from __future__ import annotations import time as _time from datetime import datetime, timezone, timedelta from typing import Dict, Any, Optional from fastapi import HTTPException from loguru import logger from web.core import ( _cache, CACHE_TTL, CACHE_TTL_ANKARA, CITIES, CITY_RISK_PROFILES, SETTLEMENT_SOURCE_LABELS, _is_excluded_model_name, _market_layer, _sf, _weather, ) from src.analysis.deb_algorithm import calculate_dynamic_weights from src.analysis.settlement_rounding import apply_city_settlement from src.analysis.metar_narrator import describe_metar_report from src.data_collection.city_registry import ALIASES def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]: """Fetch, analyse, and return structured weather data for one city.""" # Check cache ttl = CACHE_TTL_ANKARA if city.lower() == "ankara" else CACHE_TTL if not force_refresh: cached = _cache.get(city) if cached and _time.time() - cached["t"] < ttl: return cached["d"] info = CITIES[city] lat, lon, is_f = info["lat"], info["lon"], info["f"] sym = "°F" if is_f else "°C" settlement_source = str(info.get("settlement_source") or "metar").strip().lower() or "metar" settlement_source_label = SETTLEMENT_SOURCE_LABELS.get( settlement_source, settlement_source.upper(), ) # ── 1. Fetch raw data ── raw = _weather.fetch_all_sources( city, lat=lat, lon=lon, force_refresh=force_refresh, ) om = raw.get("open-meteo", {}) metar = raw.get("metar", {}) mgm = raw.get("mgm") or {} settlement_current = raw.get("settlement_current") or {} ens_raw = raw.get("ensemble", {}) mm = raw.get("multi_model", {}) if not isinstance(om, dict): om = {} if not isinstance(metar, dict): metar = {} if not isinstance(mgm, dict): mgm = {} if not isinstance(settlement_current, dict): settlement_current = {} if not isinstance(ens_raw, dict): ens_raw = {} if not isinstance(mm, dict): mm = {} risk = CITY_RISK_PROFILES.get(city, {}) # ── 2. Current conditions (city-specific settlement source first, then METAR/MGM fallback) ── mc = metar.get("current", {}) if metar else {} mg_cur = mgm.get("current", {}) if mgm else {} sc_cur = settlement_current.get("current", {}) if settlement_current else {} use_settlement_current = settlement_source in {"hko", "cwa"} and bool(sc_cur) primary_current = sc_cur if use_settlement_current else mc cur_temp = _sf(primary_current.get("temp")) if cur_temp is None: cur_temp = _sf(mc.get("temp")) if cur_temp is None: cur_temp = _sf(mg_cur.get("temp")) max_so_far = _sf(primary_current.get("max_temp_so_far")) if max_so_far is None: max_so_far = _sf(mc.get("max_temp_so_far")) if max_so_far is None: max_so_far = _sf(mg_cur.get("mgm_max_temp")) max_temp_time = primary_current.get("max_temp_time") if not max_temp_time and not use_settlement_current: max_temp_time = mc.get("max_temp_time") if not max_temp_time: max_temp_time = mg_cur.get("time", "") if " " in max_temp_time: max_temp_time = max_temp_time.split(" ")[1][:5] if max_temp_time == "": max_temp_time = None wu_settle = apply_city_settlement(city.lower(), max_so_far) if max_so_far is not None else None # Observation time → local obs_time_str = "" metar_age_min = None obs_t = "" if use_settlement_current: obs_t = str(settlement_current.get("observation_time") or "").strip() if not obs_t: obs_t = metar.get("observation_time", "") if metar else "" # 优先从 API 获取偏移；若缺失则尝试 NWS 动态偏移；最后回退静态配置 utc_offset = om.get("utc_offset") if utc_offset is None: try: nws_periods = (raw.get("nws", {}) or {}).get("forecast_periods", []) or [] if nws_periods: first_start = nws_periods[0].get("start_time") if first_start: maybe_dt = datetime.fromisoformat(str(first_start)) if maybe_dt.utcoffset() is not None: utc_offset = int(maybe_dt.utcoffset().total_seconds()) except Exception: utc_offset = None if utc_offset is None: utc_offset = info.get("tz", 0) if obs_t and "T" in obs_t: try: dt = datetime.fromisoformat(str(obs_t).replace("Z", "+00:00")) if