Add TAF timing markers and market-aware intraday cues
This commit is contained in:
2569718930@qq.com
+228
-47
@@ -295,67 +295,238 @@ def _build_vertical_profile_signal(
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def _build_taf_signal(
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taf_data: Dict[str, Any],
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city: str,
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local_date: str,
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utc_offset: 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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if str(city or "").strip().lower() == "hong kong":
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return {}
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raw_taf = str((taf_data or {}).get("raw_taf") or "").upper().strip()
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raw_taf = re.sub(r"\s+", " ", str((taf_data or {}).get("raw_taf") or "").upper().strip())
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if not raw_taf:
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return {}
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precip_codes = re.findall(
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r"\b(?:-|\+)?(?:TSRA|TS|VCTS|SHRA|RA|DZ|SN|SHSN|SHGS)\b",
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raw_taf,
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)
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cloud_matches = re.findall(r"\b(FEW|SCT|BKN|OVC)(\d{3})\b", raw_taf)
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wind_matches = re.findall(r"\b(\d{3}|VRB)(\d{2,3})(?:G\d{2,3})?KT\b", raw_taf)
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tempo_tokens = re.findall(r"\b(?:TEMPO|BECMG|PROB30|PROB40|FM\d{6})\b", raw_taf)
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issue_raw = str((taf_data or {}).get("issue_time") or "").strip()
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issue_dt = None
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if issue_raw:
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try:
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issue_dt = datetime.fromisoformat(issue_raw.replace("Z", "+00:00"))
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except Exception:
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issue_dt = None
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if issue_dt is None:
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issue_dt = datetime.now(timezone.utc)
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local_tz = timezone(timedelta(seconds=int(utc_offset or 0)))
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valid_match = re.search(r"\b(\d{2})(\d{2})/(\d{2})(\d{2})\b", raw_taf)
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tokens = raw_taf.split()
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if not valid_match:
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return {}
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def _infer_utc(day: int, hour: int, minute: int = 0) -> datetime:
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base = issue_dt
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year = base.year
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month = base.month
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candidate = datetime(year, month, day, hour, minute, tzinfo=timezone.utc)
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if candidate < base - timedelta(days=20):
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if month == 12:
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candidate = datetime(year + 1, 1, day, hour, minute, tzinfo=timezone.utc)
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else:
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candidate = datetime(year, month + 1, day, hour, minute, tzinfo=timezone.utc)
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elif candidate > base + timedelta(days=20):
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if month == 1:
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candidate = datetime(year - 1, 12, day, hour, minute, tzinfo=timezone.utc)
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else:
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candidate = datetime(year, month - 1, day, hour, minute, tzinfo=timezone.utc)
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return candidate
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def _parse_period(token: str) -> tuple[Optional[datetime], Optional[datetime]]:
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match = re.match(r"^(\d{2})(\d{2})/(\d{2})(\d{2})$", token)
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if not match:
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return None, None
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start = _infer_utc(int(match.group(1)), int(match.group(2)))
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end = _infer_utc(int(match.group(3)), int(match.group(4)))
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if end <= start:
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end += timedelta(days=1)
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return start, end
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valid_start_utc, valid_end_utc = _parse_period(valid_match.group(0))
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if valid_start_utc is None or valid_end_utc is None:
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return {}
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segment_indexes: list[int] = []
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for idx, token in enumerate(tokens):
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if re.match(r"^FM\d{6}$", token) or token in {"TEMPO", "BECMG", "PROB30", "PROB40"}:
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segment_indexes.append(idx)
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base_start_idx = 0
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for idx, token in enumerate(tokens):
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if token == valid_match.group(0):
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base_start_idx = idx + 1
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break
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segments: list[Dict[str, Any]] = []
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first_segment_idx = segment_indexes[0] if segment_indexes else len(tokens)
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if base_start_idx < first_segment_idx:
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segments.append(
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{
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"type": "BASE",
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"start_utc": valid_start_utc,
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"end_utc": valid_end_utc,
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"tokens": tokens[base_start_idx:first_segment_idx],
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}
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)
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idx_pos = 0
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while idx_pos < len(segment_indexes):
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start_idx = segment_indexes[idx_pos]
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end_idx = segment_indexes[idx_pos + 1] if idx_pos + 1 < len(segment_indexes) else len(tokens)
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token = tokens[start_idx]
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seg_type = token
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seg_start = valid_start_utc
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seg_end = valid_end_utc
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payload_start = start_idx + 1
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if re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token):
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match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token)
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seg_type = "FM"
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seg_start = _infer_utc(int(match.group(1)), int(match.group(2)), int(match.group(3)))
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if idx_pos + 1 < len(segment_indexes):
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next_token = tokens[segment_indexes[idx_pos + 1]]
