from __future__ import annotations import re import time as _time import threading from concurrent.futures import ThreadPoolExecutor 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, CACHE_TTL_KOREAN_AMOS, CITIES, CITY_RISK_PROFILES, SETTLEMENT_SOURCE_LABELS, _is_excluded_model_name, _sf, _weather, ) from src.analysis.deb_algorithm import calculate_dynamic_weights from src.analysis.settlement_rounding import apply_city_settlement from src.data_collection.country_networks import build_country_network_snapshot from src.data_collection.city_registry import ALIASES, CITY_REGISTRY from src.data_collection.city_time import get_city_utc_offset_seconds from src.data_collection.nmc_sources import NMC_CITY_REFERENCES from src.database.runtime_state import IntradayPathSnapshotRepository from src.models.lgbm_daily_high import predict_lgbm_daily_high from web.services.groq_commentary import ( build_groq_commentary_context as _groq_context_builder, clean_commentary_text as _groq_clean_text, groq_commentary_enabled as _groq_enabled, maybe_enrich_dynamic_commentary_with_groq as _groq_enrich, normalize_groq_commentary_payload as _groq_normalize_payload, request_groq_commentary as _groq_request, ) from web.services.city_payloads import ( build_city_detail_payload as _city_payload_detail, build_city_market_scan_payload as _city_payload_market_scan, build_city_summary_payload as _city_payload_summary, ) TURKISH_MGM_CITIES = {"ankara", "istanbul"} _ANALYSIS_CACHE_STATS_LOCK = threading.Lock() _ANALYSIS_CACHE_STATS: Dict[str, Any] = { "total_requests": 0, "cache_hits": 0, "cache_misses": 0, "force_refresh_requests": 0, "last_cache_hit_at": None, "last_cache_miss_at": None, "last_city": None, } _SUMMARY_CACHE_LOCK = threading.Lock() _SUMMARY_CACHE: Dict[str, Dict[str, Any]] = {} def _dedupe_forecast_daily(rows: Any) -> list[Dict[str, Any]]: if not isinstance(rows, list): return [] seen = set() out = [] for row in rows: if not isinstance(row, dict): continue date = str(row.get("date") or "").strip() if not date or date in seen: continue seen.add(date) out.append(row) return out def _format_observation_time_local(value: Any, utc_offset: int) -> str: raw = str(value or "").strip() if not raw: return "" if "T" in raw: try: dt = datetime.fromisoformat(raw.replace("Z", "+00:00")) if dt.tzinfo is None: dt = dt.replace(tzinfo=timezone.utc) return dt.astimezone(timezone(timedelta(seconds=utc_offset))).strftime("%H:%M") except Exception: pass match = re.search(r"(\d{1,2}):(\d{2})", raw) if match: return f"{int(match.group(1)):02d}:{match.group(2)}" return raw[:16] def _fetch_nmc_current_fallback(city: str, *, use_fahrenheit: bool) -> Dict[str, Any]: city_key = str(city or "").strip().lower() if city_key not in NMC_CITY_REFERENCES: return {} try: payload = _weather.fetch_nmc_region_current( city_key, use_fahrenheit=use_fahrenheit, ) return payload if isinstance(payload, dict) else {} except Exception as exc: logger.debug("NMC current fallback failed city={}: {}", city_key, exc) return {} def _is_plausible_city_temp(city: str, value: Any, unit: str = "°C") -> bool: temp = _sf(value) if temp is None: return False meta = CITY_REGISTRY.get(str(city or "").strip().lower(), {}) or {} min_c = _sf(meta.get("min_plausible_metar_temp_c")) if min_c is None: return True min_value = min_c * 9 / 5 + 32 if str(unit or "").upper().endswith("F") else min_c return temp >= min_value def _parse_utc_datetime(value: Any) -> Optional[datetime]: raw = str(value or "").strip() if not raw or "T" not in raw: return None try: dt = datetime.fromisoformat(raw.replace("Z", "+00:00")) except Exception: return None if dt.tzinfo is None: dt = dt.replace(tzinfo=timezone.utc) return dt.astimezone(timezone.utc) def _metar_is_current_local_day( metar: Dict[str, Any], *, local_date: str, utc_offset: int, ) -> bool: if not isinstance(metar, dict) or not metar: return False if metar.get("stale_for_today") is True: return False observation_local_date = str(metar.get("observation_local_date") or "").strip() if observation_local_date: return observation_local_date == local_date obs_dt = _parse_utc_datetime(metar.get("observation_time")) if obs_dt is None: return True local_dt = obs_dt.astimezone(timezone(timedelta(seconds=utc_offset))) return local_dt.strftime("%Y-%m-%d") == local_date _OBSERVATION_SOURCE_PROFILES: Dict[str, Dict[str, Any]] = { "amos": { "label": "AMOS", "native_update_interval_sec": 60, "fresh_window_sec": 180, "expected_grace_sec": 180, "stale_after_sec": 900, }, "amsc_awos": { "label": "AMSC AWOS", "native_update_interval_sec": 60, "fresh_window_sec": 180, "expected_grace_sec": 180, "stale_after_sec": 900, }, "jma": { "label": "JMA", "native_update_interval_sec": 600, "fresh_window_sec": 900, "expected_grace_sec": 600, "stale_after_sec": 2700, }, "fmi": { "label": "FMI", "native_update_interval_sec": 600, "fresh_window_sec": 900, "expected_grace_sec": 600, "stale_after_sec": 2700, }, "knmi": { "label": "KNMI", "native_update_interval_sec": 600, "fresh_window_sec": 900, "expected_grace_sec": 600, "stale_after_sec": 2700, }, "hko": { "label": "HKO", "native_update_interval_sec": 600, "fresh_window_sec": 900, "expected_grace_sec": 600, "stale_after_sec": 2700, }, "cwa": { "label": "CWA", "native_update_interval_sec": 600, "fresh_window_sec": 900, "expected_grace_sec": 600, "stale_after_sec": 2700, }, "mgm": { "label": "MGM", "native_update_interval_sec": 900, "fresh_window_sec": 900, "expected_grace_sec": 900, "stale_after_sec": 3600, }, "metar": { "label": "METAR", "native_update_interval_sec": 900, "fresh_window_sec": 600, "expected_grace_sec": 900, "stale_after_sec": 3600, }, "noaa": { "label": "NOAA", "native_update_interval_sec": 900, "fresh_window_sec": 600, "expected_grace_sec": 900, "stale_after_sec": 3600, }, "wunderground": { "label": "METAR", "native_update_interval_sec": 900, "fresh_window_sec": 600, "expected_grace_sec": 900, "stale_after_sec": 3600, }, "nmc": { "label": "NMC", "native_update_interval_sec": 3600, "fresh_window_sec": 3600, "expected_grace_sec": 1800, "stale_after_sec": 7200, }, } def _canonical_observation_source_code(value: Any) -> str: raw = str(value or "").strip().lower() if not raw: return "metar" if "amos" in raw: return "amos" if "jma" in raw: return "jma" if "fmi" in raw: return "fmi" if "knmi" in raw: return "knmi" if "hko" in raw: return "hko" if "cwa" in raw: return "cwa" if "mgm" in raw: return "mgm" if "noaa" in raw: return "noaa" if "nmc" in raw: return "nmc" if "wunderground" in raw or raw == "wu": return "wunderground" return raw def _observation_age_min(value: Any, now_utc: Optional[datetime] = None) -> Optional[int]: obs_dt = _parse_utc_datetime(value) if obs_dt is None: return None now = now_utc or datetime.now(timezone.utc) return max(0, int((now - obs_dt).total_seconds() / 60)) def _optional_str(value: Any) -> Optional[str]: raw = str(value or "").strip() return raw or None def _build_observation_freshness( *, source_code: Any, source_label: Any = None, observed_at: Any = None, observed_at_local: Any = None, ingested_at: Any = None, age_min: Optional[int] = None, now_utc: Optional[datetime] = None, ) -> Dict[str, Any]: code = _canonical_observation_source_code(source_code or source_label) profile = _OBSERVATION_SOURCE_PROFILES.get(code) or _OBSERVATION_SOURCE_PROFILES["metar"] now = now_utc or datetime.now(timezone.utc) obs_dt = _parse_utc_datetime(observed_at) age_sec = None if age_min is not None: try: age_sec = max(0, int(age_min) * 60) except Exception: age_sec = None if age_sec is None and obs_dt is not None: age_sec = max(0, int((now - obs_dt).total_seconds())) if age_sec is None: status = "unknown" reason = "observation_time_missing" elif age_sec <= int(profile["fresh_window_sec"]): status = "fresh" reason = "within_native_fresh_window" elif age_sec <= int(profile["native_update_interval_sec"]) + int(profile["expected_grace_sec"]): status = "expected_wait" reason = "within_source_expected_cadence" elif age_sec <= int(profile["stale_after_sec"]): status = "delayed" reason = "past_expected_cadence" else: status = "stale" reason = "past_stale_threshold" expected_next = ( obs_dt + timedelta(seconds=int(profile["native_update_interval_sec"])) if obs_dt is not None else None ) return { "source_code": code, "source_label": str(source_label or profile["label"]), "observed_at": obs_dt.isoformat() if obs_dt is not None else _optional_str(observed_at), "observed_at_local": _optional_str(observed_at_local), "ingested_at": _optional_str(ingested_at), "native_update_interval_sec": int(profile["native_update_interval_sec"]), "expected_next_update_at": expected_next.isoformat() if expected_next is not None else None, "freshness_status": status, "freshness_reason": reason, "age_sec": age_sec, } def _record_analysis_cache_event(*, city: str, hit: bool, force_refresh: bool) -> None: now = datetime.now(timezone.utc).isoformat() with _ANALYSIS_CACHE_STATS_LOCK: _ANALYSIS_CACHE_STATS["total_requests"] = int(_ANALYSIS_CACHE_STATS.get("total_requests") or 0) + 1 _ANALYSIS_CACHE_STATS["last_city"] = str(city or "") if force_refresh: _ANALYSIS_CACHE_STATS["force_refresh_requests"] = int(_ANALYSIS_CACHE_STATS.get("force_refresh_requests") or 0) + 1 if hit: _ANALYSIS_CACHE_STATS["cache_hits"] = int(_ANALYSIS_CACHE_STATS.get("cache_hits") or 0) + 1 _ANALYSIS_CACHE_STATS["last_cache_hit_at"] = now else: _ANALYSIS_CACHE_STATS["cache_misses"] = int(_ANALYSIS_CACHE_STATS.get("cache_misses") or 0) + 1 _ANALYSIS_CACHE_STATS["last_cache_miss_at"] = now def get_analysis_cache_stats() -> Dict[str, Any]: with _ANALYSIS_CACHE_STATS_LOCK: stats = dict(_ANALYSIS_CACHE_STATS) hits = int(stats.get("cache_hits") or 0) misses = int(stats.get("cache_misses") or 0) eligible = hits + misses hit_rate = (hits / eligible) if eligible > 0 else None miss_rate = (misses / eligible) if eligible > 0 else None stats["hit_rate"] = round(hit_rate, 4) if hit_rate is not None