refactor: extract analysis signal builders
This commit is contained in:
2569718930@qq.com
+7
-734
@@ -42,6 +42,13 @@ from web.services.city_payloads import (
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build_city_market_scan_payload as _city_payload_market_scan,
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build_city_summary_payload as _city_payload_summary,
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)
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from web.services.analysis_signals import (
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_build_deviation_monitor,
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_build_taf_signal,
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_build_vertical_profile_signal,
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_interpolate_hourly_value, # noqa: F401 - compatibility re-export
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_wind_components, # noqa: F401 - compatibility re-export
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)
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TURKISH_MGM_CITIES = {"ankara", "istanbul"}
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HIGH_FREQ_AIRPORT_ANALYSIS_CITIES = {
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@@ -495,747 +502,13 @@ def _maybe_enrich_dynamic_commentary_with_groq(
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return _groq_enrich(city, result)
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def _interpolate_hourly_value(
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times: list,
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values: list,
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local_date: str,
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target_hour_frac: float,
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) -> Optional[float]:
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points = []
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for ts, raw_value in zip(times or [], values or []):
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if not str(ts).startswith(local_date):
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continue
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value = _sf(raw_value)
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if value is None:
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continue
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try:
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hh_mm = str(ts).split("T")[1]
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hour = int(hh_mm[:2])
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minute = int(hh_mm[3:5]) if len(hh_mm) >= 5 else 0
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except Exception:
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continue
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points.append((hour + minute / 60.0, value))
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if not points:
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return None
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points.sort(key=lambda item: item[0])
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if target_hour_frac <= points[0][0]:
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return float(points[0][1])
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if target_hour_frac >= points[-1][0]:
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return float(points[-1][1])
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for idx in range(1, len(points)):
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left_hour, left_value = points[idx - 1]
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right_hour, right_value = points[idx]
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if target_hour_frac > right_hour:
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continue
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if right_hour == left_hour:
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return float(right_value)
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ratio = (target_hour_frac - left_hour) / (right_hour - left_hour)
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return float(left_value + (right_value - left_value) * ratio)
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return float(points[-1][1])
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def _build_deviation_monitor(
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*,
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current_temp: Optional[float],
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deb_prediction: Optional[float],
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om_today: Optional[float],
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hourly_times: list,
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hourly_temps: list,
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local_date: str,
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local_hour_frac: float,
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observation_points: list,
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) -> Dict[str, Any]:
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if current_temp is None or deb_prediction is None or om_today is None:
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return {}
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offset = _sf(deb_prediction) - _sf(om_today)
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if offset is None:
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return {}
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expected_now = _interpolate_hourly_value(
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hourly_times,
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[(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps],
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local_date,
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local_hour_frac,
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)
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if expected_now is None:
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return {}
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delta = float(current_temp) - expected_now
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abs_delta = abs(delta)
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if abs_delta < 0.8:
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direction = "normal"
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severity = "normal"
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elif delta <= -1.8:
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direction = "cold"
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severity = "strong"
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elif delta >= 1.8:
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direction = "hot"
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severity = "strong"
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elif delta < 0:
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direction = "cold"
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severity = "light"
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else:
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direction = "hot"
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severity = "light"
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deviation_series = []
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for item in observation_points or []:
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if not isinstance(item, dict):
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continue
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obs_temp = _sf(item.get("temp"))
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raw_time = str(item.get("time") or "").strip()
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if obs_temp is None:
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continue
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match = re.search(r"(\d{1,2}):(\d{2})", raw_time)
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if not match:
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continue
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obs_hour_frac = int(match.group(1)) + int(match.group(2)) / 60.0
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ref_temp = _interpolate_hourly_value(
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hourly_times,
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[(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps],
