2569718930@qq.com
ba352feb34
- analysis_service.py：AMOS 数据覆盖 airport_current 的 raw_metar、wind、pressure - source_label 设为 "AMOS"（区别于 aviationweather.gov METAR） - stale_for_today 强制 false（AMOS 数据本身证明了时效性） - observation_time 优先使用 AMOS 时间戳 - AI 读取的 city_snapshot.current.raw_metar 来自 AMOS 跑道传感器 优先级：AMOS raw_metar > aviationweather.gov METAR（首尔/釜山） Reason: AMOS 直连韩国机场系统，延迟更低，更新更快
3147 lines
128 KiB
Python
3147 lines
128 KiB
Python
from __future__ import annotations
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import hashlib
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import json
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import os
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import re
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import time as _time
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import threading
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime, timezone, timedelta
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from typing import Dict, Any, Optional
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import httpx
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from fastapi import HTTPException
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from loguru import logger
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from web.core import (
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_cache,
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CACHE_TTL,
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CACHE_TTL_ANKARA,
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CITIES,
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CITY_RISK_PROFILES,
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SETTLEMENT_SOURCE_LABELS,
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_is_excluded_model_name,
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_market_layer,
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_sf,
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_weather,
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)
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from src.analysis.deb_algorithm import calculate_dynamic_weights
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from src.analysis.settlement_rounding import apply_city_settlement
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from src.data_collection.country_networks import build_country_network_snapshot
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from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
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from src.data_collection.city_time import get_city_utc_offset_seconds
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from src.data_collection.nmc_sources import NMC_CITY_REFERENCES
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from src.database.runtime_state import IntradayPathSnapshotRepository
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from src.models.lgbm_daily_high import predict_lgbm_daily_high
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TURKISH_MGM_CITIES = {"ankara", "istanbul"}
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_ANALYSIS_CACHE_STATS_LOCK = threading.Lock()
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_ANALYSIS_CACHE_STATS: Dict[str, Any] = {
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"total_requests": 0,
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"cache_hits": 0,
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"cache_misses": 0,
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"force_refresh_requests": 0,
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"last_cache_hit_at": None,
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"last_cache_miss_at": None,
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"last_city": None,
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}
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_SUMMARY_CACHE_LOCK = threading.Lock()
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_SUMMARY_CACHE: Dict[str, Dict[str, Any]] = {}
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_GROQ_COMMENTARY_CACHE_LOCK = threading.Lock()
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_GROQ_COMMENTARY_CACHE: Dict[str, Dict[str, Any]] = {}
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_GROQ_COMMENTARY_CACHE_TTL_SEC = int(
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os.getenv("POLYWEATHER_GROQ_COMMENTARY_CACHE_TTL_SEC", "1800")
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)
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def _dedupe_forecast_daily(rows: Any) -> list[Dict[str, Any]]:
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if not isinstance(rows, list):
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return []
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seen = set()
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out = []
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for row in rows:
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if not isinstance(row, dict):
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continue
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date = str(row.get("date") or "").strip()
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if not date or date in seen:
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continue
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seen.add(date)
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out.append(row)
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return out
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def _format_observation_time_local(value: Any, utc_offset: int) -> str:
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raw = str(value or "").strip()
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if not raw:
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return ""
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if "T" in raw:
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try:
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dt = datetime.fromisoformat(raw.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt.astimezone(timezone(timedelta(seconds=utc_offset))).strftime("%H:%M")
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except Exception:
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pass
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match = re.search(r"(\d{1,2}):(\d{2})", raw)
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if match:
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return f"{int(match.group(1)):02d}:{match.group(2)}"
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return raw[:16]
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def _fetch_nmc_current_fallback(city: str, *, use_fahrenheit: bool) -> Dict[str, Any]:
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city_key = str(city or "").strip().lower()
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if city_key not in NMC_CITY_REFERENCES:
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return {}
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try:
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payload = _weather.fetch_nmc_region_current(
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city_key,
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use_fahrenheit=use_fahrenheit,
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)
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return payload if isinstance(payload, dict) else {}
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except Exception as exc:
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logger.debug("NMC current fallback failed city={}: {}", city_key, exc)