else None stats["miss_rate"] = round(miss_rate, 4) if miss_rate is not None else None return stats KOREAN_AMOS_CITIES = {"seoul", "busan"} def _analysis_ttl_for_city(city: str) -> int: city_lower = city.lower() if city_lower in TURKISH_MGM_CITIES: return CACHE_TTL_ANKARA if city_lower in KOREAN_AMOS_CITIES: return CACHE_TTL_KOREAN_AMOS return CACHE_TTL def _analysis_cache_key(city: str, detail_mode: str = "full") -> str: normalized_raw = str(detail_mode or "").strip().lower() if normalized_raw == "panel": normalized_mode = "panel" elif normalized_raw == "market": normalized_mode = "market" elif normalized_raw == "nearby": normalized_mode = "nearby" else: normalized_mode = "full" return f"{city}::{normalized_mode}" def _get_cached_analysis( city: str, ttl: int, detail_modes: tuple[str, ...] = ("panel", "market", "nearby", "full"), ) -> Optional[Dict[str, Any]]: now_ts = _time.time() freshest_payload: Optional[Dict[str, Any]] = None freshest_ts = 0.0 for detail_mode in detail_modes: cached = _cache.get(_analysis_cache_key(city, detail_mode)) if not cached: continue cached_ts = float(cached.get("t", 0)) payload = cached.get("d") if ( cached_ts and now_ts - cached_ts < ttl and isinstance(payload, dict) and cached_ts >= freshest_ts ): freshest_payload = payload freshest_ts = cached_ts return freshest_payload def _get_cached_summary(city: str, ttl: int) -> Optional[Dict[str, Any]]: now_ts = _time.time() with _SUMMARY_CACHE_LOCK: cached = _SUMMARY_CACHE.get(city) if cached and now_ts - float(cached.get("t", 0)) < ttl: payload = cached.get("d") if isinstance(payload, dict): return dict(payload) return None def _set_cached_summary(city: str, payload: Dict[str, Any]) -> None: with _SUMMARY_CACHE_LOCK: _SUMMARY_CACHE[city] = {"t": _time.time(), "d": dict(payload)} def _groq_commentary_enabled() -> bool: return _groq_enabled() def _clean_commentary_text(value: Any, *, limit: int = 240) -> str: return _groq_clean_text(value, limit=limit) def _build_groq_commentary_context(result: Dict[str, Any]) -> Dict[str, Any]: return _groq_context_builder(result) def _normalize_groq_commentary_payload(payload: Dict[str, Any]) -> Dict[str, Any]: return _groq_normalize_payload(payload) def _request_groq_commentary(context: Dict[str, Any]) -> Optional[Dict[str, Any]]: return _groq_request(context) def _maybe_enrich_dynamic_commentary_with_groq( city: str, result: Dict[str, Any], ) -> Dict[str, Any]: return _groq_enrich(city, result) def _interpolate_hourly_value( times: list, values: list, local_date: str, target_hour_frac: float, ) -> Optional[float]: points = [] for ts, raw_value in zip(times or [], values or []): if not str(ts).startswith(local_date): continue value = _sf(raw_value) if value is None: continue try: hh_mm = str(ts).split("T")[1] hour = int(hh_mm[:2]) minute = int(hh_mm[3:5]) if len(hh_mm) >= 5 else 0 except Exception: continue points.append((hour + minute / 60.0, value)) if not points: return None points.sort(key=lambda item: item[0]) if target_hour_frac <= points[0][0]: return float(points[0][1]) if target_hour_frac >= points[-1][0]: return float(points[-1][1]) for idx in range(1, len(points)): left_hour, left_value = points[idx - 1] right_hour, right_value = points[idx] if target_hour_frac > right_hour: continue if right_hour == left_hour: return float(right_value) ratio = (target_hour_frac - left_hour) / (right_hour - left_hour) return float(left_value + (right_value - left_value) * ratio) return float(points[-1][1]) def _build_deviation_monitor( *, current_temp: Optional[float], deb_prediction: Optional[float], om_today: Optional[float], hourly_times: list, hourly_temps: list, local_date: str, local_hour_frac: float, observation_points: list, ) -> Dict[str, Any]: if current_temp is None or deb_prediction is None or om_today is None: return {} offset = _sf(deb_prediction) - _sf(om_today) if offset is None: return {} expected_now = _interpolate_hourly_value( hourly_times, [(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps], local_date, local_hour_frac, ) if expected_now is None: return {} delta = float(current_temp) - expected_now abs_delta = abs(delta) if abs_delta < 0.8: direction = "normal" severity = "normal" elif delta <= -1.8: direction = "cold" severity = "strong" elif delta >= 1.8: direction = "hot" severity = "strong" elif delta < 0: direction = "cold" severity = "light" else: direction = "hot" severity = "light" deviation_series = [] for item in observation_points or []: if not isinstance(item, dict): continue obs_temp = _sf(item.get("temp")) raw_time = str(item.get("time") or "").strip() if obs_temp is None: continue match = re.search(r"(\d{1,2}):(\d{2})", raw_time) if not match: continue obs_hour_frac = int(match.group(1)) + int(match.group(2)) / 60.0 ref_temp = _interpolate_hourly_value( hourly_times, [(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps], local_date, obs_hour_frac, ) if ref_temp is None: continue deviation_series.append(float(obs_temp) - ref_temp) trend = "stable" if len(deviation_series) >= 2: latest = deviation_series[-1] previous = deviation_series[-2] if latest * previous > 0: if abs(latest) - abs(previous) >= 0.3: trend = "expanding" elif abs(previous) - abs(latest) >= 0.3: trend = "contracting" if direction == "normal": label_zh = f"正常 ±{abs_delta:.1f}°C" label_en = f"Normal ±{abs_delta:.1f}°C" elif direction == "cold": label_zh = f"偏冷 {delta:.1f}°C" label_en = f"Cool bias {delta:.1f}°C" else: label_zh = f"偏热 +{abs_delta:.1f}°C" label_en = f"Warm bias +{abs_delta:.1f}°C" trend_zh = { "contracting": "收敛中", "expanding": "扩大中", "stable": "稳定", }.get(trend, "稳定") trend_en = { "contracting": "contracting", "expanding": "expanding", "stable": "stable", }.get(trend, "stable") return { "available": True, "current_delta": round(delta, 1), "reference_temp": round(expected_now, 1), "direction": direction, "severity": severity, "trend": trend, "label_zh": label_zh, "label_en": label_en, "trend_label_zh": trend_zh, "trend_label_en": trend_en, } def _wind_components(speed: Optional[float], direction: Optional[float]) -> tuple[Optional[float], Optional[float]]: if speed is None or direction is None: return None, None try: import math rad = math.radians(float(direction)) spd = float(speed) u = -spd * math.sin(rad) v = -spd * math.cos(rad) return u, v except Exception: return None, None def _build_vertical_profile_signal( hourly_next_48h: Dict[str, list], local_date: str, local_hour: int, first_peak_h: int, last_peak_h: int, ) -> Dict[str, Any]: times = hourly_next_48h.get("times") or [] if not times: return {} preferred_start = max(local_hour, max(0, first_peak_h - 2)) preferred_end = min(23, last_peak_h + 1) candidate_indexes = [ index for index, ts in enumerate(times) if str(ts).startswith(local_date) and preferred_start <= int(str(ts).split("T")[1][:2]) <= preferred_end ] if not candidate_indexes: candidate_indexes = [ index for index, ts in enumerate(times) if str(ts).startswith(local_date) ] if not candidate_indexes: return {} def _series(name: str) -> list: values = hourly_next_48h.get(name) or [] return [values[idx] if idx < len(values) else None for idx in candidate_indexes] def _max_numeric(values: list) -> Optional[float]: valid = [_sf(value) for value in values if _sf(value) is not None] return max(valid) if valid else None def _min_numeric(values: list) -> Optional[float]: valid = [_sf(value) for value in values if _sf(value) is not None] return min(valid) if valid else None def _level_label(level: str, locale: str) -> str: mapping = { "high": {"zh": "高", "en": "high"}, "medium": {"zh": "中", "en": "medium"}, "low": {"zh": "低", "en": "low"}, "strong": {"zh": "强", "en": "strong"}, "weak": {"zh": "弱", "en": "weak"}, } return mapping.get(level, {}).get(locale, level) cape_max = _max_numeric(_series("cape")) cin_min = _min_numeric(_series("convective_inhibition")) lifted_index_min = _min_numeric(_series("lifted_index")) boundary_layer_height_max = _max_numeric(_series("boundary_layer_height")) shear_values: list[float] = [] speed_10m = hourly_next_48h.get("wind_speed_10m") or [] direction_10m = hourly_next_48h.get("wind_direction_10m") or [] speed_180m = hourly_next_48h.get("wind_speed_180m") or [] direction_180m = hourly_next_48h.get("wind_direction_180m") or [] for idx in candidate_indexes: s10 = _sf(speed_10m[idx]) if idx < len(speed_10m) else None d10 = _sf(direction_10m[idx]) if idx < len(direction_10m) else None s180 = _sf(speed_180m[idx]) if idx < len(speed_180m) else None d180 = _sf(direction_180m[idx]) if idx < len(direction_180m) else None u10, v10 = _wind_components(s10, d10) u180, v180 = _wind_components(s180, d180) if None in (u10, v10, u180, v180): continue import math shear_values.append(math.sqrt((u180 - u10) ** 2 + (v180 - v10) ** 2)) shear_10m_180m_max = max(shear_values) if shear_values else None suppression_risk = "low" if (cape_max is not None and cape_max >= 700) or ( cin_min is not None and cin_min <= -50 ): suppression_risk = "high" elif (cape_max is not None and cape_max >= 150) or ( cin_min is not None and cin_min <= -15 ): suppression_risk = "medium" trigger_risk = "low" if ( cape_max is not None and cape_max >= 550 and lifted_index_min is not None and lifted_index_min <= -1.5 ): trigger_risk = "high" elif ( cape_max is not None and cape_max >= 120 and lifted_index_min is not None and lifted_index_min <= 0.5 ): trigger_risk = "medium" mixing_strength = "weak" if boundary_layer_height_max is not None and boundary_layer_height_max >= 1400: mixing_strength = "strong" elif boundary_layer_height_max is not None and boundary_layer_height_max >= 700: mixing_strength = "medium" shear_risk = "low" if shear_10m_180m_max is not None and shear_10m_180m_max >= 8: shear_risk = "high" elif shear_10m_180m_max is not None and shear_10m_180m_max >= 4: shear_risk = "medium" heating_setup = "neutral" heating_score = 0 if suppression_risk == "high": heating_score -= 2 elif