移除 Polymarket 价格拉取：删除 _market_layer、market_scan 返回空、清理健康检查和配置验证
This commit is contained in:
2569718930@qq.com
+23
-131
@@ -4,8 +4,7 @@ from __future__ import annotations
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from typing import Any, Dict, Optional
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from src.analysis.settlement_rounding import apply_city_settlement
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from web.core import _is_excluded_model_name, _market_layer, _sf
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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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@@ -22,7 +21,9 @@ def build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
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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("settlement_source_label"),
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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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@@ -41,135 +42,13 @@ def build_city_market_scan_payload(
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lite: bool = False,
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scan_filters: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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city = str(data.get("name") or "").strip().lower()
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local_date = str(data.get("local_date") or "").strip()
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requested_date = str(target_date or "").strip()
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selected_date = requested_date or local_date
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multi_model_daily = data.get("multi_model_daily") or {}
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selected_daily = (
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multi_model_daily.get(selected_date)
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if isinstance(multi_model_daily, dict)
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else None
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)
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if not isinstance(selected_daily, dict):
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selected_daily = {}
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selected_date = local_date
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distribution = selected_daily.get("probabilities")
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if not isinstance(distribution, list) or not distribution:
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distribution = data.get("probabilities", {}).get("distribution", []) or []
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distribution_all = selected_daily.get("probabilities_all")
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if not isinstance(distribution_all, list) or not distribution_all:
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distribution_all = data.get("probabilities", {}).get("distribution_all", []) or []
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if not distribution_all:
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distribution_all = distribution
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model_map = selected_daily.get("models") or data.get("multi_model") or {}
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if not isinstance(model_map, dict):
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model_map = {}
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anchor_temp = None
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anchor_model = None
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for model_name, raw_value in model_map.items():
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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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if anchor_temp is None or value > anchor_temp:
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anchor_temp = value
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anchor_model = str(model_name or "").strip() or None
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anchor_temp_c = anchor_temp
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temp_symbol = str(data.get("temp_symbol") or "")
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if anchor_temp_c is not None and "F" in temp_symbol.upper():
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anchor_temp_c = (anchor_temp_c - 32.0) * 5.0 / 9.0
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anchor_settlement = apply_city_settlement(city, anchor_temp_c) if anchor_temp_c is not None else None
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primary_bucket = None
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if isinstance(distribution, list) and distribution:
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ranked_buckets = []
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temp_symbol_upper = str(temp_symbol or "").upper()
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max_primary_bucket_delta = 16.0 if "F" in temp_symbol_upper else 8.0
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for idx, row in enumerate(distribution_all):
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if not isinstance(row, dict):
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continue
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bucket_value = _sf(
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row.get("temp")
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if row.get("temp") is not None
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else row.get("value")
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if row.get("value") is not None
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else row.get("lower")
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)
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if (
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anchor_temp is not None
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and bucket_value is not None
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and abs(float(bucket_value) - float(anchor_temp)) > max_primary_bucket_delta
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):
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continue
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bucket_prob = _sf(row.get("probability"))
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prob_rank = bucket_prob if bucket_prob is not None else -1.0
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ranked_buckets.append((-prob_rank, idx, row))
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if ranked_buckets:
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ranked_buckets.sort(key=lambda x: (x[0], x[1]))
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primary_bucket = ranked_buckets[0][2]
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elif anchor_temp is None:
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primary_bucket = distribution[0]
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model_probability = None
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if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None:
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try:
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raw_probability = float(primary_bucket.get("probability"))
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model_probability = raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
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except Exception:
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model_probability = None
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fallback_sparkline = [
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p.get("probability", 0)
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for p in distribution_all[:8]
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if isinstance(p, dict)
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]
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current = data.get("current") or {}
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selected_deb = selected_daily.get("deb") if isinstance(selected_daily.get("deb"), dict) else {}
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current_deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
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scan_context = {
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"local_date": data.get("local_date"),
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"local_time": data.get("local_time"),
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"peak": data.get("peak") or {},
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"current_max_so_far": current.get("max_so_far"),
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"current_temp": current.get("temp"),
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"trend": data.get("trend") or {},
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"network_lead_signal": data.get("network_lead_signal") or {},
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"models": model_map,
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"deb_prediction": selected_deb.get("prediction") or current_deb.get("prediction"),
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}
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market_scan = _market_layer.build_market_scan(
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city=data.get("name"),
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target_date=selected_date or data.get("local_date"),
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temperature_bucket=primary_bucket if isinstance(primary_bucket, dict) else None,
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model_probability=model_probability,
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probability_distribution=distribution_all,
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temp_symbol=temp_symbol,
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fallback_sparkline=fallback_sparkline,
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forced_market_slug=market_slug,
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include_related_buckets=not lite,
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scan_filters=scan_filters,
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scan_context=scan_context,
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)
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if isinstance(market_scan, dict):
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market_scan["anchor_model"] = anchor_model
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market_scan["anchor_high"] = anchor_temp
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market_scan["anchor_settlement"] = anchor_settlement
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market_scan["open_meteo_settlement"] = anchor_settlement
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probabilities = data.get("probabilities") or {}
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market_scan["probability_engine"] = str(
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probabilities.get("engine") or "legacy"
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).strip() or "legacy"
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market_scan["probability_calibration_mode"] = str(
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probabilities.get("calibration_mode") or "legacy"
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).strip() or "legacy"
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return {
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"market_scan": market_scan,
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"selected_date": selected_date or data.get("local_date"),
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"market_scan": {"available": False},
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"selected_date": selected_date,
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"fetched_at": data.get("updated_at"),
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}
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@@ -200,7 +79,9 @@ def build_city_detail_payload(
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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("settlement_source_label"),
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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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@@ -221,7 +102,12 @@ def build_city_detail_payload(
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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") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
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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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@@ -248,7 +134,8 @@ def build_city_detail_payload(
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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") or {"summary": "", "notes": []},
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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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@@ -266,7 +153,12 @@ def build_city_detail_payload(
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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") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
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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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