class.items(): + vals.sort() + if not vals: continue + n = len(vals) + def q(p): return vals[max(0, min(n-1, int(p*n)))] + out[k] = {"n": n, "min": vals[0], "p25": q(0.25), "p50": q(0.5), + "p75": q(0.75), "max": vals[-1]} + return {"district": district, "by_class": out} + + +@app.get("/api/district-time-machine/{district}") +def district_time_machine(district: str): + """12-month series of: deals_count, median_price, sold_volume, distinct_corpuses.""" + import datetime as dt + today = dt.date.today().replace(day=1) + months = [] + y, m = today.year, today.month + for _ in range(12): + months.append(f"{y:04d}-{m:02d}") + m -= 1 + if m == 0: m = 12; y -= 1 + months.reverse() + series = [] + with _conn() as c: + for mo in months: + row = c.execute(""" + SELECT COUNT(*) AS deals, + AVG(CASE WHEN price_per_m2>0 THEN price_per_m2/1000.0 END) AS price, + SUM(CASE WHEN area_pd>0 THEN area_pd END) AS volume_m2, + COUNT(DISTINCT project||'·'||corpus) AS corpuses + FROM objective_lots + WHERE district=? AND substr(register_date,1,7)=? + """, (district, mo)).fetchone() + series.append({ + "month": mo, + "deals": row["deals"] or 0, + "median_price_kr": round(row["price"], 1) if row["price"] else None, + "volume_m2": round(row["volume_m2"], 1) if row["volume_m2"] else 0, + "active_corpuses": row["corpuses"] or 0, + }) + return {"district": district, "series": series} + + +@app.get("/api/smart-suggestions") +def smart_suggestions(lat: float, lon: float, audience: str = "balanced", + radius_km: float = 3.0, top_n: int = 5): + """Top-N ЖК within radius_km that score higher than this point under selected audience. + Useful as 'nearby alternatives'.""" + weights = AUDIENCES.get(audience, AUDIENCES["balanced"]) + weights = {k: v for k, v in weights.items() if k != "label"} + with _conn() as c: + rows = c.execute(""" + SELECT s.site_id, s.name, s.developer, s.obj_class, s.lat, s.lon, + sd.district AS obj_district, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='education') AS edu, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='health') AS health, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='retail') AS retail, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='transit') AS transit, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='leisure') AS leisure, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='economic') AS economic, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='market') AS market + FROM sites s + LEFT JOIN site_district sd USING (site_id) + WHERE s.kind='jk'""").fetchall() + out = [] + for r in rows: + d = hav(lat, lon, r["lat"], r["lon"]) + if d > radius_km*1000: continue + comps = {"education": r["edu"] or 0, "health": r["health"] or 0, + "retail": r["retail"] or 0, "transit": r["transit"] or 0, + "leisure": r["leisure"] or 0, "economic": r["economic"] or 0, + "market": r["market"] or 0} + weighted = sum(weights.get(k,0)*v for k,v in comps.items()) + out.append({ + "site_id": r["site_id"], "name": r["name"], "developer": r["developer"], + "obj_class": r["obj_class"], "obj_district": r["obj_district"], + "lat": r["lat"], "lon": r["lon"], + "distance_m": round(d), "weighted": round(weighted, 1), + }) + out.sort(key=lambda x: -x["weighted"]) + return {"audience": audience, "radius_km": radius_km, "results": out[:top_n]} + + +@app.get("/api/export/geojson") +def export_geojson(audience: str = "balanced", min_score: float = 0): + """All ЖК as GeoJSON FeatureCollection with audience-weighted score in props.""" + weights = {k: v for k, v in AUDIENCES.get(audience, AUDIENCES["balanced"]).items() if k != "label"} + with _conn() as c: + rows = c.execute(""" + SELECT s.site_id, s.name, s.developer, s.obj_class, s.lat, s.lon, + sd.district, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='education') edu, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='health') health, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='retail') retail, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='transit') transit, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='leisure') leisure, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='economic') economic, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='market') market + FROM sites s LEFT JOIN site_district sd USING (site_id) + WHERE s.kind='jk'""").fetchall() + feats = [] + for r in rows: + comps = {"education":r["edu"] or 0, "health":r["health"] or 0, + "retail":r["retail"] or 0, "transit":r["transit"] or 