suppression_risk == "medium": heating_score -= 1 if trigger_risk == "high": heating_score -= 2 elif trigger_risk == "medium": heating_score -= 1 if mixing_strength == "strong": heating_score += 2 elif mixing_strength == "medium": heating_score += 1 else: heating_score -= 1 if shear_risk == "high": heating_score -= 1 if heating_score >= 2: heating_setup = "supportive" elif heating_score <= -2: heating_setup = "suppressed" has_profile_data = any( value is not None for value in ( cape_max, cin_min, lifted_index_min, boundary_layer_height_max, shear_10m_180m_max, ) ) zh_parts = [] en_parts = [] if suppression_risk == "high": zh_parts.append("午后对流压温风险偏高。") en_parts.append("Afternoon convective suppression risk is elevated.") elif suppression_risk == "medium": zh_parts.append("存在一定云雨压温风险。") en_parts.append("There is some cloud and shower suppression risk.") elif has_profile_data: zh_parts.append("高空对流压温风险暂时不高。") en_parts.append("Upper-air suppression risk remains limited for now.") if mixing_strength == "strong": zh_parts.append("边界层混合较深，若无云雨打断仍有冲高空间。") en_parts.append("Deep boundary-layer mixing still supports additional warming if convection stays limited.") elif mixing_strength == "medium": zh_parts.append("白天混合条件中等。") en_parts.append("Daytime mixing potential is moderate.") elif has_profile_data: zh_parts.append("边界层混合偏浅。") en_parts.append("Boundary-layer mixing remains shallow.") if shear_risk == "high": zh_parts.append("高空风切变较强，午后结构波动可能加大。") en_parts.append("Upper-level shear is relatively strong and may increase afternoon volatility.") elif shear_risk == "medium": zh_parts.append("高空风切变有一定存在感。") en_parts.append("Upper-level shear is noticeable.") elif has_profile_data: zh_parts.append("高空风切变扰动有限。") en_parts.append("Upper-level shear disruption remains limited.") if trigger_risk == "high": zh_parts.append("抬升触发条件较好，需警惕午后云团发展。") en_parts.append("Trigger conditions are favorable enough to watch for afternoon convective development.") elif trigger_risk == "medium": zh_parts.append("午后具备一定触发条件。") en_parts.append("There is some afternoon trigger potential.") elif has_profile_data: zh_parts.append("午后触发条件偏弱。") en_parts.append("Afternoon trigger potential remains weak.") if not has_profile_data: zh_parts.append("高空剖面字段暂缺，当前仅保留基础默认信号。") en_parts.append("Upper-air profile fields are currently unavailable, so only a fallback signal is shown.") elif not zh_parts: zh_parts.append("高空结构整体平稳，暂未看到明显压温信号。") if not en_parts: en_parts.append("The upper-air structure looks fairly stable, without a strong suppression signal yet.") if has_profile_data: summary_tokens_zh = [] summary_tokens_en = [] window_start = str(times[candidate_indexes[0]]).split("T")[1][:5] window_end = str(times[candidate_indexes[-1]]).split("T")[1][:5] zh_parts.append(f"判断窗口：{window_start}-{window_end}。") en_parts.append(f"Signal window: {window_start}-{window_end}.") if cape_max is not None: summary_tokens_zh.append(f"CAPE≈{round(cape_max)}") summary_tokens_en.append(f"CAPE≈{round(cape_max)}") if cin_min is not None: summary_tokens_zh.append(f"CIN≈{round(cin_min)}") summary_tokens_en.append(f"CIN≈{round(cin_min)}") if boundary_layer_height_max is not None: summary_tokens_zh.append(f"混合层≈{round(boundary_layer_height_max)}m") summary_tokens_en.append(f"mixing≈{round(boundary_layer_height_max)}m") if shear_10m_180m_max is not None: summary_tokens_zh.append(f"切变≈{shear_10m_180m_max:.1f}") summary_tokens_en.append(f"shear≈{shear_10m_180m_max:.1f}") zh_parts.append( f"压温{_level_label(suppression_risk, 'zh')}、触发{_level_label(trigger_risk, 'zh')}、混合{_level_label(mixing_strength, 'zh')}、切变{_level_label(shear_risk, 'zh')}。" ) en_parts.append( f"Suppression { _level_label(suppression_risk, 'en') }, trigger { _level_label(trigger_risk, 'en') }, mixing { _level_label(mixing_strength, 'en') }, shear { _level_label(shear_risk, 'en') }." ) if heating_setup == "supportive": zh_parts.append("整体更偏向支持白天冲高。") en_parts.append("Overall, the profile is more supportive of daytime heating.") elif heating_setup == "suppressed": zh_parts.append("整体更偏向抑制午后冲高。") en_parts.append("Overall, the profile leans more toward suppressing the afternoon peak.") else: zh_parts.append("整体更像中性环境，仍需结合地面信号。") en_parts.append("Overall, the profile looks fairly neutral and still needs surface confirmation.") if summary_tokens_zh: zh_parts.append(" / ".join(summary_tokens_zh) + "。") if summary_tokens_en: en_parts.append(" / ".join(summary_tokens_en) + ".") return { "source": "open-meteo-gfs", "window_start": times[candidate_indexes[0]] if candidate_indexes else None, "window_end": times[candidate_indexes[-1]] if candidate_indexes else None, "cape_max": cape_max, "cin_min": cin_min, "lifted_index_min": lifted_index_min, "boundary_layer_height_max": boundary_layer_height_max, "shear_10m_180m_max": shear_10m_180m_max, "suppression_risk": suppression_risk, "trigger_risk": trigger_risk, "mixing_strength": mixing_strength, "shear_risk": shear_risk, "heating_setup": heating_setup, "heating_score": heating_score, "summary_zh": "".join(zh_parts), "summary_en": " ".join(en_parts), } def _build_taf_signal( taf_data: Dict[str, Any], city: str, local_date: str, utc_offset: int, first_peak_h: int, last_peak_h: int, ) -> Dict[str, Any]: if str(city or "").strip().lower() == "hong kong": return {} raw_taf = re.sub(r"\s+", " ", str((taf_data or {}).get("raw_taf") or "").upper().strip()) if not raw_taf: return {} issue_raw = str((taf_data or {}).get("issue_time") or "").strip() issue_dt = None if issue_raw: try: issue_dt = datetime.fromisoformat(issue_raw.replace("Z", "+00:00")) except Exception: issue_dt = None if issue_dt is None: issue_dt = datetime.now(timezone.utc) local_tz = timezone(timedelta(seconds=int(utc_offset or 0))) valid_match = re.search(r"\b(\d{2})(\d{2})/(\d{2})(\d{2})\b", raw_taf) tokens = raw_taf.split() if not valid_match: return {} def _infer_utc(day: int, hour: int, minute: int = 0) -> datetime: base = issue_dt year = base.year month = base.month day_offset = 0 normalized_hour = hour if normalized_hour >= 24: day_offset += normalized_hour // 24 normalized_hour = normalized_hour % 24 candidate = datetime( year, month, day, normalized_hour, minute, tzinfo=timezone.utc, ) if day_offset: candidate += timedelta(days=day_offset) if candidate < base - timedelta(days=20): if month == 12: candidate = datetime( year + 1, 1, day, normalized_hour, minute, tzinfo=timezone.utc, ) + timedelta(days=day_offset) else: candidate = datetime( year, month + 1, day, normalized_hour, minute, tzinfo=timezone.utc, ) + timedelta(days=day_offset) elif candidate > base + timedelta(days=20): if month == 1: candidate = datetime( year - 1, 12, day, normalized_hour, minute, tzinfo=timezone.utc, ) + timedelta(days=day_offset) else: candidate = datetime( year, month - 1, day, normalized_hour, minute, tzinfo=timezone.utc, ) + timedelta(days=day_offset) return candidate def _parse_period(token: str) -> tuple[Optional[datetime], Optional[datetime]]: match = re.match(r"^(\d{2})(\d{2})/(\d{2})(\d{2})$", token) if not match: return None, None start = _infer_utc(int(match.group(1)), int(match.group(2))) end = _infer_utc(int(match.group(3)), int(match.group(4))) if end <= start: end += timedelta(days=1) return start, end valid_start_utc, valid_end_utc = _parse_period(valid_match.group(0)) if valid_start_utc is None or valid_end_utc is None: return {} segment_indexes: list[int] = [] for idx, token in enumerate(tokens): if re.match(r"^FM\d{6}$", token) or token in {"TEMPO", "BECMG", "PROB30", "PROB40"}: segment_indexes.append(idx) base_start_idx = 0 for idx, token in enumerate(tokens): if token == valid_match.group(0): base_start_idx = idx + 1 break segments: list[Dict[str, Any]] = [] first_segment_idx = segment_indexes[0] if segment_indexes else len(tokens) if base_start_idx < first_segment_idx: segments.append( { "type": "BASE", "start_utc": valid_start_utc, "end_utc": valid_end_utc, "tokens": tokens[base_start_idx:first_segment_idx], } ) idx_pos = 0 while idx_pos < len(segment_indexes): start_idx = segment_indexes[idx_pos] end_idx = segment_indexes[idx_pos + 1] if idx_pos + 1 < len(segment_indexes) else len(tokens) token = tokens[start_idx] seg_type = token seg_start = valid_start_utc seg_end = valid_end_utc payload_start = start_idx + 1 if re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token): match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token) seg_type = "FM" seg_start = _infer_utc(int(match.group(1)), int(match.group(2)), int(match.group(3))) if idx_pos + 1 < len(segment_indexes): next_token = tokens[segment_indexes[idx_pos + 1]] next_match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", next_token) if next_match: seg_end = _infer_utc(int(next_match.group(1)), int(next_match.group(2)), int(next_match.group(3))) else: seg_end = valid_end_utc else: seg_end = valid_end_utc elif token in {"TEMPO", "BECMG"}: seg_type = token if payload_start < len(tokens): seg_start, seg_end = _parse_period(tokens[payload_start]) payload_start += 1 elif token in {"PROB30", "PROB40"}: seg_type = token if payload_start < len(tokens) and tokens[payload_start] == "TEMPO": seg_type = f"{token} TEMPO" payload_start += 1 if payload_start < len(tokens): seg_start, seg_end = _parse_period(tokens[payload_start]) payload_start += 1 if seg_start is None or seg_end is None: idx_pos += 1 continue if seg_end <= seg_start: seg_end = seg_start + timedelta(hours=1) segments.append( { "type": seg_type, "start_utc": seg_start, "end_utc": seg_end, "tokens": tokens[payload_start:end_idx], } ) idx_pos += 1 peak_window_start = datetime.strptime(f"{local_date} {max(0, first_peak_h - 2):02d}:00", "%Y-%m-%d %H:%M").replace(tzinfo=local_tz) peak_window_end = datetime.strptime(f"{local_date} {min(23, last_peak_h + 1):02d}:00", "%Y-%m-%d %H:%M").replace(tzinfo=local_tz) precip_rank = {"low": 0, "medium": 1, "high": 2} suppression_level = "low" disruption_level = "low" low_ceiling_ft = None ceiling_cover = None wind_regimes: list[str] = [] markers: list[Dict[str, Any]] = [] active_segments: list[Dict[str, Any]] = [] def _segment_precip_level(tokens_block: list[str]) -> str: joined = " ".join(tokens_block) if re.search(r"\b(?:-|\+)?