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local_date,
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obs_hour_frac,
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)
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if ref_temp is None:
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continue
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deviation_series.append(float(obs_temp) - ref_temp)
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trend = "stable"
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if len(deviation_series) >= 2:
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latest = deviation_series[-1]
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previous = deviation_series[-2]
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if latest * previous > 0:
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if abs(latest) - abs(previous) >= 0.3:
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trend = "expanding"
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elif abs(previous) - abs(latest) >= 0.3:
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trend = "contracting"
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if direction == "normal":
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label_zh = f"正常 ±{abs_delta:.1f}°C"
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label_en = f"Normal ±{abs_delta:.1f}°C"
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elif direction == "cold":
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label_zh = f"偏冷 {delta:.1f}°C"
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label_en = f"Cool bias {delta:.1f}°C"
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else:
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label_zh = f"偏热 +{abs_delta:.1f}°C"
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label_en = f"Warm bias +{abs_delta:.1f}°C"
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trend_zh = {
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"contracting": "收敛中",
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"expanding": "扩大中",
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"stable": "稳定",
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}.get(trend, "稳定")
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trend_en = {
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"contracting": "contracting",
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"expanding": "expanding",
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"stable": "stable",
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}.get(trend, "stable")
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return {
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"available": True,
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"current_delta": round(delta, 1),
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"reference_temp": round(expected_now, 1),
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"direction": direction,
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"severity": severity,
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"trend": trend,
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"label_zh": label_zh,
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"label_en": label_en,
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"trend_label_zh": trend_zh,
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"trend_label_en": trend_en,
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}
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def _wind_components(speed: Optional[float], direction: Optional[float]) -> tuple[Optional[float], Optional[float]]:
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if speed is None or direction is None:
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return None, None
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try:
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import math
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rad = math.radians(float(direction))
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spd = float(speed)
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u = -spd * math.sin(rad)
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v = -spd * math.cos(rad)
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return u, v
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except Exception:
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return None, None
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def _build_vertical_profile_signal(
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hourly_next_48h: Dict[str, list],
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local_date: str,
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local_hour: int,
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first_peak_h: int,
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last_peak_h: int,
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) -> Dict[str, Any]:
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times = hourly_next_48h.get("times") or []
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if not times:
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return {}
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preferred_start = max(local_hour, max(0, first_peak_h - 2))
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preferred_end = min(23, last_peak_h + 1)
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candidate_indexes = [
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index
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for index, ts in enumerate(times)
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if str(ts).startswith(local_date)
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and preferred_start <= int(str(ts).split("T")[1][:2]) <= preferred_end
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]
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if not candidate_indexes:
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candidate_indexes = [
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index
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for index, ts in enumerate(times)
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if str(ts).startswith(local_date)
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]
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if not candidate_indexes:
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return {}
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def _series(name: str) -> list:
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values = hourly_next_48h.get(name) or []
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return [values[idx] if idx < len(values) else None for idx in candidate_indexes]
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def _max_numeric(values: list) -> Optional[float]:
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valid = [_sf(value) for value in values if _sf(value) is not None]
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return max(valid) if valid else None
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def _min_numeric(values: list) -> Optional[float]:
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valid = [_sf(value) for value in values if _sf(value) is not None]
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return min(valid) if valid else None
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def _level_label(level: str, locale: str) -> str:
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mapping = {
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"high": {"zh": "高", "en": "high"},
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"medium": {"zh": "中", "en": "medium"},
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"low": {"zh": "低", "en": "low"},
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"strong": {"zh": "强", "en": "strong"},
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"weak": {"zh": "弱", "en": "weak"},
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}
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return mapping.get(level, {}).get(locale, level)
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cape_max = _max_numeric(_series("cape"))
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cin_min = _min_numeric(_series("convective_inhibition"))
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lifted_index_min = _min_numeric(_series("lifted_index"))
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boundary_layer_height_max = _max_numeric(_series("boundary_layer_height"))