dt.tzinfo is None: dt = dt.replace(tzinfo=timezone.utc) local_dt = dt.astimezone(timezone(timedelta(seconds=utc_offset))) obs_time_str = local_dt.strftime("%H:%M") metar_age_min = int( (datetime.now(timezone.utc) - dt.astimezone(timezone.utc)).total_seconds() / 60 ) except Exception: obs_time_str = str(obs_t)[:16] settlement_today_obs = [] if use_settlement_current: if obs_time_str and cur_temp is not None: settlement_today_obs.append({"time": obs_time_str, "temp": cur_temp}) if ( max_temp_time and max_so_far is not None and str(max_temp_time) != str(obs_time_str) ): settlement_today_obs.append({"time": str(max_temp_time), "temp": max_so_far}) metar_today_obs_payload = ( [] if use_settlement_current else [ {"time": t, "temp": v} for t, v in (metar.get("today_obs", []) if metar else []) ] ) metar_recent_obs_payload = ( [] if use_settlement_current else (metar.get("recent_obs", []) if metar else []) ) # ── 3. Local time parsing ── local_time_full = om.get("current", {}).get("local_time", "") local_hour, local_minute = 12, 0 now_utc = datetime.now(timezone.utc) local_now = now_utc + timedelta(seconds=utc_offset) local_date_str = local_now.strftime("%Y-%m-%d") try: if local_time_full: local_date_str = local_time_full.split(" ")[0] tp = local_time_full.split(" ")[1].split(":") local_hour = int(tp[0]) local_minute = int(tp[1]) if len(tp) > 1 else 0 else: local_hour = local_now.hour local_minute = local_now.minute except Exception: local_hour = local_now.hour local_minute = local_now.minute local_time_str = f"{local_hour:02d}:{local_minute:02d}" local_hour_frac = local_hour + local_minute / 60 # ── 4. Daily forecast ── daily = om.get("daily", {}) dates = daily.get("time", [])[:5] maxtemps = daily.get("temperature_2m_max", [])[:5] sunrises = daily.get("sunrise", []) sunsets = daily.get("sunset", []) sunshine = daily.get("sunshine_duration", []) om_today = _sf(maxtemps[0]) if maxtemps else None forecast_daily = [{"date": d, "max_temp": t} for d, t in zip(dates, maxtemps)] if om_today is None: nws_high = _sf(raw.get("nws", {}).get("today_high")) mgm_high = _sf(mgm.get("today_high")) if mgm else None fallback_high = ( nws_high if nws_high is not None else mgm_high if mgm_high is not None else max_so_far if max_so_far is not None else cur_temp ) if fallback_high is not None: om_today = float(fallback_high) if not forecast_daily: forecast_daily = [{"date": local_date_str, "max_temp": om_today}] sunrise = ( sunrises[0].split("T")[1][:5] if sunrises and "T" in str(sunrises[0]) else "" ) sunset = ( sunsets[0].split("T")[1][:5] if sunsets and "T" in str(sunsets[0]) else "" ) sunshine_h = round(sunshine[0] / 3600, 1) if sunshine else 0 # ── 5. Multi-model forecasts ── current_forecasts: Dict[str, float] = {} if om_today is not None: current_forecasts["Open-Meteo"] = om_today for m, v in mm.get("forecasts", {}).items(): if v is not None and not _is_excluded_model_name(m): current_forecasts[m] = _sf(v) nws_high = _sf(raw.get("nws", {}).get("today_high")) if nws_high is not None: current_forecasts["NWS"] = nws_high mgm_high = _sf(mgm.get("today_high")) if mgm else None if mgm_high is not None: current_forecasts["MGM"] = mgm_high # ── 6. DEB fusion ── deb_val, deb_weights = None, "" if current_forecasts: blended, winfo = calculate_dynamic_weights(city, current_forecasts) if blended is not None: deb_val = blended deb_weights = winfo # ── 7. Ensemble stats ── ens_data = { "median": _sf(ens_raw.get("median")), "p10": _sf(ens_raw.get("p10")), "p90": _sf(ens_raw.get("p90")), } # ── 8. METAR trend ── recent_temps = metar.get("recent_temps", []) if metar else [] trend_info = { "direction": "unknown", "recent": [{"time": t, "temp": v} for t, v in recent_temps[:6]], "is_cooling": False, "is_dead_market": False, } if len(recent_temps) >= 2: t_only = [t for _, t in recent_temps] latest, prev = t_only[0], t_only[1] diff = latest - prev if len(t_only) >= 3: n = min(3, len(t_only)) all_same = all(t == latest for t in t_only[:n]) all_rising = all(t_only[i] >= t_only[i + 1] for i in range(n - 1)) all_falling = all(t_only[i] <= t_only[i + 1] for i in range(n - 1)) if all_same: trend_info["direction"] = "stagnant" elif all_rising and diff > 0: trend_info["direction"] = "rising" elif all_falling and diff < 0: trend_info["direction"] = "falling" else: trend_info["direction"] = "mixed" elif diff > 0: trend_info["direction"] = "rising" elif diff < 0: trend_info["direction"] = "falling" else: trend_info["direction"] = "stagnant" trend_info["is_cooling"] = trend_info["direction"] in ("falling", "stagnant") # ── 9. Peak hour detection ── hourly = om.get("hourly", {}) h_times = hourly.get("time", []) h_temps = hourly.get("temperature_2m", []) h_rad = hourly.get("shortwave_radiation", []) h_dew = hourly.get("dew_point_2m", []) h_pressure = hourly.get("pressure_msl", []) h_wspd = hourly.get("wind_speed_10m", []) h_wdir = hourly.get("wind_direction_10m", []) h_precip_prob = hourly.get("precipitation_probability", []) h_cloud_cover = hourly.get("cloud_cover", []) if (not h_times or not h_temps) and metar: metar_today_obs = metar.get("today_obs", []) or [] parsed_obs = [] for item in metar_today_obs: try: t_str, t_val = item if t_str is None or t_val is None: continue hh, minute_part = str(t_str).split(":") parsed_obs.append((int(hh), int(minute_part), float(t_val))) except Exception: continue if parsed_obs: parsed_obs.sort(key=lambda x: (x[0], x[1])) h_times = [f"{local_date_str}T{hh:02d}:{mm:02d}" for hh, mm, _ in parsed_obs] h_temps = [v for _, _, v in parsed_obs] h_rad = [0 for _ in parsed_obs] h_dew = [None for _ in parsed_obs] h_pressure = [None for _ in parsed_obs] h_wspd = [None for _ in parsed_obs] h_wdir = [None for _ in parsed_obs] h_precip_prob = [None for _ in parsed_obs] h_cloud_cover = [None for _ in parsed_obs] peak_hours = [] if h_times and h_temps and om_today is not None: for ts, tmp in zip(h_times, h_temps): if ts.startswith(local_date_str) and abs(tmp - om_today) <= 0.2: hr = int(ts.split("T")[1][:2]) if 8 <= hr <= 19: peak_hours.append(ts.split("T")[1][:5]) first_peak_h = int(peak_hours[0].split(":")[0]) if peak_hours else 13 last_peak_h = int(peak_hours[-1].split(":")[0]) if peak_hours else 15 if local_hour_frac > last_peak_h: peak_status = "past" elif first_peak_h <= local_hour_frac <= last_peak_h: peak_status = "in_window" else: peak_status = "before" # ── 10. Shared analysis (probability, trend, AI) via trend_engine ── # This single call replaces the duplicate probability engine, dead market # detection, forecast bust grading, and AI context building. from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze, calculate_prob_distribution probabilities = [] shadow_probabilities = [] mu = None probability_engine = "legacy" probability_calibration_mode = "legacy" probability_calibration_version = None probability_raw_mu = None probability_raw_sigma = None probability_calibrated_mu = None probability_calibrated_sigma = None ai_text = "" dynamic_commentary = {"summary": "", "notes": []} try: _, _ai_context, sd = _trend_analyze(raw, sym, city) # Use structured data from shared engine mu = sd.get("mu") probabilities = sd.get("probabilities", []) shadow_probabilities = sd.get("shadow_probabilities", []) probability_engine = sd.get("probability_engine", "legacy") probability_calibration_mode = sd.get("probability_calibration_mode", "legacy") probability_calibration_version = sd.get("probability_calibration_version") probability_raw_mu = sd.get("probability_raw_mu") probability_raw_sigma = sd.get("probability_raw_sigma") probability_calibrated_mu = sd.get("probability_calibrated_mu") probability_calibrated_sigma = sd.get("probability_calibrated_sigma") dynamic_commentary = sd.get("dynamic_commentary") or dynamic_commentary