diff --git a/.cursor/rules/preset-scan-scope.mdc b/.cursor/rules/preset-scan-scope.mdc index d4e6eb7..f25b4e7 100644 --- a/.cursor/rules/preset-scan-scope.mdc +++ b/.cursor/rules/preset-scan-scope.mdc @@ -8,7 +8,11 @@ alwaysApply: false When changing `PresetSwitchingScope`, `PRESET_SWITCHING_SCOPES`, or how [cleave/viz/preset_switching.py](cleave/viz/preset_switching.py) builds the projectM playlist (`add_path` recurse flag, directory vs other roots), also update **scan set derivation** for `cleave scan`. -Plan: [docs/presets-scan-plan.md](docs/presets-scan-plan.md). +Plan: [docs/legacy-plans/presets-scan-plan.md](docs/legacy-plans/presets-scan-plan.md). + +## Experimental / low trust + +`cleave scan` and `cleave scan-golden` are **experimental**. Classifier thresholds are tuned on the golden set only; treat project and bulk scan results as best-effort until validated on a pack. CLI prints a stderr warning on every `scan` / `scan-golden` invocation. ## Current contract (v1) @@ -22,6 +26,6 @@ Plan: [docs/presets-scan-plan.md](docs/presets-scan-plan.md). 1. Mirror the new live rule in the scan-target builder (same module family as [cleave/preset_scan.py](cleave/preset_scan.py)). 2. Extend JSON report `layers` / metadata if attribution changes. 3. Add or adjust unit tests for target enumeration (no GL required). -4. Update [docs/presets-scan-plan.md](docs/presets-scan-plan.md) scan set section. +4. Update [docs/legacy-plans/presets-scan-plan.md](docs/legacy-plans/presets-scan-plan.md) scan set section. Do not let play and scan disagree on which presets are in rotation. diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1c060c4..ba2afcb 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -12,6 +12,8 @@ jobs: SDL_VIDEODRIVER: dummy steps: - uses: actions/checkout@v4 + with: + submodules: recursive - uses: actions/setup-python@v5 with: diff --git a/.gitignore b/.gitignore index dd28f4a..1dd397b 100644 --- a/.gitignore +++ b/.gitignore @@ -21,4 +21,8 @@ __pycache__/ # venv venv/ .venv/ -cleave-env/ \ No newline at end of file +cleave-env/ + +# scratch / local temp +tmp/ +tmp.txt \ No newline at end of file diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..b120ae6 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,9 @@ +[submodule "assets/milkdrop-presets/presets-cream-of-the-crop"] + path = assets/milkdrop-presets/presets-cream-of-the-crop + url = https://github.com/projectM-visualizer/presets-cream-of-the-crop +[submodule "assets/milkdrop-presets/presets-milkdrop-original"] + path = assets/milkdrop-presets/presets-milkdrop-original + url = https://github.com/projectM-visualizer/presets-milkdrop-original +[submodule "assets/milkdrop-presets/presets-milkdrop-texture-pack"] + path = assets/milkdrop-presets/presets-milkdrop-texture-pack + url = https://github.com/projectM-visualizer/presets-milkdrop-texture-pack diff --git a/README.md b/README.md index 351245d..7ab4a1a 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,22 @@ Built on [projectM](https://github.com/projectM-visualizer/projectM) and [Demucs WSL2 audio glitches: see [microsoft/wslg#1257](https://github.com/microsoft/wslg/issues/1257). Disabling `systemd-timesyncd` should help. +## Checkout + +Clone with submodules so preset packs are available for `cleave scan-golden --probe` and other preset work: + +```bash +git clone --recurse-submodules +``` + +If you already cloned without submodules: + +```bash +git submodule update --init --recursive +``` + +Preset packs live under [assets/milkdrop-presets/](assets/milkdrop-presets/) (projectM community repos). Play and project configs still default to `~/milkdrop-presets` in [cleave-viz.yaml](./cleave-viz.yaml); point `paths.preset_root` there or at `assets/milkdrop-presets` if you prefer the checkout copy. + ## Setup Create a virtual environment... @@ -50,20 +66,19 @@ pip install -r requirements-dev.txt ## Quick Start -### Download Some Milkdrop Presets +### Milkdrop presets + +The repo vendors three projectM preset packs as submodules under [assets/milkdrop-presets/](assets/milkdrop-presets/) (see **Checkout** above). For everyday browsing you can instead use a separate tree: ```bash -# make a preset_root mkdir ~/milkdrop-presets cd ~/milkdrop-presets - -# there are thousands of community written presets to choose from... git clone https://github.com/projectM-visualizer/presets-cream-of-the-crop git clone https://github.com/projectM-visualizer/presets-milkdrop-original git clone https://github.com/projectM-visualizer/presets-milkdrop-texture-pack ``` -`preset_root` is defined in [cleave-viz.yaml](./cleave-viz.yaml) +`preset_root` is defined in [cleave-viz.yaml](./cleave-viz.yaml). ### `cleave` a track @@ -93,37 +108,13 @@ This will separate the track into its component stem tracks (bass, drums, vocals * `play` accepts a source audio file or project slug/path * `separate` can be run on its own without launching the visualizer * `render` accepts a project slug or path (not a source audio file). +* `scan` (**experimental / low confidence**) classifies Milkdrop presets for load failures and output quality (project config or bulk `--presets-dir`); classification is only trusted on the golden set (`scan-golden`). `--quarantine` / `--delete` act on `load_failed` and `black` by default. Design docs: [docs/legacy-plans/presets-scan-plan.md](docs/legacy-plans/presets-scan-plan.md). * `backup` archives a full project directory (mix, stems, configs, renders) to a `.cleave-tar.gz` file. * `restore` unpacks a `.cleave-tar.gz` archive into `projects//` (slug from `project.yaml`). ### Visualizer -Controls... - -* `Up` / `Down` - * move up / down menu items -* `CTRL` + `Up` / `Down` - * move up / down layers -* `Right` / `Left` - * expand / collapse - * increment / decrement value by 1 - * forward / back 10 secs - * next / previous milkdrop preset -* `CTRL` + `Right` / `Left` - * enable / disable layer - * increment / decrement value by 10 - * forward / back 30 secs - * up / down the preset directory tree -* `SHIFT` + `Right` / `Left` - * layers: solo / unsolo layer - * timeline: override mode -* `Enter` - * move a stem layer up or down the z-order (not available on **Render: OVERLAY**) -* `CTRL` + `Enter` - * lock / unlock stem layer -* `CTRL` + `q` - * quit -* `t` - * open timeline panel + +Controls: press `h` for help in the visualizer to show context sensitive controls + help. #### Compositing diff --git a/assets/milkdrop-presets/presets-cream-of-the-crop b/assets/milkdrop-presets/presets-cream-of-the-crop new file mode 160000 index 0000000..0180df2 --- /dev/null +++ b/assets/milkdrop-presets/presets-cream-of-the-crop @@ -0,0 +1 @@ +Subproject commit 0180df21f5e0bd39b9060cc5de420ed2f1f9e509 diff --git a/assets/milkdrop-presets/presets-milkdrop-original b/assets/milkdrop-presets/presets-milkdrop-original new file mode 160000 index 0000000..e03b83e --- /dev/null +++ b/assets/milkdrop-presets/presets-milkdrop-original @@ -0,0 +1 @@ +Subproject commit e03b83e3338d8f1ed6cbcf908c719f249ef24288 diff --git a/assets/milkdrop-presets/presets-milkdrop-texture-pack b/assets/milkdrop-presets/presets-milkdrop-texture-pack new file mode 160000 index 0000000..ff8edf2 --- /dev/null +++ b/assets/milkdrop-presets/presets-milkdrop-texture-pack @@ -0,0 +1 @@ +Subproject commit ff8edf2a8fa07e55ad562f1af97076526c484f7d diff --git a/cleave/cli.py b/cleave/cli.py index 13ba9be..6d2f25f 100644 --- a/cleave/cli.py +++ b/cleave/cli.py @@ -4,8 +4,40 @@ import time from pathlib import Path -from cleave.config import VIZ_CONFIG_FILENAME, ensure_project_viz_config +from cleave.config import ( + VIZ_CONFIG_FILENAME, + ensure_project_viz_config, + find_config_path, + load_config, +) from cleave.paths import resolve_project +from cleave.preset_scan import ( + PresetScanResult, + PresetResultCategory, + ProbeProfile, + ScanMode, + ScanReport, + build_scan_report, + delete_presets, + destructive_scan_categories, + existing_report_status, + load_resume_results, + probe_profile, + quarantine_presets, + run_scan, + scan_report_summary, + scanned_preset_dirs, + validate_quarantine_dir, + write_scan_report, +) +from cleave.preset_scan_targets import ScanTargets, build_bulk_targets, build_project_targets + + +def _experimental_notice() -> None: + print( + "warning: 'scan' is experimental; classification is only trusted on the golden set", + file=sys.stderr, + ) from cleave.separate import ( project_stems_complete, resolve_separate_target, @@ -164,6 +196,450 @@ def cmd_backup(args: argparse.Namespace) -> None: print(f"Backed up to {archive_path}") +def _dedupe_texture_paths(paths: list[Path]) -> tuple[Path, ...]: + seen: set[Path] = set() + ordered: list[Path] = [] + for path in paths: + resolved = path.expanduser().resolve() + if resolved in seen: + continue + seen.add(resolved) + ordered.append(resolved) + return tuple(ordered) + + +def _print_scan_summary(summary: dict[str, int]) -> None: + print( + "Scan complete: " + f"{summary['total']} preset(s); " + f"ok={summary['ok']}, dim={summary['dim']}, " + f"black={summary['black']}, washed_out={summary['washed_out']}, " + f"load_failed={summary['load_failed']}", + file=sys.stderr, + ) + + +def _delete_candidate_count( + results: list[PresetScanResult] | tuple[PresetScanResult, ...], + *, + categories: frozenset[PresetResultCategory], +) -> int: + return sum( + 1 + for result in results + if result.result in categories and result.path.is_file() + ) + + +def _confirm_delete(count: int, *, yes: bool) -> None: + if count == 0: + return + if yes: + return + if not sys.stdin.isatty(): + _exit_error("error: --delete requires --yes when stdin is not a TTY") + try: + answer = input(f"Delete {count} preset file(s) flagged by scan? [y/N] ").strip() + except EOFError: + answer = "" + if answer.lower() not in ("y", "yes"): + _exit_error("error: delete cancelled") + + +def _warn_resume_mismatch( + *, + field: str, + resume_value: str, + current_value: str, +) -> None: + print( + f"warning: resume report {field} is {resume_value}, " + f"current run is {current_value}", + file=sys.stderr, + ) + + +def _format_scan_resume_command(args: argparse.Namespace) -> str: + parts = [sys.argv[0], "scan"] + if args.presets_dir is not None: + parts.extend(["--presets-dir", str(args.presets_dir)]) + for texture_path in args.texture_path: + parts.extend(["--texture-path", str(texture_path)]) + if args.recursive: + parts.append("--recursive") + elif args.project_dir is not None: + parts.append(str(args.project_dir)) + if args.config is not None: + parts.extend(["-c", str(args.config)]) + assert args.report is not None + parts.extend(["--report", str(args.report)]) + parts.append("--resume") + return " ".join(parts) + + +def _guard_incomplete_report( + report_path: Path | None, + args: argparse.Namespace, + target_total: int, +) -> None: + if report_path is None or args.resume or not report_path.is_file(): + return + try: + scanned, complete = existing_report_status(report_path) + except ValueError: + return + if complete: + return + resume_cmd = _format_scan_resume_command(args) + _exit_error( + f"error: {report_path} is incomplete ({scanned}/{target_total}).\n" + f"Resume with: {resume_cmd}\n" + f"Or delete the file to start over." + ) + + +def _handle_scan_keyboard_interrupt( + report_path: Path | None, + args: argparse.Namespace, + target_total: int, +) -> None: + if report_path is None: + print( + "Scan interrupted; results were not saved " + "(use --report to enable resume)", + file=sys.stderr, + ) + else: + try: + scanned, _ = existing_report_status(report_path) + except ValueError: + scanned = 0 + print( + f"Scan interrupted; {scanned}/{target_total} saved (complete: false).\n" + f"Resume: {_format_scan_resume_command(args)}", + file=sys.stderr, + ) + sys.exit(130) + + +def _execute_preset_scan( + *, + args: argparse.Namespace, + profile: ProbeProfile, + prior_results: list[PresetScanResult], + skip_paths: frozenset[Path], + report_path: Path | None, + scan_mode: ScanMode, + scan_targets: ScanTargets, + report_targets: ScanTargets, + run_texture_paths: tuple[Path, ...] | None = None, + project_dir: Path | None = None, + config_path: Path | None = None, +) -> tuple[list[PresetScanResult], ScanReport]: + target_total = len(scan_targets.presets) + _guard_incomplete_report(report_path, args, target_total) + + def report_sink(new_results: list[PresetScanResult], complete: bool) -> None: + assert report_path is not None + report = build_scan_report( + scan_mode=scan_mode, + profile=profile, + targets=report_targets, + results=[*prior_results, *new_results], + project_dir=project_dir, + config_path=config_path, + complete=complete, + ) + write_scan_report(report_path, report) + + try: + new_results = run_scan( + scan_targets, + texture_paths=run_texture_paths, + report_sink=report_sink if report_path is not None else None, + skip_paths=skip_paths, + ) + except KeyboardInterrupt: + _handle_scan_keyboard_interrupt(report_path, args, target_total) + except (FileNotFoundError, ValueError, RuntimeError) as e: + _exit_error(f"error: {e}") + + results = [*prior_results, *new_results] + report = build_scan_report( + scan_mode=scan_mode, + profile=profile, + targets=report_targets, + results=results, + project_dir=project_dir, + config_path=config_path, + ) + return results, report + + +def cmd_scan(args: argparse.Namespace) -> None: + _experimental_notice() + bulk_mode = args.presets_dir is not None + + destructive_categories = destructive_scan_categories( + include_dim=args.include_dim, + include_washout=args.include_washout, + ) + if ( + (args.include_dim or args.include_washout) + and args.quarantine is None + and not args.delete + ): + print( + "warning: --include-dim/--include-washout have no effect " + "without --quarantine or --delete", + file=sys.stderr, + ) + + if args.resume and args.report is None: + _exit_error("error: --resume requires --report PATH") + + if bulk_mode: + if args.project_dir is not None: + _exit_error( + "error: --presets-dir cannot be used with a project directory" + ) + else: + if args.project_dir is None: + _exit_error("error: project directory required (or use --presets-dir)") + if args.texture_path: + _exit_error("error: --texture-path is only valid in bulk scan mode") + if args.recursive: + _exit_error("error: --recursive is only valid in bulk scan mode") + + prior_results: list[PresetScanResult] = [] + skip_paths: frozenset[Path] = frozenset() + resume_scan_mode: str | None = None + + if args.resume: + assert args.report is not None + resume_path = args.report.expanduser() + if not resume_path.is_file(): + _exit_error(f"error: resume report not found: {resume_path}") + try: + resume_data = load_resume_results(resume_path) + except ValueError as exc: + _exit_error(f"error: cannot read resume report {resume_path}: {exc}") + prior_results = list(resume_data.results) + skip_paths = resume_data.skip_paths + resume_scan_mode = resume_data.scan_mode + if resume_data.complete: + _exit_error( + f"error: {resume_path} is already complete " + f"({len(prior_results)} presets).\n" + f"Or delete the file to scan again." + ) + + profile = probe_profile() + current_scan_mode = "bulk" if bulk_mode else "project" + if resume_scan_mode is not None and resume_scan_mode != current_scan_mode: + _warn_resume_mismatch( + field="scan_mode", + resume_value=resume_scan_mode, + current_value=current_scan_mode, + ) + + report_path = ( + args.report.expanduser().resolve() if args.report is not None else None + ) + quarantine_dir: Path | None = None + + if bulk_mode: + texture_paths = list(args.texture_path) + config_path: Path | None = None + if args.config is not None: + config_path = args.config.expanduser().resolve() + cfg = load_config(config_path, None) + texture_paths.extend(cfg.paths.texture_paths) + resolved_textures = _dedupe_texture_paths(texture_paths) + if not resolved_textures: + _exit_error( + "error: bulk scan requires at least one --texture-path " + "or paths.texture_paths in -c config" + ) + + presets_dir = args.presets_dir.expanduser().resolve() + if not presets_dir.is_dir(): + _exit_error(f"error: presets directory not found: {presets_dir}") + + targets = build_bulk_targets(presets_dir, recursive=args.recursive) + report_targets = ScanTargets( + presets=targets.presets, + presets_dir=targets.presets_dir, + texture_paths=resolved_textures, + ) + + if args.quarantine is not None: + try: + quarantine_dir = validate_quarantine_dir( + args.quarantine, + scanned_preset_dirs(targets), + ) + except ValueError as exc: + _exit_error(f"error: {exc}") + + results, report = _execute_preset_scan( + args=args, + profile=profile, + prior_results=prior_results, + skip_paths=skip_paths, + report_path=report_path, + scan_mode="bulk", + scan_targets=targets, + report_targets=report_targets, + run_texture_paths=resolved_textures, + config_path=config_path, + ) + else: + try: + project_dir = resolve_project(Path(args.project_dir)) + except (FileNotFoundError, ValueError) as e: + _exit_error(f"error: {e}") + + config_path = find_config_path(args.config, project_dir) + if config_path is None: + _exit_error(f"error: no {VIZ_CONFIG_FILENAME} found for project") + + try: + cfg = load_config(args.config, project_dir) + except (FileNotFoundError, ValueError) as e: + _exit_error(f"error: {e}") + + targets = build_project_targets(cfg) + + if args.quarantine is not None: + try: + quarantine_dir = validate_quarantine_dir( + args.quarantine, + scanned_preset_dirs(targets), + ) + except ValueError as exc: + _exit_error(f"error: {exc}") + + results, report = _execute_preset_scan( + args=args, + profile=profile, + prior_results=prior_results, + skip_paths=skip_paths, + report_path=report_path, + scan_mode="project", + scan_targets=targets, + report_targets=targets, + project_dir=project_dir, + config_path=config_path, + ) + + summary = scan_report_summary(report) + _print_scan_summary(summary) + + if report_path is not None: + write_scan_report(report_path, report) + print(f"Wrote scan report to {report_path}") + + if quarantine_dir is not None: + moves = quarantine_presets( + results, + quarantine_dir, + categories=destructive_categories, + ) + for src, dst in moves: + print(f"Quarantined {src} -> {dst}") + + if args.delete: + delete_count = _delete_candidate_count( + results, + categories=destructive_categories, + ) + _confirm_delete(delete_count, yes=args.yes) + deleted = delete_presets(results, categories=destructive_categories) + for path in deleted: + print(f"Deleted {path}") + + +def cmd_scan_golden(args: argparse.Namespace) -> None: + _experimental_notice() + from cleave.preset_scan_golden import ( + DEFAULT_GOLDEN_SET_PATH, + DEFAULT_METRICS_CACHE_PATH, + DEFAULT_SWEEP_WARMUP_FRAMES, + DEFAULT_SWEEP_WINDOW_FRAMES, + default_threshold_sweep_variants, + evaluate, + format_probe_profile_summary, + load_golden_set, + load_metrics_cache, + print_eval_report, + probe_golden_set, + probe_profile, + sweep, + ) + + golden_path = ( + args.golden.expanduser().resolve() + if args.golden is not None + else DEFAULT_GOLDEN_SET_PATH + ) + cache_path = ( + args.cache.expanduser().resolve() + if args.cache is not None + else DEFAULT_METRICS_CACHE_PATH.resolve() + ) + + if args.probe: + golden = load_golden_set(golden_path) + probe_golden_set(golden, cache_path) + profile = probe_profile() + print( + f"Wrote cache ({format_probe_profile_summary(profile)}) to {cache_path}", + file=sys.stderr, + ) + return + + golden = load_golden_set(golden_path) + cache = load_metrics_cache(cache_path) + + if args.eval: + try: + report = evaluate( + cache, + golden, + warmup_frames=args.warmup, + window_frames=args.window, + ) + except ValueError as exc: + _exit_error(f"error: {exc}") + print_eval_report(report) + return + + results = sweep(cache, golden) + variant_count = len(default_threshold_sweep_variants()) + warmup_grid = DEFAULT_SWEEP_WARMUP_FRAMES + window_grid = DEFAULT_SWEEP_WINDOW_FRAMES + config_count = len(results) + print( + f"Sweep: {config_count} configs " + f"({variant_count} threshold variants x " + f"{len(warmup_grid)} warmups x " + f"{len(window_grid)} windows)", + file=sys.stderr, + ) + print("Sweep results (best first):", file=sys.stderr) + for index, entry in enumerate(results[:15], start=1): + threshold_note = "" + if entry.thresholds is not None: + threshold_note = f" thresholds={entry.thresholds}" + print( + f" {index}. warmup={entry.warmup_frames} window={entry.window_frames}" + f"{threshold_note}: {entry.correct}/{entry.total} " + f"({entry.accuracy * 100:.1f}%)", + file=sys.stderr, + ) + + def cmd_restore(args: argparse.Namespace) -> None: from cleave.archive import restore_project @@ -335,6 +811,155 @@ def build_parser() -> argparse.ArgumentParser: ) restore.set_defaults(func=cmd_restore) + scan = subparsers.add_parser( + "scan", + prog="cleave scan", + help="[EXPERIMENTAL] Scan Milkdrop presets for load failures and black output", + epilog=( + "For large preset directories, use --report so an interrupted scan " + "can be resumed with the same --report PATH and --resume." + ), + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + scan.add_argument( + "project_dir", + nargs="?", + help=_PROJECT_DIR_HELP, + ) + scan.add_argument( + "-c", + "--config", + type=Path, + help=f"Config path (default: /{VIZ_CONFIG_FILENAME})", + ) + scan.add_argument( + "--presets-dir", + type=Path, + help="Bulk mode: directory of .milk presets (mutually exclusive with project)", + ) + scan.add_argument( + "--texture-path", + action="append", + type=Path, + default=[], + metavar="DIR", + help="Bulk mode: texture search path (repeatable; required unless -c supplies paths)", + ) + scan.add_argument( + "--recursive", + action="store_true", + help="Bulk mode: scan subdirectories for .milk files", + ) + scan.add_argument( + "--report", + type=Path, + metavar="PATH", + help=( + "Write JSON scan report to PATH incrementally so an interrupted run " + "can be resumed with the same PATH and --resume." + ), + ) + scan.add_argument( + "--resume", + action="store_true", + help="Continue a prior scan; requires --report PATH to an existing report", + ) + scan_action = scan.add_mutually_exclusive_group() + scan_action.add_argument( + "--quarantine", + type=Path, + metavar="DIR", + help=( + "Move failed presets (load_failed, black) to DIR; " + "use --include-dim / --include-washout to extend" + ), + ) + scan_action.add_argument( + "--delete", + action="store_true", + help=( + "Delete failed presets after the scan (load_failed, black; " + "prompts unless --yes)" + ), + ) + scan.add_argument( + "--include-dim", + action="store_true", + help="Also quarantine or delete presets classified as dim", + ) + scan.add_argument( + "--include-washout", + action="store_true", + help="Also quarantine or delete presets classified as washed_out", + ) + scan.add_argument( + "--yes", + action="store_true", + help="Skip confirmation for --delete (required when stdin is not a TTY)", + ) + scan.set_defaults(func=cmd_scan) + + from cleave.paths import repo_root + from cleave.preset_scan_golden import ( + DEFAULT_GOLDEN_SET_PATH, + DEFAULT_METRICS_CACHE_PATH, + ) + + scan_golden = subparsers.add_parser( + "scan-golden", + prog="cleave scan-golden", + description="[EXPERIMENTAL] Golden-set probe and classifier evaluation for preset scan.", + help="[EXPERIMENTAL] Probe and evaluate the preset scan golden set", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + mode = scan_golden.add_mutually_exclusive_group(required=True) + mode.add_argument( + "--probe", + action="store_true", + help="GL probe all golden presets and write metrics cache", + ) + mode.add_argument( + "--eval", + action="store_true", + help="Classify cached metrics and compare to golden labels", + ) + mode.add_argument( + "--sweep", + action="store_true", + help="Grid search warmup/window settings against golden labels", + ) + scan_golden.add_argument( + "--warmup", + type=int, + metavar="N", + help="Override eval warmup frames (must match cache profile)", + ) + scan_golden.add_argument( + "--window", + type=int, + metavar="N", + help="Override eval window frames (must match cache profile)", + ) + scan_golden.add_argument( + "--cache", + type=Path, + metavar="PATH", + help=( + "Metrics cache JSON path " + f"(default: {DEFAULT_METRICS_CACHE_PATH.relative_to(repo_root())})" + ), + ) + scan_golden.add_argument( + "--golden", + type=Path, + metavar="PATH", + help=( + "Golden set YAML path " + f"(default: {DEFAULT_GOLDEN_SET_PATH.relative_to(repo_root())})" + ), + ) + scan_golden.set_defaults(func=cmd_scan_golden) + return parser diff --git a/cleave/preset_scan.py b/cleave/preset_scan.py new file mode 100644 index 0000000..40dbd55 --- /dev/null +++ b/cleave/preset_scan.py @@ -0,0 +1,1106 @@ +"""Preset scan probe profiles, classification, and JSON report types.""" + +from __future__ import annotations + +import json +import math +import os +import shutil +import sys +import tempfile +import time +from collections.abc import Callable +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +import numpy as np +import pygame +from OpenGL.GL import ( + GL_COLOR_ATTACHMENT0, + GL_DEPTH_ATTACHMENT, + GL_DEPTH_COMPONENT24, + GL_FRAMEBUFFER, + GL_FRAMEBUFFER_COMPLETE, + GL_RENDERBUFFER, + GL_RGBA, + GL_RGBA8, + GL_TEXTURE_2D, + GL_UNSIGNED_BYTE, + glBindFramebuffer, + glBindRenderbuffer, + glBindTexture, + glCheckFramebufferStatus, + glDeleteFramebuffers, + glDeleteRenderbuffers, + glDeleteTextures, + glFramebufferRenderbuffer, + glFramebufferTexture2D, + glGenFramebuffers, + glGenRenderbuffers, + glGenTextures, + glRenderbufferStorage, + glTexImage2D, +) + +from cleave.pcm_io import SAMPLE_RATE_HZ +from cleave.preset_scan_metrics import ( + FrameMetrics, + PresetMetrics, + frame_metrics_from_dict, + frame_metrics_to_dict, + peak_metrics, + sample_frame_metrics, + white_frame_fraction, +) +from cleave.preset_scan_targets import PresetTarget, ScanTargets +from cleave.projectm import PresetLoadFailure, ProjectM +from cleave.stem_pcm import samples_per_frame + +ScanMode = Literal["project", "bulk"] +PresetResultCategory = Literal["load_failed", "black", "dim", "washed_out", "ok"] + +PROBE_WARMUP_FRAMES = 15 +PROBE_WINDOW_FRAMES = 75 +PROBE_FPS = 30 +PROBE_FBO_WIDTH = 480 +PROBE_FBO_HEIGHT = 270 + +COVERAGE_LUMA_MIN = 16 +WHITE_CHANNEL_MIN = 235 + +DEFER_LOAD_FAILED_MIN_COV16 = 0.9 +BROKEN_SOFT_WHITE_MEAN_HI = 235.0 +WASHED_COV16_MIN = 0.999 + +_SCAN_THRESHOLD_SPECS: dict[str, float] = { + "black_max_luma": 1.0, + "black_coverage": 0.0003, + "dim_mean_luma": 15.0, + "dim_coverage": 0.25, + "very_sparse_dim_mean": 5.15, + "very_sparse_dim_max": 100.0, + "very_sparse_dim_cov16": 0.015, + "sparse_dim_mean": 9.95, + "sparse_dim_max": 80.0, + "sparse_dim_cov16_min": 0.102, + "sparse_dim_cov16_max": 0.128, + "capped_dim_mean": 22.0, + "capped_dim_max_lo": 50.0, + "capped_dim_max_hi": 111.0, + "capped_dim_cov16": 0.9, + "white_channel_min": float(WHITE_CHANNEL_MIN), + "white_area_frac": 0.6, + "min_white_frame_frac": 0.3, + "bright_on_black_max": 25.0, + "bright_on_black_coverage": 0.00025, + "coverage_luma_min": float(COVERAGE_LUMA_MIN), + "dim_bob_cov_lo": 0.07, + "dim_bob_cov_hi": 0.13, + "dim_bob_mean_mid": 6.0, + "dim_bob_mean_hi": 10.0, + "white_area_frac_soft": 0.15, + "min_white_frame_frac_soft": 0.55, + "washed_mean_soft": 200.0, + "washed_mean_peak": 245.0, + "washed_cov16_min": WASHED_COV16_MIN, + "washed_cov192_peak": 0.95, + "washed_max_lo": 240.0, + "washed_mean_lo": 155.0, + "washed_mean_hi": 175.0, + "washed_cov192_lo": 0.05, + "washed_max_mid": 235.0, + "washed_mean_mid_lo": 170.0, + "washed_mean_mid_hi": 188.0, + "washed_cov192_mid": 0.28, + "washed_white235_mid_max": 0.10, +} + +SCAN_THRESHOLDS: dict[str, float] = dict(_SCAN_THRESHOLD_SPECS) + +BLACK_MAX_LUMA = _SCAN_THRESHOLD_SPECS["black_max_luma"] +BLACK_COVERAGE = _SCAN_THRESHOLD_SPECS["black_coverage"] +DIM_MEAN_LUMA = _SCAN_THRESHOLD_SPECS["dim_mean_luma"] +DIM_COVERAGE = _SCAN_THRESHOLD_SPECS["dim_coverage"] +VERY_SPARSE_DIM_MEAN = _SCAN_THRESHOLD_SPECS["very_sparse_dim_mean"] +VERY_SPARSE_DIM_MAX = _SCAN_THRESHOLD_SPECS["very_sparse_dim_max"] +VERY_SPARSE_DIM_COV16 = _SCAN_THRESHOLD_SPECS["very_sparse_dim_cov16"] +SPARSE_DIM_MEAN = _SCAN_THRESHOLD_SPECS["sparse_dim_mean"] +SPARSE_DIM_MAX = _SCAN_THRESHOLD_SPECS["sparse_dim_max"] +SPARSE_DIM_COV16_MIN = _SCAN_THRESHOLD_SPECS["sparse_dim_cov16_min"] +SPARSE_DIM_COV16_MAX = _SCAN_THRESHOLD_SPECS["sparse_dim_cov16_max"] +CAPPED_DIM_MEAN = _SCAN_THRESHOLD_SPECS["capped_dim_mean"] +CAPPED_DIM_MAX_LO = _SCAN_THRESHOLD_SPECS["capped_dim_max_lo"] +CAPPED_DIM_MAX_HI = _SCAN_THRESHOLD_SPECS["capped_dim_max_hi"] +CAPPED_DIM_COV16 = _SCAN_THRESHOLD_SPECS["capped_dim_cov16"] +WHITE_AREA_FRAC = _SCAN_THRESHOLD_SPECS["white_area_frac"] +MIN_WHITE_FRAME_FRAC = _SCAN_THRESHOLD_SPECS["min_white_frame_frac"] +BRIGHT_ON_BLACK_MAX = _SCAN_THRESHOLD_SPECS["bright_on_black_max"] +BRIGHT_ON_BLACK_COVERAGE = _SCAN_THRESHOLD_SPECS["bright_on_black_coverage"] +DIM_BOB_COV_LO = _SCAN_THRESHOLD_SPECS["dim_bob_cov_lo"] +DIM_BOB_COV_HI = _SCAN_THRESHOLD_SPECS["dim_bob_cov_hi"] +DIM_BOB_MEAN_MID = _SCAN_THRESHOLD_SPECS["dim_bob_mean_mid"] +DIM_BOB_MEAN_HI = _SCAN_THRESHOLD_SPECS["dim_bob_mean_hi"] +WASHED_WHITE_AREA_FRAC_SOFT = _SCAN_THRESHOLD_SPECS["white_area_frac_soft"] +WASHED_MIN_WHITE_FRAME_FRAC_SOFT = _SCAN_THRESHOLD_SPECS[ + "min_white_frame_frac_soft" +] +WASHED_MEAN_SOFT = _SCAN_THRESHOLD_SPECS["washed_mean_soft"] +WASHED_MEAN_PEAK = _SCAN_THRESHOLD_SPECS["washed_mean_peak"] +WASHED_COV192_PEAK = _SCAN_THRESHOLD_SPECS["washed_cov192_peak"] +WASHED_MAX_LO = _SCAN_THRESHOLD_SPECS["washed_max_lo"] +WASHED_MEAN_LO = _SCAN_THRESHOLD_SPECS["washed_mean_lo"] +WASHED_MEAN_HI = _SCAN_THRESHOLD_SPECS["washed_mean_hi"] +WASHED_COV192_LO = _SCAN_THRESHOLD_SPECS["washed_cov192_lo"] +WASHED_MAX_MID = _SCAN_THRESHOLD_SPECS["washed_max_mid"] +WASHED_MEAN_MID_LO = _SCAN_THRESHOLD_SPECS["washed_mean_mid_lo"] +WASHED_MEAN_MID_HI = _SCAN_THRESHOLD_SPECS["washed_mean_mid_hi"] +WASHED_COV192_MID = _SCAN_THRESHOLD_SPECS["washed_cov192_mid"] +WASHED_WHITE235_MID_MAX = _SCAN_THRESHOLD_SPECS["washed_white235_mid_max"] + + +def scan_thresholds() -> dict[str, float]: + return dict(_SCAN_THRESHOLD_SPECS) + +REPORT_FLUSH_EVERY = 10 + +DEFAULT_DESTRUCTIVE_CATEGORIES: frozenset[PresetResultCategory] = frozenset( + ("load_failed", "black") +) + + +def destructive_scan_categories( + *, + include_dim: bool = False, + include_washout: bool = False, +) -> frozenset[PresetResultCategory]: + categories = set(DEFAULT_DESTRUCTIVE_CATEGORIES) + if include_dim: + categories.add("dim") + if include_washout: + categories.add("washed_out") + return frozenset(categories) + + +@dataclass(frozen=True) +class ProbeProfile: + warmup_frames: int + window_frames: int + + @property + def total_frames(self) -> int: + return self.warmup_frames + self.window_frames + + +@dataclass(frozen=True) +class ProbePcm: + channels: int = 1 + + def chunk(self, frame_idx: int, n_samples: int) -> np.ndarray: + return _synthetic_pcm_burst(frame_idx, n_samples) + + +@dataclass(frozen=True) +class PresetScanTimings: + load_sec: float | None = None + render_sec: float | None = None + + +@dataclass(frozen=True) +class PresetScanResult: + path: Path + result: PresetResultCategory + layers: tuple[str, ...] + error: str | None = None + timings: PresetScanTimings | None = None + luma: FrameMetrics | None = None + + +@dataclass(frozen=True) +class ScanReport: + scan_mode: ScanMode + project_dir: Path | None + config_path: Path | None + presets_dir: Path | None + preset_root: Path | None + texture_paths: tuple[Path, ...] + layers: dict[str, list[str]] + thresholds: dict[str, float] + probe_frames: int + probe_fps: int + fbo_size: tuple[int, int] + presets: tuple[PresetScanResult, ...] + complete: bool = True + + +@dataclass(frozen=True) +class ResumeData: + scan_mode: ScanMode + results: tuple[PresetScanResult, ...] + skip_paths: frozenset[Path] + complete: bool + + +def build_probe_pcm() -> ProbePcm: + return ProbePcm() + + +def probe_profile() -> ProbeProfile: + return ProbeProfile( + warmup_frames=PROBE_WARMUP_FRAMES, + window_frames=PROBE_WINDOW_FRAMES, + ) + + +def _normalize_peak_metrics( + peaks: FrameMetrics, +) -> tuple[float, float, dict[int, float]]: + return peaks.max_luma, peaks.mean_luma, peaks.coverage + + +def _peak_white_coverage( + peaks: FrameMetrics, + cutoff: int, +) -> float: + return peaks.white_coverage.get(cutoff, 0.0) + + +def _dim_bright_on_black_ok( + *, + max_luma: float, + mean_luma: float, + cov_min: float, + th: dict[str, float], +) -> bool: + if max_luma < th["bright_on_black_max"] or cov_min < th["bright_on_black_coverage"]: + return False + if "dim_bob_cov_lo" not in th: + return True + if cov_min < th["dim_bob_cov_lo"]: + return True + if mean_luma >= th["dim_bob_mean_hi"]: + return True + return th["dim_bob_cov_lo"] <= cov_min < th["dim_bob_cov_hi"] and mean_luma >= th[ + "dim_bob_mean_mid" + ] + + +def _defer_load_failed(peaks: FrameMetrics, th: dict[str, float]) -> bool: + max_luma, _, coverage = _normalize_peak_metrics(peaks) + cov_min = coverage[int(th["coverage_luma_min"])] + return ( + max_luma >= th["black_max_luma"] + and cov_min >= DEFER_LOAD_FAILED_MIN_COV16 + ) + + +def _broken_soft_white_black( + peaks: FrameMetrics, + frames: tuple[FrameMetrics, ...], + th: dict[str, float], +) -> bool: + _, mean_luma, _ = _normalize_peak_metrics(peaks) + white_frac_hard = white_frame_fraction( + frames, + warmup_frames=0, + window_frames=0, + channel_min_cutoff=int(th["white_channel_min"]), + area_frac=th["white_area_frac"], + ) + if white_frac_hard >= th["min_white_frame_frac"]: + return False + white_frac_soft = white_frame_fraction( + frames, + warmup_frames=0, + window_frames=0, + channel_min_cutoff=int(th["white_channel_min"]), + area_frac=th["white_area_frac_soft"], + ) + return ( + white_frac_soft >= th["min_white_frame_frac_soft"] + and th["washed_mean_soft"] <= mean_luma <= BROKEN_SOFT_WHITE_MEAN_HI + ) + + +def _washed_out_tiers( + peaks: FrameMetrics, + frames: tuple[FrameMetrics, ...], + th: dict[str, float], +) -> bool: + max_luma, mean_luma, coverage = _normalize_peak_metrics(peaks) + cov16 = coverage[int(th["coverage_luma_min"])] + cov192 = coverage.get(192, 0.0) + white235 = _peak_white_coverage(peaks, int(th["white_channel_min"])) + + white_frac = white_frame_fraction( + frames, + warmup_frames=0, + window_frames=0, + channel_min_cutoff=int(th["white_channel_min"]), + area_frac=th["white_area_frac"], + ) + if white_frac >= th["min_white_frame_frac"]: + return True + + white_frac_soft = white_frame_fraction( + frames, + warmup_frames=0, + window_frames=0, + channel_min_cutoff=int(th["white_channel_min"]), + area_frac=th["white_area_frac_soft"], + ) + if ( + white_frac_soft >= th["min_white_frame_frac_soft"] + and mean_luma >= th["washed_mean_soft"] + ): + return True + + if ( + mean_luma >= th["washed_mean_peak"] + and cov16 >= th["washed_cov16_min"] + and cov192 >= th["washed_cov192_peak"] + ): + return True + + if ( + cov16 >= th["washed_cov16_min"] + and max_luma >= th["washed_max_lo"] + and th["washed_mean_lo"] <= mean_luma <= th["washed_mean_hi"] + and cov192 >= th["washed_cov192_lo"] + ): + return True + + if ( + cov16 >= th["washed_cov16_min"] + and max_luma >= th["washed_max_mid"] + and th["washed_mean_mid_lo"] <= mean_luma <= th["washed_mean_mid_hi"] + and cov192 >= th["washed_cov192_mid"] + and white235 < th["washed_white235_mid_max"] + ): + return True + + return False + + +def classify_preset_result( + failures: list[PresetLoadFailure], + peaks: FrameMetrics, + *, + frames: tuple[FrameMetrics, ...] | None = None, + thresholds: dict[str, float] | None = None, +) -> tuple[PresetResultCategory, str | None]: + th = scan_thresholds() + if thresholds is not None: + th.update(thresholds) + + active_failures = failures + if failures and _defer_load_failed(peaks, th): + active_failures = [] + + if active_failures: + return "load_failed", active_failures[0].message + + if frames is not None and "washed_mean_peak" in th: + if _broken_soft_white_black(peaks, frames, th): + return "black", None + if _washed_out_tiers(peaks, frames, th): + return "washed_out", None + + max_luma, mean_luma, coverage = _normalize_peak_metrics(peaks) + cov_min = coverage[int(th["coverage_luma_min"])] + + if max_luma < th["black_max_luma"] or cov_min < th["black_coverage"]: + if _dim_bright_on_black_ok( + max_luma=max_luma, + mean_luma=mean_luma, + cov_min=cov_min, + th=th, + ): + return "ok", None + return "black", None + + if ( + mean_luma < th["very_sparse_dim_mean"] + and max_luma >= th["very_sparse_dim_max"] + and cov_min >= th["very_sparse_dim_cov16"] + ): + return "dim", None + + if ( + mean_luma < th["sparse_dim_mean"] + and max_luma >= th["sparse_dim_max"] + and th["sparse_dim_cov16_min"] <= cov_min < th["sparse_dim_cov16_max"] + ): + return "dim", None + + if ( + mean_luma < th["capped_dim_mean"] + and th["capped_dim_max_lo"] <= max_luma < th["capped_dim_max_hi"] + and cov_min >= th["capped_dim_cov16"] + ): + return "dim", None + + if mean_luma < th["dim_mean_luma"] and cov_min < th["dim_coverage"]: + if _dim_bright_on_black_ok( + max_luma=max_luma, + mean_luma=mean_luma, + cov_min=cov_min, + th=th, + ): + return "ok", None + return "dim", None + + return "ok", None + + +def build_scan_report( + *, + scan_mode: ScanMode, + profile: ProbeProfile, + targets: ScanTargets, + results: list[PresetScanResult] | tuple[PresetScanResult, ...], + project_dir: Path | None = None, + config_path: Path | None = None, + complete: bool = True, +) -> ScanReport: + layers = { + slot: [str(path) for path in paths] + for slot, paths in sorted(targets.layer_sources.items()) + } + return ScanReport( + scan_mode=scan_mode, + project_dir=project_dir.resolve() if project_dir is not None else None, + config_path=config_path.resolve() if config_path is not None else None, + presets_dir=( + targets.presets_dir.resolve() if targets.presets_dir is not None else None + ), + preset_root=( + targets.preset_root.resolve() if targets.preset_root is not None else None + ), + texture_paths=tuple(p.resolve() for p in targets.texture_paths), + layers=layers, + thresholds=scan_thresholds(), + probe_frames=profile.total_frames, + probe_fps=PROBE_FPS, + fbo_size=(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT), + presets=tuple(results), + complete=complete, + ) + + +def scan_report_summary(report: ScanReport) -> dict[str, int]: + counts: dict[str, int] = { + "total": len(report.presets), + "load_failed": 0, + "black": 0, + "dim": 0, + "washed_out": 0, + "ok": 0, + } + for preset in report.presets: + counts[preset.result] += 1 + return counts + + +def scan_report_to_dict(report: ScanReport) -> dict[str, Any]: + payload: dict[str, Any] = { + "scan_mode": report.scan_mode, + "project_dir": _path_to_str(report.project_dir), + "config_path": _path_to_str(report.config_path), + "presets_dir": _path_to_str(report.presets_dir), + "preset_root": _path_to_str(report.preset_root), + "texture_paths": [str(path) for path in report.texture_paths], + "layers": report.layers, + "thresholds": report.thresholds, + "probe_frames": report.probe_frames, + "probe_fps": report.probe_fps, + "fbo_size": list(report.fbo_size), + "presets": [_preset_result_to_dict(preset) for preset in report.presets], + "summary": scan_report_summary(report), + "complete": report.complete, + } + return payload + + +def write_scan_report(path: Path, report: ScanReport) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, tmp_name = tempfile.mkstemp(dir=path.parent, suffix=".tmp") + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as fh: + json.dump(scan_report_to_dict(report), fh, indent=2) + fh.write("\n") + os.replace(tmp_path, path) + except BaseException: + tmp_path.unlink(missing_ok=True) + raise + + +def existing_report_status(path: Path) -> tuple[int, bool]: + """Return preset count and completion flag from an on-disk report.""" + resolved = path.expanduser().resolve() + if not resolved.is_file(): + raise ValueError(f"resume report not found: {resolved}") + + try: + raw = json.loads(resolved.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + raise ValueError(f"malformed JSON: {exc}") from exc + except OSError as exc: + raise ValueError(str(exc)) from exc + + if not isinstance(raw, dict): + raise ValueError("report root must be a JSON object") + + presets_raw = raw.get("presets") + if not isinstance(presets_raw, list): + raise ValueError("missing presets array") + + complete = raw.get("complete", True) + return len(presets_raw), bool(complete) + + +def load_resume_results(path: Path) -> ResumeData: + """Load prior scan results and skip paths from a report JSON file.""" + resolved = path.expanduser().resolve() + if not resolved.is_file(): + raise ValueError(f"resume report not found: {resolved}") + + try: + raw = json.loads(resolved.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + raise ValueError(f"malformed JSON: {exc}") from exc + except OSError as exc: + raise ValueError(str(exc)) from exc + + if not isinstance(raw, dict): + raise ValueError("report root must be a JSON object") + + scan_mode = raw.get("scan_mode") + if scan_mode not in ("project", "bulk"): + raise ValueError("missing or invalid scan_mode") + + presets_raw = raw.get("presets") + if not isinstance(presets_raw, list): + raise ValueError("missing presets array") + + results: list[PresetScanResult] = [] + skip_paths: set[Path] = set() + for index, entry in enumerate(presets_raw): + try: + result = _preset_result_from_dict(entry) + except ValueError as exc: + raise ValueError(f"invalid preset entry at index {index}: {exc}") from exc + results.append(result) + skip_paths.add(result.path.resolve()) + + complete = bool(raw.get("complete", True)) + + return ResumeData( + scan_mode=scan_mode, + results=tuple(results), + skip_paths=frozenset(skip_paths), + complete=complete, + ) + + +def validate_quarantine_dir( + quarantine_dir: Path, + scanned_dirs: tuple[Path, ...], +) -> Path: + """Resolve and create *quarantine_dir*; reject paths inside scanned preset dirs.""" + resolved = quarantine_dir.expanduser().resolve() + if resolved.exists() and not resolved.is_dir(): + raise ValueError(f"--quarantine target is not a directory: {resolved}") + + try: + resolved.mkdir(parents=True, exist_ok=True) + except OSError as exc: + raise ValueError(f"cannot create quarantine directory {resolved}: {exc}") from exc + + for scanned in scanned_dirs: + scanned_resolved = scanned.expanduser().resolve() + if _path_is_inside_or_equal(resolved, scanned_resolved): + raise ValueError( + "--quarantine directory must be outside the scanned presets directory" + ) + + return resolved + + +def delete_presets( + results: list[PresetScanResult] | tuple[PresetScanResult, ...], + *, + categories: frozenset[PresetResultCategory] | None = None, +) -> list[Path]: + """Delete preset files flagged in *results* within *categories*.""" + active = ( + DEFAULT_DESTRUCTIVE_CATEGORIES + if categories is None + else categories + ) + deleted: list[Path] = [] + for result in results: + if result.result not in active: + continue + path = result.path + if not path.is_file(): + continue + path.unlink() + deleted.append(path) + return deleted + + +def quarantine_presets( + results: list[PresetScanResult] | tuple[PresetScanResult, ...], + quarantine_dir: Path, + *, + categories: frozenset[PresetResultCategory] | None = None, +) -> list[tuple[Path, Path]]: + """Move failed presets into *quarantine_dir* (flat layout, suffix on collision).""" + active = ( + DEFAULT_DESTRUCTIVE_CATEGORIES + if categories is None + else categories + ) + moves: list[tuple[Path, Path]] = [] + for result in results: + if result.result not in active: + continue + src = result.path + if not src.is_file(): + continue + dst = _unique_quarantine_path(quarantine_dir, src.name) + shutil.move(str(src), str(dst)) + moves.append((src, dst)) + return moves + + +def scanned_preset_dirs(targets: ScanTargets) -> tuple[Path, ...]: + """Directories whose preset files are included in *targets*.""" + dirs: set[Path] = set() + if targets.presets_dir is not None: + dirs.add(targets.presets_dir.resolve()) + for paths in targets.layer_sources.values(): + for path in paths: + if path.is_dir(): + dirs.add(path.resolve()) + else: + dirs.add(path.parent.resolve()) + return tuple(sorted(dirs)) + + +def configure_probe_projectm(pm: ProjectM, texture_strs: list[str]) -> None: + pm.set_window_size(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT) + pm.set_fps(PROBE_FPS) + pm.set_hard_cut_enabled(False) + pm.set_texture_paths(texture_strs) + + +class ProbeSession: + """Hidden pygame/OpenGL context and probe FBO for preset scan GL work.""" + + def __init__(self, texture_strs: list[str] | None = None) -> None: + self._texture_strs = list(texture_strs or []) + self._fbo: ProbeFbo | None = None + self.n_pcm = samples_per_frame(PROBE_FPS) + self.frame_dt = 1.0 / PROBE_FPS + + def __enter__(self) -> ProbeSession: + os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") + pygame.init() + try: + pygame.display.set_mode( + (PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT), + pygame.OPENGL | pygame.HIDDEN, + ) + except pygame.error as exc: + pygame.quit() + raise RuntimeError(f"failed to open OpenGL context: {exc}") from exc + self._fbo = ProbeFbo(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT) + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + tb: object, + ) -> None: + if self._fbo is not None: + self._fbo.destroy() + self._fbo = None + pygame.quit() + + @property + def fbo(self) -> ProbeFbo: + if self._fbo is None: + raise RuntimeError("ProbeSession is not active") + return self._fbo + + def configure_projectm(self, pm: ProjectM) -> None: + configure_probe_projectm(pm, self._texture_strs) + + +def run_scan( + targets: ScanTargets, + *, + texture_paths: tuple[Path, ...] | None = None, + report_sink: Callable[[list[PresetScanResult], bool], None] | None = None, + skip_paths: frozenset[Path] | None = None, +) -> list[PresetScanResult]: + """Probe each preset in *targets* with a hidden GL context and a fresh ProjectM.""" + profile = probe_profile() + probe_pcm = build_probe_pcm() + paths = texture_paths if texture_paths is not None else targets.texture_paths + texture_strs = [str(path) for path in paths] + + results: list[PresetScanResult] = [] + skip = skip_paths or frozenset() + total = len(targets.presets) + skip_count = len(skip) + probed = 0 + resumed_announced = False + + try: + with ProbeSession(texture_strs) as session: + for index, target in enumerate(targets.presets, start=1): + if target.path.resolve() in skip: + continue + if skip_count and not resumed_announced: + _progress(f"Resuming: {skip_count} done") + resumed_announced = True + _progress(f"Scanning {index}/{total} {target.path}...") + pm = ProjectM() + try: + session.configure_projectm(pm) + result = _probe_preset( + pm, + session.fbo, + target, + profile=profile, + pcm=probe_pcm, + n_pcm=session.n_pcm, + frame_dt=session.frame_dt, + ) + results.append(result) + probed += 1 + if report_sink is not None and probed % REPORT_FLUSH_EVERY == 0: + report_sink(results, False) + finally: + pm.destroy() + + if report_sink is not None: + report_sink(results, True) + except KeyboardInterrupt: + if report_sink is not None: + report_sink(results, False) + raise + + return results + + +def _progress(message: str) -> None: + print(message, file=sys.stderr, flush=True) + + +def _render_frames( + pm: ProjectM, + fbo: ProbeFbo, + *, + profile: ProbeProfile, + pcm: ProbePcm, + n_pcm: int, + frame_dt: float, +) -> tuple[FrameMetrics, ...]: + frame_metrics: list[FrameMetrics] = [] + for frame_idx in range(profile.total_frames): + t_sec = frame_idx * frame_dt + pm.set_frame_time(t_sec) + pm.feed_pcm(pcm.chunk(frame_idx, n_pcm), channels=pcm.channels) + fbo.bind() + pm.render_to_fbo(fbo.fbo_id) + frame_metrics.append( + sample_frame_metrics(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT) + ) + return tuple(frame_metrics) + + +def probe_preset_metrics( + pm: ProjectM, + fbo: ProbeFbo, + preset_path: Path, + *, + profile: ProbeProfile, + pcm: ProbePcm, + n_pcm: int, + frame_dt: float, +) -> PresetMetrics: + """Render *preset_path* with clean boot and return per-frame luma metrics.""" + pm.drain_preset_failures() + pm.set_preset_start_clean(True) + pm.load_preset(preset_path, smooth=False) + pm.set_preset_start_clean(False) + pm.lock_preset(True) + + frame_metrics = _render_frames( + pm, + fbo, + profile=profile, + pcm=pcm, + n_pcm=n_pcm, + frame_dt=frame_dt, + ) + + failures = pm.drain_preset_failures() + return PresetMetrics( + path=preset_path, + load_failed=bool(failures), + error=failures[0].message if failures else None, + fps=PROBE_FPS, + frames=frame_metrics, + ) + + +def _probe_preset( + pm: ProjectM, + fbo: ProbeFbo, + target: PresetTarget, + *, + profile: ProbeProfile, + pcm: ProbePcm, + n_pcm: int, + frame_dt: float, +) -> PresetScanResult: + load_started = time.perf_counter() + pm.drain_preset_failures() + pm.set_preset_start_clean(True) + pm.load_preset(target.path, smooth=False) + pm.set_preset_start_clean(False) + pm.lock_preset(True) + load_sec = time.perf_counter() - load_started + + render_started = time.perf_counter() + frame_metrics = _render_frames( + pm, + fbo, + profile=profile, + pcm=pcm, + n_pcm=n_pcm, + frame_dt=frame_dt, + ) + render_sec = time.perf_counter() - render_started + warmup = profile.warmup_frames + window = profile.window_frames + window_frames = tuple(frame_metrics[warmup : warmup + window]) + peaks = peak_metrics( + frame_metrics, + warmup_frames=warmup, + window_frames=window, + ) + failures = pm.drain_preset_failures() + category, error = classify_preset_result( + failures, + peaks, + frames=window_frames, + ) + + return PresetScanResult( + path=target.path, + result=category, + layers=target.layers, + error=error, + timings=PresetScanTimings(load_sec=load_sec, render_sec=render_sec), + luma=peaks, + ) + + +def _synthetic_pcm_burst(frame_idx: int, n_samples: int) -> np.ndarray: + """Mono PCM with energy every frame (sine plus light noise, never silence).""" + if n_samples <= 0: + return np.zeros(1, dtype=np.float32) + + t = np.arange(n_samples, dtype=np.float32) / SAMPLE_RATE_HZ + freq = 440.0 + float(frame_idx % 7) * 55.0 + phase = float(frame_idx) * 0.41 + sine = (0.65 * np.sin(2.0 * math.pi * freq * t + phase)).astype(np.float32) + rng = np.random.default_rng(frame_idx) + noise = 0.12 * rng.standard_normal(n_samples).astype(np.float32) + return np.clip(sine + noise, -1.0, 1.0).astype(np.float32) + + +def _gl_name(gen_fn, count: int = 1) -> int: + names = gen_fn(count) + try: + return int(names[0]) + except (TypeError, IndexError): + return int(names) + + +def create_probe_fbo(width: int, height: int) -> ProbeFbo: + """Create a minimal RGBA FBO for preset scan probes.""" + return ProbeFbo(width, height) + + +class ProbeFbo: + """Minimal RGBA FBO for raw projectM probe output.""" + + def __init__(self, width: int, height: int) -> None: + self.width = width + self.height = height + self._texture_id = _gl_name(glGenTextures) + self._depth_rbo_id = _gl_name(glGenRenderbuffers) + self.fbo_id = _gl_name(glGenFramebuffers) + + glBindTexture(GL_TEXTURE_2D, self._texture_id) + glTexImage2D( + GL_TEXTURE_2D, + 0, + GL_RGBA8, + width, + height, + 0, + GL_RGBA, + GL_UNSIGNED_BYTE, + None, + ) + glBindRenderbuffer(GL_RENDERBUFFER, self._depth_rbo_id) + glRenderbufferStorage( + GL_RENDERBUFFER, GL_DEPTH_COMPONENT24, width, height + ) + + glBindFramebuffer(GL_FRAMEBUFFER, self.fbo_id) + glFramebufferTexture2D( + GL_FRAMEBUFFER, + GL_COLOR_ATTACHMENT0, + GL_TEXTURE_2D, + self._texture_id, + 0, + ) + glFramebufferRenderbuffer( + GL_FRAMEBUFFER, + GL_DEPTH_ATTACHMENT, + GL_RENDERBUFFER, + self._depth_rbo_id, + ) + status = glCheckFramebufferStatus(GL_FRAMEBUFFER) + glBindFramebuffer(GL_FRAMEBUFFER, 0) + glBindRenderbuffer(GL_RENDERBUFFER, 0) + glBindTexture(GL_TEXTURE_2D, 0) + if status != GL_FRAMEBUFFER_COMPLETE: + self.destroy() + raise RuntimeError( + f"probe FBO incomplete ({width}x{height}): status 0x{status:x}" + ) + + def bind(self) -> None: + glBindFramebuffer(GL_FRAMEBUFFER, self.fbo_id) + + def destroy(self) -> None: + if self._texture_id: + glDeleteTextures(1, [self._texture_id]) + self._texture_id = 0 + if self._depth_rbo_id: + glDeleteRenderbuffers(1, [self._depth_rbo_id]) + self._depth_rbo_id = 0 + if self.fbo_id: + glDeleteFramebuffers(1, [self.fbo_id]) + self.fbo_id = 0 + + +def _path_to_str(path: Path | None) -> str | None: + if path is None: + return None + return str(path) + + +def _path_is_inside_or_equal(child: Path, parent: Path) -> bool: + child_resolved = child.resolve() + parent_resolved = parent.resolve() + if child_resolved == parent_resolved: + return True + try: + child_resolved.relative_to(parent_resolved) + except ValueError: + return False + return True + + +def _unique_quarantine_path(quarantine_dir: Path, filename: str) -> Path: + candidate = quarantine_dir / filename + if not candidate.exists(): + return candidate + stem = Path(filename).stem + suffix = Path(filename).suffix + index = 1 + while True: + candidate = quarantine_dir / f"{stem}_{index}{suffix}" + if not candidate.exists(): + return candidate + index += 1 + + +def _preset_result_from_dict(entry: Any) -> PresetScanResult: + if not isinstance(entry, dict): + raise ValueError("expected a JSON object") + + path_raw = entry.get("path") + if not isinstance(path_raw, str) or not path_raw: + raise ValueError("missing path") + + result_raw = entry.get("result") + if result_raw not in ("load_failed", "black", "dim", "washed_out", "ok"): + raise ValueError("missing or invalid result") + + layers_raw = entry.get("layers", []) + if not isinstance(layers_raw, list) or not all( + isinstance(layer, str) for layer in layers_raw + ): + raise ValueError("layers must be a string array") + + error_raw = entry.get("error") + error = error_raw if isinstance(error_raw, str) else None + + timings: PresetScanTimings | None = None + timings_raw = entry.get("timings") + if timings_raw is not None: + if not isinstance(timings_raw, dict): + raise ValueError("timings must be an object") + load_sec = timings_raw.get("load_sec") + render_sec = timings_raw.get("render_sec") + timings = PresetScanTimings( + load_sec=float(load_sec) if load_sec is not None else None, + render_sec=float(render_sec) if render_sec is not None else None, + ) + + luma_raw = entry.get("luma") + luma = frame_metrics_from_dict(luma_raw) if luma_raw is not None else None + + return PresetScanResult( + path=Path(path_raw).resolve(), + result=result_raw, + layers=tuple(layers_raw), + error=error, + timings=timings, + luma=luma, + ) + + +def _preset_result_to_dict(preset: PresetScanResult) -> dict[str, Any]: + payload: dict[str, Any] = { + "path": str(preset.path), + "result": preset.result, + "layers": list(preset.layers), + } + if preset.error is not None: + payload["error"] = preset.error + if preset.timings is not None: + timings: dict[str, float] = {} + if preset.timings.load_sec is not None: + timings["load_sec"] = preset.timings.load_sec + if preset.timings.render_sec is not None: + timings["render_sec"] = preset.timings.render_sec + if timings: + payload["timings"] = timings + if preset.luma is not None: + payload["luma"] = frame_metrics_to_dict(preset.luma) + return payload diff --git a/cleave/preset_scan_golden.py b/cleave/preset_scan_golden.py new file mode 100644 index 0000000..039bbbd --- /dev/null +++ b/cleave/preset_scan_golden.py @@ -0,0 +1,788 @@ +"""Golden-set harness for preset scan classifier tuning.""" + +from __future__ import annotations + +import itertools +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +import yaml + +from cleave.paths import repo_root +from cleave.preset_scan import ( + PROBE_FBO_HEIGHT, + PROBE_FBO_WIDTH, + PROBE_FPS, + PROBE_WARMUP_FRAMES, + PROBE_WINDOW_FRAMES, + WHITE_CHANNEL_MIN, + PresetResultCategory, + ProbeProfile, + ProbeSession, + build_probe_pcm, + classify_preset_result, + probe_preset_metrics, + probe_profile, + scan_thresholds, +) +from cleave.preset_scan_metrics import ( + METRICS_CACHE_VERSION, + FrameMetrics, + MetricsCache, + PresetMetrics, + empty_frame_metrics, + load_metrics_cache, + peak_metrics, + white_frame_fraction, + write_metrics_cache, +) +from cleave.projectm import PresetLoadFailure, ProjectM + +GOLDEN_CASE_COUNT = 50 +GoldenExpectedResult = Literal["ok", "dim", "black", "washed_out"] + +# Threshold values at or above this sentinel disable optional quick washed-out tiers. +SWEEP_RULE_DISABLED = 999.0 + +DEFAULT_SWEEP_WARMUP_FRAMES = (10, 12, 15, 18, 20) +DEFAULT_SWEEP_WINDOW_FRAMES = (60, 70, 75) + +DEFAULT_GOLDEN_SET_PATH = ( + repo_root() / "tests" / "fixtures" / "preset_scan_golden_set.yaml" +) +DEFAULT_METRICS_CACHE_PATH = ( + repo_root() / "tests" / "fixtures" / "preset_scan_golden_metrics.json" +) + +_VALID_EXPECTED: frozenset[str] = frozenset(("ok", "dim", "black", "washed_out")) + + +@dataclass(frozen=True) +class GoldenCase: + id: int + preset: Path + expected_result: GoldenExpectedResult + notes: str + + +@dataclass(frozen=True) +class GoldenSet: + version: int + preset_root: Path + texture_paths: tuple[Path, ...] + cases: tuple[GoldenCase, ...] + + +@dataclass(frozen=True) +class EvalMismatch: + id: int + preset_name: str + expected: GoldenExpectedResult + actual: PresetResultCategory + luma: FrameMetrics | None + white_frame_frac: float | None = None + white_coverage_peak: float | None = None + + +@dataclass(frozen=True) +class EvalReport: + total: int + correct: int + accuracy: float + per_category_accuracy: dict[GoldenExpectedResult, float] + confusion_matrix: dict[GoldenExpectedResult, dict[GoldenExpectedResult, int]] + mismatches: tuple[EvalMismatch, ...] + warmup_frames: int + window_frames: int + + +@dataclass(frozen=True) +class SweepResult: + warmup_frames: int + window_frames: int + thresholds: dict[str, float] | None + correct: int + total: int + accuracy: float + + +def _resolve_golden_path(raw: str) -> Path: + """Resolve a golden-set path (repo-relative, absolute, or ``~``).""" + path = Path(raw).expanduser() + if path.is_absolute(): + return path.resolve() + return (repo_root() / path).resolve() + + +def _golden_preset_key(path: Path) -> str: + """Stable lookup key for a preset across different preset_root locations.""" + normalized = path.expanduser().resolve().as_posix() + marker = "presets-milkdrop-original/" + idx = normalized.find(marker) + if idx != -1: + return normalized[idx:] + return path.name + + +def load_golden_set(path: Path | None = None) -> GoldenSet: + """Load and validate the golden-set YAML fixture.""" + resolved = (path or DEFAULT_GOLDEN_SET_PATH).expanduser().resolve() + if not resolved.is_file(): + raise ValueError(f"golden set not found: {resolved}") + + try: + raw = yaml.safe_load(resolved.read_text(encoding="utf-8")) + except yaml.YAMLError as exc: + raise ValueError(f"malformed YAML: {exc}") from exc + except OSError as exc: + raise ValueError(str(exc)) from exc + + if not isinstance(raw, dict): + raise ValueError("golden set root must be a mapping") + + version = raw.get("version") + if not isinstance(version, int): + raise ValueError("golden set missing version") + + preset_root_raw = raw.get("preset_root") + if not isinstance(preset_root_raw, str) or not preset_root_raw: + raise ValueError("golden set missing preset_root") + preset_root = _resolve_golden_path(preset_root_raw) + + texture_paths_raw = raw.get("texture_paths") + if not isinstance(texture_paths_raw, list) or not texture_paths_raw: + raise ValueError("golden set missing texture_paths") + texture_paths = tuple( + _resolve_golden_path(entry) + for entry in texture_paths_raw + if isinstance(entry, str) and entry + ) + if not texture_paths: + raise ValueError("golden set missing texture_paths") + + cases_raw = raw.get("cases") + if not isinstance(cases_raw, list): + raise ValueError("golden set missing cases array") + + cases: list[GoldenCase] = [] + for index, entry in enumerate(cases_raw): + if not isinstance(entry, dict): + raise ValueError(f"case at index {index} must be a mapping") + + case_id = entry.get("id") + if not isinstance(case_id, int): + raise ValueError(f"case at index {index} missing id") + + preset_raw = entry.get("preset") + if not isinstance(preset_raw, str) or not preset_raw: + raise ValueError(f"case {case_id} missing preset") + + expected_raw = entry.get("expected_result") + if expected_raw not in _VALID_EXPECTED: + raise ValueError(f"case {case_id} missing or invalid expected_result") + + notes_raw = entry.get("notes", "") + notes = notes_raw if isinstance(notes_raw, str) else "" + + preset_path = (preset_root / preset_raw).resolve() + cases.append( + GoldenCase( + id=case_id, + preset=preset_path, + expected_result=expected_raw, + notes=notes, + ) + ) + + if len(cases) != GOLDEN_CASE_COUNT: + raise ValueError( + f"golden set must contain {GOLDEN_CASE_COUNT} cases, got {len(cases)}" + ) + + seen_ids = {case.id for case in cases} + expected_ids = set(range(1, GOLDEN_CASE_COUNT + 1)) + if seen_ids != expected_ids: + missing = sorted(expected_ids - seen_ids) + extra = sorted(seen_ids - expected_ids) + parts: list[str] = [] + if missing: + parts.append(f"missing ids: {missing}") + if extra: + parts.append(f"unexpected ids: {extra}") + raise ValueError(f"golden set case ids invalid ({'; '.join(parts)})") + + cases_sorted = tuple(sorted(cases, key=lambda case: case.id)) + return GoldenSet( + version=version, + preset_root=preset_root, + texture_paths=texture_paths, + cases=cases_sorted, + ) + + +def probe_golden_set( + golden: GoldenSet, + cache_path: Path, +) -> MetricsCache: + """GL probe every golden case; write full-frame metrics cache.""" + profile = probe_profile() + probe_pcm = build_probe_pcm() + texture_strs = [str(path) for path in golden.texture_paths] + + presets: list[PresetMetrics] = [] + total = len(golden.cases) + with ProbeSession(texture_strs) as session: + for index, case in enumerate(golden.cases, start=1): + _progress(f"Probing {index}/{total} {case.preset}...") + pm = ProjectM() + try: + session.configure_projectm(pm) + presets.append( + probe_preset_metrics( + pm, + session.fbo, + case.preset, + profile=profile, + pcm=probe_pcm, + n_pcm=session.n_pcm, + frame_dt=session.frame_dt, + ) + ) + finally: + pm.destroy() + + cache = MetricsCache( + version=METRICS_CACHE_VERSION, + presets=tuple(presets), + probe_fps=PROBE_FPS, + fbo_size=(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT), + warmup_frames=profile.warmup_frames, + window_frames=profile.window_frames, + total_frames=profile.total_frames, + ) + write_metrics_cache(cache_path, cache) + return cache + + +def format_probe_profile_summary(profile: ProbeProfile) -> str: + return ( + f"{profile.warmup_frames} warmup + " + f"{profile.window_frames} window, {profile.total_frames} frames" + ) + + +def resolve_cache_probe_profile( + cache: MetricsCache, + *, + file: Any = None, +) -> ProbeProfile: + """Return the probe profile stored in or inferred from a metrics cache.""" + if cache.warmup_frames is not None and cache.window_frames is not None: + return ProbeProfile( + warmup_frames=cache.warmup_frames, + window_frames=cache.window_frames, + ) + + frame_counts = [len(entry.frames) for entry in cache.presets if entry.frames] + if not frame_counts: + raise ValueError("cannot infer probe profile from cache with no frame data") + + unique_counts = sorted(set(frame_counts)) + if len(unique_counts) != 1: + raise ValueError( + "cannot infer probe profile from cache with inconsistent frame counts: " + + ", ".join(str(count) for count