2569718930@qq.com
284 lines
11 KiB
Python
284 lines
11 KiB
Python
"""City payload builders for API-facing response shapes."""
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from __future__ import annotations
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from typing import Any, Dict, Optional, List
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from datetime import datetime, timezone
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import re
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from web.core import _is_excluded_model_name
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TURKISH_MGM_CITIES = {"ankara", "istanbul"}
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def build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
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return {
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"name": data.get("name"),
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"display_name": data.get("display_name"),
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"icao": data.get("risk", {}).get("icao"),
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"utc_offset_seconds": data.get("utc_offset_seconds"),
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"local_time": data.get("local_time"),
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"temp_symbol": data.get("temp_symbol"),
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"current": {
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"temp": data.get("current", {}).get("temp"),
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"obs_time": data.get("current", {}).get("obs_time"),
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"settlement_source": data.get("current", {}).get("settlement_source"),
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"settlement_source_label": data.get("current", {}).get(
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"settlement_source_label"
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),
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},
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"deb": {"prediction": data.get("deb", {}).get("prediction")},
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"deviation_monitor": data.get("deviation_monitor") or {},
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"risk": {
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"level": data.get("risk", {}).get("level"),
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"warning": data.get("risk", {}).get("warning"),
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},
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"updated_at": data.get("updated_at"),
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}
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def _parse_time_val(val: str) -> Optional[datetime]:
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if not val:
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return None
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try:
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val = str(val).strip().replace("Z", "+00:00")
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if "T" in val:
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return datetime.fromisoformat(val)
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else:
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return datetime.fromisoformat(val)
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except Exception:
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try:
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val_clean = re.sub(r'\.\d+', '', val)
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return datetime.strptime(val_clean, "%Y-%m-%d %H:%M:%S")
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except Exception:
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return None
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def aggregate_runway_history(raw_history: Dict[str, List[Dict[str, Any]]], resolution: str) -> Dict[str, List[Dict[str, Any]]]:
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if not raw_history:
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return {}
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if not resolution or resolution == "1m":
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return raw_history
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try:
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if resolution.endswith("m"):
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minutes = int(resolution[:-1])
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elif resolution.endswith("h"):
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minutes = int(resolution[:-1]) * 60
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else:
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minutes = 10
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except Exception:
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minutes = 10
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seconds = minutes * 60
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aggregated = {}
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for rwy, points in raw_history.items():
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if not points:
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continue
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buckets = {}
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for pt in points:
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t_str = pt.get("time") or pt.get("timestamp")
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temp = pt.get("temp") or pt.get("temp_c") or pt.get("value")
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if temp is None or not isinstance(t_str, str):
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continue
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dt = _parse_time_val(t_str)
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if not dt:
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continue
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ts = int(dt.timestamp())
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bucket_ts = (ts // seconds) * seconds
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if bucket_ts not in buckets:
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buckets[bucket_ts] = []
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buckets[bucket_ts].append(temp)
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bucket_points = []
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for bucket_ts in sorted(buckets.keys()):
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temps = buckets[bucket_ts]
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close_temp = temps[-1]
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bucket_dt = datetime.fromtimestamp(bucket_ts, tz=timezone.utc)
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bucket_points.append({
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"time": bucket_dt.isoformat(),
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"temp": round(close_temp, 1)
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})
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aggregated[rwy] = bucket_points
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return aggregated
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def build_runway_band_history(raw_history: Dict[str, List[Dict[str, Any]]], resolution: str) -> List[Dict[str, Any]]:
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if not raw_history:
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return []
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try:
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if resolution.endswith("m"):
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minutes = int(resolution[:-1])
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elif resolution.endswith("h"):
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minutes = int(resolution[:-1]) * 60
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else:
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minutes = 10
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except Exception:
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minutes = 10
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seconds = minutes * 60
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buckets = {}
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for rwy, points in raw_history.items():
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for pt in points:
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t_str = pt.get("time") or pt.get("timestamp")
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temp = pt.get("temp") or pt.get("temp_c") or pt.get("value")
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if temp is None or not isinstance(t_str, str):
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continue
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dt = _parse_time_val(t_str)
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if not dt:
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continue
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ts = int(dt.timestamp())
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bucket_ts = (ts // seconds) * seconds
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if bucket_ts not in buckets:
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buckets[bucket_ts] = []
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buckets[bucket_ts].append(temp)
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band_history = []
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for bucket_ts in sorted(buckets.keys()):
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temps = buckets[bucket_ts]
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if not temps:
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continue
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high_temp = max(temps)
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low_temp = min(temps)
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avg_temp = sum(temps) / len(temps)
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bucket_dt = datetime.fromtimestamp(bucket_ts, tz=timezone.utc)
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band_history.append({
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"time": bucket_dt.isoformat(),
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"high_temp": round(high_temp, 1),
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"low_temp": round(low_temp, 1),
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"avg_temp": round(avg_temp, 1),
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})
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return band_history
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def build_city_detail_payload(