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return {}
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def _is_plausible_city_temp(city: str, value: Any, unit: str = "°C") -> bool:
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temp = _sf(value)
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if temp is None:
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return False
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meta = CITY_REGISTRY.get(str(city or "").strip().lower(), {}) or {}
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min_c = _sf(meta.get("min_plausible_metar_temp_c"))
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if min_c is None:
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return True
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min_value = min_c * 9 / 5 + 32 if str(unit or "").upper().endswith("F") else min_c
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return temp >= min_value
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def _parse_utc_datetime(value: Any) -> Optional[datetime]:
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raw = str(value or "").strip()
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if not raw or "T" not in raw:
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return None
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try:
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dt = datetime.fromisoformat(raw.replace("Z", "+00:00"))
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except Exception:
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return None
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt.astimezone(timezone.utc)
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def _metar_is_current_local_day(
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metar: Dict[str, Any],
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*,
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local_date: str,
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utc_offset: int,
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) -> bool:
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if not isinstance(metar, dict) or not metar:
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return False
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if metar.get("stale_for_today") is True:
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return False
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observation_local_date = str(metar.get("observation_local_date") or "").strip()
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if observation_local_date:
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return observation_local_date == local_date
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obs_dt = _parse_utc_datetime(metar.get("observation_time"))
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if obs_dt is None:
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return True
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local_dt = obs_dt.astimezone(timezone(timedelta(seconds=utc_offset)))
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return local_dt.strftime("%Y-%m-%d") == local_date
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def _record_analysis_cache_event(*, city: str, hit: bool, force_refresh: bool) -> None:
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now = datetime.now(timezone.utc).isoformat()
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with _ANALYSIS_CACHE_STATS_LOCK:
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_ANALYSIS_CACHE_STATS["total_requests"] = int(_ANALYSIS_CACHE_STATS.get("total_requests") or 0) + 1
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_ANALYSIS_CACHE_STATS["last_city"] = str(city or "")
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if force_refresh:
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_ANALYSIS_CACHE_STATS["force_refresh_requests"] = int(_ANALYSIS_CACHE_STATS.get("force_refresh_requests") or 0) + 1
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if hit:
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_ANALYSIS_CACHE_STATS["cache_hits"] = int(_ANALYSIS_CACHE_STATS.get("cache_hits") or 0) + 1
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_ANALYSIS_CACHE_STATS["last_cache_hit_at"] = now
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else:
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_ANALYSIS_CACHE_STATS["cache_misses"] = int(_ANALYSIS_CACHE_STATS.get("cache_misses") or 0) + 1
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_ANALYSIS_CACHE_STATS["last_cache_miss_at"] = now
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def get_analysis_cache_stats() -> Dict[str, Any]:
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with _ANALYSIS_CACHE_STATS_LOCK:
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stats = dict(_ANALYSIS_CACHE_STATS)
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hits = int(stats.get("cache_hits") or 0)
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misses = int(stats.get("cache_misses") or 0)
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eligible = hits + misses
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hit_rate = (hits / eligible) if eligible > 0 else None
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miss_rate = (misses / eligible) if eligible > 0 else None
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stats["hit_rate"] = round(hit_rate, 4) if hit_rate is not None else None
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stats["miss_rate"] = round(miss_rate, 4) if miss_rate is not None else None
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return stats
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def _analysis_ttl_for_city(city: str) -> int:
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return CACHE_TTL_ANKARA if city.lower() in TURKISH_MGM_CITIES else CACHE_TTL
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def _analysis_cache_key(city: str, detail_mode: str = "full") -> str:
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normalized_raw = str(detail_mode or "").strip().lower()
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if normalized_raw == "panel":
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normalized_mode = "panel"
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elif normalized_raw == "market":
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normalized_mode = "market"
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elif normalized_raw == "nearby":
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normalized_mode = "nearby"
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else:
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normalized_mode = "full"
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return f"{city}::{normalized_mode}"
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def _get_cached_analysis(
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city: str,
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ttl: int,
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detail_modes: tuple[str, ...] = ("panel", "market", "nearby", "full"),
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) -> Optional[Dict[str, Any]]:
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now_ts = _time.time()
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freshest_payload: Optional[Dict[str, Any]] = None
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freshest_ts = 0.0
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for detail_mode in detail_modes:
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cached = _cache.get(_analysis_cache_key(city, detail_mode))
