from __future__ import annotations import re from datetime import datetime from typing import Any, Dict, List, Optional from web.scan_city_ai_helpers import _safe_float, _truncate_ai_text def _compact_ai_candidate(row: Dict[str, Any]) -> Dict[str, Any]: return { "id": row.get("id"), "action": row.get("action"), "side": row.get("side"), "target_label": row.get("target_label"), "target_value": row.get("target_value"), "target_threshold": row.get("target_threshold"), "target_unit": row.get("target_unit"), "market_probability": row.get("market_probability"), "market_event_probability": row.get("market_event_probability"), "yes_ask": row.get("yes_ask"), "no_ask": row.get("no_ask"), "ask": row.get("ask"), "spread": row.get("spread"), "quote_age_ms": row.get("quote_age_ms"), "cluster_role": row.get("cluster_role"), "model_cluster_sources": _compact_ai_model_sources(row), "metar_context": row.get("metar_context") or {}, "window_phase": row.get("window_phase"), "peak_window_label": row.get("peak_window_label"), "minutes_until_peak_start": row.get("minutes_until_peak_start"), "minutes_until_peak_end": row.get("minutes_until_peak_end"), "trend_alignment": row.get("trend_alignment"), "tradable": row.get("tradable"), "accepting_orders": row.get("accepting_orders"), } def _normalize_ai_city_key(value: Any) -> str: return str(value or "").strip().lower().replace(" ", "").replace("-", "").replace("_", "") def _compact_ai_model_sources(row: Dict[str, Any]) -> List[Dict[str, Any]]: raw_sources = row.get("model_cluster_sources") if not isinstance(raw_sources, dict): return [] sources: List[Dict[str, Any]] = [] for name, value in raw_sources.items(): if _safe_float(value) is None: continue sources.append({"model": str(name), "value": value}) return sources[:12] def _observation_sort_key(point: Dict[str, Any]) -> tuple[int, str]: raw_time = str(point.get("time") or "").strip() try: parsed = datetime.fromisoformat(raw_time.replace("Z", "+00:00")) return parsed.hour * 60 + parsed.minute, raw_time except Exception: pass match = re.search(r"(\d{1,2}):(\d{2})", raw_time) if match: hour = max(0, min(23, int(match.group(1)))) minute = max(0, min(59, int(match.group(2)))) return hour * 60 + minute, raw_time return 9999, raw_time def _compact_observation_points(raw_points: Any, limit: int = 24) -> List[Dict[str, Any]]: if not isinstance(raw_points, list): return [] points: List[Dict[str, Any]] = [] for item in raw_points: if isinstance(item, dict): temp = _safe_float(item.get("temp")) time_value = str(item.get("time") or item.get("obs_time") or item.get("time_label") or "").strip() elif isinstance(item, (list, tuple)) and len(item) >= 2: time_value = str(item[0] or "").strip() temp = _safe_float(item[1]) else: continue if temp is None or not time_value: continue points.append({"time": time_value, "temp": temp}) sorted_points = sorted(points, key=_observation_sort_key) return sorted_points[-max(1, int(limit)) :] def _compact_ai_text(value: Any, limit: int = 700) -> Optional[str]: text = _truncate_ai_text(value, limit).strip() return text or None def _compact_hourly_context(raw_hourly: Any) -> Dict[str, Any]: if not isinstance(raw_hourly, dict): return {} times = raw_hourly.get("times") or raw_hourly.get("time") or [] temps = raw_hourly.get("temps") or raw_hourly.get("temperature_2m") or [] radiation = raw_hourly.get("radiation") or raw_hourly.get("shortwave_radiation") or [] if not isinstance(times, list) or not isinstance(temps, list): return {} points: List[Dict[str, Any]] = [] for idx, raw_time in enumerate(times): temp = _safe_float(temps[idx] if idx < len(temps) else None) if temp is None: continue time_text = str(raw_time or "").strip() if "T" in time_text: time_text = time_text.split("T", 1)[1][:5] elif len(time_text) > 5: time_text = time_text[:5] point: Dict[str, Any] = {"time": time_text, "temp": temp} rad = _safe_float(radiation[idx] if isinstance(radiation, list) and idx < len(radiation) else None) if rad is not None: point["radiation"] = rad points.append(point) if not points: return {} max_point = max(points, key=lambda item: _safe_float(item.get("temp")) or -999.0) sample_indexes = { idx for idx in range(len(points)) if idx % 2 == 0 or idx >= len(points) - 4 or points[idx] is max_point } samples = [points[idx] for idx in sorted(sample_indexes)][-14:] return { "sample_count": len(points), "forecast_hourly_max": max_point, "samples": samples, } def _compact_taf_context(raw_taf_data: Any) -> Dict[str, Any]: if not isinstance(raw_taf_data, dict): return {} signal = raw_taf_data.get("signal") if isinstance(raw_taf_data.get("signal"), dict) else {} source = signal or raw_taf_data raw_taf = raw_taf_data.get("raw_taf") or source.get("raw_taf") compact: Dict[str, Any] = { "available": bool(source.get("available") or raw_taf), "raw_taf": _compact_ai_text(raw_taf, 900), "issue_time": raw_taf_data.get("issue_time") or source.get("issue_time"), "valid_time_from": raw_taf_data.get("valid_time_from") or source.get("valid_time_from"), "valid_time_to": raw_taf_data.get("valid_time_to") or source.get("valid_time_to"), "peak_window": source.get("peak_window"), "suppression_level": source.get("suppression_level"), "disruption_level": source.get("disruption_level"), "wind_shift": source.get("wind_shift"), "wind_regimes": source.get("wind_regimes"), "summary_zh": _compact_ai_text(source.get("summary_zh"), 260), "summary_en": _compact_ai_text(source.get("summary_en"), 260), } segments = source.get("segments") if isinstance(source.get("segments"), list) else [] markers = source.get("markers") if isinstance(source.get("markers"), list) else [] if segments: compact["segments"] = segments[:3] if markers: compact["markers"] = markers[:4] return {key: value for key, value in compact.items() if value not in (None, "", [])} def _compact_vertical_context(raw_vertical: Any) -> Dict[str, Any]: if not isinstance(raw_vertical, dict): return {} keys = [ "source", "window_start", "window_end", "suppression_risk", "trigger_risk", "mixing_strength", "shear_risk", "heating_setup", "heating_score", "summary_zh", "summary_en", ] compact: Dict[str, Any] = {} for key in keys: value = raw_vertical.get(key) if isinstance(value, str): value = _compact_ai_text(value, 280) if value not in (None, "", []): compact[key] = value return compact def _compact_intraday_context(raw_intraday: Any) -> Dict[str, Any]: if not isinstance(raw_intraday, dict): return {} compact: Dict[str, Any] = {} for key in [ "headline", "headline_en", "confidence", "base_case_bucket", "upside_bucket", "downside_bucket", "next_observation_time", "peak_window", ]: value = raw_intraday.get(key) if isinstance(value, str): value = _compact_ai_text(value, 220) if value not in (None, "", []): compact[key] = value signals = raw_intraday.get("signal_contributions") if isinstance(signals, list): compact["signal_contributions"] = [ { "label": item.get("label"), "label_en": item.get("label_en"), "direction": item.get("direction"), "strength": item.get("strength"), "summary": _compact_ai_text(item.get("summary"), 180), "summary_en": _compact_ai_text(item.get("summary_en"), 180), } for item in signals[:4] if isinstance(item, dict) ] return compact def _build_metar_decision_context(data: Dict[str, Any]) -> Dict[str, Any]: today_obs = _compact_observation_points(data.get("metar_today_obs"), 36) recent_obs = _compact_observation_points(data.get("metar_recent_obs"), 12) settlement_obs = _compact_observation_points(data.get("settlement_today_obs"), 36) airport_current = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {} metar_status = data.get("metar_status") if isinstance(data.get("metar_status"), dict) else {} source_obs = today_obs or recent_obs or settlement_obs trend_source = recent_obs or source_obs[-4:] last_point = source_obs[-1] if source_obs else {} first_trend = trend_source[0] if trend_source else {} last_trend = trend_source[-1] if trend_source else {} max_point = None for point in source_obs: if max_point is None or float(point["temp"]) >= float(max_point["temp"]): max_point = point last_temp = _safe_float(last_point.get("temp")) first_temp = _safe_float(first_trend.get("temp")) trend_last_temp = _safe_float(last_trend.get("temp")) trend_delta = ( trend_last_temp - first_temp if trend_last_temp is not None and first_temp is not None and len(trend_source) >= 2 else None ) station = data.get("risk") if isinstance(data.get("risk"), dict) else {} current = data.get("current") if isinstance(data.get("current"), dict) else {} settlement_station = data.get("settlement_station") if isinstance(data.get("settlement_station"), dict) else {} settlement_source = str( current.get("settlement_source") or settlement_station.get("settlement_source") or "metar" ).strip().lower() is_hko = settlement_source == "hko" source_label = "HKO" if is_hko else "METAR" return { "source": source_label, "is_airport_metar": not is_hko, "station": ( current.get("station_code") or settlement_station.get("settlement_station_code") or station.get("icao") or airport_current.get("station_code") ), "station_label": ( current.get("station_name") or settlement_station.get("settlement_station_label") or station.get("airport") or airport_current.get("station_label") ), "today_obs": today_obs[-12:], "recent_obs": recent_obs[-8:], "settlement_today_obs": settlement_obs[-12:], "obs_count": len(source_obs), "last_time": last_point.get("time"), "last_temp": last_temp, "max_temp": _safe_float((max_point or {}).get("temp")), "max_time": (max_point or {}).get("time"), "trend_delta": trend_delta, "stale_for_today": bool(metar_status.get("stale_for_today")), "available_for_today": bool(metar_status.get("available_for_today")), "last_observation_time": metar_status.get("last_observation_time"), "airport_current_temp": _safe_float(airport_current.get("temp")), "airport_max_so_far": _safe_float(airport_current.get("max_so_far")), "airport_obs_time": airport_current.get("obs_time"), "airport_report_time": airport_current.get("report_time"), "airport_raw_metar": airport_current.get("raw_metar"), "airport_wx_desc": airport_current.get("wx_desc"), "airport_cloud_desc": airport_current.get("cloud_desc"), "airport_visibility_mi": _safe_float(airport_current.get("visibility_mi")), "airport_wind_speed_kt": _safe_float(airport_current.get("wind_speed_kt")), "airport_wind_dir": _safe_float(airport_current.get("wind_dir")), "airport_humidity": _safe_float(airport_current.get("humidity")), } def _city_observation_anchor(data: Dict[str, Any]) -> Dict[str, Any]: current = data.get("current") if isinstance(data.get("current"), dict) else {} settlement_station = data.get("settlement_station") if isinstance(data.get("settlement_station"), dict) else {} airport_current = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {} risk = data.get("risk") if isinstance(data.get("risk"), dict) else {} source = str( current.get("settlement_source") or settlement_station.get("settlement_source") or "metar" ).strip().lower() is_hko = source == "hko" if is_hko: station_code = ( current.get("station_code") or settlement_station.get("settlement_station_code") or "HKO" ) station_label = ( current.get("station_name") or settlement_station.get("settlement_station_label") or "Hong Kong Observatory" ) return { "source": "hko", "source_label": "Hong Kong Observatory", "is_airport_metar": False, "station_code": station_code, "station_label": station_label, "read_label_zh": "香港天文台观测解读", "read_label_en": "Hong Kong Observatory observation read", "instruction_zh": ( "该城市使用香港天文台/HKO 官方站点观测，提供温度、今日最高/最低温、相对湿度、" "10分钟平均风速和风向数据。它不是机场 METAR，不得使用 METAR、TAF、机场报文、报文时间等称谓，" "应使用「观测时间」「风速」「风向」「湿度」等官方气象站用语。" "观测解读必须具体说明：最新观测时间、温度、风向风速对升温路径的影响（增温/降温/中性）、" "湿度是否可能压制日间升温。