in unique_counts) + ) + + count = unique_counts[0] + expected_frames = PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES + if count != expected_frames: + raise ValueError( + f"cannot infer probe profile from {count} cached frames per preset " + f"(expected {expected_frames})" + ) + + profile = probe_profile() + + out = file if file is not None else sys.stderr + print( + f"Warning: metrics cache v{cache.version} missing probe profile metadata; " + f"inferred {format_probe_profile_summary(profile)} from frame count", + file=out, + ) + return profile + + +def resolve_eval_probe_window( + cache: MetricsCache, + *, + warmup_frames: int | None = None, + window_frames: int | None = None, + strict: bool = True, + file: Any = None, +) -> tuple[int, int, ProbeProfile]: + """Resolve eval warmup/window from cache metadata, checking explicit overrides.""" + profile = resolve_cache_probe_profile(cache, file=file) + cache_warmup = profile.warmup_frames + cache_window = profile.window_frames + + resolved_warmup = cache_warmup if warmup_frames is None else warmup_frames + resolved_window = cache_window if window_frames is None else window_frames + + if strict and ( + resolved_warmup != cache_warmup or resolved_window != cache_window + ): + raise ValueError( + "eval probe profile mismatch: cache has " + f"{format_probe_profile_summary(profile)}; " + f"requested warmup={resolved_warmup} window={resolved_window}" + ) + + return resolved_warmup, resolved_window, profile + + +def _metrics_index_for_eval( + cache: MetricsCache, + *, + warmup_frames: int, + window_frames: int, +) -> dict[str, PresetMetrics]: + """Map resolved preset paths to metrics, keeping the strongest probe per path.""" + grouped: dict[str, list[PresetMetrics]] = {} + for entry in cache.presets: + grouped.setdefault(str(entry.path.resolve()), []).append(entry) + + indexed: dict[str, PresetMetrics] = {} + for path_key, entries in grouped.items(): + if len(entries) == 1: + indexed[path_key] = entries[0] + continue + indexed[path_key] = max( + entries, + key=lambda entry: peak_metrics( + entry.frames, + warmup_frames=warmup_frames, + window_frames=window_frames, + ).mean_luma, + ) + return indexed + + +def evaluate( + cache: MetricsCache, + golden: GoldenSet, + *, + warmup_frames: int | None = None, + window_frames: int | None = None, + thresholds: dict[str, float] | None = None, + strict_profile: bool = True, + file: Any = None, +) -> EvalReport: + """Classify cached metrics and compare to golden labels (GL-free).""" + resolved_warmup, resolved_window, profile = resolve_eval_probe_window( + cache, + warmup_frames=warmup_frames, + window_frames=window_frames, + strict=strict_profile, + file=file, + ) + metrics_by_path = _metrics_index_for_eval( + cache, + warmup_frames=resolved_warmup, + window_frames=resolved_window, + ) + mismatches: list[EvalMismatch] = [] + per_category_totals: dict[GoldenExpectedResult, int] = { + label: 0 for label in _VALID_EXPECTED + } + per_category_correct: dict[GoldenExpectedResult, int] = { + label: 0 for label in _VALID_EXPECTED + } + confusion: dict[GoldenExpectedResult, dict[GoldenExpectedResult, int]] = { + expected: {actual: 0 for actual in _VALID_EXPECTED} + for expected in _VALID_EXPECTED + } + + correct = 0 + for case in golden.cases: + preset_metrics = metrics_by_path.get(str(case.preset.resolve())) + if preset_metrics is None: + legacy_key = _golden_preset_key(case.preset) + preset_metrics = next( + ( + entry + for entry in cache.presets + if _golden_preset_key(entry.path) == legacy_key + ), + None, + ) + if preset_metrics is None: + raise ValueError( + f"metrics cache missing preset for case {case.id}: {case.preset}" + ) + + actual, luma, white_frac, white_cov_peak = _classify_cached_preset( + preset_metrics, + warmup_frames=resolved_warmup, + window_frames=resolved_window, + thresholds=thresholds, + ) + actual_golden = scan_result_to_golden(actual) + + per_category_totals[case.expected_result] += 1 + confusion[case.expected_result][actual_golden] += 1 + + if actual_golden == case.expected_result: + correct += 1 + per_category_correct[case.expected_result] += 1 + else: + mismatches.append( + EvalMismatch( + id=case.id, + preset_name=case.preset.name, + expected=case.expected_result, + actual=actual, + luma=luma, + white_frame_frac=white_frac, + white_coverage_peak=white_cov_peak, + ) + ) + + total = len(golden.cases) + per_category_accuracy: dict[GoldenExpectedResult, float] = {} + for label in _VALID_EXPECTED: + count = per_category_totals[label] + per_category_accuracy[label] = ( + per_category_correct[label] / count if count else 0.0 + ) + + return EvalReport( + total=total, + correct=correct, + accuracy=correct / total if total else 0.0, + per_category_accuracy=per_category_accuracy, + confusion_matrix=confusion, + mismatches=tuple(mismatches), + warmup_frames=resolved_warmup, + window_frames=resolved_window, + ) + + +def _threshold_variant_key(thresholds: dict[str, float] | None) -> tuple[tuple[str, float], ...] | None: + if thresholds is None: + return None + return tuple(sorted(thresholds.items())) + + +def _merge_threshold_variants( + *grids: tuple[dict[str, float] | None, ...], +) -> tuple[dict[str, float] | None, ...]: + merged: list[dict[str, float] | None] = [] + seen: set[tuple[tuple[str, float], ...] | None] = set() + for grid in grids: + for entry in grid: + key = _threshold_variant_key(entry) + if key in seen: + continue + seen.add(key) + merged.append(entry) + return tuple(merged) + + +def _threshold_grid( + base: dict[str, float], + axes: dict[str, tuple[float, ...]], +) -> tuple[dict[str, float], ...]: + if not axes: + return () + keys = tuple(axes.keys()) + combos = itertools.product(*(axes[key] for key in keys)) + return tuple( + { + **base, + **dict(zip(keys, combo)), + } + for combo in combos + ) + + +def _quick_luma_washed_disabled_overrides() -> dict[str, float]: + """Disable quick-only luma washed-out tiers; hard/soft white rules stay active.""" + return { + "washed_mean_soft": SWEEP_RULE_DISABLED, + "min_white_frame_frac_soft": SWEEP_RULE_DISABLED, + "washed_mean_peak": SWEEP_RULE_DISABLED, + "washed_max_lo": SWEEP_RULE_DISABLED, + "washed_max_mid": SWEEP_RULE_DISABLED, + } + + +def _hard_white_threshold_grid(base: dict[str, float]) -> tuple[dict[str, float], ...]: + return _threshold_grid( + base, + { + "white_channel_min": (224.0, 235.0, 245.0), + "white_area_frac": (0.5, 0.55, 0.6, 0.65, 0.7), + "min_white_frame_frac": (0.2, 0.25, 0.3, 0.35, 0.4), + }, + ) + + +def default_threshold_sweep_variants() -> tuple[dict[str, float] | None, ...]: + """Default threshold grids for golden sweep (GL-free; uses cached frame metrics).""" + base = scan_thresholds() + grids: list[tuple[dict[str, float] | None, ...]] = [(None,)] + + grids.append(_hard_white_threshold_grid(base)) + + luma_off = _quick_luma_washed_disabled_overrides() + grids.append( + _threshold_grid( + {**base, **luma_off}, + { + "white_channel_min": (224.0, 235.0, 245.0), + "white_area_frac": (0.5, 0.6, 0.7), + "min_white_frame_frac": (0.2, 0.3, 0.4), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "white_area_frac_soft": (0.10, 0.15, 0.20), + "min_white_frame_frac_soft": (0.40, 0.55, 0.70), + "washed_mean_soft": (180.0, 200.0, 220.0, SWEEP_RULE_DISABLED), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "washed_mean_peak": (235.0, 245.0, SWEEP_RULE_DISABLED), + "washed_cov192_peak": (0.90, 0.95, 0.99), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "washed_max_lo": (235.0, 240.0, SWEEP_RULE_DISABLED), + "washed_mean_lo": (150.0, 155.0, 160.0), + "washed_mean_hi": (170.0, 175.0, 180.0), + "washed_cov192_lo": (0.05, 0.10, 0.15, 0.20), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "washed_max_mid": (235.0, 240.0, SWEEP_RULE_DISABLED), + "washed_mean_mid_lo": (170.0, 176.0, 182.0), + "washed_mean_mid_hi": (188.0, 192.0, 196.0), + "washed_cov192_mid": (0.25, 0.30, 0.40), + "washed_white235_mid_max": (0.10, 0.20, 0.35), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "dim_bob_cov_lo": (0.05, 0.07, 0.10), + "dim_bob_cov_hi": (0.12, 0.13, 0.15), + "dim_bob_mean_mid": (5.5, 6.0, 7.0), + "dim_bob_mean_hi": (9.5, 10.0, 11.0), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "very_sparse_dim_mean": (5.10, 5.15, 5.20), + "sparse_dim_mean": (9.90, 9.95, 10.0), + "sparse_dim_cov16_max": (0.128, 0.140, 0.165), + }, + ) + ) + grids.append( + _threshold_grid( + base, + { + "white_channel_min": (224.0, 235.0, 245.0), + "min_white_frame_frac": (0.25, 0.30, 0.35), + "washed_mean_peak": (245.0, SWEEP_RULE_DISABLED), + "washed_max_lo": (240.0, SWEEP_RULE_DISABLED), + "washed_max_mid": (240.0, SWEEP_RULE_DISABLED), + }, + ) + ) + + return _merge_threshold_variants(*grids) + + +def sweep( + cache: MetricsCache, + golden: GoldenSet, + *, + warmup_frames_values: tuple[int, ...] | None = None, + window_frames_values: tuple[int, ...] | None = None, + threshold_variants: tuple[dict[str, float] | None, ...] | None = None, +) -> list[SweepResult]: + """Grid search over probe windows and thresholds; rank by golden agreement.""" + if warmup_frames_values is None: + warmups = DEFAULT_SWEEP_WARMUP_FRAMES + else: + warmups = warmup_frames_values + if window_frames_values is None: + windows = DEFAULT_SWEEP_WINDOW_FRAMES + else: + windows = window_frames_values + threshold_sets = threshold_variants or default_threshold_sweep_variants() + + results: list[SweepResult] = [] + for warmup in warmups: + for window in windows: + for thresholds in threshold_sets: + report = evaluate( + cache, + golden, + warmup_frames=warmup, + window_frames=window, + thresholds=thresholds, + strict_profile=False, + ) + results.append( + SweepResult( + warmup_frames=warmup, + window_frames=window, + thresholds=thresholds, + correct=report.correct, + total=report.total, + accuracy=report.accuracy, + ) + ) + + results.sort( + key=lambda entry: (-entry.accuracy, -entry.correct, entry.warmup_frames, entry.window_frames) + ) + return results + + +def print_eval_report(report: EvalReport, *, file: Any = None) -> None: + """Print a human-readable evaluation summary to stderr.""" + out = file if file is not None else sys.stderr + print( + f"Golden eval: {report.correct}/{report.total} correct " + f"({report.accuracy * 100:.1f}%) " + f"[warmup={report.warmup_frames}, " + f"window={report.window_frames}, " + f"{report.warmup_frames + report.window_frames} frames]", + file=out, + ) + print("Per-category accuracy:", file=out) + for label in ("ok", "dim", "black", "washed_out"): + accuracy = report.per_category_accuracy[label] + print(f" {label}: {accuracy * 100:.1f}%", file=out) + + print("Confusion matrix (expected -> actual):", file=out) + for expected in ("ok", "dim", "black", "washed_out"): + row = report.confusion_matrix[expected] + counts = ", ".join(f"{actual}={row[actual]}" for actual in row) + print(f" {expected}: {counts}", file=out) + + if report.mismatches: + print(f"Mismatches ({len(report.mismatches)}):", file=out) + for mismatch in report.mismatches: + luma_bits = "" + if mismatch.luma is not None: + luma_bits = ( + f" max={mismatch.luma.max_luma:.1f}" + f" mean={mismatch.luma.mean_luma:.1f}" + f" cov16={mismatch.luma.coverage.get(16, 0.0):.4f}" + ) + white_bits = "" + if mismatch.white_frame_frac is not None: + white_bits = f" white_frac={mismatch.white_frame_frac:.3f}" + if mismatch.white_coverage_peak is not None: + white_bits += ( + f" white_cov{int(WHITE_CHANNEL_MIN)}=" + f"{mismatch.white_coverage_peak:.4f}" + ) + print( + f" [{mismatch.id}] {mismatch.preset_name}: " + f"expected={mismatch.expected} actual={mismatch.actual}" + f"{luma_bits}{white_bits}", + file=out, + ) + + +def scan_result_to_golden(result: PresetResultCategory) -> GoldenExpectedResult: + """Map scan classifier output to golden label space.""" + if result == "load_failed": + return "black" + return result + + +def _classify_cached_preset( + preset_metrics: PresetMetrics, + *, + warmup_frames: int, + window_frames: int, + thresholds: dict[str, float] | None, +) -> tuple[PresetResultCategory, FrameMetrics | None, float | None, float | None]: + failures: list[PresetLoadFailure] = [] + if preset_metrics.load_failed: + failures = [ + PresetLoadFailure( + filename=str(preset_metrics.path), + message=preset_metrics.error or "load failed", + ) + ] + + if not preset_metrics.frames: + category, _ = classify_preset_result( + failures, empty_frame_metrics(), thresholds=thresholds + ) + return category, None, None, None + + window_slice = preset_metrics.frames[ + warmup_frames : warmup_frames + window_frames + ] + peaks = peak_metrics( + preset_metrics.frames, + warmup_frames=warmup_frames, + window_frames=window_frames, + ) + category, _ = classify_preset_result( + failures, + peaks, + frames=window_slice, + thresholds=thresholds, + ) + th = scan_thresholds() + if thresholds is not None: + th.update(thresholds) + white_frac = white_frame_fraction( + window_slice, + warmup_frames=0, + window_frames=0, + channel_min_cutoff=int(th["white_channel_min"]), + area_frac=th["white_area_frac"], + ) + white_cov_peak = ( + peaks.white_coverage.get(int(th["white_channel_min"])) + if peaks.white_coverage + else None + ) + return category, peaks, white_frac, white_cov_peak + + +def _progress(message: str) -> None: + print(message, file=sys.stderr, flush=True) diff --git a/cleave/preset_scan_metrics.py b/cleave/preset_scan_metrics.py new file mode 100644 index 0000000..9d377ab --- /dev/null +++ b/cleave/preset_scan_metrics.py @@ -0,0 +1,351 @@ +"""Per-frame luma metrics and cache serialization for preset scan probes.""" + +from __future__ import annotations + +import json +import os +import tempfile +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +from OpenGL.GL import GL_RGBA, GL_UNSIGNED_BYTE, glReadPixels + +LUMA_COVERAGE_CUTOFFS: tuple[int, ...] = (8, 16, 32, 64, 128, 192) +WHITE_COVERAGE_CUTOFFS: tuple[int, ...] = (224, 235, 245) +METRICS_CACHE_VERSION = 3 +SUPPORTED_METRICS_CACHE_VERSIONS: frozenset[int] = frozenset((3,)) + +_LUMA_R = 0.2126 +_LUMA_G = 0.7152 +_LUMA_B = 0.0722 + + +@dataclass(frozen=True) +class FrameMetrics: + max_luma: float + mean_luma: float + coverage: dict[int, float] + white_coverage: dict[int, float] + + +@dataclass(frozen=True) +class PresetMetrics: + path: Path + load_failed: bool + error: str | None + fps: int + frames: tuple[FrameMetrics, ...] + + +@dataclass(frozen=True) +class MetricsCache: + version: int + presets: tuple[PresetMetrics, ...] + probe_fps: int + fbo_size: tuple[int, int] + warmup_frames: int | None = None + window_frames: int | None = None + total_frames: int | None = None + + +def empty_frame_metrics() -> FrameMetrics: + return FrameMetrics( + max_luma=0.0, + mean_luma=0.0, + coverage={cutoff: 0.0 for cutoff in LUMA_COVERAGE_CUTOFFS}, + white_coverage={cutoff: 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS}, + ) + + +def sample_frame_metrics(width: int, height: int) -> FrameMetrics: + raw = glReadPixels(0, 0, width, height, GL_RGBA, GL_UNSIGNED_BYTE) + rgba = np.frombuffer(raw, dtype=np.uint8).reshape(height, width, 4) + r = rgba[..., 0].astype(np.float32) + g = rgba[..., 1].astype(np.float32) + b = rgba[..., 2].astype(np.float32) + luma = _LUMA_R * r + _LUMA_G * g + _LUMA_B * b + white = np.minimum(np.minimum(r, g), b) + coverage = { + cutoff: float((luma >= cutoff).mean()) for cutoff in LUMA_COVERAGE_CUTOFFS + } + white_coverage = { + cutoff: float((white >= cutoff).mean()) for cutoff in WHITE_COVERAGE_CUTOFFS + } + return FrameMetrics( + max_luma=float(luma.max()), + mean_luma=float(luma.mean()), + coverage=coverage, + white_coverage=white_coverage, + ) + + +def peak_metrics( + frames: list[FrameMetrics] | tuple[FrameMetrics, ...], + *, + warmup_frames: int, + window_frames: int, +) -> FrameMetrics: + if not frames: + return empty_frame_metrics() + + start = max(0, warmup_frames) + if window_frames <= 0: + window = frames[start:] + else: + window = frames[start : start + window_frames] + if not window: + return empty_frame_metrics() + + return FrameMetrics( + max_luma=max(frame.max_luma for frame in window), + mean_luma=max(frame.mean_luma for frame in window), + coverage={ + cutoff: max(frame.coverage[cutoff] for frame in window) + for cutoff in LUMA_COVERAGE_CUTOFFS + }, + white_coverage={ + cutoff: max(frame.white_coverage[cutoff] for frame in window) + for cutoff in WHITE_COVERAGE_CUTOFFS + }, + ) + + +def white_frame_fraction( + frames: list[FrameMetrics] | tuple[FrameMetrics, ...], + *, + warmup_frames: int, + window_frames: int, + channel_min_cutoff: int, + area_frac: float, +) -> float: + if not frames: + return 0.0 + + start = max(0, warmup_frames) + if window_frames <= 0: + window = frames[start:] + else: + window = frames[start : start + window_frames] + if not window: + return 0.0 + + white_count = sum( + 1 + for frame in window + if frame.white_coverage[channel_min_cutoff] >= area_frac + ) + return white_count / len(window) + + +def frame_metrics_to_dict(metrics: FrameMetrics) -> dict[str, Any]: + return { + "max_luma": metrics.max_luma, + "mean_luma": metrics.mean_luma, + "coverage": {str(cutoff): metrics.coverage[cutoff] for cutoff in LUMA_COVERAGE_CUTOFFS}, + "white_coverage": { + str(cutoff): metrics.white_coverage[cutoff] + for cutoff in WHITE_COVERAGE_CUTOFFS + }, + } + + +def frame_metrics_from_dict(data: Any) -> FrameMetrics: + if not isinstance(data, dict): + raise ValueError("frame metrics must be a JSON object") + + max_luma = data.get("max_luma") + mean_luma = data.get("mean_luma") + if not isinstance(max_luma, (int, float)) or not isinstance(mean_luma, (int, float)): + raise ValueError("frame metrics missing max_luma or mean_luma") + + coverage_raw = data.get("coverage") + if not isinstance(coverage_raw, dict): + raise ValueError("frame metrics missing coverage object") + + coverage: dict[int, float] = {} + for cutoff in LUMA_COVERAGE_CUTOFFS: + value = coverage_raw.get(str(cutoff), coverage_raw.get(cutoff)) + if not isinstance(value, (int, float)): + raise ValueError(f"frame metrics missing coverage for cutoff {cutoff}") + coverage[cutoff] = float(value) + + white_coverage_raw = data.get("white_coverage") + if not isinstance(white_coverage_raw, dict): + raise ValueError("frame metrics missing white_coverage object") + + white_coverage: dict[int, float] = {} + for cutoff in WHITE_COVERAGE_CUTOFFS: + value = white_coverage_raw.get(str(cutoff), white_coverage_raw.get(cutoff)) + if not isinstance(value, (int, float)): + raise ValueError(f"frame metrics missing white_coverage for cutoff {cutoff}") + white_coverage[cutoff] = float(value) + + return FrameMetrics( + max_luma=float(max_luma), + mean_luma=float(mean_luma), + coverage=coverage, + white_coverage=white_coverage, + ) + + +def preset_metrics_to_dict(metrics: PresetMetrics) -> dict[str, Any]: + payload: dict[str, Any] = { + "path": str(metrics.path), + "load_failed": metrics.load_failed, + "fps": metrics.fps, + "frames": [frame_metrics_to_dict(frame) for frame in metrics.frames], + } + if metrics.error is not None: + payload["error"] = metrics.error + return payload + + +def preset_metrics_from_dict(data: Any) -> PresetMetrics: + if not isinstance(data, dict): + raise ValueError("preset metrics must be a JSON object") + + path_raw = data.get("path") + if not isinstance(path_raw, str) or not path_raw: + raise ValueError("preset metrics missing path") + + load_failed = data.get("load_failed") + if not isinstance(load_failed, bool): + raise ValueError("preset metrics missing load_failed") + + fps = data.get("fps") + if not isinstance(fps, int): + raise ValueError("preset metrics missing fps") + + frames_raw = data.get("frames") + if not isinstance(frames_raw, list): + raise ValueError("preset metrics missing frames array") + + error_raw = data.get("error") + error = error_raw if isinstance(error_raw, str) else None + + frames = tuple(frame_metrics_from_dict(entry) for entry in frames_raw) + return PresetMetrics( + path=Path(path_raw).resolve(), + load_failed=load_failed, + error=error, + fps=fps, + frames=frames, + ) + + +def metrics_cache_to_dict(cache: MetricsCache) -> dict[str, Any]: + payload: dict[str, Any] = { + "version": cache.version, + "probe_fps": cache.probe_fps, + "fbo_size": list(cache.fbo_size), + "presets": [preset_metrics_to_dict(preset) for preset in cache.presets], + } + if cache.warmup_frames is not None: + payload["warmup_frames"] = cache.warmup_frames + if cache.window_frames is not None: + payload["window_frames"] = cache.window_frames + if cache.total_frames is not None: + payload["total_frames"] = cache.total_frames + return payload + + +def _optional_int_field(data: dict[str, Any], key: str) -> int | None: + value = data.get(key) + if value is None: + return None + if not isinstance(value, int): + raise ValueError(f"metrics cache field {key!r} must be an integer") + return value + + +def metrics_cache_from_dict(data: Any) -> MetricsCache: + if not isinstance(data, dict): + raise ValueError("metrics cache root must be a JSON object") + + version = data.get("version") + if version not in SUPPORTED_METRICS_CACHE_VERSIONS: + if version == 2: + raise ValueError( + f"metrics cache version {version} is stale (current is " + f"{METRICS_CACHE_VERSION}); regenerate with " + "cleave scan-golden --probe" + ) + raise ValueError(f"unsupported metrics cache version: {version!r}") + + probe_fps = data.get("probe_fps") + if not isinstance(probe_fps, int): + raise ValueError("metrics cache missing probe_fps") + + fbo_size_raw = data.get("fbo_size") + if ( + not isinstance(fbo_size_raw, list) + or len(fbo_size_raw) != 2 + or not all(isinstance(value, int) for value in fbo_size_raw) + ): + raise ValueError("metrics cache missing fbo_size") + + presets_raw = data.get("presets") + if not isinstance(presets_raw, list): + raise ValueError("metrics cache missing presets array") + + warmup_frames = _optional_int_field(data, "warmup_frames") + window_frames = _optional_int_field(data, "window_frames") + total_frames = _optional_int_field(data, "total_frames") + + if version == METRICS_CACHE_VERSION: + missing = [ + name + for name, value in ( + ("warmup_frames", warmup_frames), + ("window_frames", window_frames), + ("total_frames", total_frames), + ) + if value is None + ] + if missing: + raise ValueError( + f"metrics cache v{METRICS_CACHE_VERSION} missing required fields: " + + ", ".join(missing) + ) + + presets = tuple(preset_metrics_from_dict(entry) for entry in presets_raw) + return MetricsCache( + version=version, + presets=presets, + probe_fps=probe_fps, + fbo_size=(fbo_size_raw[0], fbo_size_raw[1]), + warmup_frames=warmup_frames, + window_frames=window_frames, + total_frames=total_frames, + ) + + +def write_metrics_cache(path: Path, cache: MetricsCache) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, tmp_name = tempfile.mkstemp(dir=path.parent, suffix=".tmp") + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as fh: + json.dump(metrics_cache_to_dict(cache), fh, indent=2) + fh.write("\n") + os.replace(tmp_path, path) + except BaseException: + tmp_path.unlink(missing_ok=True) + raise + + +def load_metrics_cache(path: Path) -> MetricsCache: + resolved = path.expanduser().resolve() + if not resolved.is_file(): + raise ValueError(f"metrics cache not found: {resolved}") + + try: + raw = json.loads(resolved.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + raise ValueError(f"malformed JSON: {exc}") from exc + except OSError as exc: + raise ValueError(str(exc)) from exc + + return metrics_cache_from_dict(raw) diff --git a/cleave/preset_scan_targets.py b/cleave/preset_scan_targets.py new file mode 100644 index 0000000..3b92c16 --- /dev/null +++ b/cleave/preset_scan_targets.py @@ -0,0 +1,91 @@ +"""Derive preset scan sets for project and bulk ``cleave scan`` modes.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path + +from cleave.config import CleaveConfig +from cleave.preset_playlist import milk_files_in_dir, scan_preset_playlist + + +@dataclass(frozen=True) +class PresetTarget: + """One deduplicated preset path and the layer slots that reference it.""" + + path: Path + layers: tuple[str, ...] + + +@dataclass(frozen=True) +class ScanTargets: + """Preset paths to probe plus project-mode attribution metadata.""" + + presets: tuple[PresetTarget, ...] + preset_root: Path | None = None + texture_paths: tuple[Path, ...] = () + presets_dir: Path | None = None + layer_sources: dict[str, tuple[Path, ...]] = field(default_factory=dict) + + +def build_project_targets(cfg: CleaveConfig) -> ScanTargets: + """Collect presets from on-disk config, matching live rotation scope.""" + by_path: dict[Path, list[str]] = {} + layer_sources: dict[str, tuple[Path, ...]] = {} + + for slot in cfg.layer_z_order: + layer = cfg.layers.get(slot) + if layer is None: + continue + + playlist = scan_preset_playlist(layer.preset) + anchor_dir = playlist.current_dir + sources: list[Path] = [anchor_dir] + + for preset_path in milk_files_in_dir(anchor_dir): + _register_preset(by_path, preset_path, slot) + + if layer.preset_switching == "user_defined": + for preset_path in layer.preset_switching_presets: + sources.append(preset_path) + _register_preset(by_path, preset_path, slot) + + layer_sources[slot] = tuple(sources) + + presets = _finalize_presets(by_path) + return ScanTargets( + presets=presets, + preset_root=cfg.paths.preset_root.resolve(), + texture_paths=tuple(p.resolve() for p in cfg.paths.texture_paths), + layer_sources=layer_sources, + ) + + +def build_bulk_targets(presets_dir: Path, *, recursive: bool = False) -> ScanTargets: + """Collect presets from a directory for bulk scan mode.""" + resolved_dir = presets_dir.resolve() + if recursive: + paths = tuple(sorted(p.resolve() for p in resolved_dir.rglob("*.milk"))) + else: + paths = milk_files_in_dir(resolved_dir) + + presets = tuple(PresetTarget(path=path, layers=()) for path in paths) + return ScanTargets(presets=presets, presets_dir=resolved_dir) + + +def _register_preset( + by_path: dict[Path, list[str]], + path: Path, + slot: str, +) -> None: + resolved = path.resolve() + slots = by_path.setdefault(resolved, []) + if slot not in slots: + slots.append(slot) + + +def _finalize_presets(by_path: dict[Path, list[str]]) -> tuple[PresetTarget, ...]: + return tuple( + PresetTarget(path=path, layers=tuple(slots)) + for path, slots in sorted(by_path.items(), key=lambda item: str(item[0])) + ) diff --git a/cleave/projectm.py b/cleave/projectm.py index 23678b4..0d6f1bb 100644 --- a/cleave/projectm.py +++ b/cleave/projectm.py @@ -5,8 +5,13 @@ import ctypes import os import subprocess +import sys +from collections import deque +from collections.abc import Callable from ctypes import ( + CFUNCTYPE, POINTER, + byref, c_bool, c_char_p, c_double, @@ -14,9 +19,11 @@ c_int32, c_size_t, c_uint, + c_uint8, c_uint32, c_void_p, ) +from dataclasses import dataclass from pathlib import Path import numpy as np @@ -27,6 +34,32 @@ PROJECTM_STEREO = 2 _lib: ctypes.CDLL | None = None +_log_callback: CFUNCTYPE | None = None + +PROJECTM_LOG_LEVEL_DEBUG = 2 + +PresetSwitchFailedEvent = CFUNCTYPE(None, c_char_p, c_char_p, c_void_p) +LogCallback = CFUNCTYPE(None, c_char_p, c_int32, c_void_p) + + +class ProjectmTextureLoadData(ctypes.Structure): + _fields_ = [ + ("data", POINTER(c_uint8)), + ("width", c_uint32), + ("height", c_uint32), + ("channels", c_uint32), + ("texture_id", c_uint32), + ] + + +TextureLoadEvent = CFUNCTYPE(None, c_char_p, POINTER(ProjectmTextureLoadData), c_void_p) + + +@dataclass(frozen=True) +class PresetLoadFailure: + filename: str + message: str + exhausted: bool = False class ProjectMLibraryError(OSError): @@ -200,6 +233,86 @@ def _bind_functions(lib: ctypes.CDLL, path: str) -> None: lib.projectm_set_preset_start_clean.argtypes = [c_void_p, c_bool] lib.projectm_set_preset_start_clean.restype = None + _bind_optional_functions(lib) + + +def _bind_optional_functions(lib: ctypes.CDLL) -> None: + if hasattr(lib, "projectm_set_preset_switch_failed_event_callback"): + lib.projectm_set_preset_switch_failed_event_callback.argtypes = [ + c_void_p, + PresetSwitchFailedEvent, + c_void_p, + ] + lib.projectm_set_preset_switch_failed_event_callback.restype = None + + if hasattr(lib, "projectm_set_texture_load_event_callback"): + lib.projectm_set_texture_load_event_callback.argtypes = [ + c_void_p, + TextureLoadEvent, + c_void_p, + ] + lib.projectm_set_texture_load_event_callback.restype = None + + if hasattr(lib, "projectm_set_log_callback"): + lib.projectm_set_log_callback.argtypes = [ + LogCallback, + c_bool, + c_void_p, + ] + lib.projectm_set_log_callback.restype = None + + if hasattr(lib, "projectm_set_log_level"): + lib.projectm_set_log_level.argtypes = [c_int32, c_bool] + lib.projectm_set_log_level.restype = None + + if hasattr(lib, "projectm_get_version_components"): + lib.projectm_get_version_components.argtypes = [ + POINTER(c_int32), + POINTER(c_int32), + POINTER(c_int32), + ] + lib.projectm_get_version_components.restype = None + + if hasattr(lib, "projectm_get_version_string"): + lib.projectm_get_version_string.argtypes = [] + lib.projectm_get_version_string.restype = c_char_p + + if hasattr(lib, "projectm_get_vcs_version_string"): + lib.projectm_get_vcs_version_string.argtypes = [] + lib.projectm_get_vcs_version_string.restype = c_char_p + + if hasattr(lib, "projectm_free_string"): + lib.projectm_free_string.argtypes = [c_void_p] + lib.projectm_free_string.restype = None + + +def _decode_lib_string(ptr: c_char_p) -> str | None: + if not ptr: + return None + return ctypes.string_at(ptr).decode("utf-8") + + +def _free_lib_string(lib: ctypes.CDLL, ptr: c_char_p) -> None: + if ptr and hasattr(lib, "projectm_free_string"): + lib.projectm_free_string(ptr) + + +def _enable_debug_logging(lib: ctypes.CDLL) -> None: + global _log_callback + if os.environ.get("CLEAVE_PROJECTM_LOG") != "1": + return + if not hasattr(lib, "projectm_set_log_callback"): + return + + def _on_log(message: bytes, _level: int, _user_data: c_void_p) -> None: + if message: + print(message.decode("utf-8", errors="replace"), file=sys.stderr) + + _log_callback = LogCallback(_on_log) + lib.projectm_set_log_callback(_log_callback, c_bool(False), c_void_p()) + if hasattr(lib, "projectm_set_log_level"): + lib.projectm_set_log_level(c_int32(PROJECTM_LOG_LEVEL_DEBUG), c_bool(False)) + def _get_lib() -> ctypes.CDLL: global _lib @@ -214,6 +327,7 @@ def _get_lib() -> ctypes.CDLL: except (OSError, ProjectMLibraryError) as exc: errors.append(f"{path}: {exc}") continue + _enable_debug_logging(loaded) _lib = loaded return loaded @@ -238,6 +352,9 @@ def __init__(self) -> None: self._texture_path_storage: list[bytes] = [] self._beat_sensitivity = DEFAULT_BEAT_SENSITIVITY self._pcm_channels = 1 + self._failure_queue: deque[PresetLoadFailure] = deque() + self._switch_failed_callback: PresetSwitchFailedEvent | None = None + self.set_preset_switch_failed_handler(self._enqueue_preset_failure) @property def handle(self) -> c_void_p: @@ -360,3 +477,82 @@ def set_easter_egg(self, value: float) -> None: def set_preset_start_clean(self, enabled: bool) -> None: _get_lib().projectm_set_preset_start_clean(self._handle, c_bool(enabled)) + + def _enqueue_preset_failure( + self, + filename: str, + message: str, + *, + exhausted: bool = False, + ) -> None: + self._failure_queue.append( + PresetLoadFailure( + filename=filename, + message=message, + exhausted=exhausted, + ) + ) + + def set_preset_switch_failed_handler( + self, callback: Callable[[str, str], None] + ) -> None: + lib = _get_lib() + if not hasattr(lib, "projectm_set_preset_switch_failed_event_callback"): + return + + def _on_failed( + filename: bytes, message: bytes, _user_data: c_void_p + ) -> None: + callback( + filename.decode("utf-8") if filename else "", + message.decode("utf-8") if message else "", + ) + + self._switch_failed_callback = PresetSwitchFailedEvent(_on_failed) + lib.projectm_set_preset_switch_failed_event_callback( + self._handle, self._switch_failed_callback, c_void_p() + ) + + def clear_preset_switch_failed_handler(self) -> None: + lib = _get_lib() + if hasattr(lib, "projectm_set_preset_switch_failed_event_callback"): + lib.projectm_set_preset_switch_failed_event_callback( + self._handle, PresetSwitchFailedEvent(), c_void_p() + ) + self._switch_failed_callback = None + + def drain_preset_failures(self) -> list[PresetLoadFailure]: + failures = list(self._failure_queue) + self._failure_queue.clear() + return failures + + def version_info(self) -> dict[str, int | str]: + lib = _get_lib() + info: dict[str, int | str] = {} + if hasattr(lib, "projectm_get_version_components"): + major = c_int32() + minor = c_int32() + patch = c_int32() + lib.projectm_get_version_components( + byref(major), byref(minor), byref(patch) + ) + info["major"] = major.value + info["minor"] = minor.value + info["patch"] = patch.value + if hasattr(lib, "projectm_get_version_string"): + ptr = lib.projectm_get_version_string() + try: + version = _decode_lib_string(ptr) + if version is not None: + info["version"] = version + finally: + _free_lib_string(lib, ptr) + if hasattr(lib, "projectm_get_vcs_version_string"): + ptr = lib.projectm_get_vcs_version_string() + try: + vcs = _decode_lib_string(ptr) + if vcs is not None: + info["vcs"] = vcs + finally: + _free_lib_string(lib, ptr) + return info diff --git a/cleave/projectm_health.py b/cleave/projectm_health.py new file mode 100644 index 0000000..352e76b --- /dev/null +++ b/cleave/projectm_health.py @@ -0,0 +1,48 @@ +"""Drain projectM preset load failures for live stem layers.""" + +from __future__ import annotations + +import time +from collections.abc import Callable +from dataclasses import dataclass, field +from pathlib import Path + +from cleave.viz.layer import StemLayer +from cleave.viz.preset_switching import EMPTY_ROTATION_NOTIFICATION + +PRESET_SKIP_NOTIFICATION_INTERVAL_SEC = 10.0 + + +@dataclass +class PresetSkipNotifyTracker: + last_notify: dict[str, float] = field(default_factory=dict) + + +def drain_stem_layers_preset_failures( + layers: list[StemLayer], + *, + on_notification: Callable[[str], None] | None = None, + skip_notify_tracker: PresetSkipNotifyTracker | None = None, +) -> None: + """Drain queued preset failures; optionally show rate-limited panel messages.""" + tracker = ( + skip_notify_tracker + if skip_notify_tracker is not None + else PresetSkipNotifyTracker() + ) + now = time.monotonic() + for layer in layers: + failures = layer.pm.drain_preset_failures() + for failure in failures: + if failure.exhausted: + if on_notification is not None: + on_notification(EMPTY_ROTATION_NOTIFICATION) + continue + if on_notification is None: + continue + last = tracker.last_notify.get(layer.slot, 0.0) + if now - last < PRESET_SKIP_NOTIFICATION_INTERVAL_SEC: + continue + basename = Path(failure.filename).name if failure.filename else "preset" + on_notification(f"Skipped preset: {basename}") + tracker.last_notify[layer.slot] = now diff --git a/cleave/projectm_playlist.py b/cleave/projectm_playlist.py index b778d4b..eef730f 100644 --- a/cleave/projectm_playlist.py +++ b/cleave/projectm_playlist.py @@ -13,6 +13,8 @@ _lib: ctypes.CDLL | None = None +DEFAULT_RETRY_COUNT = 500 + class ProjectMPlaylistLibraryError(OSError): """libprojectM playlist shared library not found or failed to load.""" @@ -89,8 +91,8 @@ def _library_candidates() -> list[str]: "projectm_playlist_set_shuffle", ) -PresetLoadEvent = CFUNCTYPE(c_bool, c_uint32, c_char_p, c_bool, c_void_p) PresetSwitchedEvent = CFUNCTYPE(None, c_bool, c_uint32, c_void_p) +PresetSwitchFailedEvent = CFUNCTYPE(None, c_char_p, c_char_p, c_void_p) def _bind_functions(lib: ctypes.CDLL, path: str) -> None: @@ -121,21 +123,33 @@ def _bind_functions(lib: ctypes.CDLL, path: str) -> None: lib.projectm_playlist_set_shuffle.argtypes = [c_void_p, c_bool] lib.projectm_playlist_set_shuffle.restype = None - if hasattr(lib, "projectm_playlist_set_preset_load_event_callback"): - lib.projectm_playlist_set_preset_load_event_callback.argtypes = [ + if hasattr(lib, "projectm_playlist_set_preset_switched_event_callback"): + lib.projectm_playlist_set_preset_switched_event_callback.argtypes = [ c_void_p, c_void_p, c_void_p, ] - lib.projectm_playlist_set_preset_load_event_callback.restype = None + lib.projectm_playlist_set_preset_switched_event_callback.restype = None - if hasattr(lib, "projectm_playlist_set_preset_switched_event_callback"): - lib.projectm_playlist_set_preset_switched_event_callback.argtypes = [ + if hasattr(lib, "projectm_playlist_set_preset_switch_failed_event_callback"): + lib.projectm_playlist_set_preset_switch_failed_event_callback.argtypes = [ c_void_p, c_void_p, c_void_p, ] - lib.projectm_playlist_set_preset_switched_event_callback.restype = None + lib.projectm_playlist_set_preset_switch_failed_event_callback.restype = None + + if hasattr(lib, "projectm_playlist_play_next"): + lib.projectm_playlist_play_next.argtypes = [c_void_p, c_bool] + lib.projectm_playlist_play_next.restype = c_uint32 + + if hasattr(lib, "projectm_playlist_get_retry_count"): + lib.projectm_playlist_get_retry_count.argtypes = [c_void_p] + lib.projectm_playlist_get_retry_count.restype = c_uint32 + + if hasattr(lib, "projectm_playlist_set_retry_count"): + lib.projectm_playlist_set_retry_count.argtypes = [c_void_p, c_uint32] + lib.projectm_playlist_set_retry_count.restype = None if hasattr(lib, "projectm_playlist_size"): lib.projectm_playlist_size.argtypes = [c_void_p] @@ -210,8 +224,8 @@ class ProjectMPlaylist: def __init__(self, handle: c_void_p, *, pm: ProjectM | None = None) -> None: self._handle = handle self._pm = pm - self._preset_load_callback: PresetLoadEvent | None = None self._preset_switched_callback: PresetSwitchedEvent | None = None + self._switch_failed_callback: PresetSwitchFailedEvent | None = None self._on_preset_loaded: Callable[[Path], None] | None = None @classmethod @@ -240,21 +254,24 @@ def connect( _get_lib().projectm_playlist_connect(self._handle, pm_handle) self._pm = pm if pm is not None: + self.set_retry_count(DEFAULT_RETRY_COUNT) self._install_callbacks() def _clear_callbacks(self) -> None: lib = _get_lib() if self._handle: - if hasattr(lib, "projectm_playlist_set_preset_load_event_callback"): - lib.projectm_playlist_set_preset_load_event_callback( - self._handle, c_void_p(), c_void_p() - ) if hasattr(lib, "projectm_playlist_set_preset_switched_event_callback"): lib.projectm_playlist_set_preset_switched_event_callback( self._handle, c_void_p(), c_void_p() ) - self._preset_load_callback = None + if hasattr( + lib, "projectm_playlist_set_preset_switch_failed_event_callback" + ): + lib.projectm_playlist_set_preset_switch_failed_event_callback( + self._handle, c_void_p(), c_void_p() + ) self._preset_switched_callback = None + self._switch_failed_callback = None def _notify_preset_loaded(self, path: Path) -> None: if self._on_preset_loaded is not None: @@ -266,21 +283,6 @@ def _install_callbacks(self) -> None: if pm is None: return - if hasattr(lib, "projectm_playlist_set_preset_load_event_callback"): - def _on_preset_load( - _index: int, filename: bytes, hard_cut: bool, _user_data: c_void_p - ) -> bool: - if filename: - decoded = filename.decode("utf-8") - pm.load_preset(decoded, smooth=not hard_cut) - self._notify_preset_loaded(Path(decoded)) - return True - - self._preset_load_callback = PresetLoadEvent(_on_preset_load) - lib.projectm_playlist_set_preset_load_event_callback( - self._handle, self._preset_load_callback, c_void_p() - ) - if hasattr(lib, "projectm_playlist_set_preset_switched_event_callback"): playlist = self @@ -296,6 +298,43 @@ def _on_preset_switched( self._handle, self._preset_switched_callback, c_void_p() ) + if hasattr(lib, "projectm_playlist_set_preset_switch_failed_event_callback"): + def _on_switch_failed( + filename: bytes, message: bytes, _user_data: c_void_p + ) -> None: + pm._enqueue_preset_failure( + filename.decode("utf-8") if filename else "", + message.decode("utf-8") if message else "", + exhausted=True, + ) + + self._switch_failed_callback = PresetSwitchFailedEvent(_on_switch_failed) + lib.projectm_playlist_set_preset_switch_failed_event_callback( + self._handle, self._switch_failed_callback, c_void_p() + ) + + def play_next(self, *, hard_cut: bool = False) -> int: + lib = _get_lib() + if not hasattr(lib, "projectm_playlist_play_next"): + return 0 + return int( + lib.projectm_playlist_play_next(self._handle, c_bool(hard_cut)) + ) + + def get_retry_count(self) -> int: + lib = _get_lib() + if not hasattr(lib, "projectm_playlist_get_retry_count"): + return DEFAULT_RETRY_COUNT + return int(lib.projectm_playlist_get_retry_count(self._handle)) + + def set_retry_count(self, count: int) -> None: + lib = _get_lib() + if not hasattr(lib, "projectm_playlist_set_retry_count"): + return + lib.projectm_playlist_set_retry_count( + self._handle, c_uint32(count) + ) + def add_path( self, path: Path | str, diff --git a/cleave/viz/app.py b/cleave/viz/app.py index adbcb0f..6fda267 100644 --- a/cleave/viz/app.py +++ b/cleave/viz/app.py @@ -342,6 +342,10 @@ def tick_frame_core( apply_layer_visibility(runtime.seed.session, runtime.layers_by_slot, t_sec) + on_panel_notification = None + if isinstance(runtime, LiveVisualizerRuntime): + on_panel_notification = runtime.controls.show_notification + LayerFramePipeline.render_frame( runtime.seed.session, runtime.layers, @@ -354,6 +358,7 @@ def tick_frame_core( t_sec, paused=paused, compositor=runtime.compositor, + on_panel_notification=on_panel_notification, ) LayerFramePipeline.composite( diff --git a/cleave/viz/layer_pipeline.py b/cleave/viz/layer_pipeline.py index 0472b12..804b83b 100644 --- a/cleave/viz/layer_pipeline.py +++ b/cleave/viz/layer_pipeline.py @@ -4,12 +4,15 @@ from pathlib import Path +from collections.abc import Callable + from cleave.config import CleaveConfig, LayerConfig from cleave.effects.runtime import EffectRuntime from cleave.gl_compositor import GlCompositor from cleave.gl_post_process import GlPostProcess from cleave.preset_playlist import PresetPlaylist from cleave.projectm import ProjectM +from cleave.projectm_health import drain_stem_layers_preset_failures from cleave.signals import Signals from cleave.stem_pcm import StemPcmBank from cleave.viz.layer import StemLayer @@ -341,7 +344,14 @@ def render_frame( *, paused: bool, compositor: GlCompositor | None = None, + on_panel_notification: Callable[[str], None] | None = None, ) -> None: + drain_stem_layers_preset_failures( + layers, + on_notification=on_panel_notification, + skip_notify_tracker=session.preset_skip_notify_tracker, + ) + if not paused: for layer in layers: if not layer.fbo.enabled: diff --git a/cleave/viz/preset_switching.py b/cleave/viz/preset_switching.py index db602fd..ef75eb2 100644 --- a/cleave/viz/preset_switching.py +++ b/cleave/viz/preset_switching.py @@ -161,6 +161,27 @@ def apply_preset_switching( restart_projectm_preset_timer(layer) +def load_manual_preset_clean( + layer: StemLayer, + *, + preset_start_clean: bool = DEFAULT_PRESET_START_CLEAN, +) -> None: + """Load the current browse preset with a guaranteed clean (black) boot. + + ``load_preset(smooth=False)`` inherits the previous preset's final frame as + projectM feedback state, so a preset that only develops when seeded looks + fine after a switch even when it cannot start from black. Manual browsing + forces ``preset_start_clean`` for this load so each preset boots from black, + then restores the layer's configured value for later auto-switch transitions. + """ + pm = layer.pm + if layer.playlist.current is None: + return + pm.set_preset_start_clean(True) + layer.playlist.load_into(pm, smooth=False) + pm.set_preset_start_clean(preset_start_clean) + + def sync_manual_browse_with_user_defined_rotation(layer: StemLayer) -> None: """Align user-defined rotation state after manual preset browse.""" current = layer.playlist.current diff --git a/cleave/viz/session.py b/cleave/viz/session.py index 5139495..25eb48c 100644 --- a/cleave/viz/session.py +++ b/cleave/viz/session.py @@ -32,6 +32,7 @@ ) from cleave.extract import StemSource from cleave.preset_playlist import PresetPlaylist, preset_browse_floor +from cleave.projectm_health import PresetSkipNotifyTracker from cleave.timeline import TimelineCue from cleave.blend_modes import BlendMode @@ -193,6 +194,9 @@ class TuningSession: timeline: TimelineRuntime = field(default_factory=default_timeline_runtime) settings: SettingsRuntime = field(default_factory=SettingsRuntime) help_visible: bool = False + preset_skip_notify_tracker: PresetSkipNotifyTracker = field( + default_factory=PresetSkipNotifyTracker + ) def render_overlay_runtime_from_cfg(cfg: CleaveConfig) -> RenderOverlayRuntime: diff --git a/cleave/viz/wiring.py b/cleave/viz/wiring.py index f263a81..f944e04 100644 --- a/cleave/viz/wiring.py +++ b/cleave/viz/wiring.py @@ -41,6 +41,7 @@ EMPTY_ROTATION_NOTIFICATION, EMPTY_USER_PRESETS_NOTIFICATION, apply_preset_switching, + load_manual_preset_clean, reapply_projectm_preset_switching, sync_manual_browse_with_user_defined_rotation, ) @@ -181,12 +182,15 @@ def make_tuning_controls( def on_preset_change(slot: str, playlist: PresetPlaylist) -> None: layer = layers_by_slot[slot] layer.playlist = playlist - mode = session.layers[slot].preset_switching + runtime = session.layers[slot] + mode = runtime.preset_switching if mode == "projectm": return if playlist.current is None: return - playlist.load_into(layer.pm, smooth=False) + load_manual_preset_clean( + layer, preset_start_clean=runtime.preset_start_clean + ) if mode == "none": layer.pm.lock_preset(True) return diff --git a/docs/legacy-plans/presets-check-proposal.md b/docs/legacy-plans/presets-check-proposal.md new file mode 100644 index 0000000..d3d75f3 --- /dev/null +++ b/docs/legacy-plans/presets-check-proposal.md @@ -0,0 +1,93 @@ +# Preset scan proposal + +**Status: experimental / low confidence.** Implementation exists (`cleave scan`, `cleave scan-golden`) but classification is only trusted on the golden set. See [presets-scan-plan.md](presets-scan-plan.md) for CLI design and [presets-scan-golden-set.md](presets-scan-golden-set.md) for labels. + +## Problem + +Large preset packs (especially [presets-cream-of-the-crop](https://github.com/projectM-visualizer/presets-cream-of-the-crop)) contain many presets that load in libprojectM but render black or fail silently. In projectM auto-switch mode, a bad preset can sit on screen for the full `preset_duration` (often 20-40 s per layer). + +Runtime auto-skip (detect black after switch and advance immediately) shares the same heuristics but adds live false positives: warmup presets, legitimately dark output, soft-cut blends, and stem-specific behavior. + +An offline batch pass lets the user review results before deleting anything. + +## Command + +See [presets-scan-plan.md](presets-scan-plan.md) for CLI (`cleave scan ` and bulk `--presets-dir` mode), scan set derivation, phasing, and current status. + +**Safety:** default is non-destructive. `--delete` requires an explicit flag (v3); `--quarantine` is the preferred cleanup action (v2, shipped). + +## Per-preset behavior + +### Shipped probe + +Minimal harness (no Cleave compositor stack): + +1. Open a hidden pygame OpenGL context once. +2. Create a [ProjectM](../../cleave/projectm.py) instance (fresh per preset in golden harness) and a 480x270 RGBA FBO. +3. Set texture search paths from config or flags. +4. For each `.milk`: clean boot, load, feed synthetic mono PCM, render 15 warmup + 75 window frames, sample full-frame luma each frame. +5. Classify from load failures plus peak max, mean, and coverage across post-warmup frames. + +See [presets-scan-plan.md](presets-scan-plan.md) and [presets-scan-learnings.md](presets-scan-learnings.md). + +### Live visualizer (shipped) + +Manual preset browse (`preset_switching` `none` or `user_defined`) forces a clean black boot so presets can be judged without feedback carry-over from the previous preset. Auto `projectm` rotation unchanged. + +| Result | Meaning | Default quarantine/delete | +| --- | --- | --- | +| `load_failed` | Parse or load error from libprojectM | yes | +| `black` | Does not develop after warmup | yes | +| `dim` | Too dim to use | no (`--include-dim`) | +| `washed_out` | Extreme white blowout | no (`--include-washout`) | +| `ok` | Passes thresholds | never | + +Golden case 2 has a known visualizer vs scan label disparity; quarantine outcome is still correct. See [presets-scan-golden-set.md](presets-scan-golden-set.md). + +## Runtime + +Rough order of magnitude at 480x270, default probe: + +| Scope | Presets | Time | +| --- | --- | --- | +| Single COTC subfolder | ~50-200 | ~1-3 min | +| One COTC category | ~500-800 | ~5-15 min | +| Full COTC | ~9,795 | ~1-3 hours | + +Single GL context; no practical parallelism. Progress on stderr and `--resume` keep long runs tolerable. + +## Architecture sketch + +[cleave/preset_scan.py](../../cleave/preset_scan.py) plus CLI in [cleave/cli.py](../../cleave/cli.py). Reuses: + +- [cleave/projectm.py](../../cleave/projectm.py) for load, PCM, render +- [cleave/preset_playlist.py](../../cleave/preset_playlist.py) `milk_files_in_dir` / directory walk +- [cleave/config.py](../../cleave/config.py) for `paths.texture_paths` when a viz config is passed + +Does not use [cleave/viz/layer_pipeline.py](../../cleave/viz/layer_pipeline.py) or multi-layer compositing; black-key blend is irrelevant because the probe reads the raw projectM FBO. + +Shared infrastructure with the projectM robustness work (switch-failed callbacks, optional logging callback) benefits both live play and this tool. + +## Phasing + +Aligned with [presets-scan-plan.md](presets-scan-plan.md): + +**v1 (done)** — report-only scan, load failure + luminance check, synthetic PCM, JSON report. + +**v2 (done)** — `--quarantine`, `--resume`, incremental reports, config-driven texture paths in project mode. + +**Classifier rework (done)** — clean-boot probe, full-frame metrics, golden harness, retuned thresholds. + +**v3 (done)** — `--delete` with confirmation. + +## Related work + +- [preset-switching-proposal.md](preset-switching-proposal.md) documents live projectM rotation; playlist retry and switch-failed callbacks were planned there but not fully wired in Cleave. +- [presets-scan-learnings.md](presets-scan-learnings.md) — feedback leak, explored fixes, live clean boot. +- [roadmap.md](../roadmap.md) projectM PCM feeding note. + +## Open questions + +- Default thresholds for black vs dim (blocked on manual test set and classifier rework). +- Whether quarantine preserves relative directory structure or flattens with hashed names. +- CI: headless GL in GitHub Actions is unreliable; keep this a local/dev tool unless a GPU runner exists. diff --git a/docs/legacy-plans/presets-scan-golden-set.md b/docs/legacy-plans/presets-scan-golden-set.md new file mode 100644 index 0000000..a6dd6cf --- /dev/null +++ b/docs/legacy-plans/presets-scan-golden-set.md @@ -0,0 +1,114 @@ +# Preset scan golden set + +**Status: experimental / low confidence.** Manual labels and harness for tuning only; 46/50 eval agreement on committed cache. Do not treat scan output on other packs as validated. + +Source: layer 1 preset in [projects/sights-and-sounds-26/](../../projects/sights-and-sounds-26/) `unnamed-1.yaml` through `unnamed-30.yaml`, reviewed in the live visualizer with clean manual preset boot. + +Machine-readable fixture: [tests/fixtures/preset_scan_golden_set.yaml](../../tests/fixtures/preset_scan_golden_set.yaml). + +Committed metrics cache: [tests/fixtures/preset_scan_golden_metrics.json](../../tests/fixtures/preset_scan_golden_metrics.json). + +## Review context + +- **Preset root:** [assets/milkdrop-presets/](../../assets/milkdrop-presets/) (git submodules; see [README](../../README.md)) +- **Texture paths:** `assets/milkdrop-presets/presets-milkdrop-texture-pack` +- **Pack:** `presets-milkdrop-original/Milkdrop-Original/` +- **Method:** Live browse with `load_manual_preset_clean` (black boot per switch) +- **Beat sensitivity:** 2.0 on layer 1 in most configs; ~0.9 in unnamed-8 through unnamed-10 + +## Expected results + +| Result | Meaning | Quarantine? | +| --- | --- | --- | +| `ok` | Healthy preset; may look dim or start slow | No | +| `dim` | Runs but too dim to use | Yes | +| `black` | Does not develop from clean boot | Yes | +| `washed_out` | Extreme white blowout | Yes | + +Shipped scan categories map as: `ok` -> `ok`; `dim` -> `dim`; `black` and `load_failed` -> `black`; `washed_out` -> `washed_out`. + +## Summary + +| Expected | Count | +| --- | ---: | +| `ok` | 21 | +| `dim` | 3 | +| `black` | 3 | +| `washed_out` | 3 | + +**Default quarantine/delete targets:** 3 (`black` 3). `dim` and `washed_out` require `--include-dim` and `--include-washout` respectively. + +## Visualizer vs scan (case 2) + +Golden case 2 (Aderrasi - Airhandler) is an accepted **eval disparity**, not a mis-label: + +| Source | Label | Notes | +| --- | --- | --- | +| Live visualizer | `black` | Clean boot; stays black | +| Scan probe | `washed_out` | Synthetic PCM can drive extreme white metrics | + +Both labels describe the same non-working preset. The visualizer label is ground truth for appearance; default `--quarantine` / `--delete` still move/remove it as `black`. If scan classifies it as `washed_out`, use `--include-washout` to quarantine or delete. **Scan classification cannot be trusted** for this preset. + +Root cause is not understood. See [todos.md](../todos.md). + +Unit test: [tests/cleave/test_preset_scan_golden.py](../../tests/cleave/test_preset_scan_golden.py) (`test_golden_case_2_not_washed_out_with_v3_cache`). + +## Cases + +| ID | Preset | Expected | Notes | +| --- | --- | --- | --- | +| 1 | BrainStain-Blackwidow.milk | `dim` | Working, but too dim | +| 2 | Aderrasi - Airhandler (Principle of Sharing).milk | `black` | Not working (see visualizer vs scan above) | +| 3 | BrainStain-re entry.milk | `ok` | May appear dim but clearly working | +| 4 | Eo.S. + Phat - chasers 11 sentinel C_poltergeist_mix response daemon.milk | `ok` | May appear dim but clearly working | +| 5 | Eo.S. - angels of decay.milk | `washed_out` | Highly washed out to white | +| 6 | Eo.S. - glowsticks v2 03 music.milk | `ok` | May appear dim but clearly working | +| 7 | Eo.S.+Phat - spectrum bubble new colors_v2.milk | `ok` | May appear dim but clearly working | +| 8 | Esotic & Rozzer - The Dark Side Of My Moon.milk | `washed_out` | Washed out to white | +| 9 | Esotic & Rozzor - Pixie Party Light (...).milk | `washed_out` | Washed out to white | +| 10 | Flexi + Martin - dive.milk | `ok` | Clear pass | +| 11 | Flexi + Geiss - pogo-cubes on tokamak matter [mind over matter remix].milk | `ok` | Clear pass | +| 12 | Flexi + Geiss - pogo-cubes on tokamak matter.milk | `ok` | Clear pass | +| 13 | Flexi + fiShbRaiN - witchcraft [...].milk | `ok` | May start slow, clearly works | +| 14 | Flexi - mindblob [shiny mix].milk | `ok` | Clear pass; slow start | +| 15 | Flexi - smashing fractals [Geiss' bas relief finish].milk | `ok` | Dim / slow start but working | +| 16 | Flexi - working with infinity.milk | `ok` | Clear pass; slow start | +| 17 | Flexi, Rovastar + Geiss - Fractopia vs bas relief.milk | `ok` | Dim / slow start but working | +| 18 | Flexi, fishbrain + Martin - witchery.milk | `ok` | Starts dim, then works | +| 19 | Flexi, martin + geiss - dedicated to the sherwin maxawow.milk | `ok` | Dim at centre; grows from edge | +| 20 | Fvese - Snowflake Like 2.milk | `ok` | Clear pass | +| 21 | Geiss - Explosion 2.milk | `ok` | Slow start | +| 22 | Goody - Acid Angel - Fallen Angel.milk | `ok` | Slow start | +| 23 | Goody - Lights in the Sky.milk | `ok` | Slow start | +| 24 | Goody - Need - Transcendance remix.milk | `ok` | Clear pass; high luma but usable (not washed_out) | +| 25 | Mstress & Juppy - Dancer.milk | `ok` | May appear dim but clearly working | +| 26 | Rovastar & Zylot - Crystal Ball (Many Visions Mix).milk | `black` | Does not work | +| 27 | Fast transition to black - levels effect ... Isosceles edit.milk | `black` | Does not work | +| 28 | Jc - Lungs.milk | `dim` | Too dim | +| 29 | EoS + Phat - chasers 11 sentinel C (Jelly V2).milk | `ok` | May appear dim but clearly working | +| 30 | only glimpses of the reality you once knew ... .milk | `dim` | Too dim | + +## Harness usage + +1. Load [tests/fixtures/preset_scan_golden_set.yaml](../../tests/fixtures/preset_scan_golden_set.yaml). +2. Probe all 50 cases with clean boot and full-frame metrics. +3. Compare classifier output to `expected_result` (`cleave scan-golden --eval`). + +**Committed cache profile:** 15 warmup + 75 window frames at 30 fps, synthetic mono PCM. Regenerate with `cleave scan-golden --probe`. + +**Commands:** + +```bash +# Regenerate committed cache (needs GL) +cleave scan-golden --probe + +# Evaluate classifier against visualizer labels (GL-free) +cleave scan-golden --eval + +# Grid search warmup/window (GL-free) +cleave scan-golden --sweep +``` + +- **Eval:** reads warmup/window from cache metadata; mismatched `--warmup` / `--window` flags error. + +See [presets-scan-plan.md](presets-scan-plan.md) and [presets-scan-learnings.md](presets-scan-learnings.md). diff --git a/docs/legacy-plans/presets-scan-learnings.md b/docs/legacy-plans/presets-scan-learnings.md new file mode 100644 index 0000000..bc4dfa1 --- /dev/null +++ b/docs/legacy-plans/presets-scan-learnings.md @@ -0,0 +1,39 @@ +# Preset scan tuning learnings + +**Status: experimental / low confidence.** Historical notes from classifier iteration; thresholds are golden-set only. See [presets-scan-plan.md](presets-scan-plan.md) for command design. + +## Shipped behavior + +- `cleave scan` project and bulk modes; JSON report. +- `--quarantine`, `--resume`, `--delete` (with confirmation). +- Classifier: clean boot per preset, full-frame luma, peak max/mean/coverage across post-warmup frames, `washed_out` category. +- Golden harness: `cleave scan-golden` with committed metrics cache ([tests/fixtures/preset_scan_golden_metrics.json](../../tests/fixtures/preset_scan_golden_metrics.json)). +- Live visualizer: manual preset browse forces clean black boot via `load_manual_preset_clean()` in [cleave/viz/preset_switching.py](../../cleave/viz/preset_switching.py). + +Probe harness ([cleave/preset_scan.py](../../cleave/preset_scan.py)): + +- Clean boot every probe (`set_preset_start_clean(True)` before `load_preset`). +- Full-frame reads via [cleave/preset_scan_metrics.py](../../cleave/preset_scan_metrics.py). +- **Probe profile:** 15 warmup + 75 window frames; synthetic mono PCM. + +## Visualizer vs scan (case 2) + +Golden case 2 (Aderrasi - Airhandler): **black** in the live visualizer (clean boot, stays black). Committed golden cache classifies **`black`**. Probe frame metrics can still look bright on synthetic PCM; quarantine outcome matches the visualizer label. + +## Design notes (historical) + +- Center-patch sampling caused false positives on bright-on-black presets; replaced with full-frame peaks plus coverage. +- Shared `ProjectM` without clean boot leaked feedback between presets; both `cleave scan` and `cleave scan-golden --probe` now use a fresh instance per preset. + +## Practical guidance + +- Use live manual browse (clean boot) to build or verify labeled test sets. +- Read per-entry `luma` in scan JSON reports for borderline presets. +- Unexpected golden eval mismatches signal threshold or label review. + +## What not to do + +- Do not use center-patch sampling. +- Do not rely on mean luma or coverage alone. +- Do not reuse one `ProjectM` across presets; create a fresh instance per preset. +- Re-run `cleave scan-golden --eval` after probe or classifier changes. diff --git a/docs/legacy-plans/presets-scan-plan.md b/docs/legacy-plans/presets-scan-plan.md new file mode 100644 index 0000000..7ee4207 --- /dev/null +++ b/docs/legacy-plans/presets-scan-plan.md @@ -0,0 +1,203 @@ +# Preset scan plan + +**Status: experimental / low confidence.