Update build_dynamics_pairs.py

build_dynamics_pairs.py

@@ -2,11 +2,6 @@
 """
 build_dynamics_pairs.py
 =======================
-Last-mile path: historical cache → (current, next) ZoneStateTensor pairs
-for physics_dynamics.DynamicsTrainer.
-
-Previously the Dyna module was fully unit-tested but had no script that
-turned real trajectories into training pairs. This closes that gap.
 
 Contract
 --------
@@ -65,12 +60,6 @@ def _forecast_precip_array(fc: Dict[str, Any], horizon_days: int) -> np.ndarray:
 
 
 def _belief_uncertainty_from_obs(obs: Dict[str, Any]) -> Tuple[float, float]:
-    """
-    Proxy scalars so pairs need no trained agent.
-
-    belief ~ max of clipped anomaly magnitudes mapped to [0,1]
-    uncertainty ~ higher when quality_flag low / cloud high
-    """
     anom = abs(float(obs.get("precip_anomaly_idx") or 0.0))
     soil = abs(float(obs.get("soil_moisture_anom") or 0.0))
     belief = float(np.clip(max(anom, soil) / 3.0, 0.0, 1.0))
@@ -87,13 +76,6 @@ def extract_pairs_from_pkl(
     start: Optional[date] = None,
     end: Optional[date] = None,
 ) -> Tuple[List[Tuple[Any, Any]], List[float], Dict[str, Any]]:
-    """
-    Build consecutive pairs within each trajectory (same zone, ordered time).
-
-    Returns (pairs, dts, manifest_stats). dts[i] is the real-time gap in days
-    for pairs[i] (exact step_days only). Requires torch only when materializing
-    ZoneStateTensor — import deferred so --help works offline.
-    """
     import torch
     from physics_dynamics import ZoneStateTensor