trend_info["is_dead_market"] = sd.get("trend_info", {}).get("is_dead_market", False) trend_info["direction"] = sd.get("trend_info", {}).get("direction", trend_info.get("direction", "unknown")) trend_info["is_cooling"] = sd.get("trend_info", {}).get("is_cooling", False) peak_status = sd.get("peak_status", peak_status) # Use shared DEB if not already set if deb_val is None and sd.get("deb_prediction") is not None: deb_val = sd["deb_prediction"] deb_weights = sd.get("deb_weights", "") except Exception as e: logger.warning(f"Structured analysis skipped for {city}: {e}") ai_text = describe_metar_report( raw_metar=str(primary_current.get("raw_metar") or mc.get("raw_metar") or ""), temp_symbol=sym, fallback={ "icao": metar.get("icao"), "station_name": metar.get("station_name"), "temp": cur_temp, "wind_speed_kt": _sf(primary_current.get("wind_speed_kt")), "wind_dir": _sf(primary_current.get("wind_dir")), "altimeter": _sf(primary_current.get("altimeter")), "wx_desc": primary_current.get("wx_desc"), "clouds": primary_current.get("clouds", []) or mc.get("clouds", []), }, ) # ── 12. Hourly data (today only, for chart) ── today_hourly: Dict[str, list] = {"times": [], "temps": [], "radiation": []} for i, ts in enumerate(h_times): if ts.startswith(local_date_str): today_hourly["times"].append(ts.split("T")[1][:5]) today_hourly["temps"].append(h_temps[i] if i < len(h_temps) else None) today_hourly["radiation"].append(h_rad[i] if i < len(h_rad) else None) # ── 12b. Next 48h hourly block for future-date analysis modal ── next_48h_hourly = { "times": [], "temps": [], "radiation": [], "dew_point": [], "pressure_msl": [], "wind_speed_10m": [], "wind_direction_10m": [], "precipitation_probability": [], "cloud_cover": [], } try: local_anchor = datetime.strptime( f"{local_date_str} {local_time_str}", "%Y-%m-%d %H:%M" ) except Exception: local_anchor = None if local_anchor is not None: horizon = local_anchor + timedelta(hours=48) for i, ts in enumerate(h_times): try: ts_dt = datetime.fromisoformat(ts) except Exception: continue if ts_dt < local_anchor or ts_dt > horizon: continue next_48h_hourly["times"].append(ts) next_48h_hourly["temps"].append(h_temps[i] if i < len(h_temps) else None) next_48h_hourly["radiation"].append(h_rad[i] if i < len(h_rad) else None) next_48h_hourly["dew_point"].append(h_dew[i] if i < len(h_dew) else None) next_48h_hourly["pressure_msl"].append( h_pressure[i] if i < len(h_pressure) else None ) next_48h_hourly["wind_speed_10m"].append( h_wspd[i] if i < len(h_wspd) else None ) next_48h_hourly["wind_direction_10m"].append( h_wdir[i] if i < len(h_wdir) else None ) next_48h_hourly["precipitation_probability"].append( h_precip_prob[i] if i < len(h_precip_prob) else None ) next_48h_hourly["cloud_cover"].append( h_cloud_cover[i] if i < len(h_cloud_cover) else None ) # ── 13. Cloud description (METAR primary, MGM fallback) ── clouds = mc.get("clouds", []) cloud_desc = "" if clouds: c_map = { "BKN": "多云", "OVC": "阴天", "FEW": "少云", "SCT": "散云", "SKC": "晴", "CLR": "晴", } main = clouds[-1] cloud_desc = c_map.get(main.get("cover"), main.get("cover", "")) if not cloud_desc and mgm: mgc_cover = mgm.get("current", {}).get("cloud_cover") if mgc_cover is not None: cloud_desc_map = { 0: "晴朗", 1: "少云", 2: "少云", 3: "散云", 4: "散云", 5: "多云", 6: "多云", 7: "阴天", 8: "阴天", } cloud_desc = cloud_desc_map.get(mgc_cover, "") # Final fallback: If we have ANY actual observation but no cloud info, it's usually clear. if not cloud_desc: if mc.get("temp") is not None or (mgm and mgm.get("current", {}).get("temp") is not None): # If weather phenomenon exists (e.g. rain), we'll let app.js handle wx_desc priority. # Otherwise, clear skies. if not mc.get("wx_desc"): cloud_desc = "晴朗" # ── 14. MGM data (Ankara-specific) ── mgm_data = {} if mgm: mgc = mgm.get("current", {}) mgm_time_str = mgc.get("time", "") # MGM time is usually "2026-03-04T10:40:00.000Z" (UTC) if mgm_time_str and "T" in mgm_time_str: try: # Handle ISO format with Z or +00:00 ts = mgm_time_str.replace("Z", "+00:00") if "+" in ts: base, offset_part = ts.split("+", 1) if "." in base: base = base.split(".")