feat(effects): add plugin architecture with performance monitoring

engine/effects/performance.py

@@ -0,0 +1,103 @@
+from collections import deque
+from dataclasses import dataclass
+
+
+@dataclass
+class EffectTiming:
+    name: str
+    duration_ms: float
+    buffer_chars_in: int
+    buffer_chars_out: int
+
+
+@dataclass
+class FrameTiming:
+    frame_number: int
+    total_ms: float
+    effects: list[EffectTiming]
+
+
+class PerformanceMonitor:
+    """Collects and stores performance metrics for effect pipeline."""
+
+    def __init__(self, max_frames: int = 60):
+        self._max_frames = max_frames
+        self._frames: deque[FrameTiming] = deque(maxlen=max_frames)
+        self._current_frame: list[EffectTiming] = []
+
+    def start_frame(self, frame_number: int) -> None:
+        self._current_frame = []
+
+    def record_effect(
+        self, name: str, duration_ms: float, chars_in: int, chars_out: int
+    ) -> None:
+        self._current_frame.append(
+            EffectTiming(
+                name=name,
+                duration_ms=duration_ms,
+                buffer_chars_in=chars_in,
+                buffer_chars_out=chars_out,
+            )
+        )
+
+    def end_frame(self, frame_number: int, total_ms: float) -> None:
+        self._frames.append(
+            FrameTiming(
+                frame_number=frame_number,
+                total_ms=total_ms,
+                effects=self._current_frame,
+            )
+        )
+
+    def get_stats(self) -> dict:
+        if not self._frames:
+            return {"error": "No timing data available"}
+
+        total_times = [f.total_ms for f in self._frames]
+        avg_total = sum(total_times) / len(total_times)
+        min_total = min(total_times)
+        max_total = max(total_times)
+
+        effect_stats: dict[str, dict] = {}
+        for frame in self._frames:
+            for effect in frame.effects:
+                if effect.name not in effect_stats:
+                    effect_stats[effect.name] = {"times": [], "total_chars": 0}
+                effect_stats[effect.name]["times"].append(effect.duration_ms)
+                effect_stats[effect.name]["total_chars"] += effect.buffer_chars_out
+
+        for name, stats in effect_stats.items():
+            times = stats["times"]
+            stats["avg_ms"] = sum(times) / len(times)
+            stats["min_ms"] = min(times)
+            stats["max_ms"] = max(times)
+            del stats["times"]
+
+        return {
+            "frame_count": len(self._frames),
+            "pipeline": {
+                "avg_ms": avg_total,
+                "min_ms": min_total,
+                "max_ms": max_total,
+            },
+            "effects": effect_stats,
+        }
+
+    def reset(self) -> None:
+        self._frames.clear()
+        self._current_frame = []
+
+
+_monitor: PerformanceMonitor | None = None
+
+
+def get_monitor() -> PerformanceMonitor:
+    global _monitor
+    if _monitor is None:
+        _monitor = PerformanceMonitor()
+    return _monitor
+
+
+def set_monitor(monitor: PerformanceMonitor) -> None:
+    global _monitor
+    _monitor = monitor