feat(pipeline): improve new pipeline architecture
- Add TransformDataSource for filtering/mapping source items - Add MetricsDataSource for rendering live pipeline metrics as ASCII art - Fix display stage registration in StageRegistry - Register sources with both class name and simple name aliases - Fix DisplayStage.init() to pass reuse parameter - Simplify create_default_pipeline to use DataSourceStage wrapper - Set pygame as default display - Remove old pipeline tasks from mise.toml - Add tests for new pipeline architecture
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@@ -9,6 +9,7 @@ Each data source implements a common interface:
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"""
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from abc import ABC, abstractmethod
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import Any
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@@ -125,6 +126,115 @@ class PipelineDataSource(DataSource):
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return self.fetch()
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class MetricsDataSource(DataSource):
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"""Data source that renders live pipeline metrics as ASCII art.
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Wraps a Pipeline and displays active stages with their average execution
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time and approximate FPS impact. Updates lazily when camera is about to
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focus on a new node (frame % 15 == 12).
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"""
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def __init__(
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self,
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pipeline: Any,
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viewport_width: int = 80,
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viewport_height: int = 24,
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):
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self.pipeline = pipeline
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self.viewport_width = viewport_width
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self.viewport_height = viewport_height
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self.frame = 0
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self._cached_metrics: dict | None = None
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@property
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def name(self) -> str:
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return "metrics"
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@property
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def is_dynamic(self) -> bool:
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return True
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def fetch(self) -> list[SourceItem]:
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if self.frame % 15 == 12:
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self._cached_metrics = None
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if self._cached_metrics is None:
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self._cached_metrics = self._fetch_metrics()
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buffer = self._render_metrics(self._cached_metrics)
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self.frame += 1
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content = "\n".join(buffer)
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return [
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SourceItem(content=content, source="metrics", timestamp=f"f{self.frame}")
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]
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def _fetch_metrics(self) -> dict:
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if hasattr(self.pipeline, "get_metrics_summary"):
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metrics = self.pipeline.get_metrics_summary()
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if "error" not in metrics:
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return metrics
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return {"stages": {}, "pipeline": {"avg_ms": 0}}
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def _render_metrics(self, metrics: dict) -> list[str]:
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stages = metrics.get("stages", {})
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if not stages:
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return self._render_empty()
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active_stages = {
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name: stats for name, stats in stages.items() if stats.get("avg_ms", 0) > 0
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}
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if not active_stages:
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return self._render_empty()
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total_avg = sum(s["avg_ms"] for s in active_stages.values())
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if total_avg == 0:
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total_avg = 1
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lines: list[str] = []
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lines.append("═" * self.viewport_width)
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lines.append(" PIPELINE METRICS ".center(self.viewport_width, "─"))
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lines.append("─" * self.viewport_width)
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header = f"{'STAGE':<20} {'AVG_MS':>8} {'FPS %':>8}"
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lines.append(header)
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lines.append("─" * self.viewport_width)
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for name, stats in sorted(active_stages.items()):
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avg_ms = stats.get("avg_ms", 0)
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fps_impact = (avg_ms / 16.67) * 100 if avg_ms > 0 else 0
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row = f"{name:<20} {avg_ms:>7.2f} {fps_impact:>7.1f}%"
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lines.append(row[: self.viewport_width])
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lines.append("─" * self.viewport_width)
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total_row = (
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f"{'TOTAL':<20} {total_avg:>7.2f} {(total_avg / 16.67) * 100:>7.1f}%"
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)
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lines.append(total_row[: self.viewport_width])
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lines.append("─" * self.viewport_width)
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lines.append(
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f" Frame:{self.frame:04d} Cache:{'HIT' if self._cached_metrics else 'MISS'}"
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)
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while len(lines) < self.viewport_height:
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lines.append(" " * self.viewport_width)
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return lines[: self.viewport_height]
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def _render_empty(self) -> list[str]:
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lines = [" " * self.viewport_width for _ in range(self.viewport_height)]
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msg = "No metrics available"
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y = self.viewport_height // 2
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x = (self.viewport_width - len(msg)) // 2
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lines[y] = " " * x + msg + " " * (self.viewport_width - x - len(msg))
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return lines
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def get_items(self) -> list[SourceItem]:
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return self.fetch()
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class CachedDataSource(DataSource):
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"""Data source that wraps another source with caching."""
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@@ -146,6 +256,44 @@ class CachedDataSource(DataSource):
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return self._items
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class TransformDataSource(DataSource):
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"""Data source that transforms items from another source.
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Applies optional filter and map functions to each item.
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This enables chaining: source → transform → transformed output.
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Args:
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source: The source to fetch items from
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filter_fn: Optional function(item: SourceItem) -> bool
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map_fn: Optional function(item: SourceItem) -> SourceItem
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"""
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def __init__(
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self,
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source: DataSource,
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filter_fn: Callable[[SourceItem], bool] | None = None,
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map_fn: Callable[[SourceItem], SourceItem] | None = None,
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):
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self.source = source
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self.filter_fn = filter_fn
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self.map_fn = map_fn
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@property
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def name(self) -> str:
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return f"transform:{self.source.name}"
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def fetch(self) -> list[SourceItem]:
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items = self.source.fetch()
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if self.filter_fn:
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items = [item for item in items if self.filter_fn(item)]
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if self.map_fn:
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items = [self.map_fn(item) for item in items]
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return items
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class CompositeDataSource(DataSource):
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"""Data source that combines multiple sources."""
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