feat(effects): add entropy parameter to effect plugins

- Add entropy field to EffectConfig (0.0 = calm, 1.0 = chaotic)
- Provide compute_entropy() method in EffectContext for dynamic scoring
- Update Fade, Firehose, Glitch, Noise plugin defaults with entropy values
- Enables finer control: intensity (strength) vs entropy (randomness)

This separates deterministic effect strength from probabilistic chaos, allowing more expressive control in UI panel and presets.

Fixes #32

engine/effects/types.py

@@ -44,6 +44,11 @@ class PartialUpdate:
 
 @dataclass
 class EffectContext:
+    """Context passed to effect plugins during processing.
+
+    Contains terminal dimensions, camera state, frame info, and real-time sensor values.
+    """
+
     terminal_width: int
     terminal_height: int
     scroll_cam: int
@@ -56,6 +61,26 @@ class EffectContext:
     items: list = field(default_factory=list)
     _state: dict[str, Any] = field(default_factory=dict, repr=False)
 
+    def compute_entropy(self, effect_name: str, data: Any) -> float:
+        """Compute entropy score for an effect based on its output.
+
+        Args:
+            effect_name: Name of the effect
+            data: Processed buffer or effect-specific data
+
+        Returns:
+            Entropy score 0.0-1.0 representing visual chaos
+        """
+        # Default implementation: use effect name as seed for deterministic randomness
+        # Better implementations can analyze actual buffer content
+        import hashlib
+
+        data_str = str(data)[:100] if data else ""
+        hash_val = hashlib.md5(f"{effect_name}:{data_str}".encode()).hexdigest()
+        # Convert hash to float 0.0-1.0
+        entropy = int(hash_val[:8], 16) / 0xFFFFFFFF
+        return min(max(entropy, 0.0), 1.0)
+
     def get_sensor_value(self, sensor_name: str) -> float | None:
         """Get a sensor value from context state.
 
@@ -80,6 +105,7 @@ class EffectContext:
 class EffectConfig:
     enabled: bool = True
     intensity: float = 1.0
+    entropy: float = 0.0  # Visual chaos metric (0.0 = calm, 1.0 = chaotic)
     params: dict[str, Any] = field(default_factory=dict)