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if not cached:
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continue
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cached_ts = float(cached.get("t", 0))
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payload = cached.get("d")
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if (
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cached_ts
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and now_ts - cached_ts < ttl
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and isinstance(payload, dict)
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and cached_ts >= freshest_ts
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):
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freshest_payload = payload
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freshest_ts = cached_ts
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return freshest_payload
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def _get_cached_summary(city: str, ttl: int) -> Optional[Dict[str, Any]]:
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now_ts = _time.time()
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with _SUMMARY_CACHE_LOCK:
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cached = _SUMMARY_CACHE.get(city)
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if cached and now_ts - float(cached.get("t", 0)) < ttl:
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payload = cached.get("d")
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if isinstance(payload, dict):
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return dict(payload)
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return None
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def _set_cached_summary(city: str, payload: Dict[str, Any]) -> None:
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with _SUMMARY_CACHE_LOCK:
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_SUMMARY_CACHE[city] = {"t": _time.time(), "d": dict(payload)}
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def _groq_commentary_enabled() -> bool:
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enabled = str(
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os.getenv("POLYWEATHER_GROQ_COMMENTARY_ENABLED", "false")
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).strip().lower()
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api_key = str(os.getenv("GROQ_API_KEY") or "").strip()
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return enabled in {"1", "true", "yes", "on"} and bool(api_key)
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def _clean_commentary_text(value: Any, *, limit: int = 240) -> str:
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text = str(value or "").strip()
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if not text:
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return ""
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text = re.sub(r"\s+", " ", text)
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return text[:limit].strip()
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def _build_groq_commentary_context(result: Dict[str, Any]) -> Dict[str, Any]:
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dynamic = result.get("dynamic_commentary") or {}
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vertical = result.get("vertical_profile_signal") or {}
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taf_signal = ((result.get("taf") or {}).get("signal") or {}) if isinstance(result.get("taf"), dict) else {}
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network = result.get("network_lead_signal") or {}
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peak = result.get("peak") or {}
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current = result.get("current") or {}
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airport_primary = result.get("airport_primary") or {}
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notes = dynamic.get("notes") if isinstance(dynamic.get("notes"), list) else []
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compact_notes = [_clean_commentary_text(item, limit=180) for item in notes]
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compact_notes = [item for item in compact_notes if item][:4]
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return {
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"city": result.get("display_name") or result.get("name"),
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"local_date": result.get("local_date"),
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"local_time": result.get("local_time"),
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"temp_symbol": result.get("temp_symbol"),
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"current_temp": current.get("temp"),
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"day_high_so_far": current.get("max_so_far"),
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"airport_anchor_temp": airport_primary.get("temp"),
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"airport_vs_network_delta": result.get("airport_vs_network_delta"),
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"peak_hours": peak.get("hours") or [],
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"peak_status": peak.get("status"),
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"network_lead_status": network.get("status"),
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"network_lead_note": _clean_commentary_text(network.get("note"), limit=180),
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"rules_summary": _clean_commentary_text(dynamic.get("summary"), limit=260),
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"rules_notes": compact_notes,
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"upper_air_summary_zh": _clean_commentary_text(vertical.get("summary_zh"), limit=260),
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"upper_air_summary_en": _clean_commentary_text(vertical.get("summary_en"), limit=260),
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"taf_summary_zh": _clean_commentary_text(taf_signal.get("summary_zh"), limit=220),
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"taf_summary_en": _clean_commentary_text(taf_signal.get("summary_en"), limit=220),
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"taf_peak_window": _clean_commentary_text(taf_signal.get("peak_window"), limit=80),
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}
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def _normalize_groq_commentary_payload(payload: Dict[str, Any]) -> Dict[str, Any]:
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def _headline(value: Any, fallback: str) -> str:
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text = _clean_commentary_text(value, limit=90)
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return text or fallback
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def _bullets(value: Any) -> list[str]:
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items = value if isinstance(value, list) else []
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cleaned = [_clean_commentary_text(item, limit=120) for item in items]
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cleaned = [item for item in cleaned if item]
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return cleaned[:3]
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zh_headline = _headline(payload.get("headline_zh"), "结构信号以现有规则结论为主。")
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en_headline = _headline(payload.get("headline_en"), "Structural read stays anchored to the existing rule-based signal.")