0, + "leisure":r["leisure"] or 0, "economic":r["economic"] or 0, + "market":r["market"] or 0} + w = sum(weights.get(k,0)*v for k,v in comps.items()) + if w < min_score: continue + feats.append({"type":"Feature", + "geometry":{"type":"Point","coordinates":[r["lon"], r["lat"]]}, + "properties":{"site_id":r["site_id"],"name":r["name"], + "developer":r["developer"],"obj_class":r["obj_class"], + "district":r["district"], + "weighted_score":round(w,2), + **{f"score_{k}": round(v,1) for k,v in comps.items()}}}) + return {"type":"FeatureCollection","features":feats, + "_metadata":{"audience":audience,"weights":weights,"count":len(feats)}} + + +@app.get("/api/export/csv") +def export_csv(audience: str = "balanced"): + """Same data as flat CSV for Excel/Google Sheets.""" + import io, csv + weights = {k: v for k, v in AUDIENCES.get(audience, AUDIENCES["balanced"]).items() if k != "label"} + with _conn() as c: + rows = c.execute(""" + SELECT s.site_id, s.name, s.developer, s.obj_class, s.lat, s.lon, s.flat_count, + sd.district, + de.real_median_price_m2, de.real_velocity_6mo, de.real_sold_pct, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='education') edu, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='health') health, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='retail') retail, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='transit') transit, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='leisure') leisure, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='economic') economic, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='market') market + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN district_economics de USING (district) + WHERE s.kind='jk'""").fetchall() + buf = io.StringIO() + buf.write("") # BOM for Excel + w = csv.writer(buf, delimiter=";") + cols = ["site_id","name","developer","obj_class","district","lat","lon","flat_count", + "median_price_kr","velocity_6mo","sold_pct", + "edu","health","retail","transit","leisure","economic","market","weighted"] + w.writerow(cols) + for r in rows: + comps = {k: r[k] or 0 for k in ("edu","health","retail","transit","leisure","economic","market")} + comp_full = {"education":comps["edu"],"health":comps["health"],"retail":comps["retail"], + "transit":comps["transit"],"leisure":comps["leisure"], + "economic":comps["economic"],"market":comps["market"]} + weighted = sum(weights.get(k,0)*v for k,v in comp_full.items()) + w.writerow([r["site_id"],r["name"],r["developer"],r["obj_class"],r["district"], + r["lat"],r["lon"],r["flat_count"], + r["real_median_price_m2"], r["real_velocity_6mo"], r["real_sold_pct"], + *[comps[k] for k in ("edu","health","retail","transit","leisure","economic","market")], + round(weighted,2)]) + from fastapi.responses import Response + return Response(content=buf.getvalue(), media_type="text/csv; charset=utf-8", + headers={"Content-Disposition": f'attachment; filename="gendesign_jk_{audience}.csv"'}) + + +@app.get("/api/poi-vs-district/{site_id:path}") +def poi_vs_district(site_id: str): + """Compare POI counts of one site vs district average.""" + with _conn() as c: + s = c.execute("""SELECT s.lat, s.lon, sd.district FROM sites s + LEFT JOIN site_district sd USING (site_id) + WHERE s.site_id=?""", (site_id,)).fetchone() + if not s: raise HTTPException(404) + district = s["district"] + if not district: + return {"site_id": site_id, "district": None, "comparison": []} + + peers = [r["site_id"] for r in c.execute( + "SELECT site_id FROM site_district WHERE district=? AND site_id!=?", + (district, site_id)).fetchall()] + if not peers: + return {"site_id": site_id, "district": district, "comparison": []} + + cats = ["kindergarten","school","pharmacy","clinic","shop_big","shop_med", + "bus_stop","tram_stop","park","playground","sports","cafe","restaurant", + "atm","post"] + out = [] + for cat in cats: + mine = c.execute( + "SELECT COUNT(*) FROM pois WHERE site_id=? AND category=? AND distance_m<=1000", + (site_id, cat)).fetchone()[0] + placeholders = ",".join("?"