This PR tracks the v2.3.4 polish release.

.gitattributes

@@ -0,0 +1,2 @@
+*.json filter=lfs diff=lfs merge=lfs -text
+samples/runs/ filter=lfs diff=lfs merge=lfs -text

src/v2.3.3/ForenSynth_ai_v2_3_3_visual.py

@@ -209,10 +209,18 @@ def parse_args() -> AppConfig:
 
     p.add_argument("--export-evidence-csv", action="store_true")
     p.add_argument("--chart-style", choices=["heatmap", "pies", "both", "off"], default="both")
-    p.add_argument("--limit-detections", type=int, default=0,
-                   help="If > 0, limit the number of detections processed after loading (applied post-                  Chainsaw).")
-    p.add_argument("--sample-step", type=int, default=0,
-                   help="If > 1, keep every Nth detection before applying the limit (simple stratified     sample).")
+    p.add_argument(
+        "--limit-detections",
+        type=int,
+        default=0,
+        help="If > 0, limit the number of detections processed after loading (applied post-                  Chainsaw).",
+    )
+    p.add_argument(
+        "--sample-step",
+        type=int,
+        default=0,
+        help="If > 1, keep every Nth detection before applying the limit (simple stratified     sample).",
+    )
 
     a = p.parse_args()
     prefer = [s.strip() for s in a.prefer.split(",") if s.strip()]
@@ -259,7 +267,7 @@ def parse_args() -> AppConfig:
         export_evidence_csv=a.export_evidence_csv,
         chart_style=a.chart_style,
         limit_detections=a.limit_detections,
-        sample_step=a.sample_step
+        sample_step=a.sample_step,
     )
 
 
@@ -832,10 +840,13 @@ def write_ioc(name: str, items: List[str]):
     write_ioc("md5", iocs.get("md5", []))
     write_ioc("paths", iocs.get("paths", []))
 
-# ──────────────────────────────────────────────────────────────────────────
+
+# ────────────────────────────────────────────────────────────────────────────
 # Sampling Helper
-# ──────────────────────────────────────────────────────────────────────────
-def apply_sampling(dets: List[Dict[str, Any]], limit: int = 0, step: int = 0, sort_time: bool = False) -> List[Dict[str, Any]]:
+# ────────────────────────────────────────────────────────────────────────────
+def apply_sampling(
+    dets: List[Dict[str, Any]], limit: int = 0, step: int = 0, sort_time: bool = False
+) -> List[Dict[str, Any]]:
     """
     Optionally sub-sample and/or cap detections before the LLM phases.
     - step > 1  → keep every Nth record (0, N, 2N, ...)
@@ -857,9 +868,12 @@ def apply_sampling(dets: List[Dict[str, Any]], limit: int = 0, step: int = 0, so
         dets = dets[:limit]
 
     if len(dets) != original:
-        info(f"Sampling applied: {original} → {len(dets)} (step={step or 1}, limit={limit or 'none'})")
+        info(
+            f"Sampling applied: {original} → {len(dets)} (step={step or 1}, limit={limit or 'none'})"
+        )
     return dets
 
+
 # ────────────────────────────────────────────────────────────────────────────
 # Heatmap & Donut helpers + HTML
 # ────────────────────────────────────────────────────────────────────────────