2569718930@qq.com
37494a7192
将 web/routes.py 拆分为模块化 router + service 架构
- 新增 web/app_factory.py 集中注册 7 个域名 router
- 新增 web/routers/ 薄壳路由层（auth/city/system/scan/ops/payments/analytics）
- 新增 web/services/ 业务函数下沉（每域独立 service 文件）
- web/routes.py 缩减为 city_runtime 的兼容重导出 facade
- analysis_service.py/app.py 适配新入口并清理冗余导入
Scope-risk: LOW — 全量 170 测试通过，router 注册顺序与原路由一致
Tested: python -m pytest -q (170 passed), ruff check . (All checks passed)
@
278 lines
12 KiB
Python
278 lines
12 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
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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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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("settlement_source_label"),
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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 build_city_market_scan_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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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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"fetched_at": data.get("updated_at"),
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}
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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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) -> Dict[str, Any]:
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market_payload = build_city_market_scan_payload(
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data,
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market_slug=market_slug,
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target_date=target_date,
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)
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market_scan = market_payload.get("market_scan")
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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("settlement_source_label"),
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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") or ("mgm" if str(data.get("name") or "").lower() in TURKISH_MGM_CITIES else "metar_cluster"),
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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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"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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"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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"market_scan": market_scan,
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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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"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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"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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