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feat: Implement PolyWeather web map API and its multi-source weather data collection module.

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This commit is contained in:
2569718930@qq.com
2026-03-05 01:50:38 +08:00
parent eee159c533
commit 7c087b2c81
2 changed files with 35 additions and 27 deletions
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web/app.py
+29 -21
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@@ -43,19 +43,19 @@ _weather = WeatherDataCollector(_config)
# City Registry
# ──────────────────────────────────────────────────────────
CITIES: Dict[str, Dict[str, Any]] = {
"ankara": {"lat": 40.1281, "lon": 32.9951, "f": False}, # LTAC (Esenboğa)
"london": {"lat": 51.5048, "lon": 0.0522, "f": False}, # EGLC (London City)
"paris": {"lat": 49.0097, "lon": 2.5480, "f": False}, # LFPG (Charles de Gaulle)
"seoul": {"lat": 37.4602, "lon": 126.4407, "f": False}, # RKSI (Incheon)
"toronto": {"lat": 43.6777, "lon": -79.6248, "f": False}, # CYYZ (Pearson)
"buenos aires": {"lat": -34.8222, "lon": -58.5358, "f": False}, # SAEZ (Ezeiza)
"wellington": {"lat": -41.3272, "lon": 174.8053, "f": False}, # NZWN (Wellington)
"new york": {"lat": 40.7769, "lon": -73.8740, "f": True}, # KLGA (LaGuardia)
"chicago": {"lat": 41.9742, "lon": -87.9073, "f": True}, # KORD (O'Hare)
"dallas": {"lat": 32.8471, "lon": -96.8518, "f": True}, # KDAL (Dallas Love Field)
"miami": {"lat": 25.7959, "lon": -80.2870, "f": True}, # KMIA (Miami)
"atlanta": {"lat": 33.6407, "lon": -84.4277, "f": True}, # KATL (Hartsfield-Jackson)
"seattle": {"lat": 47.4502, "lon": -122.3088, "f": True}, # KSEA (Sea-Tac)
"ankara": {"lat": 40.1281, "lon": 32.9951, "f": False, "tz": 10800},
"london": {"lat": 51.5048, "lon": 0.0522, "f": False, "tz": 0},
"paris": {"lat": 49.0097, "lon": 2.5480, "f": False, "tz": 3600},
"seoul": {"lat": 37.4602, "lon": 126.4407, "f": False, "tz": 32400},
"toronto": {"lat": 43.6777, "lon": -79.6248, "f": False, "tz": -18000},
"buenos aires": {"lat": -34.8222, "lon": -58.5358, "f": False, "tz": -10800},
"wellington": {"lat": -41.3272, "lon": 174.8053, "f": False, "tz": 46800},
"new york": {"lat": 40.7769, "lon": -73.8740, "f": True, "tz": -18000},
"chicago": {"lat": 41.9742, "lon": -87.9073, "f": True, "tz": -21600},
"dallas": {"lat": 32.8471, "lon": -96.8518, "f": True, "tz": -21600},
"miami": {"lat": 25.7959, "lon": -80.2870, "f": True, "tz": -18000},
"atlanta": {"lat": 33.6407, "lon": -84.4277, "f": True, "tz": -18000},
"seattle": {"lat": 47.4502, "lon": -122.3088, "f": True, "tz": -28800},
}
ALIASES = {
@@ -135,7 +135,8 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
obs_time_str = ""
metar_age_min = None
obs_t = metar.get("observation_time", "") if metar else ""
utc_offset = om.get("utc_offset", 0)
# 优先从 API 获取偏移，若失败则使用 CITIES 预设的静态偏移 (兜底当地时间)
utc_offset = om.get("utc_offset", info.get("tz", 0))
if obs_t and "T" in obs_t:
try:
dt = datetime.fromisoformat(obs_t.replace("Z", "+00:00"))
@@ -150,15 +151,22 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
# ── 3. Local time parsing ──
local_time_full = om.get("current", {}).get("local_time", "")
local_hour, local_minute = 12, 0
local_date_str = datetime.now().strftime("%Y-%m-%d")
now_utc = datetime.now(timezone.utc)
local_now = now_utc + timedelta(seconds=utc_offset)
local_date_str = local_now.strftime("%Y-%m-%d")
try:
local_date_str = local_time_full.split(" ")[0]
tp = local_time_full.split(" ")[1].split(":")
local_hour = int(tp[0])
local_minute = int(tp[1]) if len(tp) > 1 else 0
if local_time_full:
local_date_str = local_time_full.split(" ")[0]
tp = local_time_full.split(" ")[1].split(":")
local_hour = int(tp[0])
local_minute = int(tp[1]) if len(tp) > 1 else 0
else:
local_hour = local_now.hour
local_minute = local_now.minute
except Exception:
local_hour = datetime.now().hour
local_minute = datetime.now().minute
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