[0] ts = base + "+" + offset_part dt = datetime.fromisoformat(ts) local_dt = dt.astimezone(timezone(timedelta(seconds=utc_offset or 0))) mgm_time_str = local_dt.strftime("%H:%M") except Exception as e: logger.debug(f"MGM time conversion failed: {e}") pass mgm_data = { "temp": _sf(mgc.get("temp")), "time": mgm_time_str, "feels_like": _sf(mgc.get("feels_like")), "humidity": _sf(mgc.get("humidity")), "wind_dir": _sf(mgc.get("wind_dir")), "wind_speed_ms": _sf(mgc.get("wind_speed_ms")), "pressure": _sf(mgc.get("pressure")), "cloud_cover": mgc.get("cloud_cover"), "rain_24h": _sf(mgc.get("rain_24h")), "today_high": _sf(mgm.get("today_high")), "today_low": _sf(mgm.get("today_low")), "hourly": [], } mgm_hourly = mgm.get("hourly", []) for h in mgm_hourly: dt_str = h.get("time") val = _sf(h.get("temp")) if dt_str and "T" in dt_str and val is not None: try: dt = datetime.fromisoformat(dt_str.replace("Z", "+00:00")) local_dt = dt.astimezone(timezone(timedelta(seconds=utc_offset))) mgm_data["hourly"].append({ "time": local_dt.strftime("%Y-%m-%dT%H:%M"), "temp": val }) except Exception: pass # ── 15. Extended Multi-Model Daily ── multi_model_daily = {} mm_daily_raw = mm.get("daily_forecasts", {}) for i, d_str in enumerate(dates): if i == 0: day_m = current_forecasts.copy() d_val, d_winfo = deb_val, deb_weights else: day_m = mm_daily_raw.get(d_str, {}).copy() if i < len(maxtemps) and maxtemps[i] is not None: day_m["Open-Meteo"] = _sf(maxtemps[i]) # Add MGM per-day forecast mgm_daily = mgm.get("daily_forecasts", {}) if d_str in mgm_daily: day_m["MGM"] = _sf(mgm_daily[d_str]) day_m = { m: v for m, v in day_m.items() if not _is_excluded_model_name(m) } d_val, d_winfo = None, "" d_probs = [] if day_m: try: blended, winfo = calculate_dynamic_weights(city, day_m) if blended is not None: d_val = blended d_winfo = winfo # Calculate future probability based on model divergence m_vals = [v for v in day_m.values() if v is not None] if len(m_vals) > 1: # Use spread as a proxy for sigma. # sigma = (max-min)/2 with a floor of 0.6 d_sigma = max(0.6, (max(m_vals) - min(m_vals)) / 2.0) else: d_sigma = 1.0 prob_obj = calculate_prob_distribution(d_val, d_sigma, None, sym) d_probs = prob_obj.get("probabilities", []) except Exception: pass if day_m: multi_model_daily[d_str] = { "models": day_m, "deb": {"prediction": d_val, "weights_info": d_winfo}, "probabilities": d_probs if i > 0 else probabilities # Use today's real prob for today } # ── Assemble result ── result = { "name": city, "display_name": city.title(), "lat": lat, "lon": lon, "temp_symbol": sym, "local_time": local_time_str, "local_date": local_date_str, "risk": { "level": risk.get("risk_level", "low"), "emoji": risk.get("risk_emoji", "🟢"), "airport": risk.get("airport_name", ""), "icao": risk.get("icao", ""), "distance_km": risk.get("distance_km", 0), "warning": risk.get("warning", ""), }, "current": { "temp": cur_temp, "max_so_far": max_so_far, "max_temp_time": max_temp_time, "wu_settlement": wu_settle, "settlement_source": settlement_source, "settlement_source_label": settlement_source_label, "obs_time": obs_time_str, "obs_age_min": metar_age_min, "report_time": metar.get("report_time") if metar else None, "receipt_time": metar.get("receipt_time") if metar else None, "obs_time_epoch": metar.get("obs_time_epoch") if metar else None, "wind_speed_kt": _sf(primary_current.get("wind_speed_kt")), "wind_dir": _sf(primary_current.get("wind_dir")), "humidity": _sf(primary_current.get("humidity")), "cloud_desc": cloud_desc, "clouds_raw": [ {"cover": c.get("cover"), "base": c.get("base")} for c in clouds ], "visibility_mi": _sf(primary_current.get("visibility_mi")), "wx_desc": primary_current.get("wx_desc"), "raw_metar": primary_current.get("raw_metar"), }, "mgm": mgm_data, "mgm_nearby": raw.get("mgm_nearby", []), "forecast": { "today_high": om_today, "daily": forecast_daily, "sunrise": sunrise, "sunset": sunset, "sunshine_hours": sunshine_h, }, "source_forecasts": { "weather_gov": raw.get("nws") or {}, }, "multi_model": {k: v for k, v in current_forecasts.items() if v is not None}, "multi_model_daily": multi_model_daily, "deb": {"prediction": deb_val, "weights_info": deb_weights}, "ensemble": ens_data, "probabilities": { "mu": round(mu, 1) if mu is not None else None, "distribution": probabilities, "engine": probability_engine, "calibration_mode": probability_calibration_mode, "calibration_version": probability_calibration_version, "raw_mu": probability_raw_mu, "raw_sigma": probability_raw_sigma, "calibrated_mu": probability_calibrated_mu, "calibrated_sigma": probability_calibrated_sigma, "shadow_distribution": shadow_probabilities, }, "trend": trend_info, "peak": { "hours": peak_hours, "first_h": first_peak_h, "last_h": last_peak_h, "status": peak_status, }, "dynamic_commentary": dynamic_commentary, "hourly": today_hourly, "hourly_next_48h": next_48h_hourly, "metar_today_obs": metar_today_obs_payload, "metar_recent_obs": metar_recent_obs_payload, "settlement_today_obs": settlement_today_obs, "ai_analysis": ai_text, "updated_at": datetime.now(timezone.utc).isoformat(), } _cache[city] = {"t": _time.time(), "d": result} return result def _normalize_city_or_404(name: str) -> str: city = name.lower().strip().replace("-", " ") city = ALIASES.get(city, city) if city not in CITIES: raise HTTPException(404, detail=f"Unknown city: {city}") return city def _build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]: return { "name": data.get("name"), "display_name": data.get("display_name"), "icao": data.get("risk", {}).get("icao"), "local_time": data.get("local_time"), "temp_symbol": data.get("temp_symbol"), "current": { "temp": data.get("current", {}).get("temp"), "obs_time": data.get("current", {}).get("obs_time"), "settlement_source": data.get("current", {}).get("settlement_source"), "settlement_source_label": data.get("current", {}).get("settlement_source_label"), }, "deb": {"prediction": data.get("deb", {}).get("prediction")}, "risk": { "level": data.get("risk", {}).get("level"), "warning": data.get("risk", {}).get("warning"), }, "updated_at": data.get("updated_at"), } def _build_city_detail_payload( data: Dict[str, Any], market_slug: Optional[str] = None, target_date: Optional[str] = None, ) -> Dict[str, Any]: city = str(data.get("name") or "").strip().lower() local_date = str(data.get("local_date") or "").strip() requested_date = str(target_date or "").strip() selected_date = requested_date or local_date multi_model_daily = data.get("multi_model_daily") or {} selected_daily = ( multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else None ) if not isinstance(selected_daily, dict): selected_daily = {} selected_date = local_date distribution = selected_daily.get("probabilities") if not isinstance(distribution, list) or not distribution: distribution = data.get("probabilities", {}).get("distribution", []) or [] model_map = selected_daily.get("models") or data.get("multi_model") or {} if not isinstance(model_map, dict): model_map = {} anchor_temp = None anchor_model = None for model_name, raw_value in model_map.items(): value = _sf(raw_value) if value is None: continue if anchor_temp is None or value > anchor_temp: anchor_temp = value anchor_model = str(model_name or "").strip() or None anchor_temp_c = anchor_temp temp_symbol = str(data.get("temp_symbol") or "") if anchor_temp_c is not None and "F" in temp_symbol.upper(): anchor_temp_c = (anchor_temp_c - 32.0) * 5.0 / 9.0 anchor_settlement = apply_city_settlement(city, anchor_temp_c) if