style: apply ruff formatting

Change-Id: I04fb9ad0a7145958d4071630e03a1ccf1b17332b

Author: kkkapu

scripts/parse_log_and_summarize.py

@@ -160,16 +160,14 @@ def analyze_step_time_breakdown(log_file_path):
 
                 step_match = train_step_pattern.search(line)
                 if step_match:
-                    events.append({
-                        "type": "step",
-                        "step": int(step_match.group(1)),
-                        "train_time": float(step_match.group(2))
-                    })
+                    events.append(
+                        {"type": "step", "step": int(step_match.group(1)), "train_time": float(step_match.group(2))}
+                    )
     except Exception as e:
         print(f"Error analyzing breakdown: {e}")
         return []
 
-   # 2. Calculate Dynamic Threshold
+    # 2. Calculate Dynamic Threshold
     # This threshold represents the minimum expected time interval between
     # the timestamps of two consecutive steps.
     THRESHOLD_BUFFER_SECONDS = 2.0
@@ -210,11 +208,7 @@ def analyze_step_time_breakdown(log_file_path):
                             finish_time = events[j]["val"]
                             break
 
-            processed_steps.append({
-                "step": step_num,
-                "finish_time": finish_time,
-                "train_time": train_time
-            })
+            processed_steps.append({"step": step_num, "finish_time": finish_time, "train_time": train_time})
 
     # 4. Calculate final gaps
     for i in range(1, len(processed_steps)):
@@ -224,13 +218,15 @@ def analyze_step_time_breakdown(log_file_path):
         if curr["step"] > prev["step"]:
             time_delta = max((curr["finish_time"] - prev["finish_time"]).total_seconds(), 0.0)
             other_time = time_delta - curr["train_time"]
-            results.append({
-                "step": curr["step"],
-                "timestamp": curr["finish_time"],
-                "total_gap": time_delta,
-                "train_time": curr["train_time"],
-                "other_time": other_time,
-            })
+            results.append(
+                {
+                    "step": curr["step"],
+                    "timestamp": curr["finish_time"],
+                    "total_gap": time_delta,
+                    "train_time": curr["train_time"],
+                    "other_time": other_time,
+                }
+            )
 
     return results