*len(peers)) + avg = c.execute( + f"SELECT AVG(c) FROM (SELECT COUNT(*) c FROM pois WHERE site_id IN ({placeholders}) AND category=? AND distance_m<=1000 GROUP BY site_id)", + (*peers, cat)).fetchone()[0] or 0 + out.append({"category": cat, "mine": mine, "district_avg": round(avg, 1), + "delta_pct": round(100*(mine-avg)/avg, 1) if avg else None}) + return {"site_id": site_id, "district": district, "comparison": out} + + +@app.get("/api/district-compare") +def district_compare(districts: str): + """Compare 2-3 districts on key metrics. Body: 'A,B,C'.""" + names = [n.strip() for n in districts.split(",") if n.strip()][:5] + out = [] + with _conn() as c: + for name in names: + r = c.execute("""SELECT * FROM district_economics WHERE district=?""", (name,)).fetchone() + if r: out.append(dict(r)) + return {"districts": names, "data": out} + + +@app.get("/api/district-roi-ranking") +def district_roi_ranking(obj_class: str = "комфорт", far: float = 2.0, + parcel_area_m2: float = 3000): + """For a hypothetical parcel size + class, rank ALL districts by IRR/margin. + Useful for investor scouting — 'where to look for parcels'.""" + CLASSES = { + "эконом": {"price_baseline": 100, "construction": 60000, "saleable": 0.65, "avg_unit": 38}, + "комфорт": {"price_baseline": 130, "construction": 75000, "saleable": 0.62, "avg_unit": 48}, + "бизнес": {"price_baseline": 200, "construction": 110000, "saleable": 0.55, "avg_unit": 65}, + "премиум": {"price_baseline": 320, "construction": 170000, "saleable": 0.50, "avg_unit": 95}, + } + cfg = CLASSES.get(obj_class, CLASSES["комфорт"]) + out = [] + with _conn() as c: + rows = c.execute("""SELECT district, real_median_price_m2, real_velocity_6mo, + real_sold_pct, n_projects + FROM district_economics + WHERE real_median_price_m2 IS NOT NULL""").fetchall() + for r in rows: + # adjust class price by district base ratio + ratio = (r["real_median_price_m2"] or 130) / 130 + price_kr = cfg["price_baseline"] * ratio + gba = parcel_area_m2 * far + saleable = gba * cfg["saleable"] + n_units = round(saleable / cfg["avg_unit"]) + revenue = saleable * price_kr * 1000 + construction = gba * cfg["construction"] + soft = revenue * 0.10 + profit = revenue - construction - soft + margin = 100 * profit / revenue if revenue else 0 + # crude ROI-per-month: profit / (construction × months) × 12 + # assume sellout = n_units / district_velocity_per_corp (capped at 30 mo) + v = r["real_velocity_6mo"] or 1.0 + months = max(18, min(60, n_units / max(v, 0.5))) + roi_annual = (profit / construction) * 12 / months * 100 if construction else 0 + out.append({ + "district": r["district"], + "median_price_kr": round(r["real_median_price_m2"], 1), + "velocity_6mo": round(v, 2), + "sold_pct": round(r["real_sold_pct"] or 0, 1), + "n_projects": r["n_projects"], + "price_kr_class": round(price_kr, 1), + "n_units": n_units, + "revenue_mln": round(revenue/1e6, 1), + "profit_mln": round(profit/1e6, 1), + "margin_pct": round(margin, 1), + "months_to_sellout": round(months, 1), + "roi_annual_pct": round(roi_annual, 1), + }) + out.sort(key=lambda x: -x["roi_annual_pct"]) + return {"obj_class": obj_class, "far": far, "parcel_area_m2": parcel_area_m2, + "results": out} + + +@app.get("/api/risk-score") +def risk_score(lat: float, lon: float): + """Risk: distance to railways, highways, industry — closer = higher noise/dust.""" + import math + risks = { + "railway": {"items": [], "ideal_m": 200, "max_m": 800, "weight": 0.30}, + "highway": {"items": [], "ideal_m": 80, "max_m": 400, "weight": 0.30}, + "industry": {"items": [], "ideal_m": 200, "max_m": 800, "weight": 0.25}, + "fuel": {"items": [], "ideal_m": 80, "max_m": 300, "weight": 0.05}, + "fire": {"items": [], "ideal_m": 500, "max_m": 2000,"weight": 0.10}, + } + # Use cached OSM raw — railway / highway data lives in osm_buildings_all only as buildings, + # so for risk we sample fuel + fire from main cache (already there). + for cat in ("fuel", "fire"): + for el in _osm_data.get(cat, []): + if el["type"] == "node": + la, lo = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {}; la, lo = c.get("lat"), c.get("lon") + if la is None: continue + d = hav(lat, lon, la, lo) + if d <= risks[cat]["max_m"] * 2: + risks[cat]["items"].append({"distance_m": round(d, 1)}) + + # Railway/highway from OSM Overpass live — small bbox (1km around point) + try: + import requests + bbox = (lat - 0.012, lon - 0.020, lat + 0.012, lon + 0.020) # ~1.5km + q = f'[out:json][timeout:30];(way["railway"]({bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]});way["highway"~"^(motorway|trunk|primary|secondary)$"]({bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}););out geom;' + r = requests.post("https://overpass-api.de/api/interpreter", data={"data": q}, + timeout=40, headers={"User-Agent":"gendesign/0.7"}) + if r.ok: + for el in r.json().get("elements", []): + if not el.get("geometry"): continue + cat = "railway" if el.get("tags",{}).get("railway") else "highway" + # nearest point of way + min_d = min(hav(lat, lon, p["lat"], p["lon"]) for p in el["geometry"]) + risks[cat]["items"].append({"distance_m": round(min_d, 1), + "name": el.get("tags",{}).get("name","")}) + except Exception as e: + risks["_overpass_err"] = str(e)[:120] + + # Score per category — closer to ideal = high penalty + out = {"lat": lat, "lon": lon, "risks": {}} + total_penalty = 0 + for cat, cfg in risks.items(): + if cat.startswith("_"): continue + if not cfg["items"]: + out["risks"][cat] = {"min_distance_m": None, "n_within_buffer": 0, "penalty_pct": 0, "weight": cfg["weight"]} + continue + nearest = min(cfg["items"], key=lambda x: x["distance_m"]) + d = nearest["distance_m"] + if d <= cfg["ideal_m"]: + penalty = cfg["weight"] * 100 + elif d >= cfg["max_m"]: + penalty = 0 + else: + penalty = cfg["weight"] * 100 * (cfg["max_m"] - d) / (cfg["max_m"] - cfg["ideal_m"]) + out["risks"][cat] = { + "min_distance_m": d, + "n_within_buffer": sum(1 for x in cfg["items"] if x["distance_m"] <= cfg["max_m"]), + "penalty_pct": round(penalty, 1), + "weight": cfg["weight"], + } + total_penalty += penalty + out["total_penalty"] = round(total_penalty, 1) + out["risk_score"] = round(max(0, 100 - total_penalty), 1) # 100 = no risks + return out + + +@app.post("/api/tep-multiclass") +def tep_multiclass(payload: dict): + """Compare TEP across 3 classes (econom/comfort/business) for same parcel.""" + base_district = payload.get("district") + parcel_area = payload.get("parcel_area_m2", 2789) + far = payload.get("far", 2.0) + + CLASSES = { + "эконом": {"price_kr": 100, "construction": 60000, "saleable": 0.65, "avg_unit": 38}, + "комфорт": {"price_kr": 130, "construction": 75000, "saleable": 0.62, "avg_unit": 48}, + "бизнес": {"price_kr": 200, "construction": 110000, "saleable": 0.55, "avg_unit": 65}, + "премиум": {"price_kr": 320, "construction": 170000, "saleable": 0.50, "avg_unit": 95}, + } + # If we have real district median, use class-relative multipliers + if base_district: + with _conn() as c: + row = c.execute("SELECT real_median_price_m2 FROM district_economics WHERE district=?", + (base_district,)).fetchone() + if row and row["real_median_price_m2"]: + base = row["real_median_price_m2"] + # adjust class prices proportionally to district base + ratio = base / 130 # comfort baseline + for cls in CLASSES.values(): + cls["price_kr"] = round(cls["price_kr"] * ratio, 1) + + results = [] + for cls_name, cfg in CLASSES.items(): + gba = parcel_area * far + saleable = gba * cfg["saleable"] + n_units = round(saleable / cfg["avg_unit"]) + revenue = saleable * cfg["price_kr"] * 1000 + construction = gba * cfg["construction"] + soft = revenue * 0.10 + profit = revenue - construction - soft + margin = 100 * profit / revenue if revenue else 0 + results.append({ + "class": cls_name, + "price_kr_per_m2": cfg["price_kr"], + "construction_per_m2": cfg["construction"], + "n_units": n_units, + "revenue_mln": round(revenue/1e6, 1), + "cost_mln": round(construction/1e6, 1), + "profit_mln": round(profit/1e6, 1), + "margin_pct": round(margin, 1), + }) + results.sort(key=lambda x: -x["margin_pct"]) + return {"district": base_district, "parcel_area_m2": parcel_area, "far": far, + "results": results, "best_class": results[0]["class"]} + + +@app.get("/api/insolation") +def insolation(lat: float, lon: float, radius_m: int = 100): + """Approximate insolation analysis for a parcel point. + + Returns: density of OSM buildings around the point per octant (N/NE/E/SE/.../NW), + so we can identify which sides have shadow risk (dense) vs open (sun). + """ + # Use full OSM building centroids (61k+) cached at cache/osm_buildings_all.geojson + fc_path = ROOT / "cache" / "osm_buildings_all.geojson" + nearby = [] + if fc_path.exists(): + fc = json.loads(fc_path.read_text()) + import math + for feat in fc.get("features", []): + coords = feat.get("geometry", {}).get("coordinates") + if not coords: continue + clon, clat = coords[0], coords[1] + d = hav(lat, lon, clat, clon) + if d <= radius_m * 5: + dlon = math.radians(clon - lon) + la1, la2 = math.radians(lat), math.radians(clat) + y = math.sin(dlon) * math.cos(la2) + x = math.cos(la1)*math.sin(la2) - math.sin(la1)*math.cos(la2)*math.cos(dlon) + bearing = (math.degrees(math.atan2(y, x)) + 360) % 360 + nearby.append({"distance_m": round(d), "bearing_deg": round(bearing, 1)}) + # Aggregate per octant + south exposure score + octants = ["N","NE","E","SE","S","SW","W","NW"] + counts = {o: 0 for o in