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Clarify scan terminal service boundaries

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The scan terminal service had accumulated cache, payload, filtering, AI prompt, AI merge, METAR gate, ranking, and city-row construction details in one file. This splits those stable responsibilities into focused modules while preserving the endpoint payload shape and existing behavior.

Constraint: User-visible behavior and release version must remain unchanged for this internal refactor

Rejected: Rewrite the terminal scan flow around a new abstraction | too risky while production behavior is being stabilized

Confidence: high

Scope-risk: moderate

Directive: Keep scan_terminal_service.py as orchestration; add detailed rule changes to the focused modules instead of re-growing the service file

Tested: py_compile for extracted modules; ruff check .; pytest tests/test_scan_terminal_modules.py tests/test_web_observability.py; full pytest; npm run test:business; npm run build; git diff --cached --check
This commit is contained in:
2569718930@qq.com
2026-04-28 17:13:45 +08:00
parent b122e7cbae
commit 20516000a2
11 changed files with 1696 additions and 1309 deletions
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web/scan_terminal_city_row.py
+182
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from __future__ import annotations
from datetime import datetime, timedelta
from typing import Any, Dict, List
from web.core import CITIES
from web.analysis_service import _analyze, _build_city_market_scan_payload
from web.scan_city_ai_helpers import _safe_float
from web.scan_terminal_ai_compact import _build_metar_decision_context
from web.scan_terminal_filters import (
market_region_from_tz_offset as _market_region_from_tz_offset,
safe_int as _safe_int,
)
def _resolve_time_range_dates(data: Dict[str, Any], time_range: str) -> List[str]:
local_date = str(data.get("local_date") or "").strip()
multi_model_daily = data.get("multi_model_daily") or {}
available_dates = sorted(
str(date_key).strip()
for date_key in (multi_model_daily.keys() if isinstance(multi_model_daily, dict) else [])
if str(date_key).strip()
)
if not local_date:
return available_dates[:1]
if time_range == "today":
return [local_date]
try:
local_dt = datetime.fromisoformat(local_date)
except Exception:
return available_dates[:7] if time_range == "week" else available_dates[:1]
if time_range == "tomorrow":
target = (local_dt + timedelta(days=1)).strftime("%Y-%m-%d")
if target in available_dates:
return [target]
future_dates = [date_key for date_key in available_dates if date_key > local_date]
return future_dates[:1]
if time_range == "week":
target_dates = [date_key for date_key in available_dates if date_key >= local_date]
if local_date not in target_dates:
target_dates.insert(0, local_date)
deduped: List[str] = []
for date_key in target_dates:
if date_key not in deduped:
deduped.append(date_key)
if len(deduped) >= 7:
break
return deduped
return [local_date]
def _build_terminal_row(
*,
city: str,
data: Dict[str, Any],
scan: Dict[str, Any],
row: Dict[str, Any],
) -> Dict[str, Any]:
current = data.get("current") or {}
multi_model_daily = data.get("multi_model_daily") or {}
selected_date = str(row.get("selected_date") or scan.get("selected_date") or data.get("local_date") or "").strip()
daily_entry = multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else {}
if not isinstance(daily_entry, dict):
daily_entry = {}
display_name = str(data.get("display_name") or city).strip() or city
market_slug = str(row.get("market_slug") or "").strip()
side = str(row.get("side") or "").strip().lower()
edge_percent = _safe_float(row.get("edge_percent"))
final_score = _safe_float(row.get("final_score"))
volume = _safe_float(row.get("volume")) or 0.0
primary_signal = scan.get("primary_signal") or {}
city_meta = CITIES.get(city) or {}
tz_offset = _safe_int(city_meta.get("tz"), 0)
market_region = _market_region_from_tz_offset(tz_offset)
metar_context = _build_metar_decision_context(data)
return {
**row,
"id": str(row.get("id") or f"{city}|{selected_date}|{market_slug}|{side}"),
"city": city,
"city_display_name": display_name,
"trading_region": market_region["key"],
"trading_region_label": market_region["label_en"],
"trading_region_label_zh": market_region["label_zh"],
"tz_offset_seconds": tz_offset,
"selected_date": selected_date or None,
"local_date": data.get("local_date"),
"local_time": data.get("local_time"),
"temp_symbol": data.get("temp_symbol"),
"current_temp": current.get("temp"),
"current_max_so_far": current.get("max_so_far"),
"metar_context": metar_context,
"metar_today_obs": metar_context.get("today_obs") or [],
"metar_recent_obs": metar_context.get("recent_obs") or [],
"settlement_today_obs": metar_context.get("settlement_today_obs") or [],
"metar_status": {
"available_for_today": metar_context.get("available_for_today"),
"stale_for_today": metar_context.get("stale_for_today"),
"last_observation_time": metar_context.get("last_observation_time"),
"last_temp": metar_context.get("last_temp"),
},
"deb_prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
"display_name": display_name,
"airport": ((data.get("risk") or {}).get("airport") if isinstance(data.get("risk"), dict) else None),
"risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
"distribution_bias": scan.get("distribution_bias"),
"distribution_preview": scan.get("distribution_preview") or row.get("distribution_preview") or [],
"distribution_full": scan.get("distribution_full") or scan.get("distribution_preview") or row.get("distribution_preview") or [],
"model_cluster_sources": daily_entry.get("models") if isinstance(daily_entry.get("models"), dict) else data.get("multi_model"),
"window_phase": row.get("window_phase") or scan.get("window_phase"),
"window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
"signal_status": scan.get("signal_status"),
"candidate_count": scan.get("candidate_count"),
"resolved_market_type": scan.get("resolved_market_type") or "maxtemp",
"market_key": f"{city}|{selected_date}|{market_slug}",
"is_primary_signal": bool(primary_signal and primary_signal.get("id") == row.get("id")),
"signal_confidence": final_score,
"edge_percent": edge_percent,
"final_score": final_score,
"volume": volume,
}
def _scan_city_terminal_rows(
city: str,
filters: Dict[str, Any],
*,
force_refresh: bool = False,
) -> Dict[str, Any]:
data = _analyze(
city,
force_refresh=force_refresh,
include_llm_commentary=False,
detail_mode="market",
)
target_dates = _resolve_time_range_dates(data, filters["time_range"])
rows: List[Dict[str, Any]] = []
primary_scores: List[float] = []
candidate_total = 0
for target_date in target_dates:
payload = _build_city_market_scan_payload(
data,
market_slug=None,
target_date=target_date,
lite=True,
scan_filters=filters,
)
scan = payload.get("market_scan") or {}
candidate_total += int(scan.get("candidate_count") or 0)
raw_rows = scan.get("scan_rows")
if not isinstance(raw_rows, list) or not raw_rows:
raw_rows = [scan.get("primary_signal")] if isinstance(scan.get("primary_signal"), dict) else []
if not raw_rows:
continue
for raw_row in raw_rows:
if not isinstance(raw_row, dict) or not raw_row:
continue
row = _build_terminal_row(
city=city,
data=data,
scan=scan,
row=raw_row,
)
rows.append(row)
score = _safe_float(row.get("final_score"))
if score is not None and row.get("is_primary_signal"):
primary_scores.append(score)
return {
"city": city,
"rows": rows,
"candidate_total": candidate_total,
"primary_scores": primary_scores,
}