foo

metrics_stats.py

@@ -196,6 +196,84 @@ def derived_tier1(temporal: dict) -> dict:
     }
 
 
+MATCHED_PAIR_CONFIGS = (
+    "aggressive_sigma20",
+    "aggressive_sigma30",
+    "nonaggressive_sigma20",
+    "nonaggressive_sigma30",
+)
+
+
+def derived_matched_pair_workflow(temporal: dict) -> dict:
+    """Per-config BtI vs ItB NSE_PC/RMSE pairs and site-level consistency flags."""
+    per_config = []
+    nse_deltas: list[float] = []
+    nse_bti_wins_count = 0
+    residual_bti_wins_count = 0
+
+    for config in MATCHED_PAIR_CONFIGS:
+        kb = config
+        ki = f"{config}_itb"
+        tb = temporal.get(kb) or {}
+        ti = temporal.get(ki) or {}
+        nse_bti = tb.get("nse_pc")
+        nse_itb = ti.get("nse_pc")
+        rmse_bti = tb.get("rmse")
+        rmse_itb = ti.get("rmse")
+        mb = (tb.get("residual_vs_phenocam") or {}).get("mean")
+        mi = (ti.get("residual_vs_phenocam") or {}).get("mean")
+
+        delta_nse = None
+        delta_rmse = None
+        bti_wins = None
+        residual_bti_wins = None
+
+        if isinstance(nse_bti, (int, float)) and isinstance(nse_itb, (int, float)):
+            delta_nse = float(nse_bti) - float(nse_itb)
+            bti_wins = delta_nse > 0
+            nse_deltas.append(delta_nse)
+            if bti_wins:
+                nse_bti_wins_count += 1
+
+        if isinstance(rmse_bti, (int, float)) and isinstance(rmse_itb, (int, float)):
+            delta_rmse = float(rmse_bti) - float(rmse_itb)
+
+        if isinstance(mb, (int, float)) and isinstance(mi, (int, float)):
+            if float(mb) > float(mi):
+                residual_bti_wins_count += 1
+                residual_bti_wins = True
+            elif float(mb) < float(mi):
+                residual_bti_wins = False
+            else:
+                residual_bti_wins = None
+
+        per_config.append(
+            {
+                "config": config,
+                "nse_pc_bti": float(nse_bti) if isinstance(nse_bti, (int, float)) else None,
+                "nse_pc_itb": float(nse_itb) if isinstance(nse_itb, (int, float)) else None,
+                "rmse_bti": float(rmse_bti) if isinstance(rmse_bti, (int, float)) else None,
+                "rmse_itb": float(rmse_itb) if isinstance(rmse_itb, (int, float)) else None,
+                "delta_nse_bti_minus_itb": delta_nse,
+                "delta_rmse_bti_minus_itb": delta_rmse,
+                "bti_wins": bti_wins,
+                "residual_bti_wins": residual_bti_wins,
+            }
+        )
+
+    mean_delta_nse = (
+        float(sum(nse_deltas) / len(nse_deltas)) if nse_deltas else None
+    )
+    return {
+        "per_config": per_config,
+        "consistency": nse_bti_wins_count,
+        "nse_bti_wins_count": nse_bti_wins_count,
+        "residual_bti_wins_count": residual_bti_wins_count,
+        "residual_nse_mismatch": residual_bti_wins_count != nse_bti_wins_count,
+        "mean_delta_nse": mean_delta_nse,
+    }
+
+
 def calculate_phenocam_stats(phenocam_ts):
     """Calculate phenocam summary statistics."""
     values = [v for v in phenocam_ts.values() if v is not None]
@@ -407,7 +485,11 @@ def calculate_all_metrics(season, site_name, site_position):
                 results["temporal"][scenario_name] = temporal_metrics
 
     if results["temporal"]:
-        results["derived"] = derived_tier1(results["temporal"])
+        derived = derived_tier1(results["temporal"])
+        derived["matched_pair_workflow"] = derived_matched_pair_workflow(
+            results["temporal"]
+        )
+        results["derived"] = derived
 
     # Save results
     output_path = Path(f"data/{site_name}/{season}/metrics.json")