octants} + weighted_shadow_S = 0 # higher = more buildings to the South (BAD for insolation) + for b in nearby: + idx = int(((b["bearing_deg"] + 22.5) % 360) / 45) + counts[octants[idx]] += 1 + # Building is south of parcel if bearing in (135°, 225°) + if 135 < b["bearing_deg"] < 225: + weighted_shadow_S += 1 / (1 + b["distance_m"]/50) + # Insolation score: 100 = wide open south, 0 = dense south wall + insolation_score = max(0, 100 - weighted_shadow_S * 10) + return { + "lat": lat, "lon": lon, + "n_buildings_500m": len(nearby), + "octants": counts, + "insolation_score": round(insolation_score, 1), + "shadow_pressure_S": round(weighted_shadow_S, 2), + "nearby_sample": sorted(nearby, key=lambda x: x["distance_m"])[:8], + } + + +@app.get("/api/poi-points") +def poi_points(category: str = "*"): + """Returns flat array of [lat, lon, weight] for heatmap rendering. + category in {*, education, health, retail, transit, leisure, cafe}.""" + GROUPS = { + "education": ["kindergarten","school","university"], + "health": ["pharmacy","clinic","hospital"], + "retail": ["shop_big","shop_med","shop_small"], + "transit": ["bus_stop","tram_stop","metro"], + "leisure": ["park","playground","sports"], + "cafe": ["cafe","restaurant"], + } + target_cats = GROUPS.get(category) # None means all + pts = [] + seen = set() + for cat, elems in _osm_data.items(): + if target_cats and cat not in target_cats: continue + for el in elems: + if el["type"] == "node": + la, lo = el.get("lat"), el.get("lon") + else: + c = el.get("center") or {}; la, lo = c.get("lat"), c.get("lon") + if la is None: continue + key = (round(la, 5), round(lo, 5)) + if key in seen: continue + seen.add(key) + pts.append([la, lo, 0.5]) + return {"points": pts, "count": len(pts), "category": category} + + +@app.get("/api/leaderboard") +def leaderboard(audience: str = "balanced", limit: int = 30, + obj_class: Optional[str] = None, district: Optional[str] = None, + min_score: float = 0): + """Top-N ЖК ranked by chosen audience profile. + + Audience profile re-weights the per-component scores already cached in `scores`. + """ + weights = AUDIENCES.get(audience, AUDIENCES["balanced"]) + weights = {k: v for k, v in weights.items() if k != "label"} + with _conn() as c: + rows = c.execute(""" + SELECT s.site_id, s.name, s.developer, s.obj_class, s.flat_count, + s.lat, s.lon, sd.district AS obj_district, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='education') AS edu, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='health') AS health, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='retail') AS retail, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='transit') AS transit, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='leisure') AS leisure, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='economic') AS economic, + (SELECT score_0_100 FROM scores WHERE site_id=s.site_id AND component='market') AS market, + m.project, + (SELECT real_median_price_m2 FROM district_economics + WHERE district=sd.district) AS price, + (SELECT real_velocity_6mo FROM district_economics + WHERE district=sd.district) AS vel + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN jk_objective_match m USING (site_id) + WHERE s.kind='jk' + """).fetchall() + out = [] + for r in rows: + if obj_class and r["obj_class"] != obj_class: continue + if district and r["obj_district"] != district: continue + comps = {"education": r["edu"] or 0, "health": r["health"] or 0, + "retail": r["retail"] or 0, "transit": r["transit"] or 0, + "leisure": r["leisure"] or 0, "economic": r["economic"] or 0, + "market": r["market"] or 0} + weighted = sum(weights.get(k, 0) * v for k, v in comps.items()) + if weighted < min_score: continue + out.append({ + "site_id": r["site_id"], "name": r["name"], "developer": r["developer"], + "obj_class": r["obj_class"], "flat_count": r["flat_count"], + "obj_district": r["obj_district"], "lat": r["lat"], "lon": r["lon"], + "weighted": round(weighted, 2), + "matched_objective": bool(r["project"]), + "price_m2_kr": round(r["price"], 1) if r["price"] else None, + "velocity_6mo": round(r["vel"], 2) if r["vel"] else None, + "components": {k: round(v, 1) for k, v in comps.items()}, + }) + out.sort(key=lambda x: -x["weighted"]) + return {"audience": audience, "weights": weights, "total_matched": len(out), + "results": out[:limit]} + + +@app.post("/api/bulk-analyze") +def bulk_analyze(payload: dict): + """Analyze a batch of {cad,lat,lon} entries. + Body: {points: [{cad?,lat,lon,name?