(?:TSRA|TS|VCTS|SHRA|SHSN|SHGS)\b", joined): return "high" if re.search(r"\b(?:-|\+)?(?:RA|DZ|SN)\b", joined): return "medium" return "low" for segment in segments: start_local = segment["start_utc"].astimezone(local_tz) end_local = segment["end_utc"].astimezone(local_tz) overlap_start = max(start_local, peak_window_start) overlap_end = min(end_local, peak_window_end) if overlap_end <= overlap_start: continue active_segments.append(segment) joined = " ".join(segment["tokens"]) level = _segment_precip_level(segment["tokens"]) if precip_rank[level] > precip_rank[suppression_level]: suppression_level = level cloud_matches = re.findall(r"\b(FEW|SCT|BKN|OVC)(\d{3})\b", joined) for cover, base in cloud_matches: if cover not in {"BKN", "OVC"}: continue try: base_ft = int(base) * 100 except Exception: continue if low_ceiling_ft is None or base_ft < low_ceiling_ft: low_ceiling_ft = base_ft ceiling_cover = cover if low_ceiling_ft is not None and low_ceiling_ft <= 4000 and suppression_level == "low": suppression_level = "medium" wind_matches = re.findall(r"\b(\d{3}|VRB)(\d{2,3})(?:G\d{2,3})?KT\b", joined) segment_regimes = [] for direction, _speed in wind_matches: if direction == "VRB": segment_regimes.append("variable") continue deg = int(direction) if 135 <= deg <= 225: segment_regimes.append("southerly") elif deg >= 315 or deg <= 45: segment_regimes.append("northerly") else: segment_regimes.append("cross") for item in segment_regimes: if item not in wind_regimes: wind_regimes.append(item) if segment["type"] in {"TEMPO", "BECMG", "PROB30", "PROB40", "PROB30 TEMPO", "PROB40 TEMPO"}: disruption_level = "medium" if disruption_level == "low" else disruption_level if segment["type"] in {"PROB30 TEMPO", "PROB40 TEMPO"} or level == "high": disruption_level = "high" marker_time_local = overlap_start marker_hour = marker_time_local.strftime("%H:00") hazards = [] if level != "low": hazards.append(level) if low_ceiling_ft is not None and segment_regimes is not None: hazards.append("cloud") if segment_regimes: hazards.append("wind") summary_zh = ( f"{segment['type']} {overlap_start.strftime('%H:%M')}-{overlap_end.strftime('%H:%M')} " f"{'有阵雨/雷暴扰动' if level == 'high' else '有云雨扰动' if level == 'medium' else '以稳定为主'}" ) summary_en = ( f"{segment['type']} {overlap_start.strftime('%H:%M')}-{overlap_end.strftime('%H:%M')} " f"{'shows shower/thunder disruption' if level == 'high' else 'shows cloud/rain disruption' if level == 'medium' else 'stays relatively stable'}" ) markers.append( { "label_time": marker_hour, "marker_type": segment["type"], "start_local": overlap_start.strftime("%H:%M"), "end_local": overlap_end.strftime("%H:%M"), "suppression_level": level, "summary_zh": summary_zh, "summary_en": summary_en, } ) wind_shift = len(wind_regimes) >= 2 or "variable" in wind_regimes peak_window = f"{peak_window_start.strftime('%H:%M')}-{peak_window_end.strftime('%H:%M')}" if suppression_level == "high": summary_zh = f"TAF 在峰值窗口（{peak_window}）提示阵雨或雷暴扰动，机场最高温可能被云雨压低。" summary_en = f"TAF flags shower or thunderstorm disruption around the peak window ({peak_window}), airport high may get capped by showers/storms." elif suppression_level == "medium": summary_zh = f"TAF 在峰值窗口（{peak_window}）提示云量或弱降水扰动，需要防峰值被压低。" summary_en = f"TAF points to cloud or light-precip disruption around the peak window ({peak_window}); the airport high may be capped." else: summary_zh = f"TAF 在峰值窗口（{peak_window}）暂未提示明显云雨压温。" summary_en = f"TAF does not flag a strong cloud/rain suppression signal around the peak window ({peak_window})." if wind_shift: summary_zh += " 同时机场预报风向存在阶段性切换。" summary_en += " Airport wind direction also shifts by regime during the window." return { "available": True, "source": "aviationweather-taf", "raw_taf": raw_taf, "issue_time": (taf_data or {}).get("issue_time"), "valid_time_from": (taf_data or {}).get("valid_time_from"), "valid_time_to": (taf_data or {}).get("valid_time_to"), "peak_window": peak_window, "segments": [ { "type": seg["type"], "start_local": seg["start_utc"].astimezone(local_tz).strftime("%H:%M"), "end_local": seg["end_utc"].astimezone(local_tz).strftime("%H:%M"), "tokens": seg["tokens"], } for seg in active_segments ], "markers": markers, "low_ceiling_ft": low_ceiling_ft, "ceiling_cover": ceiling_cover, "wind_regimes": wind_regimes, "wind_shift": wind_shift, "suppression_level": suppression_level, "disruption_level": disruption_level, "summary_zh": summary_zh, "summary_en": summary_en, } def _clock_minutes(value: Any) -> Optional[int]: text = str(value or "").strip() match = re.search(r"\b(\d{1,2}):(\d{2})\b", text) if not match: return None hour = int(match.group(1)) minute = int(match.group(2)) if hour < 0 or hour > 23 or minute < 0 or minute > 59: return None return hour * 60 + minute def _format_clock_minutes(value: int) -> str: value = max(0, min(23 * 60 + 59, int(value))) return f"{value // 60:02d}:{value % 60:02d}" def _next_observation_clock(local_time: Any) -> str: minutes = _clock_minutes(local_time) if minutes is None: return "--" next_slot = ((minutes // 30) + 1) * 30 if next_slot > 23 * 60 + 59: return "23:59" return _format_clock_minutes(next_slot) def _bucket_label_from_value(value: Optional[float], unit: str) -> Optional[str]: if value is None: return None try: return f"{int(round(float(value)))}{unit or '°C'}" except Exception: return None def _top_probability_bucket(distribution: Any) -> Optional[Dict[str, Any]]: if not isinstance(distribution, list): return None candidates = [row for row in distribution if isinstance(row, dict)] if not candidates: return None return max(candidates, key=lambda row: _sf(row.get("probability")) or -1.0) def _bucket_label(row: Optional[Dict[str, Any]], unit: str) -> Optional[str]: if not isinstance(row, dict): return None for key in ("label", "bucket", "range"): raw = str(row.get(key) or "").strip() if raw: return raw return _bucket_label_from_value(_sf(row.get("value")), unit) def _add_signal( signals: list, *, label: str, direction: str, strength: str, summary: str, label_en: Optional[str] = None, summary_en: Optional[str] = None, ) -> None: signals.append( { "label": label, "label_en": label_en or label, "direction": direction, "strength": strength, "summary": summary, "summary_en": summary_en or summary, } ) def _build_intraday_meteorology(data: Dict[str, Any]) -> Dict[str, Any]: """Build a paid-product intraday meteorology read from existing layers.""" current = data.get("current") or {} probabilities = data.get("probabilities") or {} distribution = probabilities.get("distribution") or [] top_bucket = _top_probability_bucket(distribution) unit = str(data.get("temp_symbol") or "°C") deb = data.get("deb") or {} peak = data.get("peak") or {} deviation = data.get("deviation_monitor") or {} taf_signal = ( ((data.get("taf") or {}).get("signal") or {}) if isinstance(data.get("taf"), dict) else {} ) vertical = data.get("vertical_profile_signal") or {} current_temp = _sf(current.get("temp")) max_so_far = _sf(current.get("max_so_far")) deb_prediction = _sf(deb.get("prediction")) base_value = _sf(top_bucket.get("value")) if isinstance(top_bucket, dict) else None if base_value is None: base_value = deb_prediction if base_value is None: base_value = max_so_far if max_so_far is not None else current_temp base_case_bucket = _bucket_label(top_bucket, unit) or _bucket_label_from_value(base_value, unit) upside_bucket = _bucket_label_from_value(base_value + 1.0, unit) if base_value is not None else None downside_bucket = _bucket_label_from_value(base_value - 1.0, unit) if base_value is not None else None signals: list = [] support_score = 0 suppress_score = 0 available_layers = 0 direction = str(deviation.get("direction") or "").lower() severity = str(deviation.get("severity") or "normal").lower() delta = _sf(deviation.get("current_delta")) if direction: available_layers += 1 strength = "strong" if severity == "strong" else ("medium" if severity == "light" else "weak") if direction == "hot": support_score += 2 if strength == "strong" else 1 _add_signal( signals, label="日内节奏", label_en="Intraday pace", direction="support", strength=strength, summary=f"实测较预期路径偏高 {abs(delta or 0):.1f}{unit}，峰值仍有上修空间。", summary_en=f"Observed temperature is running {abs(delta or 0):.1f}{unit} above the expected path; the peak still has upside room.", ) elif direction == "cold": suppress_score += 2 if strength == "strong" else 1 _add_signal( signals, label="日内节奏", label_en="Intraday pace", direction="suppress", strength=strength, summary=f"实测较预期路径偏低 {abs(delta or 0):.1f}{unit}，追更高温档需要等待后续观测确认。", summary_en=f"Observed temperature is running {abs(delta or 0):.1f}{unit} below the expected path; higher buckets need confirmation from later observations.", ) else: _add_signal( signals, label="日内节奏", label_en="Intraday pace", direction="neutral", strength="weak", summary="实测大体贴近当前预期路径，下一步主要看峰值窗口内是否继续抬升。", summary_en="Observed temperature is broadly tracking the expected path; the next question is whether it keeps lifting through the peak window.", ) heating_setup = str(vertical.get("heating_setup") or "").lower() suppression_risk = str(vertical.get("suppression_risk") or "").lower() if heating_setup or suppression_risk: available_layers += 1 if heating_setup == "supportive": support_score += 2 _add_signal( signals, label="边界层结构", label_en="Boundary-layer setup", direction="support", strength="strong", summary=str(vertical.get("summary_zh") or "边界层结构支持白天继续混合升温。"), summary_en=str(vertical.get("summary_en") or "The boundary-layer setup supports continued daytime mixing and warming."), ) elif heating_setup == "suppressed" or suppression_risk == "high": suppress_score += 2 _add_signal( signals, label="边界层结构", label_en="Boundary-layer setup", direction="suppress", strength="strong", summary=str(vertical.get("summary_zh") or "边界层或云雨结构对午后峰值形成压制。"), summary_en=str(vertical.get("summary_en") or "Boundary-layer or cloud/rain structure is capping the afternoon peak."), ) else: _add_signal( signals, label="边界层结构", label_en="Boundary-layer setup", direction="neutral", strength="medium", summary=str(vertical.get("summary_zh") or "边界层结构暂未给出单边信号。"), summary_en=str(vertical.get("summary_en") or "The boundary-layer setup does not yet provide a one-sided signal."), ) taf_suppression = str(taf_signal.get("suppression_level") or "").lower() taf_disruption = str(taf_signal.get("disruption_level") or "").lower() taf_has_cloud_rain_cap = taf_suppression in {"medium", "high"} or taf_disruption in { "medium", "high", } structural_cap = False if taf_signal.get("available") or taf_suppression: available_layers += 1 if taf_suppression == "high" or taf_disruption == "high": suppress_score += 2 direction_value = "suppress" strength = "strong" elif taf_suppression == "medium" or taf_disruption == "medium": suppress_score += 1 direction_value = "suppress" strength = "medium" else: support_score += 1 direction_value = "support" strength = "weak" _add_signal( signals, label="TAF 云雨扰动", label_en="TAF cloud/rain disruption", direction=direction_value, strength=strength, summary=str(taf_signal.get("summary_zh") or "TAF 暂未提示强云雨压温信号。"), summary_en=str(taf_signal.get("summary_en") or "TAF does not yet flag a strong cloud/rain temperature cap."), ) airport_delta = _sf(data.get("airport_vs_network_delta")) lead_signal = data.get("network_lead_signal") or {} if airport_delta is not None: available_layers += 1 leader = str(lead_signal.get("leader_station_label") or lead_signal.get("leader_station_code") or "").strip() sync_status = str(lead_signal.get("leader_sync_status") or "").strip().lower() sync_delta = _sf(lead_signal.get("leader_time_delta_vs_anchor_minutes")) sync_suffix_zh = "" sync_suffix_en = "" if sync_status in {"near_realtime", "lagged"} and sync_delta is not None: sync_suffix_zh = f"；但与机场锚点约差 {sync_delta:.0f} 分钟，作为降权信号处理" sync_suffix_en = f"; timing differs from the airport anchor by about {sync_delta:.0f} minutes, so this signal is down-weighted" elif sync_status == "unknown": sync_suffix_zh = "；周边站观测时间不可完全校验，作为弱参考" sync_suffix_en = "; station timing is not fully verified, so this is treated as a weak reference" if airport_delta <= -0.4: support_score += 1 _add_signal( signals, label="站网对比", label_en="Station-network comparison", direction="support", strength="weak" if sync_suffix_zh else "medium", summary=f"周边站网较机场锚点偏热 {abs(airport_delta):.1f}{unit}{f'，领先点位 {leader}' if leader else ''}{sync_suffix_zh}。", summary_en=f"Nearby stations are {abs(airport_delta):.1f}{unit} warmer than the airport anchor{f'; leading site: {leader}' if leader else ''}{sync_suffix_en}.", ) elif airport_delta >= 0.4: suppress_score += 1 _add_signal( signals, label="站网对比", label_en="Station-network comparison", direction="suppress", strength="weak" if sync_suffix_zh else "medium", summary=f"机场锚点较周边站网偏热 {abs(airport_delta):.1f}{unit}，继续上修需要机场自身后续报文确认{sync_suffix_zh}。", summary_en=f"The airport anchor is {abs(airport_delta):.1f}{unit} warmer than nearby stations; further upside needs confirmation from later airport reports{sync_suffix_en}.", ) else: _add_signal( signals, label="站网对比", label_en="Station-network comparison", direction="neutral", strength="weak", summary="机场锚点与周边站网基本同步，暂不构成单独上修或下修理由。", summary_en="The airport anchor and nearby station network are broadly aligned, so this layer does not independently argue for upside or downside.", ) peak_status = str(peak.get("status") or "").lower() first_h = _sf(peak.get("first_h")) last_h = _sf(peak.get("last_h")) peak_window = ( f"{int(first_h):02d}:00-{int(last_h):02d}:59" if first_h is not None and last_h is not None else "--" ) if peak_status == "past": headline = "峰值窗口已过，后续更偏向确认最终高点而非继续上修。" headline_en = "The peak window has passed; the read now shifts toward confirming the final high rather than chasing further upside." confidence = "high" if available_layers >= 2 else "medium" elif suppress_score >= support_score + 2: structural_cap = any( signal.get("direction") == "suppress" and signal.get("label") in {"边界层结构", "站网对比", "日内节奏"} for signal in signals ) if taf_has_cloud_rain_cap and structural_cap: headline = "峰值同时存在 TAF 云雨扰动和结构压制，当前更偏防守高温上修。" headline_en = "Both TAF cloud/rain disruption and structural signals are capping the peak; defend against aggressive high-temperature upside for now." elif taf_has_cloud_rain_cap: headline = "TAF 提示峰值窗口有云雨扰动，当前更偏防守高温上修。" headline_en = "TAF flags cloud/rain disruption near the peak window; defend against aggressive high-temperature upside for now." else: headline = "峰值主要受结构信号压制，TAF 云雨层暂未构成主压温理由。" headline_en = "The peak is mainly capped by structural signals; TAF cloud/rain is not the primary suppression reason for now." confidence = "high" if available_layers >= 3 else "medium" elif support_score >= suppress_score + 2: headline = "峰值仍有上修空间，后续重点看峰值窗口内报文能否继续抬升。" headline_en = "The peak still has upside room; the next check is whether reports keep lifting through the peak window." confidence = "high" if available_layers >= 3 else "medium" elif available_layers == 0: headline = "关键日内层仍在补齐，先以观测锚点和下一次报文为主。" headline_en = "Key intraday layers are still filling in; anchor the read on observations and the next report." confidence = "low" else: headline = "当前处于分歧判断区，峰值窗口内的下一组观测将决定方向。" headline_en = "The setup is in a split-decision zone; the next observations inside the peak window should decide direction." confidence = "medium" if available_layers >= 2 else "low" next_observation = _next_observation_clock(data.get("local_time") or current.get("obs_time")) threshold = base_value invalidation_rules = [] invalidation_rules_en = [] confirmation_rules = [] confirmation_rules_en = [] if peak_status == "past": invalidation_rules.append("若后续官方结算源补录更高值，以结算源最终高点为准。") invalidation_rules_en.append("If the official settlement source later backfills a higher reading, defer to the final settlement-source high.") confirmation_rules.append("若峰值窗口后连续两次观测不再创新高，当前高点基本确认。") confirmation_rules_en.append("If two consecutive post-peak observations fail to make a new high, the current high is broadly confirmed.") else: watch_clock = _format_clock_minutes(int(first_h or 13) * 60 + 30) if threshold is not None: invalidation_rules.append(f"{watch_clock} 前若仍未接近 {threshold:.0f}{unit}，上修路径降级。") invalidation_rules_en.append(f"If observations are still not near {threshold:.0f}{unit} before {watch_clock}, downgrade the upside path.") confirmation_rules.append(f"峰值窗口内任一结算源观测触达或超过 {threshold:.0f}{unit}，基准路径确认度上升。") confirmation_rules_en.append(f"If any settlement-source observation reaches or exceeds {threshold:.0f}{unit} inside the peak window, confidence in the base path rises.") invalidation_rules.append("若 TAF 或实况报文出现阵雨、雷暴或低云/云雨压制，高温上沿需要下调。") invalidation_rules_en.append("If TAF or live reports show showers, thunderstorms, or low-cloud/cloud-rain suppression, lower the upper temperature bound.") confirmation_rules.append("若实测继续贴近 DEB 曲线且云雨信号不增强，维持当前主路径。") confirmation_rules_en.append("If observations keep tracking the DEB curve and cloud/rain signals do not strengthen, maintain the current main path.") if not signals: _add_signal( signals, label="数据完整性", label_en="Data completeness", direction="neutral", strength="weak", summary="当前缺少足够的日内结构层，等待下一次观测刷新后再提高判断权重。", summary_en="There are not enough intraday structure layers yet; wait for the next observation refresh before raising confidence.", ) return { "headline": headline, "headline_en": headline_en, "confidence": confidence, "base_case_bucket": base_case_bucket, "upside_bucket": upside_bucket, "downside_bucket": downside_bucket, "next_observation_time": next_observation, "peak_window": peak_window, "invalidation_rules": invalidation_rules[:4], "invalidation_rules_en": invalidation_rules_en[:4], "confirmation_rules": confirmation_rules[:3], "confirmation_rules_en": confirmation_rules_en[:3], "signal_contributions": signals[:5], } def _archive_intraday_path_snapshot(city: str, result: Dict[str, Any]) -> None: """Persist replayable intraday path inputs visible at analysis time.""" hourly = result.get("hourly") or {} times = hourly.get("times") if isinstance(hourly, dict) else [] temps = hourly.get("temps") if isinstance(hourly, dict) else [] if not isinstance(times, list) or not isinstance(temps, list) or not times: return forecast = result.get("forecast") or {} deb = result.get("deb") or {} current = result.get("current") or {} forecast_today_high = _sf(forecast.get("today_high")) deb_prediction = _sf(deb.get("prediction")) offset = ( deb_prediction - forecast_today_high if deb_prediction is not None and forecast_today_high is not None else 0.0 ) deb_base_temps = [ round(float(value) + offset, 1) if _sf(value) is not None else None for value in temps ] utc_offset = int(result.get("utc_offset_seconds") or 0) snapshot_time = datetime.now(timezone.utc).astimezone( timezone(timedelta(seconds=utc_offset)) ).isoformat(timespec="seconds") payload = { "schema_version": 1, "city": city, "target_date": str(result.get("local_date") or "").strip(), "snapshot_time": snapshot_time, "local_time": str(result.get("local_time") or "").strip(), "utc_offset_seconds": utc_offset, "temp_symbol": result.get("temp_symbol"), "deb_prediction": deb_prediction, "forecast_today_high": forecast_today_high, "deb_base_path": { "times": [str(item) for item in times], "temps": deb_base_temps, "source": "hourly_plus_deb_offset", "offset": round(offset, 3), }, "hourly": { "times": [str(item) for item in times], "temps": temps, }, "metar_today_obs": result.get("metar_today_obs") or [], "settlement_today_obs": result.get("settlement_today_obs") or [], "current": { "temp": _sf(current.get("temp")), "max_so_far": _sf(current.get("max_so_far")), "obs_time": current.get("obs_time"), "settlement_source": current.get("settlement_source"), "settlement_source_label": current.get("settlement_source_label"), }, "forecast": { "today_high": forecast_today_high, "sunrise": forecast.get("sunrise"), "sunset": forecast.get("sunset"), }, "peak": result.get("peak") or {}, "metar_status": result.get("metar_status") or {}, } try: IntradayPathSnapshotRepository().append_snapshot(payload) except Exception as exc: logger.debug(f"intraday path snapshot archive skipped for {city}: {exc}") def _analyze( city: str, force_refresh: bool = False, include_llm_commentary: bool = False, detail_mode: str = "full", ) -> Dict[str, Any]: """Fetch, analyse, and return structured weather data for one city.""" # Check cache ttl = _analysis_ttl_for_city(city) normalized_detail_mode_raw = str(detail_mode or "full").strip().lower() if normalized_detail_mode_raw == "panel": normalized_detail_mode = "panel" elif normalized_detail_mode_raw == "market": normalized_detail_mode = "market" elif normalized_detail_mode_raw == "nearby": normalized_detail_mode = "nearby" else: normalized_detail_mode = "full" cache_key = _analysis_cache_key(city, normalized_detail_mode) if not force_refresh: cached = _cache.get(cache_key) if cached and _time.time() - cached["t"] < ttl: if include_llm_commentary: cached_payload = cached["d"] dynamic = cached_payload.get("dynamic_commentary") or {} if not dynamic.get("headline_zh"): cached_payload["dynamic_commentary"] = _maybe_enrich_dynamic_commentary_with_groq( city, cached_payload, ) _record_analysis_cache_event(city=city, hit=True, force_refresh=False) return cached["d"] _record_analysis_cache_event(city=city, hit=False, force_refresh=force_refresh) 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 ── is_panel_mode = normalized_detail_mode == "panel" is_market_mode = normalized_detail_mode == "market" is_nearby_mode = normalized_detail_mode == "nearby" raw = _weather.fetch_all_sources( city, lat=lat, lon=lon, force_refresh=force_refresh, include_taf=not is_panel_mode and not is_nearby_mode and not is_market_mode, include_nearby=not is_panel_mode and not is_market_mode, include_ensemble=not is_panel_mode and not is_nearby_mode and not is_market_mode, include_multi_model=not is_panel_mode and not is_nearby_mode, include_mgm=not is_market_mode, ) om = raw.get("open-meteo", {}) metar = raw.get("metar", {}) taf = raw.get("taf", {}) 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, {}) network_snapshot = ( build_country_network_snapshot(city, raw) if not is_panel_mode and not is_market_mode 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。 # 当前日期/时间必须来自运行时钟，不能使用 Open-Meteo 缓存里的 local_time。 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 = get_city_utc_offset_seconds(city) try: utc_offset = int(utc_offset or 0) except Exception: utc_offset = get_city_utc_offset_seconds(city) now_utc = datetime.now(timezone.utc) local_now = now_utc + timedelta(seconds=utc_offset) local_date_str = local_now.strftime("%Y-%m-%d") 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 metar_current_is_today = _metar_is_current_local_day( metar, local_date=local_date_str, utc_offset=int(utc_offset or 0), ) # ── 2. Current conditions (settlement > AMOS runway sensors > METAR > MGM > NMC 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 {} amos_data = raw.get("amos") or {} if amos_data: logger.info("AMOS _analyze: found amos data for city={} temp_c={} source={}", city, amos_data.get("temp_c"), amos_data.get("source")) use_settlement_current = settlement_source in {"hko", "cwa", "noaa", "wunderground"} and bool(sc_cur) live_mc = mc if metar_current_is_today else {} primary_current = sc_cur if use_settlement_current else live_mc current_source = settlement_source current_source_label = settlement_source_label current_station_code = settlement_current.get("station_code") current_station_name = settlement_current.get("station_name") cur_temp = _sf(primary_current.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None # AMOS runway sensor: authoritative for Korean airports (RKSI/RKPK) if cur_temp is None: amos_temp = _sf(amos_data.get("temp_c")) if amos_temp is not None and _is_plausible_city_temp(city, amos_temp, sym): cur_temp = amos_temp current_source = "amos" current_source_label = amos_data.get("source_label") or "AMOS" current_station_code = amos_data.get("icao") current_station_name = amos_data.get("station_label") if cur_temp is None: cur_temp = _sf(live_mc.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None if cur_temp is None: cur_temp = _sf(mg_cur.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None if cur_temp is None: nmc_fallback = _fetch_nmc_current_fallback(city, use_fahrenheit=is_f) nmc_cur = nmc_fallback.get("current") or {} nmc_temp = _sf(nmc_cur.get("temp")) if nmc_temp is not None: cur_temp = nmc_temp current_source = "nmc" current_source_label = "NMC" current_station_code = nmc_fallback.get("station_code") current_station_name = nmc_fallback.get("station_name") max_so_far = _sf(primary_current.get("max_temp_so_far")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = _sf(live_mc.get("max_temp_so_far")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = _sf(mg_cur.get("mgm_max_temp")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = cur_temp max_temp_time = primary_current.get("max_temp_time") if not max_temp_time and not use_settlement_current: max_temp_time = live_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 raw_settlement_max = max_so_far wu_settle = apply_city_settlement(city.lower(), raw_settlement_max) if raw_settlement_max is not None else None display_settlement_max = wu_settle if settlement_source == "wunderground" and wu_settle is not None else raw_settlement_max # 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 and metar_current_is_today: obs_t = metar.get("observation_time", "") if metar else "" if obs_t and "T" in obs_t: try: dt = _parse_utc_datetime(obs_t) if dt is None: raise ValueError("invalid observation time") 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] if not obs_time_str and current_source == "amos": amos_obs_time = amos_data.get("observation_time") if amos_obs_time: obs_time_str = _format_observation_time_local(amos_obs_time, int(utc_offset or 0)) if not obs_time_str and current_source == "nmc": nmc_fallback = _fetch_nmc_current_fallback(city, use_fahrenheit=is_f) obs_time_str = _format_observation_time_local( nmc_fallback.get("publish_time") or nmc_fallback.get("timestamp"), int(utc_offset or 0), ) current_obs_raw = obs_t if current_source == "amos": current_obs_raw = amos_data.get("observation_time") elif current_source == "nmc": current_obs_raw = ( nmc_fallback.get("publish_time") or nmc_fallback.get("timestamp") if isinstance(nmc_fallback, dict) else None ) current_age_min = metar_age_min if current_obs_raw: current_age_min = _observation_age_min(current_obs_raw, now_utc) or current_age_min current_freshness = _build_observation_freshness( source_code=current_source, source_label=current_source_label, observed_at=current_obs_raw, observed_at_local=obs_time_str, ingested_at=primary_current.get("receipt_time") or primary_current.get("report_time"), age_min=current_age_min, now_utc=now_utc, ) airport_source_code = amos_data.get("source") if current_source == "amos" else "metar" airport_source_code = airport_source_code or ("amos" if current_source == "amos" else "metar") airport_source_label = amos_data.get("source_label") if current_source == "amos" else "METAR" airport_source_label = airport_source_label or ("AMOS" if current_source == "amos" else "METAR") airport_obs_raw = amos_data.get("observation_time") if current_source == "amos" else (metar.get("observation_time") if metar else None) airport_age_min = _observation_age_min(airport_obs_raw, now_utc) if airport_obs_raw else metar_age_min if airport_age_min is None: airport_age_min = metar_age_min airport_temp = _sf(amos_data.get("temp_c")) if current_source == "amos" else _sf(live_mc.get("temp")) if airport_temp is not None and not _is_plausible_city_temp(city, airport_temp, sym): airport_temp = None airport_freshness = _build_observation_freshness( source_code=airport_source_code, source_label=airport_source_label, observed_at=airport_obs_raw, observed_at_local=obs_time_str, ingested_at=metar.get("receipt_time") if metar else None, age_min=airport_age_min, now_utc=now_utc, ) airport_primary_current = dict(network_snapshot.get("airport_primary_current") or {}) if ( airport_primary_current.get("source_code") == "metar" and metar and not metar_current_is_today ): airport_primary_current["temp"] = None airport_primary_current["stale_for_today"] = True airport_primary_current["last_observation_local_date"] = metar.get("observation_local_date") airport_primary_current["current_local_date"] = local_date_str if ( airport_primary_current.get("source_code") == "metar" and obs_time_str and not use_settlement_current ): airport_primary_current["obs_time"] = obs_time_str airport_primary_current["obs_age_min"] = metar_age_min settlement_today_obs = [] if use_settlement_current: explicit_settlement_obs = settlement_current.get("today_obs") or [] normalized_obs = [] for item in explicit_settlement_obs: if isinstance(item, dict): raw_time = str(item.get("time") or "").strip() raw_temp = _sf(item.get("temp")) elif isinstance(item, (list, tuple)) and len(item) >= 2: raw_time = str(item[0] or "").strip() raw_temp = _sf(item[1]) else: continue if not raw_time or raw_temp is None: continue normalized_obs.append({"time": raw_time, "temp": raw_temp}) if normalized_obs: settlement_today_obs = normalized_obs else: 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 = [ {"time": t, "temp": v} for t, v in ( metar.get("today_obs", []) if metar and metar_current_is_today else [] ) if _is_plausible_city_temp(city, v, sym) ] metar_recent_obs_payload = [ point for point in ( metar.get("recent_obs", []) if metar and metar_current_is_today else [] ) if isinstance(point, dict) and _is_plausible_city_temp(city, point.get("temp"), sym) ] airport_max_so_far = None airport_max_temp_time = None for point in metar_today_obs_payload: value = _sf(point.get("temp")) if isinstance(point, dict) else None if value is None: continue if airport_max_so_far is None or value >= airport_max_so_far: airport_max_so_far = value airport_max_temp_time = str(point.get("time") or "") or None # ── 3. 