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shear_values: list[float] = []
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speed_10m = hourly_next_48h.get("wind_speed_10m") or []
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direction_10m = hourly_next_48h.get("wind_direction_10m") or []
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speed_180m = hourly_next_48h.get("wind_speed_180m") or []
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direction_180m = hourly_next_48h.get("wind_direction_180m") or []
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for idx in candidate_indexes:
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s10 = _sf(speed_10m[idx]) if idx < len(speed_10m) else None
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d10 = _sf(direction_10m[idx]) if idx < len(direction_10m) else None
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s180 = _sf(speed_180m[idx]) if idx < len(speed_180m) else None
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d180 = _sf(direction_180m[idx]) if idx < len(direction_180m) else None
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u10, v10 = _wind_components(s10, d10)
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u180, v180 = _wind_components(s180, d180)
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if None in (u10, v10, u180, v180):
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continue
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import math
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shear_values.append(math.sqrt((u180 - u10) ** 2 + (v180 - v10) ** 2))
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shear_10m_180m_max = max(shear_values) if shear_values else None
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suppression_risk = "low"
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if (cape_max is not None and cape_max >= 700) or (
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cin_min is not None and cin_min <= -50
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):
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suppression_risk = "high"
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elif (cape_max is not None and cape_max >= 150) or (
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cin_min is not None and cin_min <= -15
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):
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suppression_risk = "medium"
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trigger_risk = "low"
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if (
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cape_max is not None
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and cape_max >= 550
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and lifted_index_min is not None
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and lifted_index_min <= -1.5
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):
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trigger_risk = "high"
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elif (
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cape_max is not None
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and cape_max >= 120
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and lifted_index_min is not None
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and lifted_index_min <= 0.5
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):
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trigger_risk = "medium"
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mixing_strength = "weak"
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if boundary_layer_height_max is not None and boundary_layer_height_max >= 1400:
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mixing_strength = "strong"
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elif boundary_layer_height_max is not None and boundary_layer_height_max >= 700:
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mixing_strength = "medium"
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shear_risk = "low"
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if shear_10m_180m_max is not None and shear_10m_180m_max >= 8:
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shear_risk = "high"
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elif shear_10m_180m_max is not None and shear_10m_180m_max >= 4:
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shear_risk = "medium"
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heating_setup = "neutral"
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heating_score = 0
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if suppression_risk == "high":
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heating_score -= 2
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elif suppression_risk == "medium":
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heating_score -= 1
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if trigger_risk == "high":
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heating_score -= 2
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elif trigger_risk == "medium":
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heating_score -= 1
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if mixing_strength == "strong":
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heating_score += 2
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elif mixing_strength == "medium":
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heating_score += 1
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else:
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heating_score -= 1
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if shear_risk == "high":
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heating_score -= 1
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if heating_score >= 2:
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heating_setup = "supportive"
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elif heating_score <= -2:
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heating_setup = "suppressed"
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has_profile_data = any(
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value is not None
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for value in (
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cape_max,
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cin_min,
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lifted_index_min,
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boundary_layer_height_max,
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shear_10m_180m_max,
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)
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)
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zh_parts = []
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en_parts = []
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if suppression_risk == "high":
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zh_parts.append("午后对流压温风险偏高。")
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en_parts.append("Afternoon convective suppression risk is elevated.")
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elif suppression_risk == "medium":
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zh_parts.append("存在一定云雨压温风险。")
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en_parts.append("There is some cloud and shower suppression risk.")
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elif has_profile_data:
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zh_parts.append("高空对流压温风险暂时不高。")
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en_parts.append("Upper-air suppression risk remains limited for now.")
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if mixing_strength == "strong":
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zh_parts.append("边界层混合较深，若无云雨打断仍有冲高空间。")
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en_parts.append("Deep boundary-layer mixing still supports additional warming if convection stays limited.")
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elif mixing_strength == "medium":
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zh_parts.append("白天混合条件中等。")
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en_parts.append("Daytime mixing potential is moderate.")
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elif has_profile_data:
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zh_parts.append("边界层混合偏浅。")
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en_parts.append("Boundary-layer mixing remains shallow.")