}], audience: 'family'} + """ + points = payload.get("points") or [] + audience = payload.get("audience") or "balanced" + weights = AUDIENCES.get(audience, AUDIENCES["balanced"]) + weights = {k: v for k, v in weights.items() if k != "label"} + results = [] + for p in points[:50]: # cap 50 + try: + res = score_point(p.get("lat"), p.get("lon"), weights) + results.append({ + "cad": p.get("cad"), "name": p.get("name"), + "lat": p.get("lat"), "lon": p.get("lon"), + "weighted": res["weighted"], "rank": res["rank_overall"], + "district": res["district"], "scores": res["scores"], + }) + except Exception as e: + results.append({"cad": p.get("cad"), "error": str(e)[:120]}) + results.sort(key=lambda x: -(x.get("weighted") or 0)) + return {"audience": audience, "n": len(results), "results": results} + + +@app.get("/api/jk-sellout/{site_id:path}") +def jk_sellout(site_id: str): + """Months-to-sellout for a specific ЖК: current stock / 6mo velocity.""" + with _conn() as c: + m = c.execute("SELECT project FROM jk_objective_match WHERE site_id=?", + (site_id,)).fetchone() + if not m: + return {"site_id": site_id, "matched": False} + project = m["project"] + agg = c.execute("""SELECT + COUNT(CASE WHEN status IN ('в продаже','резерв') THEN 1 END) AS stock_lots, + COUNT(CASE WHEN status IN ('в продаже','резерв') THEN 1 END) + * AVG(CASE WHEN area_pd>0 THEN area_pd END) AS stock_m2, + COUNT(CASE WHEN register_date >= date('now','-6 months') THEN 1 END)/6.0 AS vel_6mo, + COUNT(*) AS total, + SUM(CASE WHEN sold='да' THEN 1 ELSE 0 END) AS sold_n + FROM objective_lots WHERE project=?""", (project,)).fetchone() + d = dict(agg) if agg else {} + d["site_id"] = site_id + d["project"] = project + d["matched"] = True + d["months_to_sellout"] = (d["stock_lots"] / d["vel_6mo"]) if d.get("vel_6mo") and d["vel_6mo"] > 0 else None + return d + + +@app.get("/api/nearby-jk-velocity") +def nearby_jk_velocity(lat: float, lon: float, radius_m: int = 1500): + """Per-complex velocity for ЖК within radius_m of point. + + Returns list with: site_id, name, distance_m, velocity_6mo (deals/month), + n_lots_district, sold_pct_district, weighted_score. + Useful for direct-competitor analysis. + """ + with _conn() as c: + sites = c.execute("""SELECT s.site_id, s.name, s.lat, s.lon, s.developer, s.obj_class, + sd.district AS obj_district, + st.weighted, st.rank_overall, + m.project + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN scores_total st USING (site_id) + LEFT JOIN jk_objective_match m USING (site_id) + WHERE s.kind='jk'""").fetchall() + # per-project velocity from objective_lots + proj_vel = {r["project"]: (r["v"], r["sold"], r["nlots"]) for r in c.execute(""" + SELECT project, + COUNT(CASE WHEN register_date >= date('now','-6 months') THEN 1 END)/6.0 AS v, + ROUND(100.0 * SUM(CASE WHEN sold='да' THEN 1 ELSE 0 END) / COUNT(*), 1) AS sold, + COUNT(*) AS nlots + FROM objective_lots + WHERE project IS NOT NULL + GROUP BY 1""").fetchall()} + out = [] + for s in sites: + d = hav(lat, lon, s["lat"], s["lon"]) + if d <= radius_m: + vel, sold, nlots = proj_vel.get(s["project"], (None, None, None)) + out.append({ + "site_id": s["site_id"], "name": s["name"], "lat": s["lat"], "lon": s["lon"], + "distance_m": round(d, 1), "developer": s["developer"], "obj_class": s["obj_class"], + "obj_district": s["obj_district"], "weighted": s["weighted"], "rank": s["rank_overall"], + "project_in_objective": s["project"], + "velocity_6mo": vel, "sold_pct": sold, "n_lots": nlots, + }) + out.sort(key=lambda x: x["distance_m"]) + return out + + +@app.post("/api/fetch-polygon/{cad}") +def fetch_polygon(cad: str): + """Pull parcel polygon from Rosreestr via rosreestr2coord lib (bypasses NSPD WAF). + + Caches to cache/parcel_polygons/.geojson and returns the FeatureCollection. + Workaround for NSPD WAF — the lib uses a different upstream that still works. + """ + try: + from rosreestr2coord.parser import Area + import shapely.geometry as sg + import pyproj + except ImportError as e: + raise HTTPException(500, f"Missing dependency: {e}. Install rosreestr2coord shapely pyproj.") + + a = Area(cad, area_type=1, with_log=False, use_cache=False, timeout=20) + if not a.feature: + raise HTTPException(404, f"No polygon found for {cad}") + + feature = dict(a.feature) + # rosreestr2coord returns WGS84 already despite metadata + feature["geometry"]["crs"] = {"type":"name","properties":{"name":"EPSG:4326"}} + poly = sg.shape(feature["geometry"]) + centroid = poly.centroid + geod = pyproj.Geod(ellps="WGS84") + area_m2 = abs(geod.geometry_area_perimeter(poly)[0]) + + fc = { + "type":"FeatureCollection", + "features":[feature], + "_centroid": {"lat": centroid.y, "lon": centroid.x}, + "_area_m2": round(area_m2, 1), + "_source": "rosreestr2coord", + "_props": feature["properties"].get("options", {}), + } + fname = (ROOT / "cache" / "parcel_polygons" / f"{cad.replace(':','_')}.geojson") + try: + fname.parent.mkdir(parents=True, exist_ok=True) + fname.write_text(json.dumps(fc, ensure_ascii=False, indent=2)) + except OSError: + pass # cache mount is RO in prod — fc is still returned to caller + return fc + + +@app.post("/api/upload-polygon") +def upload_polygon(payload: dict): + """Manually upload parcel polygon (e.g., copied from NSPD). + + Body: {cad: '66:41:0204016:10', geojson: {...}} + Stored at cache/parcel_polygons/.geojson; subsequent /api/analyze + will use polygon centroid AND min-distance from polygon edges. + """ + cad = (payload.get("cad") or "").strip() + gj = payload.get("geojson") + if not cad or not gj: + raise HTTPException(400, "cad and geojson required") + fname = (ROOT / "cache" / "parcel_polygons" / f"{cad.replace(':','_')}.geojson") + try: + fname.parent.mkdir(parents=True, exist_ok=True) + fname.write_text(json.dumps(gj, ensure_ascii=False)) + except OSError as e: + raise HTTPException(503, f"cache mount is read-only: {e}") + return {"saved": str(fname.relative_to(ROOT)), "size": fname.stat().st_size} + + +@app.get("/api/parcel-polygon/{cad}") +def get_polygon(cad: str): + fname = (ROOT / "cache" / "parcel_polygons" / f"{cad.replace(':','_')}.geojson") + if not fname.exists(): + raise HTTPException(404, f"no polygon cached for {cad}") + return json.loads(fname.read_text()) + + +# Static cache for jk polygons FeatureCollection +_JK_POLYGONS = None +def _load_jk_polygons(): + global _JK_POLYGONS + if _JK_POLYGONS is None: + path = ROOT / "cache" / "jk_polygons.geojson" + if path.exists(): + _JK_POLYGONS = json.loads(path.read_text()) + # enrich features with site weighted+name from local DB + with _conn() as c: + site_meta = {r["site_id"]: dict(r) for r in c.execute( + """SELECT s.site_id, s.name, s.developer, s.obj_class, s.district, + sd.district AS obj_district, st.weighted, st.rank_overall + FROM sites s + LEFT JOIN site_district sd USING (site_id) + LEFT JOIN scores_total st USING (site_id) + WHERE s.kind='jk'""").fetchall()} + for f in _JK_POLYGONS["features"]: + meta = site_meta.get(f["properties"]["site_id"], {}) + f["properties"].update(meta) + else: + _JK_POLYGONS = {"type":"FeatureCollection","features":[]} + return _JK_POLYGONS + +@app.get("/api/jk-polygons") +def jk_polygons(): + """Building footprint polygons matched to ЖК via spatial join (within 80m). + Returns ~314 buildings for 111 of 380 ЖК (29% coverage). + Properties enriched with site name, developer, score, rank.""" + return _load_jk_polygons() + + +@app.get("/api/district-velocity-trend/{district}") +def district_velocity_trend(district: str): + """Monthly registrations count for a district over last 12 months. + Source: objective_lots.register_date. + """ + with _conn() as c: + rows = c.execute(""" + SELECT substr(register_date, 1, 7) AS m, COUNT(*) n + FROM objective_lots + WHERE district = ? + AND register_date IS NOT NULL + AND register_date >= date('now', '-12 months') + GROUP BY 1 ORDER BY 1""", (district,)).fetchall() + # Fill empty months + import datetime as dt + today = dt.date.today().replace(day=1) + months = [] + y, m = today.year, today.month + for _ in range(12): + months.append(f"{y:04d}-{m:02d}") + m -= 1 + if m == 0: m = 12; y -= 1 + months.reverse() + series = {r["m"]: r["n"] for r in rows} + return [{"month": m, "n": series.get(m, 0)} for m in months] + + +class WhatIfPOI(BaseModel): + cat: str + lat: float + lon: float + name: Optional[str] = None + + +class AnalyzeIn(BaseModel): + cad: Optional[str] = None + lat: Optional[float] = None + lon: Optional[float] = None + name: Optional[str] = None + audience: Optional[str] = "balanced" + what_if: Optional[list] = None # [{cat, lat, lon, name?