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next_match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", next_token)
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if next_match:
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seg_end = _infer_utc(int(next_match.group(1)), int(next_match.group(2)), int(next_match.group(3)))
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else:
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seg_end = valid_end_utc
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else:
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seg_end = valid_end_utc
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elif token in {"TEMPO", "BECMG"}:
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seg_type = token
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if payload_start < len(tokens):
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seg_start, seg_end = _parse_period(tokens[payload_start])
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payload_start += 1
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elif token in {"PROB30", "PROB40"}:
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seg_type = token
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if payload_start < len(tokens) and tokens[payload_start] == "TEMPO":
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seg_type = f"{token} TEMPO"
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payload_start += 1
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if payload_start < len(tokens):
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seg_start, seg_end = _parse_period(tokens[payload_start])
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payload_start += 1
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if seg_start is None or seg_end is None:
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idx_pos += 1
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continue
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if seg_end <= seg_start:
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seg_end = seg_start + timedelta(hours=1)
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segments.append(
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{
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"type": seg_type,
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"start_utc": seg_start,
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"end_utc": seg_end,
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"tokens": tokens[payload_start:end_idx],
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}
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)
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idx_pos += 1
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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)
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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)
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precip_rank = {"low": 0, "medium": 1, "high": 2}
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suppression_level = "low"
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disruption_level = "low"
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low_ceiling_ft = None
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ceiling_cover = None
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for cover, base in cloud_matches:
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if cover not in {"BKN", "OVC"}:
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continue
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try:
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base_ft = int(base) * 100
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except Exception:
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continue
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if low_ceiling_ft is None or base_ft < low_ceiling_ft:
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low_ceiling_ft = base_ft
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ceiling_cover = cover
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wind_regimes: list[str] = []
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markers: list[Dict[str, Any]] = []
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active_segments: list[Dict[str, Any]] = []
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direction_buckets: list[str] = []
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for direction, _speed in wind_matches:
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if direction == "VRB":
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direction_buckets.append("variable")
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def _segment_precip_level(tokens_block: list[str]) -> str:
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joined = " ".join(tokens_block)
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if re.search(r"\b(?:-|\+)?(?:TSRA|TS|VCTS|SHRA|SHSN|SHGS)\b", joined):
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return "high"
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if re.search(r"\b(?:-|\+)?(?:RA|DZ|SN)\b", joined):
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return "medium"
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return "low"
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for segment in segments:
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start_local = segment["start_utc"].astimezone(local_tz)
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end_local = segment["end_utc"].astimezone(local_tz)
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if end_local < peak_window_start or start_local > peak_window_end:
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continue
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try:
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active_segments.append(segment)
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joined = " ".join(segment["tokens"])
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level = _segment_precip_level(segment["tokens"])
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if precip_rank[level] > precip_rank[suppression_level]:
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suppression_level = level
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cloud_matches = re.findall(r"\b(FEW|SCT|BKN|OVC)(\d{3})\b", joined)
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for cover, base in cloud_matches:
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if cover not in {"BKN", "OVC"}:
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continue
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try:
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base_ft = int(base) * 100
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except Exception:
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continue
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if low_ceiling_ft is None or base_ft < low_ceiling_ft:
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low_ceiling_ft = base_ft
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ceiling_cover = cover
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if low_ceiling_ft is not None and low_ceiling_ft <= 4000 and suppression_level == "low":
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suppression_level = "medium"
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wind_matches = re.findall(r"\b(\d{3}|VRB)(\d{2,3})(?:G\d{2,3})?KT\b", joined)
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segment_regimes = []
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for direction, _speed in wind_matches:
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if direction == "VRB":
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segment_regimes.append("variable")
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continue
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deg = int(direction)
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except Exception:
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continue
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if 135 <= deg <= 225:
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direction_buckets.append("southerly")
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elif deg >= 315 or deg <= 45:
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direction_buckets.append("northerly")
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else:
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direction_buckets.append("cross")
