- Remove Candidate class, use PathStep for both hypothetical and actual steps
- Simplify Path.step() to accept a PathStep
- Fix DCA Hamiltonian to return visit_count directly instead of normalized score
- Tests pass and DCA properly discriminates
- Add callback and interval parameters to find_stochastic_path() for adaptive weights
- Add get_influence() method to compute weighted score contribution per factor
- Rename graph_node/output_chord to source_node/destination_node/source_chord/destination_chord
- Rename voice_stay_count to sustain_count_before/after
- Rename node_visit_counts to last_visited_count_before/after
- Remove redundant internal state from Path - derive from steps
- Each PathStep now fully self-contained with before/after state
Changed voice stay count calculation in Path.step() to compare
by position (position i with position i) instead of tracking
voice identity. This makes dca_voice_movement factor behave
identically to master.
- Move transposition, voice mapping into Path.step()
- Add node_visit_counts and voice_stay_count to each PathStep
- Fix voice tracking to use voice identity, not position
- Clean up unused last_graph_nodes code
- Full encapsulation: Path handles all voice-leading state
- Add src/path.py with Path and PathStep classes
- Path stores initial_chord, steps, weights_config
- PathStep stores graph_node, output_chord, transposition, movements, scores, candidates
- Refactor find_stochastic_path to use candidates approach
- Separate _build_candidates (raw scores) from _compute_weights
- Simplify return type to Path only (graph_chords available via property)
- Update io.py to use new Path API
Michael Winter