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 = _dedupe_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_wspd_180m = hourly.get("wind_speed_180m", []) h_wdir_180m = hourly.get("wind_direction_180m", []) h_precip_prob = hourly.get("precipitation_probability", []) h_cloud_cover = hourly.get("cloud_cover", []) h_cape = hourly.get("cape", []) h_cin = hourly.get("convective_inhibition", []) h_lifted_index = hourly.get("lifted_index", []) h_boundary_layer_height = hourly.get("boundary_layer_height", []) 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_wspd_180m = [None for _ in parsed_obs] h_wdir_180m = [None for _ in parsed_obs] h_precip_prob = [None for _ in parsed_obs] h_cloud_cover = [None for _ in parsed_obs] h_cape = [None for _ in parsed_obs] h_cin = [None for _ in parsed_obs] h_lifted_index = [None for _ in parsed_obs] h_boundary_layer_height = [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" lgbm_val = None if current_forecasts and deb_val is not None: lgbm_val, _ = predict_lgbm_daily_high( city_name=city, current_forecasts=current_forecasts, deb_prediction=deb_val, current_temp=cur_temp, max_so_far=max_so_far, humidity=_sf(primary_current.get("humidity")), wind_speed_kt=_sf(primary_current.get("wind_speed_kt")), visibility_mi=_sf(primary_current.get("visibility_mi")), local_hour=local_hour, local_date=local_date_str, peak_status=peak_status, ) # LGBM is kept as an independent reference (lgbm.prediction), # not fed back into DEB to avoid circular dependency deviation_monitor = _build_deviation_monitor( current_temp=cur_temp, deb_prediction=deb_val, om_today=om_today, hourly_times=h_times, hourly_temps=h_temps, local_date=local_date_str, local_hour_frac=local_hour_frac, observation_points=( settlement_today_obs if settlement_today_obs else metar_today_obs_payload ), ) # ── 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 = [] probabilities_all = [] shadow_probabilities = [] shadow_probabilities_all = [] 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 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", []) probabilities_all = sd.get("probabilities_all", probabilities) shadow_probabilities = sd.get("shadow_probabilities", []) shadow_probabilities_all = sd.get("shadow_probabilities_all", 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}") # ── 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": [], "wind_speed_180m": [], "wind_direction_180m": [], "precipitation_probability": [], "cloud_cover": [], "cape": [], "convective_inhibition": [], "lifted_index": [], "boundary_layer_height": [], } 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["wind_speed_180m"].append( h_wspd_180m[i] if i < len(h_wspd_180m) else None ) next_48h_hourly["wind_direction_180m"].append( h_wdir_180m[i] if i < len(h_wdir_180m) 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 ) next_48h_hourly["cape"].append( h_cape[i] if i < len(h_cape) else None ) next_48h_hourly["convective_inhibition"].append( h_cin[i] if i < len(h_cin) else None ) next_48h_hourly["lifted_index"].append( h_lifted_index[i] if i < len(h_lifted_index) else None ) next_48h_hourly["boundary_layer_height"].append( h_boundary_layer_height[i] if i < len(h_boundary_layer_height) else None ) vertical_profile_signal = ( _build_vertical_profile_signal( next_48h_hourly, local_date_str, local_hour, first_peak_h, last_peak_h, ) if not is_panel_mode and not is_nearby_mode and not is_market_mode else {} ) taf_signal = ( _build_taf_signal( taf if isinstance(taf, dict) else {}, city, local_date_str, int(utc_offset or 0), first_peak_h, last_peak_h, ) if not is_panel_mode and not is_nearby_mode and not is_market_mode else {"available": False} ) # ── 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 (Turkish MGM-supported cities) ── 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 = [] d_probs_all = [] 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", []) d_probs_all = prob_obj.get("probabilities_all", d_probs) 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 "probabilities_all": d_probs_all if i > 0 else probabilities_all, } # ── Assemble result ── city_meta = CITIES.get(city, {}) or {} result = { "detail_depth": ( "panel" if is_panel_mode else "market" if is_market_mode else "nearby" if is_nearby_mode else "full" ), "name": city, "display_name": str(city_meta.get("display_name") or city_meta.get("name") or city.title()), "lat": lat, "lon": lon, "utc_offset_seconds": int(utc_offset or 0), "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": display_settlement_max, "max_temp_time": max_temp_time, "raw_max_so_far": raw_settlement_max, "wu_settlement": wu_settle, "source_code": current_source, "settlement_source": current_source, "settlement_source_label": current_source_label, "station_code": current_station_code, "station_name": current_station_name, "obs_time": obs_time_str, "obs_age_min": None if use_settlement_current else metar_age_min, "freshness": current_freshness, "observation_status": "live" if cur_temp is not None else "missing", "report_time": primary_current.get("report_time"), "receipt_time": primary_current.get("receipt_time"), "obs_time_epoch": primary_current.get("obs_time_epoch"), "wind_speed_kt": _sf(amos_data.get("wind_kt")) if current_source == "amos" else _sf(primary_current.get("wind_speed_kt")), "wind_dir": _sf(primary_current.get("wind_dir")), "humidity": _sf(primary_current.get("humidity")), "pressure_hpa": _sf(amos_data.get("pressure_hpa")) if current_source == "amos" else _sf(primary_current.get("pressure_hpa")), "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": amos_data.get("raw_metar") if current_source == "amos" else primary_current.get("raw_metar"), }, "airport_current": { "temp": airport_temp, "obs_time": obs_time_str, "max_so_far": airport_max_so_far, "max_temp_time": airport_max_temp_time, "obs_age_min": airport_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(amos_data.get("wind_kt")) if current_source == "amos" else _sf(live_mc.get("wind_speed_kt")), "wind_dir": _sf(live_mc.get("wind_dir")), "humidity": _sf(live_mc.get("humidity")), "cloud_desc": metar.get("cloud_desc") if metar else None, "visibility_mi": _sf(live_mc.get("visibility_mi")), "wx_desc": live_mc.get("wx_desc"), "raw_metar": amos_data.get("raw_metar") if current_source == "amos" else live_mc.get("raw_metar"), "source_code": airport_source_code, "source_label": airport_source_label, "freshness": airport_freshness, "stale_for_today": False if current_source == "amos" else (bool(metar) and not metar_current_is_today), "last_observation_local_date": metar.get("observation_local_date") if metar else None, "current_local_date": local_date_str, }, "settlement_station": network_snapshot.get("settlement_station") or {}, "airport_primary": airport_primary_current, "airport_primary_today_obs": network_snapshot.get("airport_primary_today_obs") or [], "official_nearby": network_snapshot.get("official_nearby") or [], "official_network_source": network_snapshot.get("official_network_source"), "official_network_status": network_snapshot.get("official_network_status") or {}, "network_lead_signal": network_snapshot.get("network_lead_signal") or {}, "network_spread_signal": network_snapshot.get("network_spread_signal") or {}, "center_station_candidate": network_snapshot.get("center_station_candidate"), "airport_vs_network_delta": network_snapshot.get("airport_vs_network_delta"), "mgm": mgm_data, "mgm_nearby": raw.get("mgm_nearby", []), "nearby_source": raw.get("nearby_source") or ("mgm" if city.lower() in TURKISH_MGM_CITIES else "metar_cluster"), "amos": amos_data if amos_data and amos_data.get("source") else None, "forecast": { "today_high": om_today, "daily": forecast_daily, "sunrise": sunrise, "sunset": sunset, "sunshine_hours": sunshine_h, }, "source_forecasts": { "weather_gov": raw.get("nws") or {}, "open_meteo_multi_model": { "source": mm.get("source"), "provider": mm.get("provider"), "dates": mm.get("dates") or [], "model_metadata": mm.get("model_metadata") or {}, "model_keys": mm.get("model_keys") or {}, "attribution": mm.get("attribution"), } if isinstance(mm, dict) and mm else {}, }, "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}, "lgbm": {"prediction": lgbm_val}, "deviation_monitor": deviation_monitor, "ensemble": ens_data, "probabilities": { "mu": round(mu, 1) if mu is not None else None, "distribution": probabilities, "distribution_all": probabilities_all or 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, "shadow_distribution_all": shadow_probabilities_all or 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, "vertical_profile_signal": vertical_profile_signal, "taf": { **(taf if isinstance(taf, dict) else {}), "signal": taf_signal, } if taf_signal or taf else {}, "metar_today_obs": metar_today_obs_payload, "metar_recent_obs": metar_recent_obs_payload, "metar_status": { "available_for_today": metar_current_is_today, "stale_for_today": bool(metar) and not metar_current_is_today, "last_observation_time": metar.get("observation_time") if metar else None, "last_observation_local_date": metar.get("observation_local_date") if metar else None, "current_local_date": local_date_str, "last_temp": _sf(mc.get("temp")) if mc else None, }, "settlement_today_obs": settlement_today_obs, "ai_analysis": "", "updated_at": datetime.now(timezone.utc).isoformat(), } result["intraday_meteorology"] = _build_intraday_meteorology(result) if