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if shear_risk == "high":
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zh_parts.append("高空风切变较强，午后结构波动可能加大。")
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en_parts.append("Upper-level shear is relatively strong and may increase afternoon volatility.")
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elif shear_risk == "medium":
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zh_parts.append("高空风切变有一定存在感。")
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en_parts.append("Upper-level shear is noticeable.")
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elif has_profile_data:
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zh_parts.append("高空风切变扰动有限。")
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en_parts.append("Upper-level shear disruption remains limited.")
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if trigger_risk == "high":
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zh_parts.append("抬升触发条件较好，需警惕午后云团发展。")
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en_parts.append("Trigger conditions are favorable enough to watch for afternoon convective development.")
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elif trigger_risk == "medium":
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zh_parts.append("午后具备一定触发条件。")
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en_parts.append("There is some afternoon trigger potential.")
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elif has_profile_data:
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zh_parts.append("午后触发条件偏弱。")
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en_parts.append("Afternoon trigger potential remains weak.")
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if not has_profile_data:
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zh_parts.append("高空剖面字段暂缺，当前仅保留基础默认信号。")
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en_parts.append("Upper-air profile fields are currently unavailable, so only a fallback signal is shown.")
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elif not zh_parts:
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zh_parts.append("高空结构整体平稳，暂未看到明显压温信号。")
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if not en_parts:
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en_parts.append("The upper-air structure looks fairly stable, without a strong suppression signal yet.")
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if has_profile_data:
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summary_tokens_zh = []
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summary_tokens_en = []
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window_start = str(times[candidate_indexes[0]]).split("T")[1][:5]
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window_end = str(times[candidate_indexes[-1]]).split("T")[1][:5]
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zh_parts.append(f"判断窗口：{window_start}-{window_end}。")
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en_parts.append(f"Signal window: {window_start}-{window_end}.")
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if cape_max is not None:
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summary_tokens_zh.append(f"CAPE≈{round(cape_max)}")
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summary_tokens_en.append(f"CAPE≈{round(cape_max)}")
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if cin_min is not None:
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summary_tokens_zh.append(f"CIN≈{round(cin_min)}")
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summary_tokens_en.append(f"CIN≈{round(cin_min)}")
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if boundary_layer_height_max is not None:
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summary_tokens_zh.append(f"混合层≈{round(boundary_layer_height_max)}m")
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summary_tokens_en.append(f"mixing≈{round(boundary_layer_height_max)}m")
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if shear_10m_180m_max is not None:
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summary_tokens_zh.append(f"切变≈{shear_10m_180m_max:.1f}")
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summary_tokens_en.append(f"shear≈{shear_10m_180m_max:.1f}")
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zh_parts.append(
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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')}。"
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)
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en_parts.append(
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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') }."
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)
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if heating_setup == "supportive":
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zh_parts.append("整体更偏向支持白天冲高。")
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en_parts.append("Overall, the profile is more supportive of daytime heating.")
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elif heating_setup == "suppressed":
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zh_parts.append("整体更偏向抑制午后冲高。")
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en_parts.append("Overall, the profile leans more toward suppressing the afternoon peak.")
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else:
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zh_parts.append("整体更像中性环境，仍需结合地面信号。")
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en_parts.append("Overall, the profile looks fairly neutral and still needs surface confirmation.")
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if summary_tokens_zh:
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zh_parts.append(" / ".join(summary_tokens_zh) + "。")
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if summary_tokens_en:
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en_parts.append(" / ".join(summary_tokens_en) + ".")
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return {
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"source": "open-meteo-gfs",
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||||
"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]:
|
||||
|
||||
@@ -0,0 +1,760 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
|
||||
def _sf(v) -> Optional[float]:
|
||||
if v is None:
|
||||
return None
|
||||
try:
|
||||
return float(v)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
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,
|
||||
}
|
||||
Reference in New Issue
Block a user