Update timesfm_wrapper.py

timesfm_wrapper.py

@@ -78,13 +78,6 @@ def _rh_to_prob_humidity(rh_max_pct: float) -> float:
 # ---------------------------------------------------------------------------
 
 class BaselineBackend:
-    """
-    Deterministic statistical forecast derived entirely from ZoneObs fields.
-
-    No randomness — deterministic forecasts let the policy learn stable
-    input-output mappings. Stochasticity enters through the observation
-    pipeline, not the forecast.
-    """
 
     def __init__(self, horizon_days: int = 30) -> None:
         self.horizon_days = horizon_days
@@ -136,18 +129,6 @@ class BaselineBackend:
 # ---------------------------------------------------------------------------
 
 class OpenMeteoBackend:
-    """
-    Real probabilistic forecast via Open-Meteo free API.
-
-    FIX vs original: lat/lon moved from forecast() into __init__() so the
-    object satisfies the ForecastBackend Protocol (forecast(self, obs) only).
-    Resolve lat/lon from the zone registry before constructing this object.
-
-    Example:
-        lat, lon = zone_registry[zone_id].centroid
-        backend  = OpenMeteoBackend(lat=lat, lon=lon, horizon_days=16)
-        result   = backend.forecast(obs)
-    """
 
     BASE_URL    = "https://api.open-meteo.com/v1/forecast"
     TIMEOUT_S   = 15
@@ -256,12 +237,6 @@ class OpenMeteoBackend:
 
 @dataclass
 class LocalTimesFMBackend:
-    """
-    Local TimesFM-2.5-200m inference. SHA256-verified. No synthetic fallback.
-
-    Download checkpoint from:
-      https://huggingface.co/google/timesfm-2.5-200m-pytorch
-    """
 
     checkpoint_path: Path
     expected_sha256: str
@@ -402,13 +377,6 @@ def create_forecast_backend(
     lat:             Optional[float] = None,
     lon:             Optional[float] = None,
 ) -> ForecastBackend:
-    """
-    Create a ForecastBackend.
-
-    mode="baseline"   — deterministic statistical forecast (default, no deps)
-    mode="openmeteo"  — real Open-Meteo API forecast (requires requests, lat, lon)
-    mode="timesfm"    — local TimesFM inference (requires checkpoint + timesfm)
-    """
     if mode == "baseline":
         return BaselineBackend(horizon_days=horizon_days)
 
@@ -439,4 +407,4 @@ def create_forecast_backend(
 
     raise ValueError(
         f"Unknown backend mode: '{mode}'. Valid: 'baseline', 'openmeteo', 'timesfm'"
-    )
+    )