feat(integration): Complete feature rewrite with pipeline architecture, effects system, and display improvements

Major changes:
- Pipeline architecture with capability-based dependency resolution
- Effects plugin system with performance monitoring
- Display abstraction with multiple backends (terminal, null, websocket)
- Camera system for viewport scrolling
- Sensor framework for real-time input
- Command-and-control system via ntfy
- WebSocket display backend for browser clients
- Comprehensive test suite and documentation

Issue #48: ADR for preset scripting language included

This commit consolidates 110 individual commits into a single
feature integration that can be reviewed and tested before
further refinement.

engine/effects/types.py

@@ -1,10 +1,54 @@
+"""
+Visual effects type definitions and base classes.
+
+EffectPlugin Architecture:
+- Uses ABC (Abstract Base Class) for interface enforcement
+- Runtime discovery via directory scanning (effects_plugins/)
+- Configuration via EffectConfig dataclass
+- Context passed through EffectContext dataclass
+
+Plugin System Research (see AGENTS.md for references):
+- VST: Standardized audio interfaces, chaining, presets (FXP/FXB)
+- Python Entry Points: Namespace packages, importlib.metadata discovery
+- Shadertoy: Shader-based with uniforms as context
+
+Current gaps vs industry patterns:
+- No preset save/load system
+- No external plugin distribution via entry points
+- No plugin metadata (version, author, description)
+"""
+
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import Any
 
 
+@dataclass
+class PartialUpdate:
+    """Represents a partial buffer update for optimized rendering.
+
+    Instead of processing the full buffer every frame, effects that support
+    partial updates can process only changed regions.
+
+    Attributes:
+        rows: Row indices that changed (None = all rows)
+        cols: Column range that changed (None = full width)
+        dirty: Set of dirty row indices
+    """
+
+    rows: tuple[int, int] | None = None  # (start, end) inclusive
+    cols: tuple[int, int] | None = None  # (start, end) inclusive
+    dirty: set[int] | None = None  # Set of dirty row indices
+    full_buffer: bool = True  # If True, process entire buffer
+
+
 @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
@@ -15,24 +59,140 @@ class EffectContext:
     frame_number: int = 0
     has_message: bool = False
     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.
+
+        Args:
+            sensor_name: Name of the sensor (e.g., "mic", "camera")
+
+        Returns:
+            Sensor value as float, or None if not available.
+        """
+        return self._state.get(f"sensor.{sensor_name}")
+
+    def set_state(self, key: str, value: Any) -> None:
+        """Set a state value in the context."""
+        self._state[key] = value
+
+    def get_state(self, key: str, default: Any = None) -> Any:
+        """Get a state value from the context."""
+        return self._state.get(key, default)
+
+    @property
+    def state(self) -> dict[str, Any]:
+        """Get the state dictionary for direct access by effects."""
+        return self._state
 
 
 @dataclass
 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)
 
 
 class EffectPlugin(ABC):
+    """Abstract base class for effect plugins.
+
+    Subclasses must define:
+    - name: str - unique identifier for the effect
+    - config: EffectConfig - current configuration
+
+    Optional class attribute:
+    - param_bindings: dict - Declarative sensor-to-param bindings
+        Example:
+            param_bindings = {
+                "intensity": {"sensor": "mic", "transform": "linear"},
+                "rate": {"sensor": "mic", "transform": "exponential"},
+            }
+
+    And implement:
+    - process(buf, ctx) -> list[str]
+    - configure(config) -> None
+
+    Effect Behavior with ticker_height=0:
+    - NoiseEffect: Returns buffer unchanged (no ticker to apply noise to)
+    - FadeEffect: Returns buffer unchanged (no ticker to fade)
+    - GlitchEffect: Processes normally (doesn't depend on ticker_height)
+    - FirehoseEffect: Returns buffer unchanged if no items in context
+
+    Effects should handle missing or zero context values gracefully by
+    returning the input buffer unchanged rather than raising errors.
+
+    The param_bindings system enables PureData-style signal routing:
+    - Sensors emit values that effects can bind to
+    - Transform functions map sensor values to param ranges
+    - Effects read bound values from context.state["sensor.{name}"]
+    """
+
     name: str
     config: EffectConfig
+    param_bindings: dict[str, dict[str, str | float]] = {}
+    supports_partial_updates: bool = False  # Override in subclasses for optimization
 
