Remove remote TimesFM integration
This commit is contained in:
2569718930@qq.com
@@ -559,38 +559,6 @@ class NwsOpenMeteoSourceMixin:
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record_source_call("open_meteo", "multi_model", "empty", (time.perf_counter() - started) * 1000.0)
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return None
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timesfm_meta = {}
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try:
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from src.models.timesfm_adapter import (
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TIMESFM_MODEL_NAME,
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predict_timesfm_daily,
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)
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timesfm_result = predict_timesfm_daily(
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city_name=city,
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forecast_dates=dates,
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daily_model_forecasts=daily_forecasts,
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)
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timesfm_predictions = (
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timesfm_result.get("predictions", {})
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if isinstance(timesfm_result, dict)
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else {}
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)
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if isinstance(timesfm_predictions, dict):
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for date_str, raw_value in timesfm_predictions.items():
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parsed = round(float(raw_value), 1) if raw_value is not None else None
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if parsed is None:
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continue
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daily_forecasts.setdefault(date_str, {})[TIMESFM_MODEL_NAME] = parsed
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if isinstance(timesfm_result, dict):
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timesfm_meta = {
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key: value
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for key, value in timesfm_result.items()
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if key != "predictions"
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}
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except Exception as exc:
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logger.warning(f"TimesFM adapter failed for {city}: {exc}")
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# 今天的预报 (向后兼容)
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today_date = dates[0] if dates else None
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forecasts = daily_forecasts.get(today_date, {})
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@@ -606,7 +574,6 @@ class NwsOpenMeteoSourceMixin:
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"daily_forecasts": daily_forecasts, # 按天 {"2026-02-23": {...}, "2026-02-24": {...}}
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"dates": dates,
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"unit": "fahrenheit" if use_fahrenheit else "celsius",
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"timesfm_meta": timesfm_meta,
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}
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with self._multi_model_cache_lock:
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self._multi_model_cache[cache_key] = {
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@@ -111,8 +111,8 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
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os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC", "900")
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)
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self.multi_model_cache_version = str(
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os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_VERSION", "v4")
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).strip() or "v4"
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os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_VERSION", "v2")
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).strip() or "v2"
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self._open_meteo_cache: Dict[str, Dict] = {}
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self._ensemble_cache: Dict[str, Dict] = {}
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self._multi_model_cache: Dict[str, Dict] = {}
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@@ -1,210 +0,0 @@
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from __future__ import annotations
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import os
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import time
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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import requests
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from loguru import logger
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from src.utils.metrics import record_source_call
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TIMESFM_MODEL_NAME = "TimesFM"
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TIMESFM_DEFAULT_MODEL_ID = "google/timesfm-2.5-200m-pytorch"
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def _sf(value: object) -> Optional[float]:
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if value is None:
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return None
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try:
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return float(value)
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except Exception:
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return None
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def _parse_date(raw: object) -> Optional[datetime]:
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text = str(raw or "").strip()
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if not text:
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return None
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try:
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return datetime.strptime(text, "%Y-%m-%d")
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except Exception:
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return None
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def _get_default_history_file() -> str:
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project_root = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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return os.path.join(project_root, "data", "daily_records.json")
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def _is_enabled() -> bool:
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return str(os.getenv("POLYWEATHER_TIMESFM_ENABLED", "false")).strip().lower() in {
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"1",
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"true",
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"yes",
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"on",
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}
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def _get_service_url() -> str:
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return str(os.getenv("POLYWEATHER_TIMESFM_SERVICE_URL", "")).strip().rstrip("/")
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def _load_actual_history(
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city_name: str,
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history_file: Optional[str] = None,
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max_points: Optional[int] = None,
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) -> List[Dict[str, object]]:
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from src.analysis.deb_algorithm import load_history
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from src.data_collection.city_registry import ALIASES
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city_key = ALIASES.get(
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str(city_name or "").strip().lower(),
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str(city_name or "").strip().lower(),
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)
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if not city_key:
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return []
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data = load_history(history_file or _get_default_history_file())
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city_data = data.get(city_key) if isinstance(data, dict) else None
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if not isinstance(city_data, dict):
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return []
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rows: List[Dict[str, object]] = []
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for date_key, record in city_data.items():
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if not isinstance(record, dict):