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zh_bullets = _bullets(payload.get("bullets_zh"))
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en_bullets = _bullets(payload.get("bullets_en"))
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while len(zh_bullets) < 3:
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zh_bullets.append("继续结合当前节奏、边界风险和峰值窗口判断。")
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while len(en_bullets) < 3:
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en_bullets.append("Keep the read anchored to pace, boundary risk, and the peak window.")
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return {
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"headline_zh": zh_headline,
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"headline_en": en_headline,
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"bullets_zh": zh_bullets[:3],
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"bullets_en": en_bullets[:3],
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"source": "groq",
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}
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def _request_groq_commentary(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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api_key = str(os.getenv("GROQ_API_KEY") or "").strip()
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if not api_key:
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return None
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model = str(os.getenv("POLYWEATHER_GROQ_COMMENTARY_MODEL") or "openai/gpt-oss-20b").strip()
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timeout_sec = float(os.getenv("POLYWEATHER_GROQ_COMMENTARY_TIMEOUT_SEC", "8"))
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payload = {
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"model": model,
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"temperature": 0.2,
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"max_tokens": 400,
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"messages": [
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{
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"role": "system",
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"content": (
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"You rewrite weather-market structure commentary. "
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"Never invent facts. Use only the provided context. "
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"Return concise bilingual output for a dashboard: "
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"one headline and exactly three bullets in Chinese, and the same in English. "
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"Keep every bullet actionable and short."
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),
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},
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{
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"role": "user",
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"content": json.dumps(context, ensure_ascii=False),
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},
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],
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"response_format": {
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"type": "json_schema",
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"json_schema": {
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"name": "polyweather_structure_commentary",
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"strict": True,
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"schema": {
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"headline_zh": {"type": "string"},
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"bullets_zh": {
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"type": "array",
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"items": {"type": "string"},
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"minItems": 3,
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"maxItems": 3,
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},
