feat: Implement Sideline plugin system with consistent terminology

This commit implements the Sideline/Mainline split with a clean plugin architecture:

## Core Changes

### Sideline Framework (New Directory)
- Created  directory containing the reusable pipeline framework
- Moved pipeline core, controllers, adapters, and registry to
- Moved display system to
- Moved effects system to
- Created plugin system with security and compatibility management in
- Created preset pack system with ASCII art encoding in
- Added default font (Corptic) to
- Added terminal ANSI constants to

### Mainline Application (Updated)
- Created  for Mainline stage component registration
- Updated  to register Mainline stages at startup
- Updated  as a compatibility shim re-exporting from sideline

### Terminology Consistency
- : Base class for all pipeline components (sources, effects, displays, cameras)
- : Base class for distributable plugin packages (was )
- : Base class for visual effects (was )
- Backward compatibility aliases maintained for existing code

## Key Features
- Plugin discovery via entry points and explicit registration
- Security permissions system for plugins
- Compatibility management with semantic version constraints
- Preset pack system for distributable configurations
- Default font bundled with Sideline (Corptic.otf)

## Testing
- Updated tests to register Mainline stages before discovery
- All StageRegistry tests passing

Note: This is a major refactoring that separates the framework (Sideline) from the application (Mainline), enabling Sideline to be used by other applications.
This commit is contained in:
2026-03-23 20:42:33 -07:00
parent 2d28e92594
commit e4b143ff36
58 changed files with 10163 additions and 50 deletions

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from sideline.effects.chain import EffectChain
from sideline.effects.performance import PerformanceMonitor, get_monitor, set_monitor
from sideline.effects.registry import EffectRegistry, get_registry, set_registry
from sideline.effects.types import (
EffectConfig,
EffectContext,
Effect,
EffectPlugin, # Backward compatibility alias
create_effect_context,
)
# Note: Legacy effects and controller are Mainline-specific and moved to engine/effects/
__all__ = [
"EffectChain",
"EffectRegistry",
"EffectConfig",
"EffectContext",
"Effect", # Primary class name
"EffectPlugin", # Backward compatibility alias
"create_effect_context",
"get_registry",
"set_registry",
"get_monitor",
"set_monitor",
"PerformanceMonitor",
]

87
sideline/effects/chain.py Normal file
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import time
from sideline.effects.performance import PerformanceMonitor, get_monitor
from sideline.effects.registry import EffectRegistry
from sideline.effects.types import EffectContext, PartialUpdate
class EffectChain:
def __init__(
self, registry: EffectRegistry, monitor: PerformanceMonitor | None = None
):
self._registry = registry
self._order: list[str] = []
self._monitor = monitor
def _get_monitor(self) -> PerformanceMonitor:
if self._monitor is not None:
return self._monitor
return get_monitor()
def set_order(self, names: list[str]) -> None:
self._order = list(names)
def get_order(self) -> list[str]:
return self._order.copy()
def add_effect(self, name: str, position: int | None = None) -> bool:
if name not in self._registry.list_all():
return False
if position is None:
self._order.append(name)
else:
self._order.insert(position, name)
return True
def remove_effect(self, name: str) -> bool:
if name in self._order:
self._order.remove(name)
return True
return False
def reorder(self, new_order: list[str]) -> bool:
all_plugins = set(self._registry.list_all().keys())
if not all(name in all_plugins for name in new_order):
return False
self._order = list(new_order)
return True
def process(self, buf: list[str], ctx: EffectContext) -> list[str]:
monitor = self._get_monitor()
frame_number = ctx.frame_number
monitor.start_frame(frame_number)
# Get dirty regions from canvas via context (set by CanvasStage)
dirty_rows = ctx.get_state("canvas.dirty_rows")
# Create PartialUpdate for effects that support it
full_buffer = dirty_rows is None or len(dirty_rows) == 0
partial = PartialUpdate(
rows=None,
cols=None,
dirty=dirty_rows,
full_buffer=full_buffer,
)
frame_start = time.perf_counter()
result = list(buf)
for name in self._order:
plugin = self._registry.get(name)
if plugin and plugin.config.enabled:
chars_in = sum(len(line) for line in result)
effect_start = time.perf_counter()
try:
# Use process_partial if supported, otherwise fall back to process
if getattr(plugin, "supports_partial_updates", False):
result = plugin.process_partial(result, ctx, partial)
else:
result = plugin.process(result, ctx)
except Exception:
plugin.config.enabled = False
elapsed = time.perf_counter() - effect_start
chars_out = sum(len(line) for line in result)
monitor.record_effect(name, elapsed * 1000, chars_in, chars_out)
total_elapsed = time.perf_counter() - frame_start
monitor.end_frame(frame_number, total_elapsed * 1000)
return result

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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

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from sideline.effects.types import EffectConfig, EffectPlugin
class EffectRegistry:
def __init__(self):
self._plugins: dict[str, EffectPlugin] = {}
self._discovered: bool = False
def register(self, plugin: EffectPlugin) -> None:
self._plugins[plugin.name] = plugin
def get(self, name: str) -> EffectPlugin | None:
return self._plugins.get(name)
def list_all(self) -> dict[str, EffectPlugin]:
return self._plugins.copy()
def list_enabled(self) -> list[EffectPlugin]:
return [p for p in self._plugins.values() if p.config.enabled]
def enable(self, name: str) -> bool:
plugin = self._plugins.get(name)
if plugin:
plugin.config.enabled = True
return True
return False
def disable(self, name: str) -> bool:
plugin = self._plugins.get(name)
if plugin:
plugin.config.enabled = False
return True
return False
def configure(self, name: str, config: EffectConfig) -> bool:
plugin = self._plugins.get(name)
if plugin:
plugin.configure(config)
return True
return False
def is_enabled(self, name: str) -> bool:
plugin = self._plugins.get(name)
return plugin.config.enabled if plugin else False
_registry: EffectRegistry | None = None
def get_registry() -> EffectRegistry:
global _registry
if _registry is None:
_registry = EffectRegistry()
return _registry
def set_registry(registry: EffectRegistry) -> None:
global _registry
_registry = registry

288
sideline/effects/types.py Normal file
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"""
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
ticker_height: int
camera_x: int = 0
mic_excess: float = 0.0
grad_offset: float = 0.0
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 Effect(ABC):
"""Abstract base class for visual effects.
Effects are pipeline stages that transform the rendered buffer.
They can apply visual transformations like noise, fade, glitch, etc.
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]:
"""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:
"""Configure the effect with new settings.
Args:
config: New configuration to apply
"""
...
# Backward compatibility alias
EffectPlugin = Effect
def create_effect_context(
terminal_width: int = 80,
terminal_height: int = 24,
scroll_cam: int = 0,
ticker_height: int = 0,
mic_excess: float = 0.0,
grad_offset: float = 0.0,
frame_number: int = 0,
has_message: bool = False,
items: list | None = None,
) -> EffectContext:
"""Factory function to create EffectContext with sensible defaults."""
return EffectContext(
terminal_width=terminal_width,
terminal_height=terminal_height,
scroll_cam=scroll_cam,
ticker_height=ticker_height,
mic_excess=mic_excess,
grad_offset=grad_offset,
frame_number=frame_number,
has_message=has_message,
items=items or [],
)
@dataclass
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