}] + remove_cats: Optional[list] = None # ["metro"] + + +@app.post("/api/analyze") +def analyze(payload: AnalyzeIn): + lat, lon = payload.lat, payload.lon + + # If only cad given, try cached polygon + if (lat is None or lon is None) and payload.cad: + fname = (ROOT / "cache" / "parcel_polygons" / + f"{payload.cad.replace(':','_')}.geojson") + if fname.exists(): + gj = json.loads(fname.read_text()) + geom = gj.get("geometry") or (gj.get("features",[{}])[0].get("geometry") + if gj.get("type")=="FeatureCollection" else None) + if geom and geom.get("type") in ("Polygon","MultiPolygon"): + pts = geom["coordinates"][0] if geom["type"]=="Polygon" else geom["coordinates"][0][0] + lon = sum(p[0] for p in pts) / len(pts) + lat = sum(p[1] for p in pts) / len(pts) + + if lat is None or lon is None: + raise HTTPException(400, "lat+lon required (or upload polygon for cad)") + + weights = AUDIENCES.get(payload.audience or "balanced", AUDIENCES["balanced"]) + weights = {k: v for k, v in weights.items() if k != "label"} + result = score_point(lat, lon, weights, + what_if=payload.what_if, + remove_cats=payload.remove_cats) + result["input"] = {"cad": payload.cad, "lat": lat, "lon": lon, + "name": payload.name, "audience": payload.audience or "balanced", + "what_if": payload.what_if, "remove_cats": payload.remove_cats} + # Include cached polygon if present + if payload.cad: + fname = (ROOT / "cache" / "parcel_polygons" / + f"{payload.cad.replace(':','_')}.geojson") + if fname.exists(): + try: result["polygon"] = json.loads(fname.read_text()) + except: pass + return result + + +# Service Worker must be served from root scope so it can intercept all tile requests +@app.get("/sw.js") +def sw(): + sw_path = STATIC / "sw.js" + if not sw_path.exists(): + raise HTTPException(404) + return FileResponse(sw_path, media_type="application/javascript", + headers={"Service-Worker-Allowed": "/", "Cache-Control": "no-cache"}) + + +# Static UI (mount last) +if STATIC.exists(): + app.mount("/", StaticFiles(directory=STATIC, html=True), name="static") diff --git a/site-finder/static/index.html b/site-finder/static/index.html new file mode 100644 index 00000000..c72f1b6c --- /dev/null +++ b/site-finder/static/index.html @@ -0,0 +1,3513 @@ + + + + +GenDesign · Анализ участка + + + + + + + + + + + + + + +
+
+
+
+
+ 🏘 ЖК + + + + +
+
+ 📍 Участок · радиусы + + + + +
+ + +
+
+ 🌐 POI плотность + + +
+
+ 🔍 Фильтр ЖК + + + +
+ +
+ +
+
+ 🗺 Подложка + +
+
+
+
Легенда
+
участок
+
ЖК · топ-25%
+
ЖК · средний
+
ЖК · ниже
+
POI
+
+
+
+ + + + + + + + + + + + + + + + + + + diff --git a/site-finder/static/sw.js b/site-finder/static/sw.js new file mode 100644 index 00000000..0f117202 --- /dev/null +++ b/site-finder/static/sw.js @@ -0,0 +1,67 @@ +// Service Worker for persistent tile + asset caching. +// Strategy: cache-first for tiles (fast repeat loads, offline-capable), +// network-first for HTML/JS (fresh code on each reload). +const VERSION = 'v18'; +const TILE_CACHE = 'gendesign-tiles-' + VERSION; +const ASSET_CACHE = 'gendesign-assets-' + VERSION; +const TILE_HOSTS = [ + 'basemaps.cartocdn.com', + 'tile.openstreetmap.org', + 'tile.openstreetmap.fr', + 'maps.2gis.com', + 'server.arcgisonline.com', + 'core-renderer-tiles.maps.yandex.net', + 'core-sat.maps.yandex.net', + 'unpkg.com', + 'cdn.jsdelivr.net', +]; + +self.addEventListener('install', e => { + self.skipWaiting(); +}); + +self.addEventListener('activate', e => { + e.waitUntil((async () => { + const keys = await caches.keys(); + await Promise.all(keys.filter(k => !k.endsWith(VERSION)).map(k => caches.delete(k))); + await self.clients.claim(); + })()); +}); + +self.addEventListener('fetch', e => { + const req = e.request; + if (req.method !== 'GET') return; + const url = new URL(req.url); + const isTile = TILE_HOSTS.some(h => url.hostname.endsWith(h)); + if (!isTile) return; // let same-origin (FastAPI) requests pass through normally + + e.respondWith((async () => { + const cacheName = url.hostname.includes('basemaps') || url.hostname.endsWith('cdn.jsdelivr.net') || url.hostname === 'unpkg.com' + ? ASSET_CACHE : TILE_CACHE; + const cache = await caches.open(cacheName); + const cached = await cache.match(req); + if (cached) { + // refresh in background (stale-while-revalidate) + fetch(req).then(r => { if (r.ok) cache.put(req, r.clone()); }).catch(()=>{}); + return cached; + } + try { + const resp = await fetch(req); + if (resp.ok && resp.status === 200) { + // size check — don't cache huge >1 MB blobs + const len = +resp.headers.get('content-length') || 0; + if (len < 2_000_000) cache.put(req, resp.clone()); + } + return resp; + } catch (err) { + // offline: try fallback (e.g. blank tile) — for now just rethrow + throw err; + } + })()); +}); + +self.addEventListener('message', e => { + if (e.data === 'clear-cache') { + caches.keys().then(ks => Promise.all(ks.map(k => caches.delete(k)))); + } +});