Add script that renders the standard Mainline visualization using the
graph-based DSL. This demonstrates the default behavior with:
- Headlines source data
- Scroll camera mode
- Terminal display
- Classic effects: noise, fade, glitch, firehose
Files added:
- examples/default_visualization.py - Main script
- examples/default_visualization.toml - TOML configuration
- examples/README.md - Documentation for all examples
Usage:
python examples/default_visualization.py
- fix(demo-lfo-effects): Fix math.sin() usage (was angle.__sin__())
- feat(pipeline): Add set_effect_intensity() method for runtime effect control
- Allows changing effect intensity during pipeline execution
- Returns False if effect not found or intensity out of range
- Used by LFO modulation demo
The demo-lfo-effects.py script now works correctly with proper
math.sin() usage and the new set_effect_intensity() method provides
a clean API for runtime effect intensity control.
Add comprehensive documentation for the graph-based pipeline DSL:
- docs/graph-dsl.md: Complete DSL reference with TOML, Python, and CLI syntax
- docs/GRAPH_SYSTEM_SUMMARY.md: Implementation overview and architecture
- examples/graph_dsl_demo.py: Demonstrates imperative Python API usage
- examples/test_graph_integration.py: Integration test verifying pipeline execution
The documentation follows a wiki-like approach with navigable structure:
- Overview section explaining the concept
- Syntax examples for each format (TOML, Python, CLI)
- Node type reference table
- Advanced features section
- Comparison with old XYZStage approach
This provides users with multiple entry points to understand and use the
new graph-based pipeline system.
Add support for loading pipelines from TOML graph configs in the
pipeline runner, maintaining full backward compatibility with presets.
- Add graph_config parameter to run_pipeline_mode() function
- Support both preset mode and graph mode with conditional logic
- Graph mode: loads from TOML file, uses graph-defined stages
- Preset mode: maintains existing behavior with manual stage building
- Handle items/context appropriately for each mode (graph uses own data sources)
- CLI display flag works in both modes
Backward compatible: graph_config defaults to None, so existing calls
to run_pipeline_mode(preset_name) continue to work unchanged.
Allow pipelines to be defined in TOML format with intuitive
node-and-connection syntax that's easy to read and edit.
- Add graph_toml.py with TOML parsing using tomllib
- Support simple format: "source": "headlines"
- Support full format: {"type": "camera", "mode": "scroll"}
- Parse connection strings in "A -> B -> C" chain format
- Add example pipeline_graph.toml demonstrating usage
Example TOML:
[nodes.source]
type = "source"
source = "headlines"
[nodes.camera]
type = "camera"
mode = "scroll"
[connections]
list = ["source -> camera -> display"]
Bridge the new graph abstraction with existing Stage-based pipeline
system for backward compatibility.
- Add GraphAdapter class to map nodes to Stage implementations
- Handle effect intensity configuration (sets global effect state)
- Map camera modes to Camera factory methods (feed, scroll, horizontal, etc.)
- Auto-inject required dependencies (render, camera_update) via pipeline capabilities
- Support for all major node types: source, camera, effect, position, display
The adapter ensures that graphs seamlessly integrate with the existing
pipeline architecture while providing a cleaner abstraction layer.
Introduce Node, Connection, and Graph classes for defining pipelines
as graphs instead of verbose XYZStage naming convention.
- Add NodeType enum (SOURCE, CAMERA, EFFECT, DISPLAY, etc.)
- Add Node, Connection, and Graph dataclasses with type hints
- Add validation for cycles and disconnected nodes using DFS
- Add factory methods: node(), connect(), chain() for easy graph building
- Support for both imperative and declarative graph construction
This provides the foundation for the graph-based DSL that replaces
the verbose XYZStage naming convention with intuitive node-and-connection syntax.
Add typed dataclasses for tuple returns:
- types.py: HeadlineItem, FetchResult, Block dataclasses with legacy tuple converters
- fetch.py: Add type hints and HeadlineTuple type alias
Add pyright for static type checking:
- Add pyright to dependencies
- Verify type coverage with pyright (0 errors in core modules)
This enables:
- Named types instead of raw tuples (better IDE support, self-documenting)
- Type-safe APIs across modules
- Backward compatibility via to_tuple/from_tuple methods
Note: Lazy imports skipped for render.py - startup impact is minimal.
- Fix pre-existing lint errors in engine/ modules using ruff --unsafe-fixes
- Add hk.pkl with pre-commit and pre-push hooks using ruff builtin
- Configure hooks to use 'uv run' prefix for tool execution
- Update mise.toml to include hk and pkl tools
- All 73 tests pass
fix: apply ruff auto-fixes and add hk git hooks
- Fix pre-existing lint errors in engine/ modules using ruff --unsafe-fixes
- Add hk.pkl with pre-commit and pre-push hooks using ruff builtin
- Configure hooks to use 'uv run' prefix for tool execution
- Update mise.toml to include hk and pkl tools
- Use 'hk install --mise' for proper mise integration
- All 73 tests pass
- Fix import sorting (isort) across all engine modules
- Fix SIM105 try-except-pass patterns (contextlib.suppress)
- Fix nested with statements in tests
- Fix unused loop variables
Run 'uv run pytest' to verify tests still pass.