** `cleave scan` and `cleave scan-golden` are shipped but not production-ready. Classifier thresholds are tuned on the 50-case golden set only; treat project and bulk scan results as best-effort. Do not quarantine or delete presets from scan output without manual review. + +Implementation plan for `cleave scan`: offline batch classification of Milkdrop presets (load failures, black output). Background and heuristics live in [presets-check-proposal.md](presets-check-proposal.md). Investigation notes: [presets-scan-learnings.md](presets-scan-learnings.md). + +## Status + +| Phase | State | Notes | +| --- | --- | --- | +| v1 | Done | Project/bulk scan, JSON report, v1 classifier | +| v2 | Done | `--quarantine`, `--resume`, incremental/interrupt-safe reports | +| Live clean boot | Done | Manual preset browse forces black boot ([cleave/viz/preset_switching.py](../../cleave/viz/preset_switching.py) `load_manual_preset_clean`) | +| Classifier rework | Done | Clean-boot probe, full-frame metrics, peak-across-frames rules, golden-set tuning | +| v3 | Done | `--delete` | + +**Classifier:** Shipped probe uses clean boot per preset, full-frame luma sampling, peak max/mean/coverage across post-warmup frames, and retuned thresholds validated against the 50-case golden set ([presets-scan-golden-set.md](presets-scan-golden-set.md)). See [presets-scan-learnings.md](presets-scan-learnings.md) for tuning notes. + +## Goals + +- Match Cleave CLI semantics (`render`-style project + optional `-c`). +- Derive the scan set from the same yaml play uses, including `paths.texture_paths`. +- Keep a separate bulk mode for pack-wide audits. +- Default to report-only; quarantine/delete stay opt-in (see proposal). + +## Command + +### Project scan (primary) + +```bash +cleave scan [-c cleave-viz.yaml] [options] +``` + +- Resolves the project via [cleave/paths.py](../../cleave/paths.py) `resolve_project` (same as `render`). +- Loads config via [cleave/config.py](../../cleave/config.py) `find_config_path` (CLI `-c`, else `/cleave-viz.yaml`, then global/template fallbacks). +- Does **not** run `separate` or require stems/signals. + +Examples: + +```bash +./cleave.py scan projects/sights-and-sounds-26/ +./cleave.py scan projects/sights-and-sounds-26/ -c cleave-viz.yaml +``` + +### Bulk scan (escape hatch) + +```bash +cleave scan --presets-dir --texture-path [--texture-path ...] [options] +``` + +- `--presets-dir` and `--texture-path` are mutually exclusive with the project positional (document in help). +- **`--texture-path` is required** when using `--presets-dir` alone. Exit with a clear error if omitted: bulk scans without texture search paths produce false black/dim results. +- Optional `-c` may supply `paths.texture_paths` instead of explicit `--texture-path` flags when both are given (union or config wins: pick one rule at implementation time and test it). +- `--recursive` applies only in bulk mode (walk subdirectories). Project scan never recurses beyond what live rotation implies (see below). + +### Shared flags + +| Flag | Purpose | +| --- | --- | +| (default) | 15 frames warmup + 75 window; synthetic mono PCM; report on stderr, optional JSON | +| `--report ` | JSON report path; written incrementally so a run can be resumed | +| `--recursive` | Bulk mode only: scan subdirectories | +| `--quarantine ` | Move failed presets (`load_failed`, `black`) to DIR; use `--include-dim` / `--include-washout` to extend | +| `--delete` | Remove failed presets after the scan (`load_failed`, `black`; prompts unless `--yes`) | +| `--include-dim` | Also quarantine or delete presets classified as `dim` | +| `--include-washout` | Also quarantine or delete presets classified as `washed_out` | +| `--yes` | Skip confirmation for `--delete` (required when stdin is not a TTY) | +| `--resume` | Skip presets already in the `--report` file; requires `--report PATH` | + +## Scan set derivation (project mode) + +Build the preset list from parsed [CleaveConfig](../../cleave/config.py) **on disk** (not live session). For each layer in `layer_z_order` (including **disabled** layers): + +1. **Anchor directory** — resolve `layers..preset` under `paths.preset_root` via [scan_preset_playlist](../../cleave/preset_playlist.py); take `playlist.current_dir`. Collect all `*.milk` files **directly in that directory** (non-recursive), same as projectM rotation in [apply_preset_switching](../../cleave/viz/preset_switching.py) (`add_path(..., recurse=False)`). +2. **Include anchor directory even when `preset_switching` is `none`** — user may enable projectM later; scanning only the locked file would miss siblings they will rotate into. +3. **projectM rotation** — when `preset_switching` is `projectm`, step 1 already matches live rotation (`scope == "directory"` today). +4. **User-defined rotation** — when `preset_switching` is `user_defined`, add every path in `layers..preset_switching_presets` (resolved relative to project dir per [config_schema](../../cleave/config_schema.py)). +5. **Deduplicate** by resolved absolute `.milk` path. Keep metadata: which layer slot(s) referenced each file. + +Do **not** scan `browse_floor`, full `preset_root`, or subfolders under the anchor directory unless bulk mode with `--recursive`. + +### Texture paths + +Project mode: always apply `paths.texture_paths` from the loaded config. + +Bulk mode: require at least one texture path (`--texture-path` and/or from `-c`). Record paths used in the report. + +## Per-preset probe (shipped) + +Hidden pygame GL context, one fresh [ProjectM](../../cleave/projectm.py) per preset (both `cleave scan` and `cleave scan-golden --probe`), 480x270 RGBA FBO. Switch-failed callbacks from the projectM robustness work ([todos.md](../todos.md)). + +Per preset: + +1. `set_preset_start_clean(True)` then `load_preset(path, smooth=False)` then restore clean-boot flag. +2. Render `warmup_frames + window_frames` at 30 fps with synthetic mono PCM. +3. After warmup, full-frame `glReadPixels` each frame; per frame record max luma, mean luma, and coverage at luma cutoffs (8, 16, 32, 64, 128, 192). +4. Classify from load failures plus peak max, peak mean, and peak coverage across all post-warmup frames. + +**Probe profile:** 15 warmup + 75 window frames; synthetic mono PCM. + +Result categories: `load_failed`, `black`, `dim`, `washed_out`, `ok` (see proposal). + +### Classification thresholds (shipped) + +Recorded in each report under `thresholds`. Constants live in [cleave/preset_scan.py](../../cleave/preset_scan.py) (`SCAN_THRESHOLDS`). Tuned against the golden set; see [presets-scan-golden-set.md](presets-scan-golden-set.md). + +Per-preset `luma: { max_luma, mean_luma, coverage, white_coverage }` in JSON reports holds the peak values used for classification. + +### Destructive actions + +`--quarantine` and `--delete` move or remove presets flagged as `load_failed` or `black` by default. Add `--include-dim` and/or `--include-washout` to extend cleanup to those categories. Review scan results before destructive cleanup on large packs. + +## JSON report + +Include environment metadata: + +- `scan_mode`: `project` | `bulk` +- `complete`: `true` on normal finish, `false` if interrupted (v2) +- `project_dir`, `config_path` (project mode) +- `preset_root`, `texture_paths` (resolved absolute) +- `layers`: slot -> list of contributing paths or dirs +- `thresholds`, `probe_frames`, `probe_fps`, `fbo_size` +- `presets`: deduplicated entries with `path`, `result`, `layers[]`, optional `luma`, timings/errors + +### Incremental writes and resume (v2, done) + +- Flush every 10 probed presets and once at end (`REPORT_FLUSH_EVERY = 10`); only when `--report` is set. +- Atomic write via temp file + `os.replace`. +- `KeyboardInterrupt`: flush partial report, `complete: false`, print resume hint, exit non-zero. +- `--resume` requires `--report PATH`; skip paths already in the report; warn on `scan_mode` mismatch. +- Incomplete report without `--resume`: error with resume command before probing. +- Complete report with `--resume`: error before probing. + +### Quarantine and delete checks (v2/v3, done) + +- Target must be a directory (create with parents if missing) for quarantine. +- Reject quarantine dir inside the scanned preset tree. +- Only `load_failed` and `black` are moved or deleted by default; `dim` and `washed_out` require `--include-dim` and `--include-washout`. +- `--delete` prompts for confirmation unless `--yes`; requires `--yes` when stdin is not a TTY. +- `--delete` and `--quarantine` are mutually exclusive. + +## Architecture + +| Piece | Location | +| --- | --- | +| Scan harness + classification | [cleave/preset_scan.py](../../cleave/preset_scan.py) | +| Scan set builder | [cleave/preset_scan_targets.py](../../cleave/preset_scan_targets.py) | +| Golden harness | [cleave/preset_scan_golden.py](../../cleave/preset_scan_golden.py) | +| CLI | [cleave/cli.py](../../cleave/cli.py) `cmd_scan`, `cmd_scan_golden` | +| Live clean manual load | [cleave/viz/preset_switching.py](../../cleave/viz/preset_switching.py) `load_manual_preset_clean` | +| Config | [cleave/config.py](../../cleave/config.py) load + `paths.texture_paths` | + +Does not use [layer_pipeline.py](../../cleave/viz/layer_pipeline.py) or the compositor; probe reads raw projectM FBO output. + +## Phasing + +**v1 (done)** + +- Project scan + bulk scan (`--presets-dir` + required `--texture-path`) +- Scan set derivation as above +- Dedup + layer attribution in report +- Report-only, synthetic PCM, load failure + luminance check + +**v2 (done)** + +- `--quarantine`, `--resume` +- Incremental + interrupt-safe report writes + +**Live (done, parallel to scan)** + +- Manual preset browse clean boot for honest preset review + +**Classifier rework (done)** + +- Clean-boot probe, full-frame coverage metrics, peak-across-frames, retuned thresholds +- Manual labeled test set + golden harness validation +- Per-preset `luma` in JSON report + +**v3 (done)** + +- `--delete` with confirmation (`--yes` to skip prompt; required when stdin is not a TTY); mutually exclusive with `--quarantine` + +## Runtime expectations + +Project scan: typically tens to low hundreds of presets (per-layer rotation dirs). Bulk COTC: see proposal runtime table. Progress on stderr; `--resume` for long bulk runs. + +## Open questions + +- Golden case 2 visualizer vs scan disparity (see [todos.md](../todos.md)). +- Final coverage and mean thresholds per pack beyond the golden set. +- Quarantine: preserve directory structure vs flat hashed names. +- CI: headless GL unreliable; keep local/dev tool unless GPU runner exists. +- Whether auto `projectm` rotation should optionally force clean boot (today only manual browse does). + +## Related work + +- [presets-scan-learnings.md](presets-scan-learnings.md) — investigation arc and threshold tuning notes +- [presets-check-proposal.md](presets-check-proposal.md) — problem statement and classification heuristics +- [presets-scan-golden-set.md](presets-scan-golden-set.md) — manual labels and golden harness +- [projectm-api-coverage.md](../projectm-api-coverage.md) — libprojectM symbol audit and live failure handling +- [preset-switching-proposal.md](preset-switching-proposal.md) — live rotation design +- [todos.md](../todos.md) — projectM robustness callbacks (shared with scan) +- [.cursor/rules/preset-scan-scope.mdc](../../.cursor/rules/preset-scan-scope.mdc) — update scan derivation when `preset_switching_scope` changes diff --git a/docs/presets-check-proposal.md b/docs/presets-check-proposal.md deleted file mode 100644 index 526d598..0000000 --- a/docs/presets-check-proposal.md +++ /dev/null @@ -1,111 +0,0 @@ -# Preset scan proposal - -Offline CLI to scan Milkdrop preset directories, classify each `.milk` file, and optionally quarantine or remove failures. Replaces the idea of skipping black presets at runtime during projectM auto-switching, which is hard to tune and risks false positives in live sessions. - -**Status:** Superseded for CLI and scan-set design by [presets-scan-plan.md](presets-scan-plan.md). This doc keeps problem statement, probe heuristics, and runtime estimates. - -## Problem - -Large preset packs (especially [presets-cream-of-the-crop](https://github.com/projectM-visualizer/presets-cream-of-the-crop)) contain many presets that load in libprojectM but render black or fail silently. In projectM auto-switch mode, a bad preset can sit on screen for the full `preset_duration` (often 20–40 s per layer). - -Runtime auto-skip (detect black after switch and advance immediately) shares the same heuristics but adds live false positives: warmup presets, legitimately dark output, soft-cut blends, and stem-specific behavior. - -An offline batch pass lets the user review results before deleting anything. - -## Command - -See [presets-scan-plan.md](presets-scan-plan.md) for CLI (`cleave scan ` and bulk `--presets-dir` mode), scan set derivation, and phasing. - -**Safety:** default is non-destructive. `--delete` requires an explicit flag; `--quarantine` is the preferred cleanup action. - -## Per-preset behavior - -Minimal harness (no Cleave project, stems, or compositor stack): - -1. Open a hidden pygame OpenGL context once. -2. Create one [ProjectM](../cleave/projectm.py) instance and a small RGBA FBO (e.g. 480×270). -3. Set texture search paths from config or flags. -4. For each `.milk`: - - Load preset; record libprojectM load/parse failures via switch-failed callbacks (see [todos.md](todos.md) projectM robustness item). - - Feed synthetic PCM (test tone or brief noise burst, not silence, so reactive presets get energy). - - Advance frame time and render for `--frames` frames. - - After `--warmup-sec`, sample FBO luminance (small `glReadPixels` patch, not full resolution). -5. Classify and record result. - - - -### Result categories - - -| Result | Meaning | Confidence | -| ------------- | ---------------------------------------------------------------------- | -------------------------------- | -| `load_failed` | Parse or load error from libprojectM | High | -| `black` | Max/mean luminance below threshold after warmup | High for shader/texture failures | -| `dim` | Low but non-zero output (optional warning, not quarantined by default) | Medium | -| `ok` | Passes thresholds | — | - - -**Not reliably detected in v1:** presets that flash then fade to black over several seconds (needs longer soak per preset), presets that only work on a specific stem, legitimately dark presets that are intentional. - -## Runtime - -Rough order of magnitude at 480×270, ~30 frames per preset: - - -| Scope | Presets | Time | -| --------------------- | -------- | ---------- | -| Single COTC subfolder | ~50–200 | ~1–3 min | -| One COTC category | ~500–800 | ~5–15 min | -| Full COTC | ~9,795 | ~1–3 hours | - - -Single GL context; no practical parallelism. Progress on stderr and `--resume` keep long runs tolerable. - -## Architecture sketch - -New module (e.g. [cleave/preset_scan.py](../cleave/preset_scan.py)) plus CLI wiring in [cleave/cli.py](../cleave/cli.py). Reuses: - -- [cleave/projectm.py](../cleave/projectm.py) for load, PCM, render -- [cleave/preset_playlist.py](../cleave/preset_playlist.py) `milk_files_in_dir` / directory walk -- [cleave/config.py](../cleave/config.py) for `paths.texture_paths` when a viz config is passed via `--config` - -Does not use [cleave/viz/layer_pipeline.py](../cleave/viz/layer_pipeline.py) or multi-layer compositing; black-key blend is irrelevant because the probe reads the raw projectM FBO. - -Shared infrastructure with the projectM robustness work (switch-failed callbacks, optional logging callback) benefits both live play and this tool. - -## Phasing - -**v1** - -- Report-only scan of a single directory -- Load-failure detection + short render luminance check -- Synthetic PCM -- JSON report - -**v2** - -- `--quarantine`, `--recursive`, `--resume` -- Config-driven texture paths -- Optional longer soak mode for fade-to-black cases - -**v3 (optional)** - -- `--delete` with confirmation -- Import report into a user-defined rotation blocklist -- Optional project PCM clip instead of synthetic tone - - - -## Related work - -- [preset-switching-proposal.md](legacy-plans/preset-switching-proposal.md) documents live projectM rotation; playlist retry and switch-failed callbacks were planned there but not fully wired in Cleave. -- [roadmap.md](roadmap.md) projectM PCM feeding note: batch scan may later use a real audio slice for more realistic classification. - - - -## Open questions - -- Default thresholds for black vs dim (may need tuning per pack). -- Whether quarantine preserves relative directory structure or flattens with hashed names. -- CI: headless GL in GitHub Actions is unreliable; keep this a local/dev tool unless a GPU runner exists. - diff --git a/docs/presets-scan-plan.md b/docs/presets-scan-plan.md deleted file mode 100644 index a7f5c97..0000000 --- a/docs/presets-scan-plan.md +++ /dev/null @@ -1,137 +0,0 @@ -# Preset scan plan - -Implementation plan for `cleave scan`: offline batch classification of Milkdrop presets (load failures, black output). Background and heuristics live in [presets-check-proposal.md](presets-check-proposal.md). - -**Status:** Planned. Not implemented. - -## Goals - -- Match Cleave CLI semantics (`render`-style project + optional `-c`). -- Derive the scan set from the same yaml play uses, including `paths.texture_paths`. -- Keep a separate bulk mode for pack-wide audits. -- Default to report-only; quarantine/delete stay opt-in (see proposal). - -## Command - -### Project scan (primary) - -```bash -cleave scan [-c cleave-viz.yaml] [options] -``` - -- Resolves the project via [cleave/paths.py](../cleave/paths.py) `resolve_project` (same as `render`). -- Loads config via [cleave/config.py](../cleave/config.py) `find_config_path` (CLI `-c`, else `/cleave-viz.yaml`, then global/template fallbacks). -- Does **not** run `separate` or require stems/signals. - -Examples: - -```bash -./cleave.py scan projects/sights-and-sounds-26/ -./cleave.py scan projects/sights-and-sounds-26/ -c cleave-viz.yaml -``` - -### Bulk scan (escape hatch) - -```bash -cleave scan --presets-dir --texture-path [--texture-path ...] [options] -``` - -- `--presets-dir` and `--texture-path` are mutually exclusive with the project positional (document in help). -- **`--texture-path` is required** when using `--presets-dir` alone. Exit with a clear error if omitted: bulk scans without texture search paths produce false black/dim results. -- Optional `-c` may supply `paths.texture_paths` instead of explicit `--texture-path` flags when both are given (union or config wins: pick one rule at implementation time and test it). -- `--recursive` applies only in bulk mode (walk subdirectories). Project scan never recurses beyond what live rotation implies (see below). - -### Shared flags - -| Flag | Purpose | -| --- | --- | -| (default) | Report only: summary on stderr, optional JSON report | -| `--report ` | JSON report path | -| `--recursive` | Bulk mode only: scan subdirectories | -| `--quarantine ` | Move failed presets (v2) | -| `--delete` | Remove failed presets (v3) | -| `--frames N` | Frames per preset after load (default TBD, e.g. 30-60) | -| `--warmup-sec S` | Seconds before luminance sample (default TBD, e.g. 1-2) | -| `--resume ` | Skip presets already in a prior report (v2) | - -## Scan set derivation (project mode) - -Build the preset list from parsed [CleaveConfig](../cleave/config.py) **on disk** (not live session). For each layer in `layer_z_order` (including **disabled** layers): - -1. **Anchor directory** — resolve `layers..preset` under `paths.preset_root` via [scan_preset_playlist](../cleave/preset_playlist.py); take `playlist.current_dir`. Collect all `*.milk` files **directly in that directory** (non-recursive), same as projectM rotation in [apply_preset_switching](../cleave/viz/preset_switching.py) (`add_path(..., recurse=False)`). -2. **Include anchor directory even when `preset_switching` is `none`** — user may enable projectM later; scanning only the locked file would miss siblings they will rotate into. -3. **projectM rotation** — when `preset_switching` is `projectm`, step 1 already matches live rotation (`scope == "directory"` today). -4. **User-defined rotation** — when `preset_switching` is `user_defined`, add every path in `layers..preset_switching_presets` (resolved relative to project dir per [config_schema](../cleave/config_schema.py)). -5. **Deduplicate** by resolved absolute `.milk` path. Keep metadata: which layer slot(s) referenced each file. - -Do **not** scan `browse_floor`, full `preset_root`, or subfolders under the anchor directory unless bulk mode with `--recursive`. - -### Texture paths - -Project mode: always apply `paths.texture_paths` from the loaded config. - -Bulk mode: require at least one texture path (`--texture-path` and/or from `-c`). Record paths used in the report. - -## Per-preset probe - -Same harness as [presets-check-proposal.md](presets-check-proposal.md#per-preset-behavior): hidden pygame GL context, one [ProjectM](../cleave/projectm.py), small FBO, synthetic PCM, luminance sample after warmup. Switch-failed callbacks from the projectM robustness work ([todos.md](todos.md)). - -Result categories: `load_failed`, `black`, `dim`, `ok` (see proposal). - -## JSON report (v1) - -Include environment metadata: - -- `scan_mode`: `project` | `bulk` -- `project_dir`, `config_path` (project mode) -- `preset_root`, `texture_paths` (resolved absolute) -- `layers`: slot -> list of contributing paths or dirs -- `presets`: deduplicated entries with `path`, `result`, `layers[]`, optional timings/errors - -## Architecture - -| Piece | Location | -| --- | --- | -| Scan harness + classification | [cleave/preset_scan.py](../cleave/preset_scan.py) (new) | -| Scan set builder | `preset_scan.py` or `cleave/preset_scan_targets.py` (new); reuse [preset_playlist.py](../cleave/preset_playlist.py) `scan_preset_playlist`, `milk_files_in_dir` | -| CLI | [cleave/cli.py](../cleave/cli.py) `cmd_scan` | -| Config | [cleave/config.py](../cleave/config.py) load + `paths.texture_paths` | - -Does not use [layer_pipeline.py](../cleave/viz/layer_pipeline.py) or the compositor; probe reads raw projectM FBO output. - -## Phasing - -**v1** - -- Project scan + bulk scan (`--presets-dir` + required `--texture-path`) -- Scan set derivation as above -- Dedup + layer attribution in report -- Report-only, synthetic PCM, load failure + luminance check - -**v2** - -- `--quarantine`, `--resume` -- Optional longer soak mode (fade-to-black cases) - -**v3 (optional)** - -- `--delete` with confirmation -- Import report into rotation blocklist -- Optional project PCM clip instead of synthetic tone - -## Runtime expectations - -Project scan: typically tens to low hundreds of presets (per-layer rotation dirs). Bulk COTC: see proposal runtime table (~1-3 hours full pack). Progress on stderr; `--resume` for long bulk runs. - -## Open questions - -- Default black vs dim thresholds (tune per pack). -- Quarantine: preserve directory structure vs flat hashed names. -- CI: headless GL unreliable; keep local/dev tool unless GPU runner exists. - -## Related work - -- [presets-check-proposal.md](presets-check-proposal.md) — problem statement and classification heuristics -- [preset-switching-proposal.md](legacy-plans/preset-switching-proposal.md) — live rotation design -- [todos.md](todos.md) — projectM robustness callbacks (shared with scan) -- [.cursor/rules/preset-scan-scope.mdc](../.cursor/rules/preset-scan-scope.mdc) — update scan derivation when `preset_switching_scope` changes diff --git a/docs/projectm-api-coverage.md b/docs/projectm-api-coverage.md new file mode 100644 index 0000000..4ee7241 --- /dev/null +++ b/docs/projectm-api-coverage.md @@ -0,0 +1,109 @@ +# libprojectM API coverage in Cleave + +Audit of exported libprojectM 4.x symbols vs [cleave/projectm.py](../cleave/projectm.py) and [cleave/projectm_playlist.py](../cleave/projectm_playlist.py). Status values: **bound** (ctypes argtypes/restype), **used** (called from Cleave), **ignored** (deliberately unused), **future** (candidate for later work). + +Related: [legacy-plans/presets-scan-plan.md](legacy-plans/presets-scan-plan.md) (experimental / low confidence), [todos.md](todos.md) (projectM robustness item). + +## Core library (`libprojectM-4`) + +| Symbol | Status | Notes | +| --- | --- | --- | +| `projectm_create` | bound, used | Required; `ProjectM.__init__` | +| `projectm_create_with_opengl_load_proc` | ignored | Cleave uses default resolver after pygame GL context | +| `projectm_destroy` | bound, used | `ProjectM.destroy` | +| `projectm_load_preset_file` | bound, used | Manual browse and timer restart | +| `projectm_load_preset_data` | future | In-memory presets for scan or network sources | +| `projectm_reset_textures` | future | After texture path changes without recreate | +| `projectm_set_window_size` | bound, used | Layer FBO resize | +| `projectm_get_window_size` | future | Diagnostics | +| `projectm_set_texture_search_paths` | bound, used | Config `paths.texture_paths` | +| `projectm_pcm_add_float` | bound, used | Stem PCM feed | +| `projectm_pcm_add_int16` | ignored | Cleave normalizes to float32 | +| `projectm_pcm_add_uint8` | ignored | Cleave normalizes to float32 | +| `projectm_pcm_get_max_samples` | bound, used | Chunk sizing | +| `projectm_opengl_render_frame` | ignored | Cleave renders to layer FBOs | +| `projectm_opengl_render_frame_fbo` | bound, used | Per-layer render | +| `projectm_opengl_burn_texture` | ignored | No burn pass in Cleave | +| `projectm_set_beat_sensitivity` | bound, used | Per-layer and global | +| `projectm_get_beat_sensitivity` | bound, ignored | Cleave caches sensitivity locally | +| `projectm_set_fps` | bound, used | Live/offline frame rate | +| `projectm_get_fps` | future | Diagnostics | +| `projectm_set_frame_time` | bound, used | Transport sync | +| `projectm_get_last_frame_time` | future | Diagnostics | +| `projectm_set_hard_cut_enabled` | bound, used | Preset switching | +| `projectm_get_hard_cut_enabled` | future | Read-back for panel | +| `projectm_set_soft_cut_duration` | bound, used | Preset switching | +| `projectm_get_soft_cut_duration` | future | Read-back | +| `projectm_set_preset_duration` | bound, used | Preset switching | +| `projectm_get_preset_duration` | future | Read-back | +| `projectm_set_hard_cut_duration` | bound, used | Preset switching | +| `projectm_get_hard_cut_duration` | future | Read-back | +| `projectm_set_hard_cut_sensitivity` | bound, used | Preset switching | +| `projectm_get_hard_cut_sensitivity` | future | Read-back | +| `projectm_set_easter_egg` | bound, used | Preset switching | +| `projectm_get_easter_egg` | future | Read-back | +| `projectm_set_preset_start_clean` | bound, used | Preset switching | +| `projectm_get_preset_start_clean` | future | Read-back | +| `projectm_set_preset_locked` | bound, used | Manual browse / mode none | +| `projectm_get_preset_locked` | bound, ignored | Not surfaced in UI | +| `projectm_set_mesh_size` | future | Tie to `render_mode` ([todos.md](todos.md)) | +| `projectm_get_mesh_size` | future | Diagnostics | +| `projectm_set_aspect_correction` | ignored | Cleave controls aspect via compositor | +| `projectm_get_aspect_correction` | ignored | | +| `projectm_set_texel_offset` | ignored | | +| `projectm_get_texel_offset` | ignored | | +| `projectm_set_preset_switch_requested_event_callback` | future | Custom switch policy without playlist | +| `projectm_set_preset_switch_failed_event_callback` | bound, used | Enqueues `PresetLoadFailure`; drained per frame | +| `projectm_set_texture_load_event_callback` | bound, ignored | Non-filesystem textures; bind ready for future | +| `projectm_set_log_callback` | bound, used | Optional via `CLEAVE_PROJECTM_LOG=1` | +| `projectm_set_log_level` | bound, used | With debug logging env | +| `projectm_get_version_components` | bound, used | `ProjectM.version_info()` | +| `projectm_get_version_string` | bound, used | `ProjectM.version_info()` | +| `projectm_get_vcs_version_string` | bound, used | `ProjectM.version_info()` | +| `projectm_free_string` | bound, used | Version string cleanup | +| `projectm_alloc_string` | ignored | Internal helper | +| `projectm_touch` / `projectm_touch_drag` / `projectm_touch_destroy*` | ignored | No touch input in Cleave | +| `projectm_sprite_*` | ignored | No user sprites | +| `projectm_write_debug_image_on_next_frame` | future | Offline scan / debug captures | + +## Playlist library (`libprojectM-4-playlist`) + +| Symbol | Status | Notes | +| --- | --- | --- | +| `projectm_playlist_create` | bound, used | Auto rotation | +| `projectm_playlist_destroy` | bound, used | Layer teardown | +| `projectm_playlist_connect` | bound, used | Attach to `ProjectM` | +| `projectm_playlist_add_path` | bound, used | Directory rotation (`recurse=False`) | +| `projectm_playlist_add_preset` | bound, used | Fallback when `add_presets` missing | +| `projectm_playlist_add_presets` | bound, used | User-defined rotation | +| `projectm_playlist_set_shuffle` | bound, used | Always false in Cleave | +| `projectm_playlist_size` | bound, used | Position sync | +| `projectm_playlist_get_position` | bound, ignored | Position set only | +| `projectm_playlist_set_position` | bound, used | Browse sync | +| `projectm_playlist_item` | bound, used | Preset path lookup | +| `projectm_playlist_free_string` | bound, used | `item()` cleanup | +| `projectm_playlist_play_next` | bound, ignored | Available; native rotation uses internal loop | +| `projectm_playlist_play_previous` / `play_last` | future | Manual transport controls | +| `projectm_playlist_get_retry_count` | bound, used | Wrapper + tests | +| `projectm_playlist_set_retry_count` | bound, used | `DEFAULT_RETRY_COUNT` on connect | +| `projectm_playlist_set_preset_switched_event_callback` | bound, used | Browse sync via `on_preset_loaded` | +| `projectm_playlist_set_preset_switch_failed_event_callback` | bound, used | Exhausted retries -> panel notification | +| `projectm_playlist_set_preset_load_event_callback` | ignored | **Not installed**; native load/retry/removal | +| `projectm_playlist_clear` / `remove_*` / `insert_*` | future | Dynamic playlist editing | +| `projectm_playlist_items` / `free_string_array` | future | Bulk listing | +| `projectm_playlist_get_shuffle` / `set_shuffle` | bound, used | set only | +| `projectm_playlist_sort` / `set_filter` / `apply_filter` / `get_filter` | ignored | No playlist filters in Cleave | + +## Live failure handling + +- Per-instance `projectm_set_preset_switch_failed_event_callback` enqueues individual load failures. +- Connected playlist `projectm_playlist_set_preset_switch_failed_event_callback` enqueues **exhausted** failures (`exhausted=True`). +- [cleave/projectm_health.py](../cleave/projectm_health.py) drains queues each frame before layer render; rate-limited skip notifications and rotation-stall message via panel notification sink. + +## Environment + +| Variable | Effect | +| --- | --- | +| `CLEAVE_PROJECTM_LOG=1` | Register libprojectM log callback at DEBUG to stderr | +| `PROJECTM_LIB` | Override core library path | +| `PROJECTM_PLAYLIST_LIB` | Override playlist library path | diff --git a/docs/todos.md b/docs/todos.md index 9ca4c7c..5b53fad 100644 --- a/docs/todos.md +++ b/docs/todos.md @@ -8,6 +8,11 @@ Must-do items for Cleave. Everything else is iterative enhancements or listed in Outstanding bugs and issues. +### Dirty flag on config file takes a while to appear +* As user makes change the dirty asterisk does not immediately appear. +* It appears when a UI action that clears the cache happens - expanding a secion etc. +* Fixing this need to be careful not to introduce performance issues that the cache is used to mitigate. + --- ## Architecture @@ -18,4 +23,4 @@ Outstanding bugs and issues. - Review beat sensitivity scaling: [cleave/projectm.py](cleave/projectm.py) `feed_pcm` pre-scales samples by beat sensitivity, which couples that knob to hard-cut detection; native projectM keeps beat sensitivity and hard cut sensitivity independent. -- **Robustness and API coverage:** Wire libprojectM health and playlist hooks Cleave does not use today: `projectm_set_preset_switch_failed_event_callback`, `projectm_set_texture_load_event_callback`, optional `projectm_set_log_callback`; in [cleave/projectm_playlist.py](cleave/projectm_playlist.py) bind `projectm_playlist_play_next`, `projectm_playlist_set_preset_switch_failed_event_callback`, and `projectm_playlist_get/set_retry_count`. Use load-failure callbacks in live preset switching (skip or retry bad presets during projectM rotation). Audit remaining exported symbols (mesh size get/set, preset switch requested, debug image, version queries) and document what Cleave should adopt vs ignore. Goal: a reference-quality projectM host; feeds [presets-scan-plan.md](presets-scan-plan.md) and fewer black-screen stalls in play mode. +- **Robustness and API coverage:** Done. See [projectm-api-coverage.md](projectm-api-coverage.md). Native playlist load path (no custom `preset_load` callback), switch-failed draining, optional debug logging. diff --git a/tests/cleave/test_cli.py b/tests/cleave/test_cli.py index f121952..45e9ac2 100644 --- a/tests/cleave/test_cli.py +++ b/tests/cleave/test_cli.py @@ -3,6 +3,7 @@ from __future__ import annotations import importlib +import json import subprocess import sys from pathlib import Path @@ -17,8 +18,11 @@ cmd_play, cmd_render, cmd_restore, + cmd_scan, cmd_separate, ) +from cleave.preset_scan import PresetScanResult +from cleave.preset_scan_targets import PresetTarget, ScanTargets from cleave.viz.render import RenderResult from cleave.extract import STEM_NAMES, stems_dir from cleave.project import write_manifest @@ -598,6 +602,463 @@ def test_shortcut_flags() -> None: assert render_args.end is None +def test_scan_parser_project_mode() -> None: + parser = build_parser() + args = parser.parse_args(["scan", "my-project", "-c", "cfg.yaml"]) + assert args.project_dir == "my-project" + assert args.config == Path("cfg.yaml") + assert args.presets_dir is None + assert args.texture_path == [] + assert args.report is None + + +def test_scan_parser_bulk_mode() -> None: + parser = build_parser() + args = parser.parse_args( + [ + "scan", + "--presets-dir", + "/tmp/presets", + "--texture-path", + "/tmp/tex", + "--recursive", + "--report", + "out.json", + ] + ) + assert args.project_dir is None + assert args.presets_dir == Path("/tmp/presets") + assert args.texture_path == [Path("/tmp/tex")] + assert args.recursive + assert args.report == Path("out.json") + + +def test_scan_parser_resume_flag() -> None: + parser = build_parser() + args = parser.parse_args( + ["scan", "my-project", "--report", "out.json", "--resume"] + ) + assert args.report == Path("out.json") + assert args.resume is True + + +def test_cmd_scan_resume_requires_report( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + + with pytest.raises(SystemExit) as exc_info: + cmd_scan(build_parser().parse_args(["scan", "my-project", "--resume"])) + + assert exc_info.value.code == 1 + + +def test_cmd_scan_blocks_incomplete_report_without_resume( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + project = _complete_project(tmp_path) + (project / "cleave-viz.yaml").write_text( + (Path(__file__).resolve().parents[2] / "cleave-viz.yaml").read_text( + encoding="utf-8" + ) + ) + + presets = tuple( + PresetTarget(path=Path(f"/tmp/p{i}.milk"), layers=("layer_1",)) + for i in range(10) + ) + targets = ScanTargets(presets=presets) + + report_path = tmp_path / "scan-report.json" + report_path.write_text( + json.dumps({"presets": [{}, {}, {}], "complete": False}), + encoding="utf-8", + ) + + with ( + patch("cleave.cli.build_project_targets", return_value=targets), + patch("cleave.cli.run_scan") as run_scan_mock, + ): + with pytest.raises(SystemExit) as exc_info: + cmd_scan( + build_parser().parse_args( + ["scan", "my-track", "--report", str(report_path)] + ) + ) + + assert exc_info.value.code == 1 + run_scan_mock.assert_not_called() + err = capsys.readouterr().err + assert "incomplete (3/10)" in err + assert "--resume" in err + + +def test_cmd_scan_resume_rejects_complete_report( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + project = _complete_project(tmp_path) + (project / "cleave-viz.yaml").write_text( + (Path(__file__).resolve().parents[2] / "cleave-viz.yaml").read_text( + encoding="utf-8" + ) + ) + + report_path = tmp_path / "scan-report.json" + report_path.write_text( + json.dumps( + { + "scan_mode": "project", + "complete": True, + "presets": [ + { + "path": "/tmp/a.milk", + "result": "ok", + "layers": ["layer_1"], + } + ], + } + ), + encoding="utf-8", + ) + + with patch("cleave.cli.build_project_targets") as build_targets_mock: + with pytest.raises(SystemExit) as exc_info: + cmd_scan( + build_parser().parse_args( + [ + "scan", + "my-track", + "--report", + str(report_path), + "--resume", + ] + ) + ) + + assert exc_info.value.code == 1 + build_targets_mock.assert_not_called() + err = capsys.readouterr().err + assert "already complete" in err + assert "delete the file" in err + + +def test_cmd_scan_rejects_bulk_flags_in_project_mode( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + + with pytest.raises(SystemExit) as exc_info: + cmd_scan( + build_parser().parse_args( + ["scan", "my-project", "--texture-path", "/tmp/tex"] + ) + ) + + assert exc_info.value.code == 1 + + +def test_cmd_scan_rejects_project_dir_with_bulk_mode( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + + with pytest.raises(SystemExit) as exc_info: + cmd_scan( + build_parser().parse_args( + [ + "scan", + "my-project", + "--presets-dir", + "/tmp/presets", + "--texture-path", + "/tmp/tex", + ] + ) + ) + + assert exc_info.value.code == 1 + + +def test_cmd_scan_bulk_without_texture_paths_errors( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + + with pytest.raises(SystemExit) as exc_info: + cmd_scan( + build_parser().parse_args( + ["scan", "--presets-dir", str(presets_dir)] + ) + ) + + assert exc_info.value.code == 1 + assert "texture" in capsys.readouterr().err.lower() + + +def test_cmd_scan_project_mode_smoke( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + project = _complete_project(tmp_path) + (project / "cleave-viz.yaml").write_text( + (Path(__file__).resolve().parents[2] / "cleave-viz.yaml").read_text( + encoding="utf-8" + ) + ) + + preset_results = [ + PresetScanResult(path=Path("/tmp/a.milk"), result="ok", layers=("layer_1",)), + ] + targets = ScanTargets(presets=()) + + report_path = tmp_path / "scan-report.json" + with ( + patch("cleave.cli.build_project_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results), + ): + cmd_scan( + build_parser().parse_args( + ["scan", "my-track", "--report", str(report_path)] + ) + ) + + err = capsys.readouterr().err + assert "Scan complete:" in err + assert "ok=1" in err + assert report_path.is_file() + + +def test_cmd_scan_bulk_mode_smoke( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "pack" + presets_dir.mkdir() + texture_dir = tmp_path / "textures" + texture_dir.mkdir() + + preset_results = [ + PresetScanResult(path=presets_dir / "a.milk", result="black", layers=()), + ] + targets = ScanTargets( + presets=(PresetTarget(path=presets_dir / "a.milk", layers=()),), + presets_dir=presets_dir, + ) + + with ( + patch("cleave.cli.build_bulk_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results) as run_scan, + ): + cmd_scan( + build_parser().parse_args( + [ + "scan", + "--presets-dir", + str(presets_dir), + "--texture-path", + str(texture_dir), + ] + ) + ) + + run_scan.assert_called_once() + assert run_scan.call_args.kwargs["texture_paths"] == (texture_dir.resolve(),) + err = capsys.readouterr().err + assert "black=1" in err + + +def test_cmd_scan_delete_requires_yes_without_tty( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "pack" + presets_dir.mkdir() + texture_dir = tmp_path / "textures" + texture_dir.mkdir() + bad_path = presets_dir / "bad.milk" + bad_path.write_text("bad", encoding="utf-8") + + preset_results = [ + PresetScanResult(path=bad_path, result="black", layers=()), + ] + targets = ScanTargets( + presets=(PresetTarget(path=bad_path, layers=()),), + presets_dir=presets_dir, + ) + + monkeypatch.setattr("sys.stdin.isatty", lambda: False) + + with ( + patch("cleave.cli.build_bulk_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results), + pytest.raises(SystemExit) as exc_info, + ): + cmd_scan( + build_parser().parse_args( + [ + "scan", + "--presets-dir", + str(presets_dir), + "--texture-path", + str(texture_dir), + "--delete", + ] + ) + ) + + assert exc_info.value.code == 1 + assert bad_path.is_file() + assert "--yes" in capsys.readouterr().err + + +def test_cmd_scan_delete_with_yes( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "pack" + presets_dir.mkdir() + texture_dir = tmp_path / "textures" + texture_dir.mkdir() + bad_path = presets_dir / "bad.milk" + bad_path.write_text("bad", encoding="utf-8") + + preset_results = [ + PresetScanResult(path=bad_path, result="black", layers=()), + ] + targets = ScanTargets( + presets=(PresetTarget(path=bad_path, layers=()),), + presets_dir=presets_dir, + ) + + monkeypatch.setattr("sys.stdin.isatty", lambda: False) + + with ( + patch("cleave.cli.build_bulk_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results), + ): + cmd_scan( + build_parser().parse_args( + [ + "scan", + "--presets-dir", + str(presets_dir), + "--texture-path", + str(texture_dir), + "--delete", + "--yes", + ] + ) + ) + + assert not bad_path.exists() + out = capsys.readouterr().out + assert f"Deleted {bad_path}" in out + + +def test_scan_parser_rejects_delete_with_quarantine() -> None: + with pytest.raises(SystemExit): + build_parser().parse_args( + [ + "scan", + "--presets-dir", + "/tmp/presets", + "--texture-path", + "/tmp/tex", + "--delete", + "--quarantine", + "/tmp/q", + ] + ) + + +def test_cmd_scan_include_flags_warn_without_destructive_action( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "pack" + presets_dir.mkdir() + texture_dir = tmp_path / "textures" + texture_dir.mkdir() + preset_path = presets_dir / "ok.milk" + preset_path.write_text("ok", encoding="utf-8") + + preset_results = [ + PresetScanResult(path=preset_path, result="ok", layers=()), + ] + targets = ScanTargets( + presets=(PresetTarget(path=preset_path, layers=()),), + presets_dir=presets_dir, + ) + + with ( + patch("cleave.cli.build_bulk_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results), + ): + cmd_scan( + build_parser().parse_args( + [ + "scan", + "--presets-dir", + str(presets_dir), + "--texture-path", + str(texture_dir), + "--include-dim", + ] + ) + ) + + err = capsys.readouterr().err + assert "warning:" in err + assert "--include-dim/--include-washout have no effect" in err + + +def test_cmd_scan_delete_skips_dim_without_include_dim( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + monkeypatch.setenv("CLEAVE_DATA", str(tmp_path)) + presets_dir = tmp_path / "pack" + presets_dir.mkdir() + texture_dir = tmp_path / "textures" + texture_dir.mkdir() + dim_path = presets_dir / "dim.milk" + dim_path.write_text("dim", encoding="utf-8") + + preset_results = [ + PresetScanResult(path=dim_path, result="dim", layers=()), + ] + targets = ScanTargets( + presets=(PresetTarget(path=dim_path, layers=()),), + presets_dir=presets_dir, + ) + + monkeypatch.setattr("sys.stdin.isatty", lambda: False) + + with ( + patch("cleave.cli.build_bulk_targets", return_value=targets), + patch("cleave.cli.run_scan", return_value=preset_results), + ): + cmd_scan( + build_parser().parse_args( + [ + "scan", + "--presets-dir", + str(presets_dir), + "--texture-path", + str(texture_dir), + "--delete", + "--yes", + ] + ) + ) + + assert dim_path.is_file() + assert "Deleted" not in capsys.readouterr().out + + def test_module_help_lists_subcommands() -> None: root = subprocess.run( [sys.executable, "-m", "cleave", "--help"], @@ -610,6 +1071,7 @@ def test_module_help_lists_subcommands() -> None: assert "analyse" not in out assert "play" in out assert "render" in out + assert "scan" in out assert "backup" in out assert "restore" in out assert out.startswith("usage: cleave [-h] target") @@ -641,3 +1103,20 @@ def test_module_help_lists_subcommands() -> None: assert "--start SEC" in render.stdout assert "--end SEC" in render.stdout assert "-hq" in render.stdout + + scan = subprocess.run( + [sys.executable, "-m", "cleave", "scan", "--help"], + capture_output=True, + text=True, + check=True, + ) + assert "--presets-dir" in scan.stdout + assert "--report" in scan.stdout + assert "--resume" in scan.stdout + + scan_golden = subprocess.run( + [sys.executable, "-m", "cleave", "scan-golden", "--help"], + capture_output=True, + text=True, + check=True, + ) diff --git a/tests/cleave/test_preset_scan.py b/tests/cleave/test_preset_scan.py new file mode 100644 index 0000000..5d9fef7 --- /dev/null +++ b/tests/cleave/test_preset_scan.py @@ -0,0 +1,1316 @@ +"""Unit tests for preset scan profiles, classification, and reports.""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path +from unittest.mock import MagicMock, call, patch + +import numpy as np +import pytest + +from cleave.preset_scan import ( + BLACK_COVERAGE, + BLACK_MAX_LUMA, + BRIGHT_ON_BLACK_COVERAGE, + BRIGHT_ON_BLACK_MAX, + COVERAGE_LUMA_MIN, + CAPPED_DIM_COV16, + CAPPED_DIM_MAX_HI, + CAPPED_DIM_MAX_LO, + CAPPED_DIM_MEAN, + DIM_COVERAGE, + DIM_MEAN_LUMA, + MIN_WHITE_FRAME_FRAC, + PROBE_FBO_HEIGHT, + PROBE_FBO_WIDTH, + PROBE_FPS, + PROBE_WARMUP_FRAMES, + PROBE_WINDOW_FRAMES, + SCAN_THRESHOLDS, + REPORT_FLUSH_EVERY, + SPARSE_DIM_COV16_MAX, + SPARSE_DIM_COV16_MIN, + SPARSE_DIM_MAX, + SPARSE_DIM_MEAN, + VERY_SPARSE_DIM_MAX, + WHITE_AREA_FRAC, + WHITE_CHANNEL_MIN, + PresetScanResult, + PresetScanTimings, + ScanReport, + build_probe_pcm, + build_scan_report, + classify_preset_result, + delete_presets, + destructive_scan_categories, + existing_report_status, + load_resume_results, + probe_profile, + quarantine_presets, + run_scan, + scan_report_summary, + scan_report_to_dict, + scan_thresholds, + validate_quarantine_dir, + write_scan_report, +) +from cleave.preset_scan_metrics import ( + LUMA_COVERAGE_CUTOFFS, + WHITE_COVERAGE_CUTOFFS, + FrameMetrics, + empty_frame_metrics, +) +from cleave.preset_scan_targets import PresetTarget, ScanTargets +from cleave.projectm import PresetLoadFailure + + +def _coverage_at( + *, + cutoff_16: float = 0.0, + cutoff_128: float = 0.0, + cutoff_192: float = 0.0, +) -> dict[int, float]: + coverage = {cutoff: 0.0 for cutoff in LUMA_COVERAGE_CUTOFFS} + coverage[COVERAGE_LUMA_MIN] = cutoff_16 + coverage[128] = cutoff_128 + coverage[192] = cutoff_192 + return coverage + + +def _white_coverage_at(*, white_235: float = 0.0) -> dict[int, float]: + white_coverage = {cutoff: 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS} + white_coverage[235] = white_235 + return white_coverage + + +def _peaks( + *, + max_luma: float, + mean_luma: float, + cutoff_16: float = 0.0, + cutoff_128: float = 0.0, + cutoff_192: float = 0.0, + white_235: float = 0.0, +) -> FrameMetrics: + return FrameMetrics( + max_luma=max_luma, + mean_luma=mean_luma, + coverage=_coverage_at( + cutoff_16=cutoff_16, + cutoff_128=cutoff_128, + cutoff_192=cutoff_192, + ), + white_coverage=_white_coverage_at(white_235=white_235), + ) + + +def _frame( + *, + max_luma: float, + mean_luma: float, + cutoff_16: float = 0.0, + white_235: float = 0.0, +) -> FrameMetrics: + return _peaks( + max_luma=max_luma, + mean_luma=mean_luma, + cutoff_16=cutoff_16, + white_235=white_235, + ) + + +def test_probe_profile() -> None: + profile = probe_profile() + assert profile.warmup_frames == PROBE_WARMUP_FRAMES + assert profile.window_frames == PROBE_WINDOW_FRAMES + assert profile.total_frames == 90 + + +def test_scan_thresholds() -> None: + assert scan_thresholds() == SCAN_THRESHOLDS + + +def test_build_scan_report_uses_thresholds() -> None: + targets = ScanTargets(presets=()) + report = build_scan_report( + scan_mode="bulk", + profile=probe_profile(), + targets=targets, + results=(), + ) + assert report.thresholds == SCAN_THRESHOLDS + + +def test_classify_preset_result_load_failed() -> None: + failures = [ + PresetLoadFailure(filename="/tmp/a.milk", message="shader error"), + PresetLoadFailure(filename="/tmp/b.milk", message="later"), + ] + result, error = classify_preset_result( + failures, _peaks(max_luma=100.0, mean_luma=100.0, cutoff_16=0.5) + ) + assert result == "load_failed" + assert error == "shader error" + + +def test_classify_preset_result_defers_load_failed_when_rendered() -> None: + failures = [ + PresetLoadFailure( + filename="/tmp/a.milk", + message="[PerFrameContext] Could not compile per-frame code", + ), + ] + peaks = _peaks(max_luma=255.0, mean_luma=120.0, cutoff_16=0.999) + frames = tuple( + _frame(max_luma=255.0, mean_luma=120.0, white_235=0.02) for _ in range(10) + ) + result, error = classify_preset_result( + failures, + peaks, + frames=frames, + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_defer_load_failed_invalid_peaks_stays_load_failed() -> None: + failures = [ + PresetLoadFailure(filename="/tmp/a.milk", message="shader error"), + ] + result, error = classify_preset_result( + failures, + empty_frame_metrics(), + ) + assert result == "load_failed" + assert error == "shader error" + + +def test_classify_preset_result_broken_soft_white_is_black() -> None: + soft_white_frames = tuple( + _frame(max_luma=255.0, mean_luma=230.0, white_235=0.2) for _ in range(5) + ) + peaks = _peaks( + max_luma=255.0, + mean_luma=230.0, + cutoff_16=0.99, + white_235=0.2, + ) + result, error = classify_preset_result( + [], + peaks, + frames=soft_white_frames, + ) + assert result == "black" + assert error is None + + +def test_classify_preset_result_black_low_max() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BLACK_MAX_LUMA - 0.01, + mean_luma=0.0, + cutoff_16=0.5, + ), + ) + assert result == "black" + assert error is None + + +def test_classify_preset_result_black_low_coverage() -> None: + result, error = classify_preset_result( + [], + _peaks(max_luma=50.0, mean_luma=25.0, cutoff_16=BLACK_COVERAGE - 0.0001), + ) + assert result == "black" + assert error is None + + +def test_classify_preset_result_black_boundary_not_black() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BLACK_MAX_LUMA, + mean_luma=0.0, + cutoff_16=BLACK_COVERAGE, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_dim() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BLACK_MAX_LUMA, + mean_luma=DIM_MEAN_LUMA - 0.01, + cutoff_16=DIM_COVERAGE - 0.001, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_dim_boundary_ok() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BLACK_MAX_LUMA, + mean_luma=DIM_MEAN_LUMA, + cutoff_16=DIM_COVERAGE, + ), + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_bright_on_black_guard() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BRIGHT_ON_BLACK_MAX, + mean_luma=DIM_MEAN_LUMA - 1.0, + cutoff_16=BRIGHT_ON_BLACK_COVERAGE, + ), + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_washed_out_persistent_white() -> None: + white_frames = tuple( + _frame(max_luma=255.0, mean_luma=255.0, white_235=0.9) for _ in range(10) + ) + dark_frames = tuple( + _frame(max_luma=50.0, mean_luma=50.0, white_235=0.1) for _ in range(20) + ) + frames = white_frames + dark_frames + peaks = _peaks(max_luma=255.0, mean_luma=255.0, cutoff_16=1.0, white_235=0.9) + result, error = classify_preset_result([], peaks, frames=frames) + assert result == "washed_out" + assert error is None + + +def test_classify_preset_result_bright_ok_not_washed_out() -> None: + """Bright usable presets with only brief white flashes stay ok.""" + frames = tuple( + _frame(max_luma=255.0, mean_luma=200.0, white_235=0.9) for _ in range(2) + ) + tuple( + _frame(max_luma=255.0, mean_luma=200.0, white_235=0.1) for _ in range(18) + ) + peaks = _peaks(max_luma=255.0, mean_luma=200.0, cutoff_16=1.0, cutoff_192=1.0) + result, error = classify_preset_result([], peaks, frames=frames) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_washed_out_boundary() -> None: + """Exactly min_white_frame_frac of white frames triggers washed_out.""" + white_count = int(10 * MIN_WHITE_FRAME_FRAC) + frames = tuple( + _frame(max_luma=255.0, mean_luma=255.0, white_235=WHITE_AREA_FRAC) + for _ in range(white_count) + ) + tuple( + _frame(max_luma=50.0, mean_luma=50.0, white_235=0.0) + for _ in range(10 - white_count) + ) + peaks = _peaks( + max_luma=255.0, + mean_luma=255.0, + cutoff_16=1.0, + white_235=WHITE_AREA_FRAC, + ) + result, error = classify_preset_result([], peaks, frames=frames) + assert result == "washed_out" + assert error is None + + +def test_classify_preset_result_high_luma_without_frames_not_washed() -> None: + """Peak luma alone does not classify washed_out without frame window.""" + result, error = classify_preset_result( + [], + _peaks( + max_luma=255.0, + mean_luma=255.0, + cutoff_16=1.0, + cutoff_192=1.0, + white_235=0.9, + ), + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_peak_washed_out() -> None: + frames = tuple( + _peaks(max_luma=255.0, mean_luma=247.0, cutoff_16=1.0, cutoff_192=1.0) + for _ in range(10) + ) + peaks = _peaks( + max_luma=255.0, + mean_luma=247.0, + cutoff_16=1.0, + cutoff_192=1.0, + ) + result, error = classify_preset_result([], peaks, frames=frames) + assert result == "washed_out" + assert error is None + + +def test_classify_preset_result_dim_bob_guard_mean_band() -> None: + """Quick dim guard: sparse mid-coverage with mean below bob mean_hi stays dim.""" + result, error = classify_preset_result( + [], + _peaks( + max_luma=SPARSE_DIM_MAX + 128.5, + mean_luma=9.6, + cutoff_16=0.164, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_dim_bob_guard_mid_cov() -> None: + """Quick dim guard: mid coverage with low mean stays dim.""" + result, error = classify_preset_result( + [], + _peaks( + max_luma=VERY_SPARSE_DIM_MAX + 136.8, + mean_luma=5.11, + cutoff_16=0.098, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_capped_dim_max_hi_boundary_ok() -> None: + """Healthy mid-max presets stay ok above capped dim max (golden case 7).""" + result, error = classify_preset_result( + [], + _peaks( + max_luma=CAPPED_DIM_MAX_HI + 3.3, + mean_luma=DIM_MEAN_LUMA - 0.6, + cutoff_16=CAPPED_DIM_COV16 + 0.0172, + ), + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_capped_dim_below_max_hi() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=CAPPED_DIM_MAX_HI - 3.4, + mean_luma=CAPPED_DIM_MEAN - 1.1, + cutoff_16=CAPPED_DIM_COV16 + 0.6342, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_sparse_dim() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=SPARSE_DIM_MAX + 20.0, + mean_luma=SPARSE_DIM_MEAN - 2.0, + cutoff_16=(SPARSE_DIM_COV16_MIN + SPARSE_DIM_COV16_MAX) / 2.0, + ), + ) + assert result == "dim" + assert error is None + + +def test_classify_preset_result_black_flash_guard() -> None: + result, error = classify_preset_result( + [], + _peaks( + max_luma=BRIGHT_ON_BLACK_MAX + 50.0, + mean_luma=0.0, + cutoff_16=BRIGHT_ON_BLACK_COVERAGE, + ), + ) + assert result == "ok" + assert error is None + + +def test_classify_preset_result_ok() -> None: + result, error = classify_preset_result( + [], + _peaks(max_luma=50.0, mean_luma=50.0, cutoff_16=0.5), + ) + assert result == "ok" + assert error is None + + +def test_synthetic_pcm_burst_never_silence() -> None: + from cleave.preset_scan import _synthetic_pcm_burst + + for frame_idx in range(8): + pcm = _synthetic_pcm_burst(frame_idx, 1470) + assert pcm.size > 0 + assert float(np.max(np.abs(pcm))) > 0.01 + + +def test_build_scan_report_project_mode() -> None: + project_dir = Path("/tmp/project") + config_path = project_dir / "cleave-viz.yaml" + preset_root = project_dir / "presets" + texture_path = project_dir / "textures" + preset_path = preset_root / "pack" / "a.milk" + anchor_dir = preset_root / "pack" + + targets = ScanTargets( + presets=(PresetTarget(path=preset_path, layers=("layer_1",)),), + preset_root=preset_root, + texture_paths=(texture_path,), + layer_sources={"layer_1": (anchor_dir,)}, + ) + results = [ + PresetScanResult( + path=preset_path, + result="ok", + layers=("layer_1",), + timings=PresetScanTimings(load_sec=0.1, render_sec=0.5), + ), + PresetScanResult( + path=preset_root / "pack" / "b.milk", + result="black", + layers=("layer_1",), + error=None, + ), + ] + report = build_scan_report( + scan_mode="project", + profile=probe_profile(), + targets=targets, + results=results, + project_dir=project_dir, + config_path=config_path, + ) + + assert report.scan_mode == "project" + assert report.project_dir == project_dir.resolve() + assert report.config_path == config_path.resolve() + assert report.presets_dir is None + assert report.preset_root == preset_root.resolve() + assert report.texture_paths == (texture_path.resolve(),) + assert report.layers == {"layer_1": [str(anchor_dir)]} + assert report.probe_frames == PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES + assert report.probe_fps == PROBE_FPS + assert report.fbo_size == (PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT) + assert len(report.presets) == 2 + + summary = scan_report_summary(report) + assert summary == { + "total": 2, + "load_failed": 0, + "black": 1, + "dim": 0, + "washed_out": 0, + "ok": 1, + } + + +def test_build_scan_report_bulk_mode() -> None: + presets_dir = Path("/tmp/presets-pack") + targets = ScanTargets( + presets=(PresetTarget(path=presets_dir / "a.milk", layers=()),), + presets_dir=presets_dir, + ) + report = build_scan_report( + scan_mode="bulk", + profile=probe_profile(), + targets=targets, + results=[ + PresetScanResult( + path=presets_dir / "a.milk", + result="load_failed", + layers=(), + error="parse error", + ), + ], + ) + + assert report.scan_mode == "bulk" + assert report.project_dir is None + assert report.config_path is None + assert report.presets_dir == presets_dir.resolve() + assert report.preset_root is None + assert report.texture_paths == () + assert report.layers == {} + + +def test_scan_report_serialization_round_trip() -> None: + preset_path = Path("/tmp/presets/a.milk") + report = ScanReport( + scan_mode="project", + project_dir=Path("/tmp/project"), + config_path=Path("/tmp/project/cleave-viz.yaml"), + presets_dir=None, + preset_root=Path("/tmp/presets"), + texture_paths=(Path("/tmp/textures"),), + layers={"layer_1": ["/tmp/presets/pack"]}, + thresholds=dict(SCAN_THRESHOLDS), + probe_frames=PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES, + probe_fps=PROBE_FPS, + fbo_size=(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT), + presets=( + PresetScanResult( + path=preset_path, + result="ok", + layers=("layer_1",), + ), + ), + ) + + payload = scan_report_to_dict(report) + assert payload["scan_mode"] == "project" + assert payload["project_dir"] == "/tmp/project" + assert payload["config_path"] == "/tmp/project/cleave-viz.yaml" + assert payload["presets_dir"] is None + assert payload["preset_root"] == "/tmp/presets" + assert payload["texture_paths"] == ["/tmp/textures"] + assert payload["layers"] == {"layer_1": ["/tmp/presets/pack"]} + assert payload["thresholds"] == SCAN_THRESHOLDS + assert payload["probe_frames"] == PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES + assert payload["probe_fps"] == PROBE_FPS + assert payload["fbo_size"] == [PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT] + assert "pcm_source" not in payload + assert "reference_clip_path" not in payload + assert payload["presets"] == [ + { + "path": str(preset_path), + "result": "ok", + "layers": ["layer_1"], + }, + ] + assert payload["summary"] == { + "total": 1, + "load_failed": 0, + "black": 0, + "dim": 0, + "washed_out": 0, + "ok": 1, + } + assert payload["complete"] is True + + with tempfile.TemporaryDirectory() as tmp: + report_path = Path(tmp) / "scan-report.json" + write_scan_report(report_path, report) + loaded = json.loads(report_path.read_text(encoding="utf-8")) + assert loaded == payload + + +def test_build_scan_report_complete_flag() -> None: + targets = ScanTargets(presets=()) + partial = build_scan_report( + scan_mode="bulk", + profile=probe_profile(), + targets=targets, + results=(), + complete=False, + ) + assert partial.complete is False + assert scan_report_to_dict(partial)["complete"] is False + + +def test_write_scan_report_atomic_round_trip() -> None: + report = ScanReport( + scan_mode="bulk", + project_dir=None, + config_path=None, + presets_dir=Path("/tmp/presets"), + preset_root=None, + texture_paths=(), + layers={}, + thresholds=dict(SCAN_THRESHOLDS), + probe_frames=PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES, + probe_fps=PROBE_FPS, + fbo_size=(PROBE_FBO_WIDTH, PROBE_FBO_HEIGHT), + presets=(), + complete=False, + ) + + with tempfile.TemporaryDirectory() as tmp: + report_path = Path(tmp) / "nested" / "scan.json" + write_scan_report(report_path, report) + assert not any(report_path.parent.glob("*.tmp")) + loaded = json.loads(report_path.read_text(encoding="utf-8")) + assert loaded["complete"] is False + assert loaded["scan_mode"] == "bulk" + + +def test_load_resume_results_valid() -> None: + preset_path = Path("/tmp/presets/a.milk").resolve() + payload = { + "scan_mode": "project", + "presets": [ + { + "path": str(preset_path), + "result": "ok", + "layers": ["layer_1"], + "timings": {"load_sec": 0.1, "render_sec": 0.5}, + }, + ], + } + + with tempfile.TemporaryDirectory() as tmp: + report_path = Path(tmp) / "resume.json" + report_path.write_text(json.dumps(payload), encoding="utf-8") + resume = load_resume_results(report_path) + + assert resume.scan_mode == "project" + assert len(resume.results) == 1 + assert resume.results[0].path == preset_path + assert resume.results[0].result == "ok" + assert resume.results[0].layers == ("layer_1",) + assert resume.skip_paths == frozenset({preset_path}) + assert resume.complete is True + + +def test_load_resume_results_incomplete_flag() -> None: + preset_path = Path("/tmp/presets/a.milk").resolve() + payload = { + "scan_mode": "bulk", + "complete": False, + "presets": [{"path": str(preset_path), "result": "ok", "layers": []}], + } + + with tempfile.TemporaryDirectory() as tmp: + report_path = Path(tmp) / "resume.json" + report_path.write_text(json.dumps(payload), encoding="utf-8") + resume = load_resume_results(report_path) + + assert resume.complete is False + + +@pytest.mark.parametrize( + ("payload", "message"), + [ + ({"presets": []}, "scan_mode"), + ({"scan_mode": "bulk"}, "presets"), + ( + { + "scan_mode": "bulk", + "presets": [{"result": "ok", "layers": []}], + }, + "path", + ), + ], +) +def test_load_resume_results_malformed( + payload: dict, message: str, tmp_path: Path +) -> None: + report_path = tmp_path / "resume.json" + report_path.write_text(json.dumps(payload), encoding="utf-8") + + with pytest.raises(ValueError, match=message): + load_resume_results(report_path) + + +def test_load_resume_results_missing_file(tmp_path: Path) -> None: + missing = tmp_path / "missing.json" + with pytest.raises(ValueError, match="resume report not found"): + load_resume_results(missing) + + +def test_load_resume_results_invalid_json(tmp_path: Path) -> None: + report_path = tmp_path / "resume.json" + report_path.write_text("{not json", encoding="utf-8") + + with pytest.raises(ValueError, match="malformed JSON"): + load_resume_results(report_path) + + +def test_validate_quarantine_dir_creates_missing(tmp_path: Path) -> None: + scanned = tmp_path / "presets" + scanned.mkdir() + quarantine = tmp_path / "quarantine" + + resolved = validate_quarantine_dir(quarantine, (scanned,)) + assert resolved == quarantine.resolve() + assert quarantine.is_dir() + + +def test_validate_quarantine_dir_rejects_file(tmp_path: Path) -> None: + scanned = tmp_path / "presets" + scanned.mkdir() + blocker = tmp_path / "not-a-dir" + blocker.write_text("x", encoding="utf-8") + + with pytest.raises(ValueError, match="not a directory"): + validate_quarantine_dir(blocker, (scanned,)) + + +def test_validate_quarantine_dir_rejects_inside_scanned(tmp_path: Path) -> None: + scanned = tmp_path / "presets" + scanned.mkdir() + inside = scanned / "quarantine" + + with pytest.raises(ValueError, match="outside the scanned presets directory"): + validate_quarantine_dir(inside, (scanned,)) + + +def test_destructive_scan_categories_default_and_opt_in() -> None: + default = destructive_scan_categories() + assert default == frozenset({"load_failed", "black"}) + + with_dim = destructive_scan_categories(include_dim=True) + assert with_dim == frozenset({"load_failed", "black", "dim"}) + + with_washout = destructive_scan_categories(include_washout=True) + assert with_washout == frozenset({"load_failed", "black", "washed_out"}) + + with_both = destructive_scan_categories(include_dim=True, include_washout=True) + assert with_both == frozenset({"load_failed", "black", "dim", "washed_out"}) + + +def test_quarantine_presets_skips_washed_out_by_default(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + quarantine_dir = tmp_path / "quarantine" + quarantine_dir.mkdir() + + washed_path = presets_dir / "washed.milk" + washed_path.write_text("washed", encoding="utf-8") + + moves = quarantine_presets( + [PresetScanResult(path=washed_path, result="washed_out", layers=())], + quarantine_dir, + ) + + assert washed_path.is_file() + assert moves == [] + + +def test_quarantine_presets_moves_washed_out_when_opted_in(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + quarantine_dir = tmp_path / "quarantine" + quarantine_dir.mkdir() + + washed_path = presets_dir / "washed.milk" + washed_path.write_text("washed", encoding="utf-8") + + moves = quarantine_presets( + [PresetScanResult(path=washed_path, result="washed_out", layers=())], + quarantine_dir, + categories=destructive_scan_categories(include_washout=True), + ) + + assert not washed_path.exists() + assert (quarantine_dir / "washed.milk").is_file() + assert len(moves) == 1 + + +def test_quarantine_presets_moves_failures_flat(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + quarantine_dir = tmp_path / "quarantine" + quarantine_dir.mkdir() + + ok_path = presets_dir / "good.milk" + bad_path = presets_dir / "bad.milk" + dup_path = presets_dir / "dup.milk" + ok_path.write_text("ok", encoding="utf-8") + bad_path.write_text("bad", encoding="utf-8") + dup_path.write_text("dup", encoding="utf-8") + (quarantine_dir / "dup.milk").write_text("existing", encoding="utf-8") + + results = [ + PresetScanResult(path=ok_path, result="ok", layers=()), + PresetScanResult(path=bad_path, result="black", layers=()), + PresetScanResult(path=dup_path, result="dim", layers=()), + ] + moves = quarantine_presets(results, quarantine_dir) + + assert ok_path.is_file() + assert not bad_path.exists() + assert dup_path.is_file() + assert (quarantine_dir / "bad.milk").is_file() + assert len(moves) == 1 + + +def test_quarantine_presets_moves_dim_when_opted_in(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + quarantine_dir = tmp_path / "quarantine" + quarantine_dir.mkdir() + + bad_path = presets_dir / "bad.milk" + dup_path = presets_dir / "dup.milk" + bad_path.write_text("bad", encoding="utf-8") + dup_path.write_text("dup", encoding="utf-8") + (quarantine_dir / "dup.milk").write_text("existing", encoding="utf-8") + + results = [ + PresetScanResult(path=bad_path, result="black", layers=()), + PresetScanResult(path=dup_path, result="dim", layers=()), + ] + moves = quarantine_presets( + results, + quarantine_dir, + categories=destructive_scan_categories(include_dim=True), + ) + + assert not bad_path.exists() + assert not dup_path.exists() + assert (quarantine_dir / "bad.milk").is_file() + assert (quarantine_dir / "dup_1.milk").is_file() + assert len(moves) == 2 + + +def _mock_run_scan_gl(monkeypatch: pytest.MonkeyPatch) -> None: + def make_pm() -> MagicMock: + fake_pm = MagicMock() + fake_pm.destroy = MagicMock() + return fake_pm + + fake_fbo = MagicMock() + fake_fbo.destroy = MagicMock() + fake_fbo.fbo_id = 1 + + class FakeProbeSession: + def __init__(self, texture_strs: list[str] | None = None) -> None: + self.fbo = fake_fbo + self.n_pcm = 1 + self.frame_dt = 1.0 / 30.0 + + def __enter__(self) -> FakeProbeSession: + return self + + def __exit__(self, *args: object) -> None: + return None + + def configure_projectm(self, pm: MagicMock) -> None: + return None + + monkeypatch.setattr("cleave.preset_scan.ProjectM", make_pm) + monkeypatch.setattr("cleave.preset_scan.ProbeSession", FakeProbeSession) + + +def test_existing_report_status_incomplete(tmp_path: Path) -> None: + report_path = tmp_path / "scan.json" + report_path.write_text( + json.dumps({"presets": [{}, {}, {}], "complete": False}), + encoding="utf-8", + ) + scanned, complete = existing_report_status(report_path) + assert scanned == 3 + assert complete is False + + +def test_run_scan_creates_fresh_projectm_per_preset( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + pm_instances: list[MagicMock] = [] + + def make_pm() -> MagicMock: + fake_pm = MagicMock() + fake_pm.destroy = MagicMock() + pm_instances.append(fake_pm) + return fake_pm + + monkeypatch.setattr("cleave.preset_scan.ProjectM", make_pm) + + fake_fbo = MagicMock() + fake_fbo.destroy = MagicMock() + fake_fbo.fbo_id = 1 + + class FakeProbeSession: + def __init__(self, texture_strs: list[str] | None = None) -> None: + self.fbo = fake_fbo + self.n_pcm = 1 + self.frame_dt = 1.0 / 30.0 + + def __enter__(self) -> FakeProbeSession: + return self + + def __exit__(self, *args: object) -> None: + return None + + def configure_projectm(self, pm: MagicMock) -> None: + return None + + monkeypatch.setattr("cleave.preset_scan.ProbeSession", FakeProbeSession) + + probe_pms: list[MagicMock] = [] + + def fake_probe(pm, fbo, target, **kwargs): + probe_pms.append(pm) + return PresetScanResult(path=target.path, result="ok", layers=target.layers) + + monkeypatch.setattr("cleave.preset_scan._probe_preset", fake_probe) + + targets = tuple( + PresetTarget(path=tmp_path / f"p{i}.milk", layers=()) + for i in range(3) + ) + scan_targets = ScanTargets(presets=targets) + + run_scan(scan_targets) + + assert len(pm_instances) == 3 + for pm in pm_instances: + pm.destroy.assert_called_once() + assert probe_pms == pm_instances + + +def test_run_scan_resume_progress_message( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + capsys: pytest.CaptureFixture[str], +) -> None: + _mock_run_scan_gl(monkeypatch) + + targets = tuple( + PresetTarget(path=tmp_path / name, layers=()) + for name in ("a.milk", "b.milk", "c.milk") + ) + scan_targets = ScanTargets(presets=targets) + + monkeypatch.setattr( + "cleave.preset_scan._probe_preset", + lambda pm, fbo, target, **kwargs: PresetScanResult( + path=target.path, result="ok", layers=target.layers + ), + ) + + skip = frozenset({(tmp_path / "a.milk").resolve()}) + run_scan(scan_targets, skip_paths=skip) + + err = capsys.readouterr().err + assert "Resuming: 1 done" in err + assert "Scanning 2/3" in err + assert "Scanning 3/3" in err + + +def test_run_scan_skip_paths_and_report_sink( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + _mock_run_scan_gl(monkeypatch) + + targets = [ + PresetTarget(path=tmp_path / "a.milk", layers=()), + PresetTarget(path=tmp_path / "b.milk", layers=()), + PresetTarget(path=tmp_path / "c.milk", layers=()), + ] + scan_targets = ScanTargets(presets=tuple(targets)) + + def fake_probe(pm, fbo, target, **kwargs): + return PresetScanResult(path=target.path, result="ok", layers=target.layers) + + monkeypatch.setattr("cleave.preset_scan._probe_preset", fake_probe) + + sink_calls: list[tuple[int, bool]] = [] + + def sink(results: list[PresetScanResult], complete: bool) -> None: + sink_calls.append((len(results), complete)) + + skip = frozenset({(tmp_path / "b.milk").resolve()}) + results = run_scan( + scan_targets, + report_sink=sink, + skip_paths=skip, + ) + + assert [result.path.name for result in results] == ["a.milk", "c.milk"] + assert sink_calls[-1] == (2, True) + assert all(complete is False for _, complete in sink_calls[:-1]) + + +def test_run_scan_report_sink_flushes_every_ten( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + _mock_run_scan_gl(monkeypatch) + + targets = tuple( + PresetTarget(path=tmp_path / f"p{i}.milk", layers=()) + for i in range(REPORT_FLUSH_EVERY + 1) + ) + scan_targets = ScanTargets(presets=targets) + + monkeypatch.setattr( + "cleave.preset_scan._probe_preset", + lambda pm, fbo, target, **kwargs: PresetScanResult( + path=target.path, result="ok", layers=target.layers + ), + ) + + partial_flushes: list[int] = [] + + def sink(results: list[PresetScanResult], complete: bool) -> None: + if not complete: + partial_flushes.append(len(results)) + + run_scan(scan_targets, report_sink=sink) + assert partial_flushes == [REPORT_FLUSH_EVERY] + + +def test_run_scan_keyboard_interrupt_flushes_sink( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + _mock_run_scan_gl(monkeypatch) + + targets = tuple( + PresetTarget(path=tmp_path / f"p{i}.milk", layers=()) + for i in range(3) + ) + scan_targets = ScanTargets(presets=targets) + + def interrupting_probe(pm, fbo, target, **kwargs): + if target.path.name == "p1.milk": + raise KeyboardInterrupt + return PresetScanResult(path=target.path, result="ok", layers=target.layers) + + monkeypatch.setattr("cleave.preset_scan._probe_preset", interrupting_probe) + + sink_calls: list[tuple[int, bool]] = [] + + def sink(results: list[PresetScanResult], complete: bool) -> None: + sink_calls.append((len(results), complete)) + + with pytest.raises(KeyboardInterrupt): + run_scan(scan_targets, report_sink=sink) + + assert sink_calls == [(1, False)] + + +def _healthy_coverage() -> dict[int, float]: + return _coverage_at(cutoff_16=0.5, cutoff_192=0.0) + + +def test_probe_preset_clean_boot(monkeypatch: pytest.MonkeyPatch) -> None: + from cleave.preset_scan import _probe_preset + + fake_pm = MagicMock() + fake_pm.drain_preset_failures.return_value = [] + fake_fbo = MagicMock() + fake_fbo.fbo_id = 1 + target = PresetTarget(path=Path("/tmp/a.milk"), layers=("layer_1",)) + profile = probe_profile() + pcm = build_probe_pcm() + frame = FrameMetrics( + max_luma=50.0, + mean_luma=25.0, + coverage=_healthy_coverage(), + white_coverage=_white_coverage_at(), + ) + + monkeypatch.setattr( + "cleave.preset_scan.sample_frame_metrics", + lambda width, height: frame, + ) + + result = _probe_preset( + fake_pm, + fake_fbo, + target, + profile=profile, + pcm=pcm, + n_pcm=100, + frame_dt=1.0 / PROBE_FPS, + ) + + assert fake_pm.set_preset_start_clean.call_args_list == [call(True), call(False)] + fake_pm.load_preset.assert_called_once_with(target.path, smooth=False) + fake_pm.lock_preset.assert_called_once_with(True) + assert result.result == "ok" + assert result.luma == frame + + +def test_probe_preset_uses_peak_metrics_over_last_frame( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from cleave.preset_scan import _probe_preset + + fake_pm = MagicMock() + fake_pm.drain_preset_failures.return_value = [] + fake_fbo = MagicMock() + fake_fbo.fbo_id = 1 + target = PresetTarget(path=Path("/tmp/a.milk"), layers=()) + + frames = [ + FrameMetrics( + max_luma=1.0, + mean_luma=1.0, + coverage=_healthy_coverage(), + white_coverage=_white_coverage_at(), + ), + FrameMetrics( + max_luma=200.0, + mean_luma=50.0, + coverage=_healthy_coverage(), + white_coverage=_white_coverage_at(), + ), + FrameMetrics( + max_luma=1.0, + mean_luma=1.0, + coverage=_healthy_coverage(), + white_coverage=_white_coverage_at(), + ), + ] + call_count = {"index": 0} + + def fake_sample(width: int, height: int) -> FrameMetrics: + index = call_count["index"] + call_count["index"] += 1 + return frames[min(index, len(frames) - 1)] + + monkeypatch.setattr("cleave.preset_scan.sample_frame_metrics", fake_sample) + monkeypatch.setattr( + "cleave.preset_scan._synthetic_pcm_burst", + lambda frame_idx, n_pcm: np.zeros(n_pcm, dtype=np.float32), + ) + + short_profile = probe_profile() + short_profile = type(short_profile)( + warmup_frames=0, + window_frames=3, + ) + pcm = build_probe_pcm() + result = _probe_preset( + fake_pm, + fake_fbo, + target, + profile=short_profile, + pcm=pcm, + n_pcm=4, + frame_dt=1.0 / PROBE_FPS, + ) + + assert result.luma is not None + assert result.luma.max_luma == 200.0 + assert result.luma.mean_luma == 50.0 + assert result.result == "ok" + + +def test_preset_result_luma_serialization_round_trip() -> None: + from cleave.preset_scan import _preset_result_from_dict, _preset_result_to_dict + + coverage = {cutoff: float(cutoff) / 256.0 for cutoff in LUMA_COVERAGE_CUTOFFS} + white_coverage = { + cutoff: float(cutoff) / 256.0 for cutoff in WHITE_COVERAGE_CUTOFFS + } + preset = PresetScanResult( + path=Path("/tmp/a.milk"), + result="ok", + layers=("layer_1",), + luma=FrameMetrics( + max_luma=42.0, + mean_luma=12.0, + coverage=coverage, + white_coverage=white_coverage, + ), + ) + + payload = _preset_result_to_dict(preset) + assert payload["luma"]["max_luma"] == 42.0 + assert payload["luma"]["mean_luma"] == 12.0 + assert payload["luma"]["coverage"]["16"] == pytest.approx(16.0 / 256.0) + + restored = _preset_result_from_dict(payload) + assert restored.luma == preset.luma + + +def test_load_resume_results_with_washed_out(tmp_path: Path) -> None: + preset_path = tmp_path / "washed.milk" + payload = { + "scan_mode": "bulk", + "presets": [ + { + "path": str(preset_path), + "result": "washed_out", + "layers": [], + }, + ], + } + report_path = tmp_path / "resume.json" + report_path.write_text(json.dumps(payload), encoding="utf-8") + + resume = load_resume_results(report_path) + assert resume.results[0].result == "washed_out" + + +def test_load_resume_results_with_luma(tmp_path: Path) -> None: + preset_path = tmp_path / "a.milk" + payload = { + "scan_mode": "bulk", + "presets": [ + { + "path": str(preset_path), + "result": "ok", + "layers": [], + "luma": { + "max_luma": 30.0, + "mean_luma": 10.0, + "coverage": {str(cutoff): 0.1 for cutoff in LUMA_COVERAGE_CUTOFFS}, + "white_coverage": { + str(cutoff): 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS + }, + }, + }, + ], + } + report_path = tmp_path / "resume.json" + report_path.write_text(json.dumps(payload), encoding="utf-8") + + resume = load_resume_results(report_path) + assert resume.results[0].luma is not None + assert resume.results[0].luma.max_luma == 30.0 + assert resume.results[0].luma.mean_luma == 10.0 + + +def test_build_probe_pcm_synthetic() -> None: + pcm = build_probe_pcm() + assert pcm.channels == 1 + chunk = pcm.chunk(3, 100) + assert chunk.shape == (100,) + assert float(np.max(np.abs(chunk))) > 0.01 + + +def test_delete_presets_removes_failures(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + ok_path = presets_dir / "good.milk" + bad_path = presets_dir / "bad.milk" + dim_path = presets_dir / "dim.milk" + ok_path.write_text("ok", encoding="utf-8") + bad_path.write_text("bad", encoding="utf-8") + dim_path.write_text("dim", encoding="utf-8") + + deleted = delete_presets( + [ + PresetScanResult(path=ok_path, result="ok", layers=()), + PresetScanResult(path=bad_path, result="black", layers=()), + PresetScanResult(path=dim_path, result="dim", layers=()), + ] + ) + + assert ok_path.is_file() + assert not bad_path.exists() + assert dim_path.is_file() + assert deleted == [bad_path] + + +def test_delete_presets_removes_dim_when_opted_in(tmp_path: Path) -> None: + presets_dir = tmp_path / "presets" + presets_dir.mkdir() + dim_path = presets_dir / "dim.milk" + dim_path.write_text("dim", encoding="utf-8") + + deleted = delete_presets( + [PresetScanResult(path=dim_path, result="dim", layers=())], + categories=destructive_scan_categories(include_dim=True), + ) + + assert not dim_path.exists() + assert deleted == [dim_path] diff --git a/tests/cleave/test_preset_scan_golden.py b/tests/cleave/test_preset_scan_golden.py new file mode 100644 index 0000000..a176237 --- /dev/null +++ b/tests/cleave/test_preset_scan_golden.py @@ -0,0 +1,407 @@ +"""Unit tests for preset scan golden-set harness.""" + +from __future__ import annotations + +import textwrap +from pathlib import Path + +import pytest + +from cleave.preset_scan import ( + BLACK_COVERAGE, + BLACK_MAX_LUMA, + DIM_COVERAGE, + DIM_MEAN_LUMA, + PROBE_WARMUP_FRAMES, + PROBE_WINDOW_FRAMES, +) +from cleave.preset_scan_golden import ( + DEFAULT_METRICS_CACHE_PATH, + GOLDEN_CASE_COUNT, + GoldenCase, + GoldenSet, + default_threshold_sweep_variants, + evaluate, + load_golden_set, + load_metrics_cache, + resolve_cache_probe_profile, + resolve_eval_probe_window, + scan_result_to_golden, + sweep, + _merge_threshold_variants, +) +from cleave.preset_scan_metrics import ( + METRICS_CACHE_VERSION, + FrameMetrics, + MetricsCache, + PresetMetrics, + LUMA_COVERAGE_CUTOFFS, + WHITE_COVERAGE_CUTOFFS, +) +from cleave.paths import repo_root + +FIXTURE_PATH = ( + repo_root() / "tests" / "fixtures" / "preset_scan_golden_set.yaml" +) + + +def _coverage( + *, + cutoff_16: float = 0.0, + cutoff_192: float = 0.0, +) -> dict[int, float]: + coverage = {cutoff: 0.0 for cutoff in LUMA_COVERAGE_CUTOFFS} + coverage[16] = cutoff_16 + coverage[192] = cutoff_192 + return coverage + + +def _white_coverage(*, white_235: float = 0.0) -> dict[int, float]: + white_coverage = {cutoff: 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS} + white_coverage[235] = white_235 + return white_coverage + + +def _frame( + *, + max_luma: float, + mean_luma: float, + cutoff_16: float = 0.0, + cutoff_192: float = 0.0, + white_235: float = 0.0, +) -> FrameMetrics: + return FrameMetrics( + max_luma=max_luma, + mean_luma=mean_luma, + coverage=_coverage(cutoff_16=cutoff_16, cutoff_192=cutoff_192), + white_coverage=_white_coverage(white_235=white_235), + ) + + +def _mini_golden(tmp_path: Path) -> GoldenSet: + preset_root = tmp_path / "presets" + preset_root.mkdir() + ok_path = preset_root / "ok.milk" + dim_path = preset_root / "dim.milk" + fail_path = preset_root / "fail.milk" + ok_path.write_text("ok", encoding="utf-8") + dim_path.write_text("dim", encoding="utf-8") + fail_path.write_text("fail", encoding="utf-8") + + return GoldenSet( + version=1, + preset_root=preset_root, + texture_paths=(tmp_path / "textures",), + cases=( + GoldenCase(id=1, preset=ok_path, expected_result="ok", notes=""), + GoldenCase(id=2, preset=dim_path, expected_result="dim", notes=""), + GoldenCase(id=3, preset=fail_path, expected_result="black", notes=""), + ), + ) + + +def _mini_cache(golden: GoldenSet) -> MetricsCache: + ok_path, dim_path, fail_path = (case.preset for case in golden.cases) + healthy = _frame(max_luma=50.0, mean_luma=50.0, cutoff_16=0.5) + dim = _frame( + max_luma=BLACK_MAX_LUMA, + mean_luma=DIM_MEAN_LUMA - 0.01, + cutoff_16=DIM_COVERAGE - 0.001, + ) + return MetricsCache( + version=METRICS_CACHE_VERSION, + probe_fps=30, + fbo_size=(480, 270), + warmup_frames=0, + window_frames=90, + total_frames=90, + presets=( + PresetMetrics( + path=ok_path, + load_failed=False, + error=None, + fps=30, + frames=(healthy,) * 90, + ), + PresetMetrics( + path=dim_path, + load_failed=False, + error=None, + fps=30, + frames=(dim,) * 90, + ), + PresetMetrics( + path=fail_path, + load_failed=True, + error="shader compile failed", + fps=30, + frames=(), + ), + ), + ) + + +def test_load_golden_set_parses_fixture() -> None: + golden = load_golden_set(FIXTURE_PATH) + + assert golden.version == 1 + assert golden.preset_root == (repo_root() / "assets" / "milkdrop-presets").resolve() + assert len(golden.texture_paths) == 1 + assert golden.texture_paths[0] == ( + repo_root() / "assets" / "milkdrop-presets" / "presets-milkdrop-texture-pack" + ).resolve() + assert len(golden.cases) == GOLDEN_CASE_COUNT + assert golden.cases[0].id == 1 + assert golden.cases[-1].id == GOLDEN_CASE_COUNT + assert golden.cases[0].preset.name == "BrainStain-Blackwidow.milk" + assert golden.cases[0].expected_result == "dim" + assert "Milkdrop-Original" in str(golden.cases[0].preset) + + +def test_load_golden_set_validates_case_count(tmp_path: Path) -> None: + yaml_path = tmp_path / "golden.yaml" + yaml_path.write_text( + textwrap.dedent( + """ + version: 1 + preset_root: /tmp/presets + texture_paths: + - /tmp/textures + cases: + - id: 1 + preset: a.milk + expected_result: ok + """ + ).strip(), + encoding="utf-8", + ) + + with pytest.raises(ValueError, match="must contain 50 cases"): + load_golden_set(yaml_path) + + +def test_scan_result_to_golden_maps_load_failed() -> None: + assert scan_result_to_golden("load_failed") == "black" + assert scan_result_to_golden("ok") == "ok" + + +def test_evaluate_with_synthetic_cache(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + report = evaluate( + cache, + golden, + warmup_frames=0, + window_frames=90, + ) + + assert report.total == 3 + assert report.correct == 3 + assert report.accuracy == 1.0 + assert report.mismatches == () + + +def test_evaluate_load_failed_counts_as_black(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + report = evaluate(cache, golden, warmup_frames=0, window_frames=90) + + assert report.confusion_matrix["black"]["black"] == 1 + mismatch_ids = {entry.id for entry in report.mismatches} + assert 3 not in mismatch_ids + + +def test_evaluate_reports_mismatch(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + ok_path = golden.cases[0].preset + blackish = _frame( + max_luma=BLACK_MAX_LUMA - 0.01, + mean_luma=0.0, + cutoff_16=BLACK_COVERAGE - 0.0001, + ) + patched = MetricsCache( + version=cache.version, + probe_fps=cache.probe_fps, + fbo_size=cache.fbo_size, + warmup_frames=cache.warmup_frames, + window_frames=cache.window_frames, + total_frames=cache.total_frames, + presets=( + PresetMetrics( + path=ok_path, + load_failed=False, + error=None, + fps=30, + frames=(blackish,) * 90, + ), + cache.presets[1], + cache.presets[2], + ), + ) + + report = evaluate(patched, golden, warmup_frames=0, window_frames=90) + + assert report.correct == 2 + assert len(report.mismatches) == 1 + assert report.mismatches[0].id == 1 + assert report.mismatches[0].expected == "ok" + assert report.mismatches[0].actual == "black" + + +def test_sweep_returns_ranked_results(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + results = sweep( + cache, + golden, + warmup_frames_values=(0,), + window_frames_values=(90,), + threshold_variants=(None,), + ) + + assert len(results) == 1 + assert results[0].accuracy == 1.0 + assert results[0].warmup_frames == 0 + assert results[0].window_frames == 90 + + +def test_sweep_ranks_better_configs_first(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + results = sweep( + cache, + golden, + warmup_frames_values=(0, 50), + window_frames_values=(90,), + threshold_variants=(None,), + ) + + assert len(results) == 2 + assert results[0].accuracy >= results[1].accuracy + + +def test_default_threshold_sweep_variants_is_expanded() -> None: + variants = default_threshold_sweep_variants() + assert variants[0] is None + assert len(variants) > 100 + assert any( + entry is not None and "washed_mean_peak" in entry for entry in variants + ) + assert any( + entry is not None and "dim_bob_cov_lo" in entry for entry in variants + ) + + +def test_merge_threshold_variants_deduplicates() -> None: + merged = _merge_threshold_variants( + (None, {"white_area_frac": 0.6}), + (None, {"white_area_frac": 0.6}, {"min_white_frame_frac": 0.3}), + ) + assert merged == (None, {"white_area_frac": 0.6}, {"min_white_frame_frac": 0.3}) + + +def test_evaluate_uses_cache_probe_profile() -> None: + golden = load_golden_set(FIXTURE_PATH) + ok_path = golden.cases[2].preset + cache = MetricsCache( + version=METRICS_CACHE_VERSION, + probe_fps=30, + fbo_size=(480, 270), + warmup_frames=PROBE_WARMUP_FRAMES, + window_frames=PROBE_WINDOW_FRAMES, + total_frames=PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES, + presets=( + PresetMetrics( + path=ok_path, + load_failed=False, + error=None, + fps=30, + frames=( + _frame(max_luma=50.0, mean_luma=50.0, cutoff_16=0.5), + ) + * (PROBE_WARMUP_FRAMES + PROBE_WINDOW_FRAMES), + ), + ), + ) + mini = GoldenSet( + version=1, + preset_root=golden.preset_root, + texture_paths=golden.texture_paths, + cases=(golden.cases[2],), + ) + + report = evaluate(cache, mini) + + assert report.warmup_frames == PROBE_WARMUP_FRAMES + assert report.window_frames == PROBE_WINDOW_FRAMES + + +def test_evaluate_rejects_profile_mismatch(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + with pytest.raises(ValueError, match="eval probe profile mismatch"): + evaluate(cache, golden, warmup_frames=10, window_frames=90) + + +def test_resolve_cache_probe_profile_infers_from_frame_count(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = MetricsCache( + version=METRICS_CACHE_VERSION, + probe_fps=30, + fbo_size=(480, 270), + presets=_mini_cache(golden).presets, + ) + + profile = resolve_cache_probe_profile(cache) + + assert profile.warmup_frames == PROBE_WARMUP_FRAMES + assert profile.window_frames == PROBE_WINDOW_FRAMES + + +def test_resolve_eval_probe_window_uses_cache_by_default(tmp_path: Path) -> None: + golden = _mini_golden(tmp_path) + cache = _mini_cache(golden) + + warmup, window, profile = resolve_eval_probe_window(cache) + + assert warmup == 0 + assert window == 90 + + +GOLDEN_CASE_2_ID = 2 + + +def test_golden_case_2_not_washed_out_with_v3_cache() -> None: + """Case 2: visualizer black; scan ok/black is accepted, not washed_out.""" + if not DEFAULT_METRICS_CACHE_PATH.is_file(): + pytest.skip("metrics cache not generated; run: cleave scan-golden --probe") + + golden = load_golden_set(FIXTURE_PATH) + try: + cache = load_metrics_cache(DEFAULT_METRICS_CACHE_PATH) + except ValueError: + pytest.skip("metrics cache outdated; run: cleave scan-golden --probe") + if cache.version != METRICS_CACHE_VERSION: + pytest.skip("metrics cache outdated; run: cleave scan-golden --probe") + + case_2 = next(case for case in golden.cases if case.id == GOLDEN_CASE_2_ID) + assert case_2.expected_result == "black" + mini = GoldenSet( + version=golden.version, + preset_root=golden.preset_root, + texture_paths=golden.texture_paths, + cases=(case_2,), + ) + report = evaluate(cache, mini) + + mismatch = next( + (entry for entry in report.mismatches if entry.id == GOLDEN_CASE_2_ID), + None, + ) + assert mismatch is None diff --git a/tests/cleave/test_preset_scan_metrics.py b/tests/cleave/test_preset_scan_metrics.py new file mode 100644 index 0000000..c555f31 --- /dev/null +++ b/tests/cleave/test_preset_scan_metrics.py @@ -0,0 +1,281 @@ +"""Unit tests for preset scan frame metrics and cache serialization.""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path +from unittest.mock import patch + +import numpy as np +import pytest + +from cleave.preset_scan_metrics import ( + LUMA_COVERAGE_CUTOFFS, + METRICS_CACHE_VERSION, + WHITE_COVERAGE_CUTOFFS, + FrameMetrics, + MetricsCache, + PresetMetrics, + frame_metrics_from_dict, + frame_metrics_to_dict, + load_metrics_cache, + metrics_cache_from_dict, + metrics_cache_to_dict, + peak_metrics, + preset_metrics_from_dict, + preset_metrics_to_dict, + sample_frame_metrics, + white_frame_fraction, + write_metrics_cache, +) + + +def _frame( + *, + max_luma: float, + mean_luma: float, + coverage_16: float = 0.0, + white_235: float = 0.0, +) -> FrameMetrics: + coverage = {cutoff: 0.0 for cutoff in LUMA_COVERAGE_CUTOFFS} + coverage[16] = coverage_16 + white_coverage = {cutoff: 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS} + white_coverage[235] = white_235 + return FrameMetrics( + max_luma=max_luma, + mean_luma=mean_luma, + coverage=coverage, + white_coverage=white_coverage, + ) + + +def test_peak_metrics_empty() -> None: + result = peak_metrics([], warmup_frames=5, window_frames=10) + assert result.max_luma == 0.0 + assert result.mean_luma == 0.0 + assert result.coverage == {cutoff: 0.0 for cutoff in LUMA_COVERAGE_CUTOFFS} + assert result.white_coverage == {cutoff: 0.0 for cutoff in WHITE_COVERAGE_CUTOFFS} + + +def test_peak_metrics_skips_warmup_and_limits_window() -> None: + frames = [ + _frame(max_luma=1.0, mean_luma=1.0, coverage_16=0.01, white_235=0.1), + _frame(max_luma=2.0, mean_luma=2.0, coverage_16=0.02, white_235=0.2), + _frame(max_luma=100.0, mean_luma=10.0, coverage_16=0.5, white_235=0.9), + _frame(max_luma=50.0, mean_luma=30.0, coverage_16=0.3, white_235=0.7), + _frame(max_luma=80.0, mean_luma=20.0, coverage_16=0.4, white_235=0.8), + ] + result = peak_metrics(frames, warmup_frames=2, window_frames=2) + assert result.max_luma == 100.0 + assert result.mean_luma == 30.0 + assert result.coverage[16] == 0.5 + assert result.white_coverage[235] == 0.9 + + +def test_peak_metrics_unbounded_window_uses_all_post_warmup() -> None: + frames = [ + _frame(max_luma=5.0, mean_luma=1.0), + _frame(max_luma=10.0, mean_luma=3.0), + _frame(max_luma=7.0, mean_luma=9.0), + ] + result = peak_metrics(frames, warmup_frames=1, window_frames=0) + assert result.max_luma == 10.0 + assert result.mean_luma == 9.0 + + +def test_peak_metrics_excludes_frames_outside_window() -> None: + frames = [ + _frame(max_luma=1.0, mean_luma=1.0), + _frame(max_luma=20.0, mean_luma=20.0), + _frame(max_luma=5.0, mean_luma=5.0), + _frame(max_luma=999.0, mean_luma=999.0), + ] + result = peak_metrics(frames, warmup_frames=1, window_frames=2) + assert result.max_luma == 20.0 + assert result.mean_luma == 20.0 + + +def test_peak_metrics_takes_peak_not_last() -> None: + frames = [ + _frame(max_luma=200.0, mean_luma=5.0, coverage_16=0.9), + _frame(max_luma=1.0, mean_luma=1.0, coverage_16=0.001), + ] + result = peak_metrics(frames, warmup_frames=0, window_frames=2) + assert result.max_luma == 200.0 + assert result.mean_luma == 5.0 + assert result.coverage[16] == 0.9 + + +def test_white_frame_fraction_counts_persistently_white_frames() -> None: + frames = [ + _frame(max_luma=255.0, mean_luma=255.0, white_235=0.9), + _frame(max_luma=255.0, mean_luma=255.0, white_235=0.7), + _frame(max_luma=50.0, mean_luma=50.0, white_235=0.1), + _frame(max_luma=255.0, mean_luma=255.0, white_235=0.5), + ] + frac = white_frame_fraction( + frames, + warmup_frames=0, + window_frames=4, + channel_min_cutoff=235, + area_frac=0.6, + ) + assert frac == pytest.approx(0.5) + + +def test_white_frame_fraction_empty() -> None: + assert ( + white_frame_fraction( + [], + warmup_frames=0, + window_frames=10, + channel_min_cutoff=235, + area_frac=0.6, + ) + == 0.0 + ) + + +def test_frame_metrics_json_round_trip() -> None: + original = FrameMetrics( + max_luma=123.5, + mean_luma=12.3, + coverage={cutoff: float(cutoff) / 256.0 for cutoff in LUMA_COVERAGE_CUTOFFS}, + white_coverage={ + cutoff: float(cutoff) / 256.0 for cutoff in WHITE_COVERAGE_CUTOFFS + }, + ) + restored = frame_metrics_from_dict(frame_metrics_to_dict(original)) + assert restored == original + + +def test_preset_metrics_json_round_trip() -> None: + original = PresetMetrics( + path=Path("/tmp/example.milk"), + load_failed=False, + error=None, + fps=30, + frames=( + _frame(max_luma=1.0, mean_luma=0.5, coverage_16=0.01), + _frame(max_luma=2.0, mean_luma=1.5, coverage_16=0.02), + ), + ) + restored = preset_metrics_from_dict(preset_metrics_to_dict(original)) + assert restored == original + + +def test_metrics_cache_json_round_trip() -> None: + original = MetricsCache( + version=METRICS_CACHE_VERSION, + probe_fps=30, + fbo_size=(480, 270), + warmup_frames=15, + window_frames=75, + total_frames=90, + presets=( + PresetMetrics( + path=Path("/tmp/a.milk"), + load_failed=True, + error="shader compile failed", + fps=30, + frames=(), + ), + PresetMetrics( + path=Path("/tmp/b.milk"), + load_failed=False, + error=None, + fps=30, + frames=(_frame(max_luma=10.0, mean_luma=5.0, coverage_16=0.1),), + ), + ), + ) + restored = metrics_cache_from_dict(metrics_cache_to_dict(original)) + assert restored == original + + +def test_write_and_load_metrics_cache() -> None: + cache = MetricsCache( + version=METRICS_CACHE_VERSION, + probe_fps=30, + fbo_size=(480, 270), + warmup_frames=15, + window_frames=75, + total_frames=90, + presets=( + PresetMetrics( + path=Path("/tmp/c.milk"), + load_failed=False, + error=None, + fps=30, + frames=(_frame(max_luma=3.0, mean_luma=2.0),), + ), + ), + ) + with tempfile.TemporaryDirectory() as tmp_dir: + path = Path(tmp_dir) / "metrics.json" + write_metrics_cache(path, cache) + loaded = load_metrics_cache(path) + assert loaded == cache + payload = json.loads(path.read_text(encoding="utf-8")) + assert payload["version"] == METRICS_CACHE_VERSION + assert payload["probe_fps"] == 30 + assert payload["warmup_frames"] == 15 + assert payload["window_frames"] == 75 + assert payload["total_frames"] == 90 + + +def test_metrics_cache_v3_requires_probe_profile() -> None: + payload = { + "version": METRICS_CACHE_VERSION, + "probe_fps": 30, + "fbo_size": [480, 270], + "presets": [], + } + with pytest.raises(ValueError, match="missing required fields"): + metrics_cache_from_dict(payload) + + +def test_metrics_cache_from_dict_rejects_bad_version() -> None: + with pytest.raises(ValueError, match="unsupported metrics cache version"): + metrics_cache_from_dict({"version": 99, "probe_fps": 30, "fbo_size": [1, 1], "presets": []}) + + +def test_metrics_cache_from_dict_rejects_v2() -> None: + payload = { + "version": 2, + "probe_fps": 30, + "fbo_size": [480, 270], + "warmup_frames": 15, + "window_frames": 75, + "total_frames": 90, + "presets": [], + } + with pytest.raises(ValueError, match="stale"): + metrics_cache_from_dict(payload) + + +@patch("cleave.preset_scan_metrics.glReadPixels") +def test_sample_frame_metrics_full_frame(mock_read_pixels) -> None: + width, height = 4, 2 + rgba = np.zeros((height, width, 4), dtype=np.uint8) + rgba[0, 0] = (255, 255, 255, 255) + rgba[1, 3] = (128, 128, 128, 255) + mock_read_pixels.return_value = rgba.tobytes() + + result = sample_frame_metrics(width, height) + + mock_read_pixels.assert_called_once() + assert result.max_luma == pytest.approx(255.0) + expected_mean = float( + np.mean( + 0.2126 * rgba[..., 0].astype(np.float32) + + 0.7152 * rgba[..., 1].astype(np.float32) + + 0.0722 * rgba[..., 2].astype(np.float32) + ) + ) + assert result.mean_luma == pytest.approx(expected_mean) + assert result.coverage[8] == pytest.approx(0.25) + assert result.coverage[192] == pytest.approx(0.125) + assert result.white_coverage[235] == pytest.approx(0.125) + assert result.white_coverage[224] == pytest.approx(0.125) diff --git a/tests/cleave/test_preset_scan_targets.py b/tests/cleave/test_preset_scan_targets.py new file mode 100644 index 0000000..0642612 --- /dev/null +++ b/tests/cleave/test_preset_scan_targets.py @@ -0,0 +1,260 @@ +"""Unit tests for preset scan target derivation.""" + +from __future__ import annotations + +import tempfile +from pathlib import Path + +from cleave.config import ( + CleaveConfig, + LayerConfig, + PathsConfig, + VisualizerConfig, +) +from cleave.config_schema import DEFAULT_LAYER_SLOTS +from cleave.preset_scan_targets import build_bulk_targets, build_project_targets +from cleave.extract import STEM_NAMES + + +def _write_milk(path: Path) -> None: + path.write_text("milk") + + +def _layer_config( + preset: Path, + *, + stem: str = "drums", + enabled: bool = True, + preset_switching: str = "projectm", + preset_switching_presets: list[Path] | None = None, +) -> LayerConfig: + return LayerConfig( + preset=preset, + stem=stem, # type: ignore[arg-type] + enabled=enabled, + preset_switching=preset_switching, # type: ignore[arg-type] + preset_switching_presets=preset_switching_presets or [], + ) + + +def _project_cfg( + tmp: Path, + *, + layers: dict[str, LayerConfig], + layer_z_order: list[str] | None = None, +) -> CleaveConfig: + texture_dir = tmp / "textures" + texture_dir.mkdir(exist_ok=True) + return CleaveConfig( + paths=PathsConfig( + preset_root=tmp / "presets", + texture_paths=(texture_dir.resolve(),), + ), + layers=layers, + visualizer=VisualizerConfig(), + config_path=tmp / "cleave-viz.yaml", + layer_z_order=layer_z_order or ["layer_1"], + ) + + +def test_project_targets_include_sibling_presets_non_recursive() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + preset_dir = root / "presets" / "pack" + preset_dir.mkdir(parents=True) + anchor = preset_dir / "anchor.milk" + sibling = preset_dir / "sibling.milk" + nested_dir = preset_dir / "nested" + nested_dir.mkdir() + _write_milk(anchor) + _write_milk(sibling) + _write_milk(nested_dir / "deep.milk") + + cfg = _project_cfg( + root, + layers={ + "layer_1": _layer_config(anchor, preset_switching="projectm"), + }, + ) + targets = build_project_targets(cfg) + + paths = {target.path for target in targets.presets} + assert paths == {anchor.resolve(), sibling.resolve()} + assert targets.layer_sources["layer_1"] == (preset_dir.resolve(),) + assert targets.preset_root == (root / "presets").resolve() + assert targets.texture_paths == ((root / "textures").resolve(),) + + +def test_project_targets_include_anchor_dir_when_preset_switching_none() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + preset_dir = root / "presets" / "locked" + preset_dir.mkdir(parents=True) + locked = preset_dir / "locked.milk" + sibling = preset_dir / "other.milk" + _write_milk(locked) + _write_milk(sibling) + + cfg = _project_cfg( + root, + layers={ + "layer_1": _layer_config(locked, preset_switching="none"), + }, + ) + targets = build_project_targets(cfg) + + paths = {target.path for target in targets.presets} + assert paths == {locked.resolve(), sibling.resolve()} + + +def test_project_targets_include_disabled_layers() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + preset_dir = root / "presets" / "off" + preset_dir.mkdir(parents=True) + anchor = preset_dir / "a.milk" + sibling = preset_dir / "b.milk" + _write_milk(anchor) + _write_milk(sibling) + + cfg = _project_cfg( + root, + layers={ + "layer_1": _layer_config(anchor, enabled=False), + }, + ) + targets = build_project_targets(cfg) + + assert len(targets.presets) == 2 + + +def test_project_targets_user_defined_adds_rotation_presets() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + anchor_dir = root / "presets" / "anchor" + extra_dir = root / "extras" + anchor_dir.mkdir(parents=True) + extra_dir.mkdir() + anchor = anchor_dir / "anchor.milk" + sibling = anchor_dir / "sibling.milk" + extra = extra_dir / "extra.milk" + _write_milk(anchor) + _write_milk(sibling) + _write_milk(extra) + + cfg = _project_cfg( + root, + layers={ + "layer_1": _layer_config( + anchor, + preset_switching="user_defined", + preset_switching_presets=[extra], + ), + }, + ) + targets = build_project_targets(cfg) + + paths = {target.path for target in targets.presets} + assert paths == { + anchor.resolve(), + sibling.resolve(), + extra.resolve(), + } + assert targets.layer_sources["layer_1"] == ( + anchor_dir.resolve(), + extra.resolve(), + ) + + +def test_project_targets_dedup_presets_across_layers() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + shared_dir = root / "presets" / "shared" + shared_dir.mkdir(parents=True) + shared = shared_dir / "shared.milk" + other = shared_dir / "other.milk" + _write_milk(shared) + _write_milk(other) + + cfg = _project_cfg( + root, + layers={ + "layer_1": _layer_config( + shared, + stem="drums", + preset_switching="none", + ), + "layer_2": _layer_config( + other, + stem="bass", + preset_switching="none", + ), + }, + layer_z_order=["layer_1", "layer_2"], + ) + targets = build_project_targets(cfg) + + shared_target = next(t for t in targets.presets if t.path == shared.resolve()) + assert shared_target.layers == ("layer_1", "layer_2") + assert len(targets.presets) == 2 + + +def test_project_targets_respect_layer_z_order_slots() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + layers: dict[str, LayerConfig] = {} + for index, slot in enumerate(DEFAULT_LAYER_SLOTS[:2]): + preset_dir = root / "presets" / slot + preset_dir.mkdir(parents=True) + preset = preset_dir / f"{slot}.milk" + _write_milk(preset) + layers[slot] = _layer_config( + preset, + stem=STEM_NAMES[index], + preset_switching="none", + ) + + cfg = _project_cfg( + root, + layers=layers, + layer_z_order=["layer_1", "layer_2"], + ) + targets = build_project_targets(cfg) + + assert len(targets.presets) == 2 + assert set(targets.layer_sources) == {"layer_1", "layer_2"} + + +def test_bulk_targets_non_recursive() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + direct = root / "direct.milk" + nested_dir = root / "nested" + nested_dir.mkdir() + nested = nested_dir / "nested.milk" + _write_milk(direct) + _write_milk(nested) + + targets = build_bulk_targets(root, recursive=False) + + assert [target.path for target in targets.presets] == [direct.resolve()] + assert targets.presets_dir == root.resolve() + assert targets.presets[0].layers == () + + +def test_bulk_targets_recursive() -> None: + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + direct = root / "direct.milk" + nested_dir = root / "nested" + nested_dir.mkdir() + nested = nested_dir / "nested.milk" + _write_milk(direct) + _write_milk(nested) + + targets = build_bulk_targets(root, recursive=True) + + assert [target.path for target in targets.presets] == [ + direct.resolve(), + nested.resolve(), + ] diff --git a/tests/cleave/test_projectm_callbacks.py b/tests/cleave/test_projectm_callbacks.py new file mode 100644 index 0000000..b31841b --- /dev/null +++ b/tests/cleave/test_projectm_callbacks.py @@ -0,0 +1,82 @@ +"""Tests for optional libprojectM callback bindings.""" + +from __future__ import annotations + +from unittest.mock import MagicMock, patch + +from cleave.projectm import PresetLoadFailure, ProjectM + + +def _mock_lib_with_callbacks() -> MagicMock: + lib = MagicMock() + for name in ( + "projectm_set_preset_switch_failed_event_callback", + "projectm_get_version_components", + "projectm_get_version_string", + "projectm_get_vcs_version_string", + "projectm_free_string", + ): + setattr(lib, name, MagicMock()) + return lib + + +def test_switch_failed_handler_enqueues_and_drains() -> None: + lib = _mock_lib_with_callbacks() + pm = ProjectM.__new__(ProjectM) + pm._handle = MagicMock() + pm._failure_queue = __import__("collections").deque() + pm._switch_failed_callback = None + + with patch("cleave.projectm._get_lib", return_value=lib): + pm.set_preset_switch_failed_handler(pm._enqueue_preset_failure) + callback = ( + lib.projectm_set_preset_switch_failed_event_callback.call_args.args[1] + ) + callback(b"/tmp/bad.milk", b"parse error", None) + + failures = pm.drain_preset_failures() + assert failures == [ + PresetLoadFailure( + filename="/tmp/bad.milk", message="parse error", exhausted=False + ) + ] + assert pm.drain_preset_failures() == [] + + +def test_clear_preset_switch_failed_handler() -> None: + lib = _mock_lib_with_callbacks() + pm = ProjectM.__new__(ProjectM) + pm._handle = MagicMock() + pm._failure_queue = __import__("collections").deque() + pm._switch_failed_callback = MagicMock() + + with patch("cleave.projectm._get_lib", return_value=lib): + pm.clear_preset_switch_failed_handler() + + lib.projectm_set_preset_switch_failed_event_callback.assert_called() + assert pm._switch_failed_callback is None + + +def test_version_info_reads_components_and_strings() -> None: + lib = _mock_lib_with_callbacks() + lib.projectm_get_version_string.return_value = b"4.2.1" + lib.projectm_get_vcs_version_string.return_value = b"abc123" + + pm = ProjectM.__new__(ProjectM) + pm._handle = MagicMock() + + with patch("cleave.projectm._get_lib", return_value=lib): + info = pm.version_info() + + lib.projectm_get_version_components.assert_called_once() + assert info["version"] == "4.2.1" + assert info["vcs"] == "abc123" + assert lib.projectm_free_string.call_count == 2 + + +def test_enqueue_preset_failure_exhausted_flag() -> None: + pm = ProjectM.__new__(ProjectM) + pm._failure_queue = __import__("collections").deque() + pm._enqueue_preset_failure("a.milk", "failed", exhausted=True) + failures = pm.drain_preset_failures() + assert failures[0].exhausted is True diff --git a/tests/cleave/test_projectm_health.py b/tests/cleave/test_projectm_health.py new file mode 100644 index 0000000..e3d7c76 --- /dev/null +++ b/tests/cleave/test_projectm_health.py @@ -0,0 +1,66 @@ +"""Tests for cleave.projectm_health live failure draining.""" + +from __future__ import annotations + +import time +from unittest.mock import MagicMock + +from cleave.projectm import PresetLoadFailure, ProjectM +from cleave.projectm_health import ( + PRESET_SKIP_NOTIFICATION_INTERVAL_SEC, + PresetSkipNotifyTracker, + drain_stem_layers_preset_failures, +) +from cleave.viz.layer import StemLayer +from cleave.viz.preset_switching import EMPTY_ROTATION_NOTIFICATION + + +def _layer_with_failures(*failures: PresetLoadFailure) -> StemLayer: + pm = ProjectM.__new__(ProjectM) + pm.drain_preset_failures = MagicMock(return_value=list(failures)) + return StemLayer( + slot="layer_1", + pm=pm, + fbo=MagicMock(), + playlist=MagicMock(), + ) + + +def test_drain_notifies_skipped_preset_rate_limited() -> None: + tracker = PresetSkipNotifyTracker() + layer = _layer_with_failures( + PresetLoadFailure(filename="/tmp/a.milk", message="bad") + ) + notify = MagicMock() + + drain_stem_layers_preset_failures( + [layer], on_notification=notify, skip_notify_tracker=tracker + ) + notify.assert_called_once_with("Skipped preset: a.milk") + + notify.reset_mock() + drain_stem_layers_preset_failures( + [layer], on_notification=notify, skip_notify_tracker=tracker + ) + notify.assert_not_called() + + tracker.last_notify["layer_1"] = ( + time.monotonic() - PRESET_SKIP_NOTIFICATION_INTERVAL_SEC - 1 + ) + drain_stem_layers_preset_failures( + [layer], on_notification=notify, skip_notify_tracker=tracker + ) + notify.assert_called_once_with("Skipped preset: a.milk") + + +def test_drain_notifies_exhausted_rotation() -> None: + layer = _layer_with_failures( + PresetLoadFailure( + filename="/tmp/bad.milk", + message="exhausted", + exhausted=True, + ) + ) + notify = MagicMock() + drain_stem_layers_preset_failures([layer], on_notification=notify) + notify.assert_called_once_with(EMPTY_ROTATION_NOTIFICATION) diff --git a/tests/cleave/test_projectm_playlist.py b/tests/cleave/test_projectm_playlist.py index 8706057..a326794 100644 --- a/tests/cleave/test_projectm_playlist.py +++ b/tests/cleave/test_projectm_playlist.py @@ -9,6 +9,7 @@ from cleave.projectm import ProjectM from cleave.projectm_playlist import ( + DEFAULT_RETRY_COUNT, ProjectMPlaylist, ProjectMPlaylistLibraryError, _bind_functions, @@ -23,8 +24,11 @@ def _mock_lib() -> MagicMock: "projectm_playlist_connect", "projectm_playlist_add_path", "projectm_playlist_set_shuffle", - "projectm_playlist_set_preset_load_event_callback", "projectm_playlist_set_preset_switched_event_callback", + "projectm_playlist_set_preset_switch_failed_event_callback", + "projectm_playlist_play_next", + "projectm_playlist_get_retry_count", + "projectm_playlist_set_retry_count", "projectm_playlist_size", "projectm_playlist_get_position", "projectm_playlist_set_position", @@ -33,6 +37,8 @@ def _mock_lib() -> MagicMock: ): setattr(lib, name, MagicMock()) lib.projectm_playlist_create.return_value = MagicMock() + lib.projectm_playlist_play_next.return_value = 2 + lib.projectm_playlist_get_retry_count.return_value = DEFAULT_RETRY_COUNT return lib @@ -72,48 +78,31 @@ def test_destroy_disconnects_before_free() -> None: lib.projectm_playlist_destroy.assert_called_once() -def test_connect_installs_instant_load_callback() -> None: +def test_connect_does_not_install_preset_load_callback() -> None: lib = _mock_lib() + lib.projectm_playlist_set_preset_load_event_callback = MagicMock() pm = ProjectM.__new__(ProjectM) pm._handle = MagicMock() - pm.load_preset = MagicMock() - loaded_paths: list[str] = [] - - with patch("cleave.projectm_playlist._get_lib", return_value=lib): - playlist = ProjectMPlaylist.create() - playlist.connect( - pm, on_preset_loaded=lambda path: loaded_paths.append(str(path)) - ) - - lib.projectm_playlist_set_preset_load_event_callback.assert_called() - install_call = lib.projectm_playlist_set_preset_load_event_callback.call_args - callback = install_call.args[1] - assert callback is not None - assert callback(0, b"/tmp/a.milk", True, None) is True - pm.load_preset.assert_called_once_with("/tmp/a.milk", smooth=False) - assert loaded_paths == ["/tmp/a.milk"] - - -def test_load_callback_honors_hard_cut_flag() -> None: - lib = _mock_lib() - pm = ProjectM.__new__(ProjectM) - pm._handle = MagicMock() - pm.load_preset = MagicMock() + pm._enqueue_preset_failure = MagicMock() with patch("cleave.projectm_playlist._get_lib", return_value=lib): playlist = ProjectMPlaylist.create() playlist.connect(pm, on_preset_loaded=lambda _path: None) - callback = lib.projectm_playlist_set_preset_load_event_callback.call_args.args[1] - callback(1, b"/tmp/b.milk", False, None) - pm.load_preset.assert_called_once_with("/tmp/b.milk", smooth=True) + lib.projectm_playlist_set_preset_load_event_callback.assert_not_called() + lib.projectm_playlist_set_preset_switched_event_callback.assert_called() + lib.projectm_playlist_set_preset_switch_failed_event_callback.assert_called() + lib.projectm_playlist_set_retry_count.assert_called_once() + retry_args = lib.projectm_playlist_set_retry_count.call_args.args + assert retry_args[0] == playlist.handle + assert retry_args[1].value == DEFAULT_RETRY_COUNT def test_preset_switched_callback_notifies_on_preset_loaded() -> None: lib = _mock_lib() pm = ProjectM.__new__(ProjectM) pm._handle = MagicMock() - pm.load_preset = MagicMock() + pm._enqueue_preset_failure = MagicMock() loaded_paths: list[str] = [] with patch("cleave.projectm_playlist._get_lib", return_value=lib): @@ -132,17 +121,59 @@ def test_preset_switched_callback_notifies_on_preset_loaded() -> None: assert loaded_paths == ["/tmp/c.milk"] -def test_destroy_clears_preset_load_callback() -> None: +def test_switch_failed_callback_enqueues_exhausted_failure() -> None: + lib = _mock_lib() + pm = ProjectM.__new__(ProjectM) + pm._handle = MagicMock() + pm._enqueue_preset_failure = MagicMock() + + with patch("cleave.projectm_playlist._get_lib", return_value=lib): + playlist = ProjectMPlaylist.create() + playlist.connect(pm) + failed = ( + lib.projectm_playlist_set_preset_switch_failed_event_callback.call_args.args[ + 1 + ] + ) + failed(b"/tmp/bad.milk", b"too many retries", None) + + pm._enqueue_preset_failure.assert_called_once_with( + "/tmp/bad.milk", + "too many retries", + exhausted=True, + ) + + +def test_play_next_and_retry_count() -> None: + lib = _mock_lib() + with patch("cleave.projectm_playlist._get_lib", return_value=lib): + playlist = ProjectMPlaylist.create() + assert playlist.play_next(hard_cut=True) == 2 + lib.projectm_playlist_play_next.assert_called_once() + playlist.set_retry_count(250) + lib.projectm_playlist_set_retry_count.assert_called() + lib.projectm_playlist_get_retry_count.return_value = 250 + assert playlist.get_retry_count() == 250 + + +def test_destroy_clears_callbacks() -> None: lib = _mock_lib() pm = ProjectM.__new__(ProjectM) pm._handle = MagicMock() + pm._enqueue_preset_failure = MagicMock() with patch("cleave.projectm_playlist._get_lib", return_value=lib): playlist = ProjectMPlaylist.create() playlist.connect(pm) playlist.destroy() - clear_call = lib.projectm_playlist_set_preset_load_event_callback.call_args_list[-1] - assert not clear_call.args[1] + clear_switched = ( + lib.projectm_playlist_set_preset_switched_event_callback.call_args_list[-1] + ) + assert not clear_switched.args[1] + clear_failed = ( + lib.projectm_playlist_set_preset_switch_failed_event_callback.call_args_list[-1] + ) + assert not clear_failed.args[1] def test_item_roundtrip_with_real_library(tmp_path: Path) -> None: diff --git a/tests/cleave/viz/test_preset_switching.py b/tests/cleave/viz/test_preset_switching.py index 651b1a8..4ee485d 100644 --- a/tests/cleave/viz/test_preset_switching.py +++ b/tests/cleave/viz/test_preset_switching.py @@ -21,6 +21,7 @@ EMPTY_ROTATION_NOTIFICATION, active_auto_preset_path, apply_preset_switching, + load_manual_preset_clean, reapply_projectm_preset_switching, reset_projectm_preset_timer, restart_projectm_preset_timer, @@ -50,6 +51,41 @@ def _stem_layer(*, paths: tuple[Path, ...], index: int = 0) -> StemLayer: return StemLayer(slot="layer_1", pm=pm, fbo=MagicMock(), playlist=playlist) +def test_load_manual_preset_clean_forces_black_boot_then_restores() -> None: + layer = _stem_layer(paths=_MILK) + layer.playlist.load_into = MagicMock() + + load_manual_preset_clean(layer, preset_start_clean=False) + + assert layer.pm.set_preset_start_clean.call_args_list == [ + call(True), + call(False), + ] + layer.playlist.load_into.assert_called_once_with(layer.pm, smooth=False) + + +def test_load_manual_preset_clean_restores_configured_start_clean() -> None: + layer = _stem_layer(paths=_MILK) + layer.playlist.load_into = MagicMock() + + load_manual_preset_clean(layer, preset_start_clean=True) + + assert layer.pm.set_preset_start_clean.call_args_list == [ + call(True), + call(True), + ] + + +def test_load_manual_preset_clean_noop_without_preset() -> None: + layer = _stem_layer(paths=()) + layer.playlist.load_into = MagicMock() + + load_manual_preset_clean(layer) + + layer.pm.set_preset_start_clean.assert_not_called() + layer.playlist.load_into.assert_not_called() + + def test_apply_none_locks_and_disables_hard_cuts() -> None: layer = _stem_layer(paths=_MILK) mock_pl = MagicMock() diff --git a/tests/cleave/viz/test_wiring.py b/tests/cleave/viz/test_wiring.py index be499e6..b6709f5 100644 --- a/tests/cleave/viz/test_wiring.py +++ b/tests/cleave/viz/test_wiring.py @@ -77,6 +77,7 @@ def test_on_preset_change_loads_preset_in_user_defined_mode() -> None: pm = ProjectM.__new__(ProjectM) pm.lock_preset = MagicMock() pm.load_preset = MagicMock() + pm.set_preset_start_clean = MagicMock() layer = StemLayer( slot="layer_1", pm=pm, @@ -109,6 +110,47 @@ def test_on_preset_change_loads_preset_in_user_defined_mode() -> None: pm.lock_preset.assert_called_once_with(False) +def test_on_preset_change_forces_clean_boot_then_restores() -> None: + session = _session_with_mode("none") + session.layers["layer_1"].preset_start_clean = False + cfg = make_test_cfg(("layer_1",)) + pm = ProjectM.__new__(ProjectM) + pm.lock_preset = MagicMock() + pm.load_preset = MagicMock() + pm.set_preset_start_clean = MagicMock() + layer = StemLayer( + slot="layer_1", + pm=pm, + fbo=MagicMock(), + playlist=session.layers["layer_1"].playlist, + ) + layers_by_slot = {"layer_1": layer} + playlist = session.layers["layer_1"].playlist + + with patch("cleave.viz.wiring.apply_preset_switching"): + controls = make_tuning_controls( + session=session, + cfg=cfg, + preset_root=cfg.paths.preset_root, + project_dir=Path("/tmp/project"), + layers_by_slot=layers_by_slot, + layers=[layer], + playback=stub_playback_state(), + duration_sec=120.0, + signals=None, + effect_runtime=MagicMock(), + ) + + bindings = controls._layer_bindings + assert bindings is not None + bindings.on_preset_change("layer_1", playlist) + + assert pm.set_preset_start_clean.call_args_list == [ + (((True,)), {}), + (((False,)), {}), + ] + + def test_on_seek_reapplies_projectm_preset_switching() -> None: session = _session_with_mode("projectm") cfg = make_test_cfg(("layer_1",)) diff --git a/tests/fixtures/preset_scan_golden_metrics.json b/tests/fixtures/preset_scan_golden_metrics.json new file mode 100644 index 0000000..d7d78bd --- /dev/null +++ b/tests/fixtures/preset_scan_golden_metrics.json @@ -0,0 +1,76864 @@ +{ + "version": 3, + "probe_fps": 30, + "fbo_size": [ + 480, + 270 + ], + "presets": [ + { + "path": "/home/fernpa/cleave/assets/milkdrop-presets/presets-milkdrop-original/Milkdrop-Original/BrainStain-Blackwidow.milk", + "load_failed": false, + "fps": 30, + "frames": [ + { + "max_luma": 18.644201278686523, + "mean_luma": 0.010090976022183895, + "coverage": { + 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+ "window_frames": 75, + "total_frames": 90 +} diff --git a/tests/fixtures/preset_scan_golden_set.yaml b/tests/fixtures/preset_scan_golden_set.yaml new file mode 100644 index 0000000..b1029da --- /dev/null +++ b/tests/fixtures/preset_scan_golden_set.yaml @@ -0,0 +1,316 @@ +# Golden set for preset scan classifier tuning. +# Manual labels from live review (clean manual preset boot) in +# +# expected_result: +# ok - pass; do not quarantine +# dim - classify as dim; quarantine/delete only with --include-dim +# black - quarantine/delete by default +# washed_out - classify as washed_out; quarantine/delete only with --include-washout +# +# Harness should resolve preset paths against preset_root and apply texture_paths. + +version: 1 +preset_root: assets/milkdrop-presets +texture_paths: + - assets/milkdrop-presets/presets-milkdrop-texture-pack + +cases: + - id: 1 + source_config: unnamed-1.yaml + preset: presets-milkdrop-original/Milkdrop-Original/BrainStain-Blackwidow.milk + expected_result: dim + notes: Working, but too dim. + + - id: 2 + source_config: unnamed-2.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi - Airhandler (Principle of Sharing).milk + expected_result: black + notes: Not working in visualizer (clean boot stays black). Both quick and slow golden caches classify black. + + - id: 3 + source_config: unnamed-3.yaml + preset: presets-milkdrop-original/Milkdrop-Original/BrainStain-re entry.milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 4 + source_config: unnamed-4.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S. + Phat - chasers 11 sentinel C_poltergeist_mix response daemon.milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 5 + source_config: unnamed-5.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S. - angels of decay.milk + expected_result: washed_out + notes: Working, but highly washed out to white. Opposite of dim; catch extreme cases. + + - id: 6 + source_config: unnamed-6.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S. - glowsticks v2 03 music.milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 7 + source_config: unnamed-7.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S.+Phat - spectrum bubble new colors_v2.milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 8 + source_config: unnamed-8.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Esotic & Rozzer - The Dark Side Of My Moon.milk + expected_result: washed_out + notes: Washed out to white. Opposite of dim; catch extreme cases. + + - id: 9 + source_config: unnamed-9.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Esotic & Rozzor - Pixie Party Light ((No Wave Invasion) Mandala Chill Red Yellow Mix).milk + expected_result: washed_out + notes: Washed out to white. Opposite of dim; catch extreme cases. + + - id: 10 + source_config: unnamed-10.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi + Martin - dive.milk + expected_result: ok + notes: Clear pass. + + - id: 11 + source_config: unnamed-11.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi + Geiss - pogo-cubes on tokamak matter [mind over matter remix].milk + expected_result: ok + notes: Clear pass. + + - id: 12 + source_config: unnamed-12.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi + Geiss - pogo-cubes on tokamak matter.milk + expected_result: ok + notes: Clear pass. + + - id: 13 + source_config: unnamed-13.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi + fiShbRaiN - witchcraft [complex terraforming - fiddling twists in the fabric of space].milk + expected_result: ok + notes: May start slow, but clearly works. + + - id: 14 + source_config: unnamed-14.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi - mindblob [shiny mix].milk + expected_result: ok + notes: Clear pass; starts slow but clearly works. + + - id: 15 + source_config: unnamed-15.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi - smashing fractals [Geiss' bas relief finish].milk + expected_result: ok + notes: May appear dim and start slow but is clearly working. + + - id: 16 + source_config: unnamed-16.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi - working with infinity.milk + expected_result: ok + notes: Clear pass; starts slow but clearly works. + + - id: 17 + source_config: unnamed-17.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi, Rovastar + Geiss - Fractopia vs bas relief.milk + expected_result: ok + notes: May appear dim and start slow but is clearly working. + + - id: 18 + source_config: unnamed-18.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi, fishbrain + Martin - witchery.milk + expected_result: ok + notes: Starts dim but clearly works after it gets going. + + - id: 19 + source_config: unnamed-19.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi, martin + geiss - dedicated to the sherwin maxawow.milk + expected_result: ok + notes: May appear dim at centre, but clearly works (grows from edge). + + - id: 20 + source_config: unnamed-20.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Fvese - Snowflake Like 2.milk + expected_result: ok + notes: Clear pass. + + - id: 21 + source_config: unnamed-21.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Geiss - Explosion 2.milk + expected_result: ok + notes: Starts slow but clearly works. + + - id: 22 + source_config: unnamed-22.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Goody - Acid Angel - Fallen Angel.milk + expected_result: ok + notes: Starts slow but clearly works. + + - id: 23 + source_config: unnamed-23.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Goody - Lights in the Sky.milk + expected_result: ok + notes: Starts slow, but clear pass. + + - id: 24 + source_config: unnamed-24.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Goody - Need - Transcendance remix.milk + expected_result: ok + notes: Clear pass. High luma but usable; not washed_out. + + - id: 25 + source_config: unnamed-25.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Mstress & Juppy - Dancer.milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 26 + source_config: unnamed-26.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Rovastar & Zylot - Crystal Ball (Many Visions Mix).milk + expected_result: black + notes: Does not work. + + - id: 27 + source_config: unnamed-27.yaml + preset: presets-cream-of-the-crop/! Transition/Fast transition to black - levels effect === Goody's Lightning (ps 2-0) --- Isosceles edit.milk + expected_result: black + notes: Does not work. + + - id: 28 + source_config: unnamed-28.yaml + preset: presets-cream-of-the-crop/Dancer/Aurora/Jc - Lungs.milk + expected_result: dim + notes: Too dim. + + - id: 29 + source_config: unnamed-29.yaml + preset: presets-cream-of-the-crop/Dancer/Comet/EoS + Phat - chasers 11 sentinel C (Jelly V2).milk + expected_result: ok + notes: May appear dim but is clearly working. + + - id: 30 + source_config: unnamed-30.yaml + preset: presets-cream-of-the-crop/Particles/Blobby/only glimpses of the reality you once knew - what good is a drug if you can't turn it's effects on and off.milk + expected_result: dim + notes: Too dim. + + - id: 31 + source_config: unnamed-31.yaml + preset: presets-cream-of-the-crop/Sparkle/Explosions/$$$ Royal - Mashup (244).milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 32 + source_config: unnamed-32.yaml + preset: presets-cream-of-the-crop/Sparkle/Explosions/$$$ Royal - Mashup (248).milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 33 + source_config: unnamed-33.yaml + preset: presets-cream-of-the-crop/Sparkle/Explosions/$$$ Royal - Mashup (409).milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 34 + source_config: unnamed-34.yaml + preset: presets-cream-of-the-crop/Sparkle/Explosions/2009 4th of July with AdamFX n Martin - into the fireworks E.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 35 + source_config: unnamed-35.yaml + preset: presets-milkdrop-original/Milkdrop-Original/3dRaGoNs & Unchained - Dragon Science.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 36 + source_config: unnamed-36.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi + Flexi - Airhandler (Last Breath - Calm) [moebius morbid vision edit].milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 37 + source_config: unnamed-37.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi - Halls Of Centrifuge.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 38 + source_config: unnamed-38.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi - Songflower (Moss Posy).milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 39 + source_config: unnamed-39.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi - The Lurker (Twin Mix) - Bitcore Tweak.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 40 + source_config: unnamed-40.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Aderrasi - What Cannot Be Undone.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 41 + source_config: unnamed-41.yaml + preset: presets-cream-of-the-crop/Sparkle/Explosions/An Digital FX Edit 2 martin - girlie affairs Internal LightFX AdamFX k.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 42 + source_config: unnamed-42.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S. + Geiss - glowsticks v2 03 music shifter edit b (water mix).milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 43 + source_config: unnamed-43.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Eo.S. - pointfield 04 arcs demon_phat edit_v3.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 44 + source_config: unnamed-44.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi + bdrv - what to do [Phat edit].milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 45 + source_config: unnamed-45.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Flexi - fractal remix weed.milk + expected_result: ok + notes: High-intensity full-screen visual; clearly working; below whiteout threshold. + + - id: 46 + source_config: unnamed-46.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Esotic & Rozzor - Pixie Party Light ((No Wave Invasion) Mandala Chill Red Yellow Mix).milk + expected_result: washed_out + notes: High-intensity but washed out to white; too blown out to use. + + - id: 47 + source_config: unnamed-47.yaml + preset: presets-cream-of-the-crop/Fractal/Nested Circle/Esotic & Rozzer - The Dark Side Of My Moon.milk + expected_result: washed_out + notes: High-intensity but washed out to white; too blown out to use. + + - id: 48 + source_config: unnamed-48.yaml + preset: presets-cream-of-the-crop/Fractal/Nested Circle/Esotic & Rozzer - Now And Later.milk + expected_result: washed_out + notes: High-intensity but washed out to white; too blown out to use. + + - id: 49 + source_config: unnamed-49.yaml + preset: presets-cream-of-the-crop/Fractal/Nested Circle/Esotic & Rozzer - Hippie Hypnotizer.milk + expected_result: washed_out + notes: High-intensity but washed out to white; too blown out to use. + + - id: 50 + source_config: unnamed-50.yaml + preset: presets-milkdrop-original/Milkdrop-Original/Rovastar & Unchained - Demonology (Vampire Soul Mix).milk + expected_result: washed_out + notes: High-intensity but washed out to white; too blown out to use.