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continue
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stamp = _parse_date(date_key)
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actual = _sf(record.get("actual_high"))
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if stamp is None or actual is None:
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continue
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rows.append(
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{
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"timestamp": stamp.strftime("%Y-%m-%d"),
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"value": round(actual, 1),
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}
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)
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rows.sort(key=lambda item: str(item.get("timestamp") or ""))
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if max_points and max_points > 0:
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rows = rows[-max_points:]
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return rows
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def predict_timesfm_daily(
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*,
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city_name: str,
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forecast_dates: List[str],
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daily_model_forecasts: Optional[Dict[str, Dict[str, object]]] = None,
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history_file: Optional[str] = None,
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) -> Dict[str, Any]:
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started = time.perf_counter()
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service_url = _get_service_url()
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timeout_sec = float(os.getenv("POLYWEATHER_TIMESFM_TIMEOUT_SEC", "12"))
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max_history_points = int(os.getenv("POLYWEATHER_TIMESFM_HISTORY_LIMIT", "60"))
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min_history_points = int(os.getenv("POLYWEATHER_TIMESFM_MIN_HISTORY_POINTS", "14"))
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if not _is_enabled():
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return {
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"predictions": {},
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"enabled": False,
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"reason": "disabled",
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}
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if not service_url:
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return {
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"predictions": {},
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"enabled": False,
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"reason": "service_not_configured",
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}
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normalized_dates = [
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str(date_str or "").strip()
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for date_str in (forecast_dates or [])
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if _parse_date(date_str) is not None
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]
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normalized_dates = list(dict.fromkeys(normalized_dates))
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if not normalized_dates:
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return {
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"predictions": {},
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"enabled": True,
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"reason": "no_valid_future_dates",
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}
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history_rows = _load_actual_history(
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city_name=city_name,
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history_file=history_file,
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max_points=max_history_points,
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)
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if len(history_rows) < min_history_points:
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return {
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"predictions": {},
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"enabled": True,
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"reason": "insufficient_history",
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"history_count": len(history_rows),
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}
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payload = {
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"city": city_name,
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"series_frequency": "D",
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"series_kind": "actual_high",
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"series": history_rows,
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"future_dates": normalized_dates,
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"daily_model_forecasts": daily_model_forecasts or {},
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}
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try:
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response = requests.post(
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f"{service_url}/predict/daily",
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json=payload,
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timeout=timeout_sec,
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)
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response.raise_for_status()
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data = response.json()
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raw_predictions = data.get("predictions", {}) if isinstance(data, dict) else {}
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predictions: Dict[str, float] = {}
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for date_str in normalized_dates:
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parsed = _sf((raw_predictions or {}).get(date_str))
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if parsed is None:
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continue
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predictions[date_str] = round(parsed, 1)
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record_source_call(
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"timesfm",
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"predict",
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"success" if predictions else "empty",
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(time.perf_counter() - started) * 1000.0,
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)
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return {
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"predictions": predictions,
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"enabled": True,
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"reason": "ok" if predictions else "empty",
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"history_count": len(history_rows),
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"service_url": service_url,
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"model": data.get("model") if isinstance(data, dict) else None,
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"model_id": data.get("model_id") if isinstance(data, dict) else None,
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"series_frequency": "D",
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"series_kind": "actual_high",
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"quantiles": data.get("quantiles") if isinstance(data, dict) else None,
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}
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except Exception as exc:
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logger.warning(f"TimesFM remote request failed for {city_name}: {exc}")
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record_source_call(
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"timesfm",
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"predict",
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"error",
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(time.perf_counter() - started) * 1000.0,
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)
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return {
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"predictions": {},
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"enabled": True,
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"reason": "request_failed",
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"history_count": len(history_rows),
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"service_url": service_url,
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"error": str(exc),
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}
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