涉及风时必须说明具体风向及来源（如「偏北风」「海风/陆风」）。" "如果有今日最高温和最低温数据，应结合判断温度走势。" ), "instruction_en": ( "This city uses Hong Kong Observatory/HKO official station observations, providing temperature, " "today's high/low, relative humidity, 10-min mean wind speed and direction. " "It is NOT an airport METAR; do not use METAR, TAF, airport bulletin, or report time terminology. " "Use terms like observation time, wind speed, wind direction, humidity. " "The observation read must be specific: latest observation time, temperature, whether the wind " "direction tends to warm, cool or be neutral for today's high path and why, and whether humidity " "may suppress daytime heating. When mentioning wind, specify the direction and source " "(e.g. northerly, sea breeze/land breeze). If today's high/low are available, use them to assess the trend." ), } return { "source": "metar", "source_label": "METAR", "is_airport_metar": True, "station_code": risk.get("icao") or airport_current.get("station_code"), "station_label": risk.get("airport") or airport_current.get("station_label"), "read_label_zh": "机场报文解读", "read_label_en": "airport-bulletin read", "instruction_zh": "该城市使用机场 METAR/TAF 作为日内实况证据。", "instruction_en": "This city uses airport METAR/TAF as intraday observation evidence.", } def _compact_ai_city_group(rows: List[Dict[str, Any]]) -> Dict[str, Any]: first = rows[0] return { "city": first.get("city"), "city_display_name": first.get("city_display_name") or first.get("display_name") or first.get("city"), "selected_date": first.get("selected_date") or first.get("local_date"), "local_time": first.get("local_time"), "temp_symbol": first.get("temp_symbol") or first.get("target_unit"), "current_temp": first.get("current_temp"), "current_max_so_far": first.get("current_max_so_far"), "window_phase": first.get("window_phase"), "remaining_window_minutes": first.get("remaining_window_minutes"), "peak_window_label": first.get("peak_window_label"), "minutes_until_peak_start": first.get("minutes_until_peak_start"), "minutes_until_peak_end": first.get("minutes_until_peak_end"), "metar_context": first.get("metar_context") or {}, "model_cluster": { "core_low": first.get("cluster_core_low"), "core_high": first.get("cluster_core_high"), "median": first.get("cluster_median"), "deb_reference": first.get("cluster_deb_reference"), "model_count": first.get("cluster_model_count"), "sources": _compact_ai_model_sources(first), }, "contracts": [_compact_ai_candidate(row) for row in rows], } def build_scan_ai_prompt(payload: Dict[str, Any], *, max_rows: int) -> Dict[str, Any]: raw_rows = [ row for row in (payload.get("rows") or [])[:max_rows] if isinstance(row, dict) and row.get("id") ] grouped: Dict[str, List[Dict[str, Any]]] = {} for row in raw_rows: key = "|".join( [ _normalize_ai_city_key(row.get("city") or row.get("city_display_name")), str(row.get("selected_date") or row.get("local_date") or ""), ] ) grouped.setdefault(key, []).append(row) cities = [_compact_ai_city_group(rows) for rows in grouped.values() if rows] sent_contracts = sum(len(city.get("contracts") or []) for city in cities) return { "schema_version": "city_forecast_v1", "snapshot_id": payload.get("snapshot_id"), "generated_at": payload.get("generated_at"), "summary": payload.get("summary") or {}, "filters": payload.get("filters") or {}, "city_count": len(cities), "candidate_row_count": len(raw_rows), "cities": cities, "_polyweather_input_meta": { "sent_cities": len(cities), "sent_contracts": sent_contracts, }, } def _compact_probability_context(probabilities: Any, deb: Any, unit: str) -> dict: if not isinstance(probabilities, dict): return {} dist = probabilities.get("distribution") if not isinstance(dist, list) or not dist: return {} top_buckets = sorted( [b for b in dist if isinstance(b, dict) and b.get("probability")], key=lambda b: float(b.get("probability", 0)), reverse=True, )[:3] compact = { "top_buckets": [ { "label": b.get("label", ""), "prob": round(float(b.get("probability", 0)) * 100), } for b in top_buckets ], } mu = probabilities.get("mu") if mu is not None: compact["mu"] = mu spread = ( round(float(probabilities.get("calibrated_sigma") or 0), 1) or round(float(probabilities.get("raw_sigma") or 0), 1) or None ) if spread is not None: compact["sigma"] = spread deb_val = deb.get("prediction") if isinstance(deb, dict) else None if deb_val is not None and mu is not None: if deb_val > mu: compact["skew"] = "right" elif deb_val < mu: compact["skew"] = "left" else: compact["skew"] = "centered" if unit: compact["unit"] = unit return compact