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data: Dict[str, Any],
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market_slug: Optional[str] = None,
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target_date: Optional[str] = None,
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resolution: Optional[str] = "10m",
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) -> Dict[str, Any]:
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return {
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"city": data.get("name"),
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"fetched_at": data.get("updated_at"),
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"overview": {
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"name": data.get("name"),
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"display_name": data.get("display_name"),
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"icao": data.get("risk", {}).get("icao"),
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"airport": data.get("risk", {}).get("airport"),
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"lat": data.get("lat"),
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"lon": data.get("lon"),
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"local_time": data.get("local_time"),
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"local_date": data.get("local_date"),
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"temp_symbol": data.get("temp_symbol"),
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"current_temp": data.get("current", {}).get("temp"),
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"settlement_source": data.get("current", {}).get("settlement_source"),
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"settlement_source_label": data.get("current", {}).get(
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"settlement_source_label"
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),
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"settlement_station": data.get("settlement_station") or {},
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"deb_prediction": data.get("deb", {}).get("prediction"),
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"risk_level": data.get("risk", {}).get("level"),
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"risk_warning": data.get("risk", {}).get("warning"),
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"updated_at": data.get("updated_at"),
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},
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"official": {
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"available": bool(data.get("current", {}).get("temp") is not None),
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"metar": {
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"observation_time": data.get("airport_current", {}).get("obs_time"),
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"obs_age_min": data.get("airport_current", {}).get("obs_age_min"),
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"report_time": data.get("airport_current", {}).get("report_time"),
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"receipt_time": data.get("airport_current", {}).get("receipt_time"),
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"raw_metar": data.get("airport_current", {}).get("raw_metar"),
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"current": data.get("airport_current") or {},
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},
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"taf": data.get("taf") or {},
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"weather_gov": {},
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"mgm": data.get("mgm") or {},
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"mgm_nearby": data.get("mgm_nearby") or [],
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"nearby_source": data.get("nearby_source")
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or (
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"mgm"
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if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES
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else "metar_cluster"
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),
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"airport_primary": data.get("airport_primary") or {},
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"airport_primary_today_obs": data.get("airport_primary_today_obs") or [],
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"official_nearby": data.get("official_nearby") or [],
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"official_network_source": data.get("official_network_source"),
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"official_network_status": data.get("official_network_status") or {},
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"network_lead_signal": data.get("network_lead_signal") or {},
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"network_spread_signal": data.get("network_spread_signal") or {},
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"center_station_candidate": data.get("center_station_candidate"),
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"airport_vs_network_delta": data.get("airport_vs_network_delta"),
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},
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"timeseries": {
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"metar_recent_obs": data.get("metar_recent_obs") or [],
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"metar_today_obs": data.get("metar_today_obs") or [],
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"settlement_today_obs": data.get("settlement_today_obs") or [],
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"hourly": data.get("hourly") or {},
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"mgm_hourly": (data.get("mgm") or {}).get("hourly", []),
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"forecast_daily": (data.get("forecast") or {}).get("daily", []),
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},
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"models": {
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k: v
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for k, v in (data.get("multi_model") or {}).items()
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if not _is_excluded_model_name(k)
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},
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"models_hourly": {
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"times": (data.get("multi_model") or {}).get("hourly_times", []),
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"curves": {
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model: values
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for model, values in (
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(data.get("multi_model") or {}).get("hourly_forecasts", {})
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).items()
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if not _is_excluded_model_name(model)
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},
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},
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"deb": data.get("deb") or {},
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"multi_model_daily": data.get("multi_model_daily") or {},
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"probabilities": data.get("probabilities") or {"mu": None, "distribution": []},
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"dynamic_commentary": data.get("dynamic_commentary")
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or {"summary": "", "notes": []},
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"intraday_meteorology": data.get("intraday_meteorology")
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or _build_intraday_meteorology(data),
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"vertical_profile_signal": data.get("vertical_profile_signal") or {},
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"taf": data.get("taf") or {},
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"runway_plate_history": aggregate_runway_history(data.get("runway_plate_history") or {}, resolution or "10m"),
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"runway_band_history": build_runway_band_history(data.get("runway_plate_history") or {}, resolution or "10m"),
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"risk": data.get("risk"),
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"settlement_station": data.get("settlement_station") or {},
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"airport_primary": data.get("airport_primary") or {},
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"official_nearby": data.get("official_nearby") or [],
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"official_network_source": data.get("official_network_source"),
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"official_network_status": data.get("official_network_status") or {},
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"network_lead_signal": data.get("network_lead_signal") or {},
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"network_spread_signal": data.get("network_spread_signal") or {},
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"center_station_candidate": data.get("center_station_candidate"),
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"airport_vs_network_delta": data.get("airport_vs_network_delta"),
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"airport_current": data.get("airport_current") or {},
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"amos": data.get("amos") or {},
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"nearby_source": data.get("nearby_source")
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or (
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"mgm"
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if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES
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else "metar_cluster"
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),
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"ai_analysis": data.get("ai_analysis") or "",
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"errors": {},
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}
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def _build_intraday_meteorology(data: Dict[str, Any]) -> Dict[str, Any]:
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from web.analysis_service import _build_intraday_meteorology as build_intraday
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return build_intraday(data)
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