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"headline_en": {"type": "string"},
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"bullets_en": {
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"type": "array",
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"items": {"type": "string"},
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"minItems": 3,
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"maxItems": 3,
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},
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},
|
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"required": [
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"headline_zh",
|
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"bullets_zh",
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"headline_en",
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"bullets_en",
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],
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},
|
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},
|
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},
|
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}
|
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with httpx.Client(timeout=timeout_sec) as client:
|
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response = client.post(
|
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"https://api.groq.com/openai/v1/chat/completions",
|
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headers={
|
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"Authorization": f"Bearer {api_key}",
|
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"Content-Type": "application/json",
|
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},
|
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json=payload,
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)
|
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response.raise_for_status()
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body = response.json()
|
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content = (
|
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(((body.get("choices") or [{}])[0]).get("message") or {}).get("content")
|
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if isinstance(body, dict)
|
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else None
|
|
)
|
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if not content:
|
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return None
|
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try:
|
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return _normalize_groq_commentary_payload(json.loads(str(content)))
|
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except Exception:
|
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logger.warning("Groq commentary returned non-JSON payload")
|
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return None
|
|
|
|
|
|
def _maybe_enrich_dynamic_commentary_with_groq(
|
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city: str,
|
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result: Dict[str, Any],
|
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) -> Dict[str, Any]:
|
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dynamic = result.get("dynamic_commentary") or {}
|
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if not _groq_commentary_enabled():
|
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return dynamic
|
|
if dynamic.get("headline_zh") and dynamic.get("bullets_zh"):
|
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return dynamic
|
|
|
|
context = _build_groq_commentary_context(result)
|
|
if not context.get("rules_summary") and not context.get("rules_notes"):
|
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return dynamic
|
|
|
|
cache_key = hashlib.sha256(
|
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json.dumps({"city": city, "context": context}, sort_keys=True, ensure_ascii=False).encode("utf-8")
|
|
).hexdigest()
|
|
now = _time.time()
|
|
with _GROQ_COMMENTARY_CACHE_LOCK:
|
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cached = _GROQ_COMMENTARY_CACHE.get(cache_key)
|
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if cached and now - float(cached.get("t") or 0) < _GROQ_COMMENTARY_CACHE_TTL_SEC:
|
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merged = dict(dynamic)
|
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merged.update(cached.get("payload") or {})
|
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return merged
|
|
|
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try:
|
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enriched = _request_groq_commentary(context)
|
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except Exception as exc:
|
|
logger.warning("Groq commentary skipped for {}: {}", city, exc)
|
|
return dynamic
|
|
if not enriched:
|
|
return dynamic
|
|
|
|
with _GROQ_COMMENTARY_CACHE_LOCK:
|
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_GROQ_COMMENTARY_CACHE[cache_key] = {"t": now, "payload": enriched}
|
|
merged = dict(dynamic)
|
|
merged.update(enriched)
|
|
return merged
|
|
|
|
|
|
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 = CACHE_TTL_ANKARA if city.lower() in TURKISH_MGM_CITIES else CACHE_TTL
|
|
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 {}
|
|
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),
|
|
)
|
|
|
|
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,
|
|
"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,
|
|
"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": _sf(live_mc.get("temp")),
|
|
"obs_time": obs_time_str,
|
|
"max_so_far": airport_max_so_far,
|
|
"max_temp_time": airport_max_temp_time,
|
|
"obs_age_min": metar_age_min,
|
|
"report_time": metar.get("report_time") if metar else None,
|
|
"receipt_time": metar.get("receipt_time") if metar else None,
|
|
"obs_time_epoch": metar.get("obs_time_epoch") if metar else None,
|
|
"wind_speed_kt": _sf(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_label": "AMOS" if current_source == "amos" else "METAR",
|
|
"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.get("temp_c") is not None 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 {},
|
|
}
|
|
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 {}
|
|
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
|
|
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 {
|
|
"name": data.get("name"),
|
|
"display_name": data.get("display_name"),
|
|
"icao": data.get("risk", {}).get("icao"),
|
|
"utc_offset_seconds": data.get("utc_offset_seconds"),
|
|
"local_time": data.get("local_time"),
|
|
"temp_symbol": data.get("temp_symbol"),
|
|
"current": {
|
|
"temp": data.get("current", {}).get("temp"),
|
|
"obs_time": data.get("current", {}).get("obs_time"),
|
|
"settlement_source": data.get("current", {}).get("settlement_source"),
|
|
"settlement_source_label": data.get("current", {}).get("settlement_source_label"),
|
|
},
|
|
"deb": {"prediction": data.get("deb", {}).get("prediction")},
|
|
"deviation_monitor": data.get("deviation_monitor") or {},
|
|
"risk": {
|
|
"level": data.get("risk", {}).get("level"),
|
|
"warning": data.get("risk", {}).get("warning"),
|
|
},
|
|
"updated_at": data.get("updated_at"),
|
|
}
|
|
|
|
|
|
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]:
|
|
city = str(data.get("name") or "").strip().lower()
|
|
local_date = str(data.get("local_date") or "").strip()
|
|
requested_date = str(target_date or "").strip()
|
|
selected_date = requested_date or local_date
|
|
|
|
multi_model_daily = data.get("multi_model_daily") or {}
|
|
selected_daily = (
|
|
multi_model_daily.get(selected_date)
|
|
if isinstance(multi_model_daily, dict)
|
|
else None
|
|
)
|
|
if not isinstance(selected_daily, dict):
|
|
selected_daily = {}
|
|
selected_date = local_date
|
|
|
|
distribution = selected_daily.get("probabilities")
|
|
if not isinstance(distribution, list) or not distribution:
|
|
distribution = data.get("probabilities", {}).get("distribution", []) or []
|
|
distribution_all = selected_daily.get("probabilities_all")
|
|
if not isinstance(distribution_all, list) or not distribution_all:
|
|
distribution_all = data.get("probabilities", {}).get("distribution_all", []) or []
|
|
if not distribution_all:
|
|
distribution_all = distribution
|
|
|
|
model_map = selected_daily.get("models") or data.get("multi_model") or {}
|
|
if not isinstance(model_map, dict):
|
|
model_map = {}
|
|
|
|
anchor_temp = None
|
|
anchor_model = None
|
|