anchor_temp_c is not None else None primary_bucket = None if isinstance(distribution, list) and distribution: if anchor_temp is None: primary_bucket = distribution[0] else: ranked_buckets = [] for idx, row in enumerate(distribution): if not isinstance(row, dict): continue bucket_temp = _sf(row.get("value")) bucket_prob = _sf(row.get("probability")) if bucket_temp is None: continue prob_rank = bucket_prob if bucket_prob is not None else -1.0 ranked_buckets.append((abs(bucket_temp - anchor_temp), -prob_rank, idx, row)) if ranked_buckets: ranked_buckets.sort(key=lambda x: (x[0], x[1], x[2])) primary_bucket = ranked_buckets[0][3] else: primary_bucket = distribution[0] model_probability = None if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None: try: raw_probability = float(primary_bucket.get("probability")) model_probability = raw_probability / 100.0 if raw_probability > 1.0 else raw_probability except Exception: model_probability = None fallback_sparkline = [ p.get("probability", 0) for p in distribution[:8] if isinstance(p, dict) ] market_scan = _market_layer.build_market_scan( city=data.get("name"), target_date=selected_date or data.get("local_date"), temperature_bucket=primary_bucket if isinstance(primary_bucket, dict) else None, model_probability=model_probability, fallback_sparkline=fallback_sparkline, forced_market_slug=market_slug, ) if isinstance(market_scan, dict): market_scan["anchor_model"] = anchor_model market_scan["anchor_high"] = anchor_temp market_scan["anchor_settlement"] = anchor_settlement market_scan["open_meteo_settlement"] = anchor_settlement return { "city": data.get("name"), "fetched_at": data.get("updated_at"), "overview": { "name": data.get("name"), "display_name": data.get("display_name"), "icao": data.get("risk", {}).get("icao"), "airport": data.get("risk", {}).get("airport"), "lat": data.get("lat"), "lon": data.get("lon"), "local_time": data.get("local_time"), "local_date": data.get("local_date"), "temp_symbol": data.get("temp_symbol"), "current_temp": data.get("current", {}).get("temp"), "settlement_source": data.get("current", {}).get("settlement_source"), "settlement_source_label": data.get("current", {}).get("settlement_source_label"), "deb_prediction": data.get("deb", {}).get("prediction"), "risk_level": data.get("risk", {}).get("level"), "risk_warning": data.get("risk", {}).get("warning"), "updated_at": data.get("updated_at"), }, "official": { "available": bool(data.get("current", {}).get("temp") is not None), "metar": { "observation_time": data.get("current", {}).get("obs_time"), "obs_age_min": data.get("current", {}).get("obs_age_min"), "report_time": data.get("current", {}).get("report_time"), "receipt_time": data.get("current", {}).get("receipt_time"), "raw_metar": data.get("current", {}).get("raw_metar"), "current": data.get("current"), }, "weather_gov": {}, "mgm": data.get("mgm") or {}, "mgm_nearby": data.get("mgm_nearby") or [], "nearby_source": "mgm" if data.get("name") == "ankara" else "metar_cluster", }, "timeseries": { "metar_recent_obs": data.get("metar_recent_obs") or [], "metar_today_obs": data.get("metar_today_obs") or [], "settlement_today_obs": data.get("settlement_today_obs") or [], "hourly": data.get("hourly") or {}, "mgm_hourly": (data.get("mgm") or {}).get("hourly", []), "forecast_daily": (data.get("forecast") or {}).get("daily", []), }, "models": { k: v for k, v in (data.get("multi_model") or {}).items() if not _is_excluded_model_name(k) }, "probabilities": data.get("probabilities") or {"mu": None, "distribution": []}, "dynamic_commentary": data.get("dynamic_commentary") or {"summary": "", "notes": []}, "market_scan": market_scan, "risk": data.get("risk"), "ai_analysis": data.get("ai_analysis") or "", "errors": {}, } # ────────────────────────────────────────────────────────── # Routes # ──────────────────────────────────────────────────────────