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unique_buckets = [bucket for bucket in dict.fromkeys(direction_buckets) if bucket]
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if 135 <= deg <= 225:
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segment_regimes.append("southerly")
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elif deg >= 315 or deg <= 45:
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segment_regimes.append("northerly")
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else:
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segment_regimes.append("cross")
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for item in segment_regimes:
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if item not in wind_regimes:
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wind_regimes.append(item)
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suppression_level = "low"
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if any(code in {"TSRA", "TS", "VCTS", "SHRA", "SHSN", "SHGS"} for code in precip_codes):
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suppression_level = "high"
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elif precip_codes or (low_ceiling_ft is not None and low_ceiling_ft <= 4000):
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suppression_level = "medium"
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if segment["type"] in {"TEMPO", "BECMG", "PROB30", "PROB40", "PROB30 TEMPO", "PROB40 TEMPO"}:
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disruption_level = "medium" if disruption_level == "low" else disruption_level
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if segment["type"] in {"PROB30 TEMPO", "PROB40 TEMPO"} or level == "high":
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disruption_level = "high"
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disruption_level = "low"
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if tempo_tokens and suppression_level == "high":
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disruption_level = "high"
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elif tempo_tokens or len(unique_buckets) >= 2:
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disruption_level = "medium"
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marker_time_local = start_local
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marker_hour = marker_time_local.strftime("%H:00")
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hazards = []
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if level != "low":
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hazards.append(level)
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if low_ceiling_ft is not None and segment_regimes is not None:
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hazards.append("cloud")
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if segment_regimes:
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hazards.append("wind")
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summary_zh = (
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f"{segment['type']} {start_local.strftime('%H:%M')}-{end_local.strftime('%H:%M')} "
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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'}"
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)
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summary_en = (
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f"{segment['type']} {start_local.strftime('%H:%M')}-{end_local.strftime('%H:%M')} "
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f"{'shows shower/thunder disruption' if level == 'high' else 'shows cloud/rain disruption' if level == 'medium' else 'stays relatively stable'}"
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)
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markers.append(
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{
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"label_time": marker_hour,
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"marker_type": segment["type"],
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"start_local": start_local.strftime("%H:%M"),
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"end_local": end_local.strftime("%H:%M"),
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"suppression_level": level,
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"summary_zh": summary_zh,
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"summary_en": summary_en,
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}
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)
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wind_shift = len(unique_buckets) >= 2 or "variable" in unique_buckets
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peak_window = f"{max(0, first_peak_h - 2):02d}:00-{min(23, last_peak_h + 1):02d}:00"
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wind_shift = len(wind_regimes) >= 2 or "variable" in wind_regimes
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peak_window = f"{peak_window_start.strftime('%H:%M')}-{peak_window_end.strftime('%H:%M')}"
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if suppression_level == "high":
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summary_zh = f"TAF 在峰值窗口（{peak_window}）提示阵雨或雷暴扰动，机场端压温风险偏高。"
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@@ -366,7 +537,6 @@ def _build_taf_signal(
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else:
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summary_zh = f"TAF 在峰值窗口（{peak_window}）暂未提示明显云雨压温。"
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summary_en = f"TAF does not flag a strong cloud/rain suppression signal around the peak window ({peak_window})."
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if wind_shift:
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summary_zh += " 同时机场预报风向存在阶段性切换。"
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summary_en += " Airport wind direction also shifts by regime during the window."
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@@ -379,10 +549,19 @@ def _build_taf_signal(
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"valid_time_from": (taf_data or {}).get("valid_time_from"),
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"valid_time_to": (taf_data or {}).get("valid_time_to"),
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"peak_window": peak_window,
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"precip_codes": precip_codes,
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"segments": [
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{
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"type": seg["type"],
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"start_local": seg["start_utc"].astimezone(local_tz).strftime("%H:%M"),
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"end_local": seg["end_utc"].astimezone(local_tz).strftime("%H:%M"),
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"tokens": seg["tokens"],
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}
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for seg in active_segments
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],
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"markers": markers,
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"low_ceiling_ft": low_ceiling_ft,
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"ceiling_cover": ceiling_cover,
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"wind_regimes": unique_buckets,
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"wind_regimes": wind_regimes,
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"wind_shift": wind_shift,
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"suppression_level": suppression_level,
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"disruption_level": disruption_level,
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@@ -875,6 +1054,8 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
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taf_signal = _build_taf_signal(
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taf if isinstance(taf, dict) else {},
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city,
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local_date_str,
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int(utc_offset or 0),
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first_peak_h,
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last_peak_h,
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)
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