for model_name, raw_value in model_map.items():
|
|
value = _sf(raw_value)
|
|
if value is None:
|
|
continue
|
|
if anchor_temp is None or value > anchor_temp:
|
|
anchor_temp = value
|
|
anchor_model = str(model_name or "").strip() or None
|
|
|
|
anchor_temp_c = anchor_temp
|
|
temp_symbol = str(data.get("temp_symbol") or "")
|
|
if anchor_temp_c is not None and "F" in temp_symbol.upper():
|
|
anchor_temp_c = (anchor_temp_c - 32.0) * 5.0 / 9.0
|
|
anchor_settlement = apply_city_settlement(city, anchor_temp_c) if anchor_temp_c is not None else None
|
|
|
|
primary_bucket = None
|
|
if isinstance(distribution, list) and distribution:
|
|
ranked_buckets = []
|
|
temp_symbol_upper = str(temp_symbol or "").upper()
|
|
max_primary_bucket_delta = 16.0 if "F" in temp_symbol_upper else 8.0
|
|
for idx, row in enumerate(distribution_all):
|
|
if not isinstance(row, dict):
|
|
continue
|
|
bucket_value = _sf(
|
|
row.get("temp")
|
|
if row.get("temp") is not None
|
|
else row.get("value")
|
|
if row.get("value") is not None
|
|
else row.get("lower")
|
|
)
|
|
if (
|
|
anchor_temp is not None
|
|
and bucket_value is not None
|
|
and abs(float(bucket_value) - float(anchor_temp)) > max_primary_bucket_delta
|
|
):
|
|
continue
|
|
bucket_prob = _sf(row.get("probability"))
|
|
prob_rank = bucket_prob if bucket_prob is not None else -1.0
|
|
ranked_buckets.append((-prob_rank, idx, row))
|
|
if ranked_buckets:
|
|
ranked_buckets.sort(key=lambda x: (x[0], x[1]))
|
|
primary_bucket = ranked_buckets[0][2]
|
|
elif anchor_temp is None:
|
|
primary_bucket = distribution[0]
|
|
|
|
model_probability = None
|
|
if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None:
|
|
try:
|
|
raw_probability = float(primary_bucket.get("probability"))
|
|
model_probability = raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
|
|
except Exception:
|
|
model_probability = None
|
|
|
|
fallback_sparkline = [
|
|
p.get("probability", 0)
|
|
for p in distribution_all[:8]
|
|
if isinstance(p, dict)
|
|
]
|
|
current = data.get("current") or {}
|
|
selected_deb = selected_daily.get("deb") if isinstance(selected_daily.get("deb"), dict) else {}
|
|
current_deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
|
|
scan_context = {
|
|
"local_date": data.get("local_date"),
|
|
"local_time": data.get("local_time"),
|
|
"peak": data.get("peak") or {},
|
|
"current_max_so_far": current.get("max_so_far"),
|
|
"current_temp": current.get("temp"),
|
|
"trend": data.get("trend") or {},
|
|
"network_lead_signal": data.get("network_lead_signal") or {},
|
|
"models": model_map,
|
|
"deb_prediction": selected_deb.get("prediction") or current_deb.get("prediction"),
|
|
}
|
|
market_scan = _market_layer.build_market_scan(
|
|
city=data.get("name"),
|
|
target_date=selected_date or data.get("local_date"),
|
|
temperature_bucket=primary_bucket if isinstance(primary_bucket, dict) else None,
|
|
model_probability=model_probability,
|
|
probability_distribution=distribution_all,
|
|
temp_symbol=temp_symbol,
|
|
fallback_sparkline=fallback_sparkline,
|
|
forced_market_slug=market_slug,
|
|
include_related_buckets=not lite,
|
|
scan_filters=scan_filters,
|
|
scan_context=scan_context,
|
|
)
|
|
if isinstance(market_scan, dict):
|
|
market_scan["anchor_model"] = anchor_model
|
|
market_scan["anchor_high"] = anchor_temp
|
|
market_scan["anchor_settlement"] = anchor_settlement
|
|
market_scan["open_meteo_settlement"] = anchor_settlement
|
|
probabilities = data.get("probabilities") or {}
|
|
market_scan["probability_engine"] = str(
|
|
probabilities.get("engine") or "legacy"
|
|
).strip() or "legacy"
|
|
market_scan["probability_calibration_mode"] = str(
|
|
probabilities.get("calibration_mode") or "legacy"
|
|
).strip() or "legacy"
|
|
return {
|
|
"market_scan": market_scan,
|
|
"selected_date": selected_date or data.get("local_date"),
|
|
"fetched_at": data.get("updated_at"),
|
|
}
|
|
|
|
|
|
def _build_city_detail_payload(
|
|
data: Dict[str, Any],
|
|
market_slug: Optional[str] = None,
|
|
target_date: Optional[str] = None,
|
|
) -> Dict[str, Any]:
|
|
market_payload = _build_city_market_scan_payload(
|
|
data,
|
|
market_slug=market_slug,
|
|
target_date=target_date,
|
|
)
|
|
market_scan = market_payload.get("market_scan")
|
|
return {
|
|
"city": data.get("name"),
|
|
"fetched_at": data.get("updated_at"),
