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Add RP5 forecast scraping support

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This commit is contained in:
2569718930@qq.com
2026-03-17 23:15:13 +08:00
parent 9ac0a13937
commit 1ae9b55509
19 changed files with 1113 additions and 278 deletions
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web/app.py
+67 -20
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@@ -36,6 +36,7 @@ from src.auth.supabase_entitlement import (
SUPABASE_ENTITLEMENT,
extract_bearer_token,
)
from src.analysis.metar_narrator import describe_metar_report
from src.database.db_manager import DBManager
from src.payments import PAYMENT_CHECKOUT, PaymentCheckoutError
@@ -431,6 +432,29 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
except Exception:
obs_time_str = str(obs_t)[:16]
settlement_today_obs = []
if use_settlement_current:
if obs_time_str and cur_temp is not None:
settlement_today_obs.append({"time": obs_time_str, "temp": cur_temp})
if (
max_temp_time
and max_so_far is not None
and str(max_temp_time) != str(obs_time_str)
):
settlement_today_obs.append({"time": str(max_temp_time), "temp": max_so_far})
metar_today_obs_payload = (
[]
if use_settlement_current
else [
{"time": t, "temp": v}
for t, v in (metar.get("today_obs", []) if metar else [])
]
)
metar_recent_obs_payload = (
[] if use_settlement_current else (metar.get("recent_obs", []) if metar else [])
)
# ── 3. Local time parsing ──
local_time_full = om.get("current", {}).get("local_time", "")
local_hour, local_minute = 12, 0
@@ -610,13 +634,12 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
# 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
from src.analysis.ai_analyzer import get_ai_analysis
probabilities = []
mu = None
ai_text = ""
try:
_, ai_context, sd = _trend_analyze(raw, sym, city)
_, _ai_context, sd = _trend_analyze(raw, sym, city)
# Use structured data from shared engine
mu = sd.get("mu")
@@ -631,17 +654,23 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
deb_val = sd["deb_prediction"]
deb_weights = sd.get("deb_weights", "")
# Append multi-model divergence for AI
if current_forecasts and ai_context:
mm_str = " | ".join(
[f"{k}:{v}{sym}" for k, v in current_forecasts.items() if v]
)
ai_context += f"\n模型分歧: {mm_str}"
if ai_context:
ai_text = get_ai_analysis(ai_context, city, sym)
except Exception as e:
logger.warning(f"Analysis/AI skipped for {city}: {e}")
logger.warning(f"Structured analysis skipped for {city}: {e}")
ai_text = describe_metar_report(
raw_metar=str(primary_current.get("raw_metar") or mc.get("raw_metar") or ""),
temp_symbol=sym,
fallback={
"icao": metar.get("icao"),
"station_name": metar.get("station_name"),
"temp": cur_temp,
"wind_speed_kt": _sf(primary_current.get("wind_speed_kt")),
"wind_dir": _sf(primary_current.get("wind_dir")),
"altimeter": _sf(primary_current.get("altimeter")),
"wx_desc": primary_current.get("wx_desc"),
"clouds": primary_current.get("clouds", []) or mc.get("clouds", []),
},
)
# ── 12. Hourly data (today only, for chart) ──
today_hourly: Dict[str, list] = {"times": [], "temps": [], "radiation": []}
@@ -911,11 +940,9 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
},
"hourly": today_hourly,
"hourly_next_48h": next_48h_hourly,
"metar_today_obs": [
{"time": t, "temp": v}
for t, v in (metar.get("today_obs", []) if metar else [])
],
"metar_recent_obs": metar.get("recent_obs", []) if metar else [],
"metar_today_obs": metar_today_obs_payload,
"metar_recent_obs": metar_recent_obs_payload,
"settlement_today_obs": settlement_today_obs,
"ai_analysis": ai_text,
"updated_at": datetime.now(timezone.utc).isoformat(),
}
@@ -1138,6 +1165,7 @@ def _build_city_detail_payload(
"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", []),
@@ -1170,7 +1198,12 @@ async def city_history(request: Request, name: str):
data = load_history(history_file)
if name not in data:
return {"history": []}
source = str(CITIES.get(name, {}).get("settlement_source") or "metar").strip().lower()
return {
"history": [],
"settlement_source": source,
"settlement_source_label": SETTLEMENT_SOURCE_LABELS.get(source, source.upper()),
}
city_data = data[name]
out = []
@@ -1178,7 +1211,15 @@ async def city_history(request: Request, name: str):
act = rec.get("actual_high")
deb = rec.get("deb_prediction")
mu = rec.get("mu")
mgm = rec.get("forecasts", {}).get("MGM")
forecasts_raw = rec.get("forecasts", {}) or {}
forecasts = {}
if isinstance(forecasts_raw, dict):
for model_name, model_value in forecasts_raw.items():
if _is_excluded_model_name(str(model_name)):
continue
fv = _sf(model_value)
forecasts[str(model_name)] = fv if fv is not None else None
mgm = forecasts.get("MGM")
# Only return items where we have at least an actual or a prediction
out.append({
@@ -1187,8 +1228,14 @@ async def city_history(request: Request, name: str):
"deb": float(deb) if deb is not None else None,
"mu": float(mu) if mu is not None else None,
"mgm": float(mgm) if mgm is not None else None,
"forecasts": forecasts,
})
return {"history": out}
source = str(CITIES.get(name, {}).get("settlement_source") or "metar").strip().lower()
return {
"history": out,
"settlement_source": source,
"settlement_source_label": SETTLEMENT_SOURCE_LABELS.get(source, source.upper()),
}
@app.get("/api/auth/me")