     @abstractmethod
-    def process(self, buf: list[str], ctx: EffectContext) -> list[str]: ...
+    def process(self, buf: list[str], ctx: EffectContext) -> list[str]:
+        """Process the buffer with this effect applied.
+
+        Args:
+            buf: List of lines to process
+            ctx: Effect context with terminal state
+
+        Returns:
+            Processed buffer (may be same object or new list)
+        """
+        ...
+
+    def process_partial(
+        self, buf: list[str], ctx: EffectContext, partial: PartialUpdate
+    ) -> list[str]:
+        """Process a partial buffer for optimized rendering.
+
+        Override this in subclasses that support partial updates for performance.
+        Default implementation falls back to full buffer processing.
+
+        Args:
+            buf: List of lines to process
+            ctx: Effect context with terminal state
+            partial: PartialUpdate indicating which regions changed
+
+        Returns:
+            Processed buffer (may be same object or new list)
+        """
+        # Default: fall back to full processing
+        return self.process(buf, ctx)
 
     @abstractmethod
-    def configure(self, config: EffectConfig) -> None: ...
+    def configure(self, config: EffectConfig) -> None:
+        """Configure the effect with new settings.
+
+        Args:
+            config: New configuration to apply
+        """
+        ...
 
 
 def create_effect_context(
@@ -40,7 +200,6 @@ def create_effect_context(
     terminal_height: int = 24,
     scroll_cam: int = 0,
     ticker_height: int = 0,
-    camera_x: int = 0,
     mic_excess: float = 0.0,
     grad_offset: float = 0.0,
     frame_number: int = 0,
@@ -53,7 +212,6 @@ def create_effect_context(
         terminal_height=terminal_height,
         scroll_cam=scroll_cam,
         ticker_height=ticker_height,
-        camera_x=camera_x,
         mic_excess=mic_excess,
         grad_offset=grad_offset,
         frame_number=frame_number,
@@ -66,3 +224,58 @@ def create_effect_context(
 class PipelineConfig:
     order: list[str] = field(default_factory=list)
     effects: dict[str, EffectConfig] = field(default_factory=dict)
+
+
+def apply_param_bindings(
+    effect: "EffectPlugin",
+    ctx: EffectContext,
+) -> EffectConfig:
+    """Apply sensor bindings to effect config.
+
+    This resolves param_bindings declarations by reading sensor values
+    from the context and applying transform functions.
+
+    Args:
+        effect: The effect with param_bindings to apply
+        ctx: EffectContext containing sensor values
+
+    Returns:
+        Modified EffectConfig with sensor-driven values applied.
+    """
+    import copy
+
+    if not effect.param_bindings:
+        return effect.config
+
+    config = copy.deepcopy(effect.config)
+
+    for param_name, binding in effect.param_bindings.items():
+        sensor_name: str = binding.get("sensor", "")
+        transform: str = binding.get("transform", "linear")
+
+        if not sensor_name:
+            continue
+
+        sensor_value = ctx.get_sensor_value(sensor_name)
+        if sensor_value is None:
+            continue
+
+        if transform == "linear":
+            applied_value: float = sensor_value
+        elif transform == "exponential":
+            applied_value = sensor_value**2
+        elif transform == "threshold":
+            threshold = float(binding.get("threshold", 0.5))
+            applied_value = 1.0 if sensor_value > threshold else 0.0
+        elif transform == "inverse":
+            applied_value = 1.0 - sensor_value
+        else:
+            applied_value = sensor_value
+
+        config.params[f"{param_name}_sensor"] = applied_value
+
+        if param_name == "intensity":
+            base_intensity = effect.config.intensity
+            config.intensity = base_intensity * (0.5 + applied_value * 0.5)
+
+    return config