|
|
"overview": {
|
|
"name": data.get("name"),
|
|
"display_name": data.get("display_name"),
|
|
"icao": data.get("risk", {}).get("icao"),
|
|
"airport": data.get("risk", {}).get("airport"),
|
|
"lat": data.get("lat"),
|
|
"lon": data.get("lon"),
|
|
"local_time": data.get("local_time"),
|
|
"local_date": data.get("local_date"),
|
|
"temp_symbol": data.get("temp_symbol"),
|
|
"current_temp": data.get("current", {}).get("temp"),
|
|
"settlement_source": data.get("current", {}).get("settlement_source"),
|
|
"settlement_source_label": data.get("current", {}).get("settlement_source_label"),
|
|
"settlement_station": data.get("settlement_station") or {},
|
|
"deb_prediction": data.get("deb", {}).get("prediction"),
|
|
"risk_level": data.get("risk", {}).get("level"),
|
|
"risk_warning": data.get("risk", {}).get("warning"),
|
|
"updated_at": data.get("updated_at"),
|
|
},
|
|
"official": {
|
|
"available": bool(data.get("current", {}).get("temp") is not None),
|
|
"metar": {
|
|
"observation_time": data.get("airport_current", {}).get("obs_time"),
|
|
"obs_age_min": data.get("airport_current", {}).get("obs_age_min"),
|
|
"report_time": data.get("airport_current", {}).get("report_time"),
|
|
"receipt_time": data.get("airport_current", {}).get("receipt_time"),
|
|
"raw_metar": data.get("airport_current", {}).get("raw_metar"),
|
|
"current": data.get("airport_current") or {},
|
|
},
|
|
"taf": data.get("taf") or {},
|
|
"weather_gov": {},
|
|
"mgm": data.get("mgm") or {},
|
|
"mgm_nearby": data.get("mgm_nearby") or [],
|
|
"nearby_source": data.get("nearby_source") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
|
|
"airport_primary": data.get("airport_primary") or {},
|
|
"airport_primary_today_obs": data.get("airport_primary_today_obs") or [],
|
|
"official_nearby": data.get("official_nearby") or [],
|
|
"official_network_source": data.get("official_network_source"),
|
|
"official_network_status": data.get("official_network_status") or {},
|
|
"network_lead_signal": data.get("network_lead_signal") or {},
|
|
"network_spread_signal": data.get("network_spread_signal") or {},
|
|
"center_station_candidate": data.get("center_station_candidate"),
|
|
"airport_vs_network_delta": data.get("airport_vs_network_delta"),
|
|
},
|
|
"timeseries": {
|
|
"metar_recent_obs": data.get("metar_recent_obs") or [],
|
|
"metar_today_obs": data.get("metar_today_obs") or [],
|
|
"settlement_today_obs": data.get("settlement_today_obs") or [],
|
|
"hourly": data.get("hourly") or {},
|
|
"mgm_hourly": (data.get("mgm") or {}).get("hourly", []),
|
|
"forecast_daily": (data.get("forecast") or {}).get("daily", []),
|
|
},
|
|
"models": {
|
|
k: v
|
|
for k, v in (data.get("multi_model") or {}).items()
|
|
if not _is_excluded_model_name(k)
|
|
},
|
|
"deb": data.get("deb") or {},
|
|
"multi_model_daily": data.get("multi_model_daily") or {},
|
|
"probabilities": data.get("probabilities") or {"mu": None, "distribution": []},
|
|
"dynamic_commentary": data.get("dynamic_commentary") or {"summary": "", "notes": []},
|
|
"intraday_meteorology": data.get("intraday_meteorology")
|
|
or _build_intraday_meteorology(data),
|
|
"vertical_profile_signal": data.get("vertical_profile_signal") or {},
|
|
"taf": data.get("taf") or {},
|
|
"market_scan": market_scan,
|
|
"risk": data.get("risk"),
|
|
"settlement_station": data.get("settlement_station") or {},
|
|
"airport_primary": data.get("airport_primary") or {},
|
|
"official_nearby": data.get("official_nearby") or [],
|
|
"official_network_source": data.get("official_network_source"),
|
|
"official_network_status": data.get("official_network_status") or {},
|
|
"network_lead_signal": data.get("network_lead_signal") or {},
|
|
"network_spread_signal": data.get("network_spread_signal") or {},
|
|
"center_station_candidate": data.get("center_station_candidate"),
|
|
"airport_vs_network_delta": data.get("airport_vs_network_delta"),
|
|
"airport_current": data.get("airport_current") or {},
|
|
"nearby_source": data.get("nearby_source") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
|
|
"ai_analysis": data.get("ai_analysis") or "",
|
|
"errors": {},
|
|
}
|
|
|
|
|
|
# ──────────────────────────────────────────────────────────
|
|
# Routes
|
|
# ──────────────────────────────────────────────────────────
|