normalized_detail_mode == "full": _archive_intraday_path_snapshot(city, result) if include_llm_commentary: result["dynamic_commentary"] = _maybe_enrich_dynamic_commentary_with_groq( city, result, ) _cache[cache_key] = {"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 _analyze_summary(city: str, force_refresh: bool = False) -> Dict[str, Any]: ttl = _analysis_ttl_for_city(city) if not force_refresh: cached_detail = _get_cached_analysis(city, ttl) if cached_detail: return cached_detail cached_summary = _get_cached_summary(city, ttl) if cached_summary: return cached_summary 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(), ) if force_refresh: try: _weather._evict_city_caches( # type: ignore[attr-defined] city=city, lat=lat, lon=lon, use_fahrenheit=is_f, ) except Exception: pass default_utc_offset = get_city_utc_offset_seconds(city) def _safe_call(fn): try: return fn() except Exception: return None jobs = { "settlement_current": lambda: _weather.fetch_settlement_current(city) or {}, "open_meteo": lambda: _weather.fetch_from_open_meteo(lat, lon, use_fahrenheit=is_f) or {}, "multi_model": lambda: _weather.fetch_multi_model(lat, lon, city=city, use_fahrenheit=is_f) or {}, } if _weather._supports_aviationweather(city): # type: ignore[attr-defined] jobs["metar"] = lambda: _weather.fetch_metar( city, use_fahrenheit=is_f, utc_offset=default_utc_offset, ) or {} if city in TURKISH_MGM_CITIES: istno, _province = _weather.TURKISH_PROVINCES.get(city, (None, None)) # type: ignore[attr-defined] if istno: jobs["mgm"] = lambda istno=istno: _weather.fetch_from_mgm(str(istno)) or {} if is_f: jobs["nws"] = lambda: _weather.fetch_nws(lat, lon) or {} if settlement_source == "hko": jobs["hko_forecast"] = lambda: _weather.fetch_hko_forecast() fetched: Dict[str, Any] = {} with ThreadPoolExecutor(max_workers=min(6, len(jobs))) as executor: future_map = { executor.submit(_safe_call, fn): key for key, fn in jobs.items() } for future, key in [(future, key) for future, key in future_map.items()]: fetched[key] = future.result() settlement_current = fetched.get("settlement_current") or {} open_meteo = fetched.get("open_meteo") or {} mm = fetched.get("multi_model") or {} utc_offset = open_meteo.get("utc_offset") if utc_offset is None: utc_offset = default_utc_offset try: utc_offset = int(utc_offset or 0) except Exception: utc_offset = default_utc_offset now_utc = datetime.now(timezone.utc) local_now = now_utc + timedelta(seconds=utc_offset) local_date_str = local_now.strftime("%Y-%m-%d") 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 / 60.0 metar = fetched.get("metar") or {} mgm = fetched.get("mgm") or {} nws = fetched.get("nws") or {} hko_forecast = fetched.get("hko_forecast") metar_current_is_today = _metar_is_current_local_day( metar, local_date=local_date_str, utc_offset=int(utc_offset or 0), ) sc_cur = settlement_current.get("current") or {} mc = metar.get("current") or {} live_mc = mc if metar_current_is_today else {} mg_cur = mgm.get("current") or {} use_settlement_current = settlement_source in {"hko", "cwa", "noaa", "wunderground"} and bool(sc_cur) primary_current = sc_cur if use_settlement_current else live_mc current_source = settlement_source current_source_label = settlement_source_label nmc_fallback: Dict[str, Any] = {} cur_temp = _sf(primary_current.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None if cur_temp is None: cur_temp = _sf(live_mc.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None if cur_temp is None: cur_temp = _sf(mg_cur.get("temp")) if cur_temp is not None and not _is_plausible_city_temp(city, cur_temp, sym): cur_temp = None if cur_temp is None: nmc_fallback = _fetch_nmc_current_fallback(city, use_fahrenheit=is_f) nmc_cur = nmc_fallback.get("current") or {} nmc_temp = _sf(nmc_cur.get("temp")) if nmc_temp is not None: cur_temp = nmc_temp current_source = "nmc" current_source_label = "NMC" max_so_far = _sf(primary_current.get("max_temp_so_far")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = _sf(live_mc.get("max_temp_so_far")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = _sf(mg_cur.get("mgm_max_temp")) if max_so_far is not None and not _is_plausible_city_temp(city, max_so_far, sym): max_so_far = None if max_so_far is None: max_so_far = cur_temp max_temp_time = primary_current.get("max_temp_time") if not max_temp_time and not use_settlement_current: max_temp_time = live_mc.get("max_temp_time") if not max_temp_time: mgm_time = str(mg_cur.get("time") or "") if " " in mgm_time: max_temp_time = mgm_time.split(" ")[1][:5] raw_settlement_max = max_so_far wu_settle = ( apply_city_settlement(city.lower(), raw_settlement_max) if raw_settlement_max is not None else None ) display_settlement_max = ( wu_settle if settlement_source == "wunderground" and wu_settle is not None else raw_settlement_max ) obs_time_str = "" obs_age_min = None obs_t = "" if use_settlement_current: obs_t = str(settlement_current.get("observation_time") or "").strip() if not obs_t and metar_current_is_today: obs_t = str(metar.get("observation_time") or "").strip() if obs_t and "T" in obs_t: try: dt = _parse_utc_datetime(obs_t) if dt is None: raise ValueError("invalid observation time") local_dt = dt.astimezone(timezone(timedelta(seconds=utc_offset))) obs_time_str = local_dt.strftime("%H:%M") obs_age_min = int( (datetime.now(timezone.utc) - dt.astimezone(timezone.utc)).total_seconds() / 60 ) except Exception: obs_time_str = str(obs_t)[:16] if not obs_time_str and current_source == "nmc": if not nmc_fallback: nmc_fallback = _fetch_nmc_current_fallback(city, use_fahrenheit=is_f) obs_time_str = _format_observation_time_local( nmc_fallback.get("publish_time") or nmc_fallback.get("timestamp"), int(utc_offset or 0), ) om_daily = (open_meteo.get("daily") or {}) if isinstance(open_meteo, dict) else {} om_hourly = (open_meteo.get("hourly") or {}) if isinstance(open_meteo, dict) else {} maxtemps = om_daily.get("temperature_2m_max", [])[:5] om_today = _sf(maxtemps[0]) if maxtemps else None nws_high = _sf((nws or {}).get("today_high")) if isinstance(nws, dict) else None mgm_high = _sf((mgm or {}).get("today_high")) if isinstance(mgm, dict) else None if om_today is 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) 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) if nws_high is not None: current_forecasts["NWS"] = nws_high if mgm_high is not None: current_forecasts["MGM"] = mgm_high if hko_forecast is not None: current_forecasts["HKO"] = _sf(hko_forecast) current_forecasts = { model_name: value for model_name, value in current_forecasts.items() if value is not None and not _is_excluded_model_name(model_name) } deb_val = None if current_forecasts: blended, _weights_info = calculate_dynamic_weights(city, current_forecasts) if blended is not None: deb_val = blended if deb_val is None: deb_val = om_today settlement_today_obs = [] if use_settlement_current: explicit_obs = settlement_current.get("today_obs") or [] for item in explicit_obs: if isinstance(item, dict): raw_time = str(item.get("time") or "").strip() raw_temp = _sf(item.get("temp")) elif isinstance(item, (list, tuple)) and len(item) >= 2: raw_time = str(item[0] or "").strip() raw_temp = _sf(item[1]) else: continue if raw_time and raw_temp is not None: settlement_today_obs.append({"time": raw_time, "temp": raw_temp}) if not settlement_today_obs and 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 = [ {"time": obs_time, "temp": obs_temp} for obs_time, obs_temp in ( (metar.get("today_obs") or []) if isinstance(metar, dict) and metar_current_is_today else [] ) ] deviation_monitor = _build_deviation_monitor( current_temp=cur_temp, deb_prediction=deb_val, om_today=om_today, hourly_times=om_hourly.get("time", []) if isinstance(om_hourly, dict) else [], hourly_temps=om_hourly.get("temperature_2m", []) if isinstance(om_hourly, dict) else [], local_date=local_date_str, local_hour_frac=local_hour_frac, observation_points=( settlement_today_obs if settlement_today_obs else metar_today_obs_payload ), ) risk = CITY_RISK_PROFILES.get(city, {}) city_meta = CITY_REGISTRY.get(city, {}) or {} result = { "name": city, "display_name": str(city_meta.get("display_name") or city_meta.get("name") or city.title()), "temp_symbol": sym, "utc_offset_seconds": int(utc_offset or 0), "local_time": local_time_str, "local_date": local_date_str, "risk": { "level": risk.get("risk_level", "low"), "warning": risk.get("warning", ""), "icao": risk.get("icao", ""), }, "current": { "temp": _sf(cur_temp), "max_so_far": _sf(display_settlement_max), "max_temp_time": max_temp_time, "wu_settlement": _sf(wu_settle), "settlement_source": current_source, "settlement_source_label": current_source_label, "obs_time": obs_time_str or None, "obs_age_min": obs_age_min, "observation_status": "live" if cur_temp is not None else "missing", }, "deb": {"prediction": _sf(deb_val)}, "deviation_monitor": deviation_monitor or {}, "updated_at": datetime.now(timezone.utc).isoformat(), } _set_cached_summary(city, result) return result def _build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]: return _city_payload_summary(data) def _build_city_market_scan_payload( data: Dict[str, Any], market_slug: Optional[str] = None, target_date: Optional[str] = None, lite: bool = False, scan_filters: Optional[Dict[str, Any]] = None, ) -> Dict[str, Any]: return _city_payload_market_scan( data, market_slug=market_slug, target_date=target_date, lite=lite, scan_filters=scan_filters, ) def _build_city_detail_payload( data: Dict[str, Any], market_slug: Optional[str] = None, target_date: Optional[str] = None, ) -> Dict[str, Any]: return _city_payload_detail( data, market_slug=market_slug, target_date=target_date, ) # ────────────────────────────────────────────────────────── # Routes # ──────────────────────────────────────────────────────────