Add transparent library health engine
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- [ ] Orphaned audio detection
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- [ ] Orphaned audio detection
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- [ ] OneDrive rename detection
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- [ ] OneDrive rename detection
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- [ ] Crate classification: static vs smart/dynamic
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- [ ] Crate classification: static vs smart/dynamic
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- [ ] Library health score
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- [x] Library health score
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## v0.3 — Safe Repair
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## v0.3 — Safe Repair
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# Library Health Engine
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## Problem
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Raw missing-reference counts do not provide a compact view of library integrity,
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but an opaque blended score would imply confidence the current data cannot support.
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## Architecture
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The health engine produces an immutable report from the core `Library`. Its score
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is only the percentage of crate references resolved by exact filename. The report
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also exposes missing references, unique missing filenames, duplicate filename
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groups, extra duplicate files, unused tracks, and missing references with matching
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candidates.
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Duplicate, unused, and candidate counts are informational. They do not affect the
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score until the project has a documented and validated weighting policy. An empty
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library has no score rather than a misleading 0% or 100%.
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## Edge Cases
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- The same missing filename referenced by several crates.
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- Several disk files sharing a filename.
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- Conflict-suffixed files that are unused but may be match candidates.
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- Libraries with no crate references.
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- Tracks referenced by filename from more than one crate.
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## Verification
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Tests assert every metric, the disclosed score basis, candidate integration, and
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empty-library behavior. Analysis remains entirely read-only.
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from collections import Counter
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from serato_doctor.matching import MatchingEngine
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from serato_doctor.models.health import HealthReport
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from serato_doctor.models.library import Library
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def analyze_health(library: Library) -> HealthReport:
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"""Calculate defensible health metrics without changing the library."""
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results = library.reconcile_by_filename()
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missing = [result for result in results if not result.exists_by_filename]
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healthy_count = len(results) - len(missing)
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score = (
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round(healthy_count / len(results) * 100, 1) if results else None
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)
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disk_name_counts = Counter(track.filename for track in library.tracks)
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duplicate_counts = [count for count in disk_name_counts.values() if count > 1]
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referenced_names = {reference.filename for reference in library.references}
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unused_count = sum(
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1 for track in library.tracks if track.filename not in referenced_names
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)
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matcher = MatchingEngine(library.tracks)
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suggested_count = sum(
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bool(matcher.candidates_for(result.reference)) for result in missing
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)
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return HealthReport(
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score=score,
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total_references=len(results),
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healthy_references=healthy_count,
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missing_references=len(missing),
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unique_missing_filenames=len(
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{result.reference.filename for result in missing}
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),
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disk_tracks=len(library.tracks),
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duplicate_filename_groups=len(duplicate_counts),
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duplicate_files=sum(count - 1 for count in duplicate_counts),
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unused_tracks=unused_count,
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suggested_matches=suggested_count,
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)
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from serato_doctor.models.crate import Crate
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from serato_doctor.models.crate import Crate
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from serato_doctor.models.health import HealthReport
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from serato_doctor.models.library import Library
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from serato_doctor.models.library import Library
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from serato_doctor.models.match import MatchEvidence, TrackMatch
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from serato_doctor.models.match import MatchEvidence, TrackMatch
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from serato_doctor.models.reference import ReferenceResult, TrackReference
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from serato_doctor.models.reference import ReferenceResult, TrackReference
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@@ -7,6 +8,7 @@ from serato_doctor.models.track import DiskTrack
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__all__ = [
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__all__ = [
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"Crate",
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"Crate",
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"DiskTrack",
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"DiskTrack",
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"HealthReport",
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"Library",
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"Library",
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"MatchEvidence",
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"MatchEvidence",
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"ReferenceResult",
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"ReferenceResult",
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from dataclasses import dataclass
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from typing import Optional
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@dataclass(frozen=True)
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class HealthReport:
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"""Transparent aggregate findings from a read-only library analysis."""
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score: Optional[float]
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total_references: int
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healthy_references: int
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missing_references: int
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unique_missing_filenames: int
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disk_tracks: int
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duplicate_filename_groups: int
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duplicate_files: int
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unused_tracks: int
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suggested_matches: int
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@property
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def score_basis(self) -> str:
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return "Resolved crate references / total crate references"
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from pathlib import Path
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from serato_doctor.health import analyze_health
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from serato_doctor.models.library import Library
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from serato_doctor.models.reference import TrackReference
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from serato_doctor.models.track import DiskTrack
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def reference(filename):
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return TrackReference(
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Path("Test.crate"), Path("/old/House") / filename, filename
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)
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def track(filename, folder="House"):
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path = Path("/new") / folder / filename
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return DiskTrack(path, filename, 100, path.suffix.lower())
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def test_health_report_exposes_each_metric():
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library = Library.build(
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[
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reference("Found.mp3"),
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reference("Conflict.mp3"),
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reference("Absent.mp3"),
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],
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[
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track("Found.mp3"),
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track("Conflict 2.mp3"),
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track("Conflict 2.mp3", "Backup"),
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track("Unused.mp3"),
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],
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)
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report = analyze_health(library)
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assert report.score == 33.3
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assert report.score_basis == (
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"Resolved crate references / total crate references"
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)
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assert report.total_references == 3
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assert report.healthy_references == 1
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assert report.missing_references == 2
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assert report.unique_missing_filenames == 2
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assert report.disk_tracks == 4
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assert report.duplicate_filename_groups == 1
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assert report.duplicate_files == 1
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assert report.unused_tracks == 3
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assert report.suggested_matches == 1
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def test_empty_library_has_no_health_score():
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report = analyze_health(Library.build([], []))
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assert report.score is None
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assert report.total_references == 0
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