Fr0zkaandClaude Opus 5.5
bd1b2a8f1d
FI/TN/VS (legacy compiled-analytic) skip ZERO tiles at every LOD and evaluate ~44k density points per LOD0/1 tile; SW skips 359/512 at LOD0. The whole-tile skip requires a native tile with a uniform verdict, and the legacy paths return Mixed/unknown (R39 notes attached). Opt-in stage timers, sampled field timer, per-LOD classification and composer counters. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016dhxM43dnWvUu2B5Ph1iG1
210 lines
12 KiB
Python
210 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Reduce the L73 per-tile capture logs into reviewable CSV tables."""
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from __future__ import annotations
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import csv
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import json
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import statistics
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[4]
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OUT = ROOT / "Saved/Linux/l73/breakdown"
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SUMMARY = OUT / "summary.json"
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EXPECTED_TILES = {0: 2850, 1: 2249, 2: 2233, 3: 2329}
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LODS = range(9)
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def median(values: list[float]) -> float:
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return statistics.median(values) if values else 0.0
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def metric(rows: list[dict], section: str, lod: int, key: str) -> float:
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return median([float(row["details"].get(section, {}).get(str(lod), {}).get(key, 0) or 0)
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for row in rows])
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def sum_metric(details: dict, section: str, key: str) -> float:
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return sum(float(row.get(key, 0) or 0)
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for row in details.get(section, {}).values())
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def write_csv(path: Path, rows: list[dict], fields: list[str]) -> None:
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with path.open("w", newline="", encoding="utf-8") as stream:
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writer = csv.DictWriter(stream, fieldnames=fields)
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writer.writeheader()
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writer.writerows(rows)
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def main() -> int:
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runs = json.loads(SUMMARY.read_text(encoding="utf-8"))
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if len(runs) != 24 or any(row["exit_code"] != 0 for row in runs):
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raise SystemExit("L73 matrix is incomplete or contains a failed run")
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signatures = {(row["plan_hash"], row["layout_identity"]) for row in runs}
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if len(signatures) != 1:
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raise SystemExit(f"plan/layout changed across runs: {sorted(signatures)}")
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for strate, expected in EXPECTED_TILES.items():
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if {row["whole_tiles"] for row in runs if row["strate"] == strate} != {expected}:
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raise SystemExit(f"strate {strate} tile count differs from {expected}")
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lod_rows = []
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strategy_rows = []
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for strate in range(4):
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all_rows = [row for row in runs if row["strate"] == strate]
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clean = [row for row in all_rows if row["mode"] == "clean"]
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profile = [row for row in all_rows if row["mode"] == "profile"]
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paired_wall_delta = [
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row["whole_view_ready_s"] - next(other["whole_view_ready_s"]
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for other in all_rows if other["pair"] == row["pair"]
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and other["mode"] == "clean") for row in profile]
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paired_worker_delta = [
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row["worker_seconds"] - next(other["worker_seconds"]
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for other in all_rows if other["pair"] == row["pair"]
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and other["mode"] == "clean") for row in profile]
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stage_keys = (
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("classify_ms", "stages_by_lod", "classify_ms"),
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("field_eval_ms_est", "classification_by_lod", "field_eval_ms_est"),
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("mc_total_ms", "worker_by_lod", "mc_exclusive_ms"),
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("mc_grid_ms", "cost_by_lod", "mc_grid_ms"),
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("mc_cell_class_ms", "cost_by_lod", "mc_cell_class_ms"),
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("mc_gradient_ms", "cost_by_lod", "mc_gradient_ms"),
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("mc_interpolation_ms", "cost_by_lod", "mc_interpolation_ms"),
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("mc_dedup_stream_ms", "cost_by_lod", "mc_dedup_stream_ms"),
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("mc_other_ms", "cost_by_lod", "mc_other_ms"),
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("skirt_ms", "worker_by_lod", "skirt_ms"),
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("ground_refine_ms", "worker_by_lod", "ground_refine_exclusive_ms"),
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("cap_refine_ms", "worker_by_lod", "cap_refine_exclusive_ms"),
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("connector_shell_ms", "cost_by_lod", "connector_shell_ms"),
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("mesh_assembly_ms", "worker_by_lod", "mesh_assembly_ms"),
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("payload_build_ms", "classification_by_lod", "payload_build_ms"),
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)
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profile_stage_sums = []
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for row in profile:
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details = row["details"]
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profile_stage_sums.append({name: sum_metric(details, section, key)
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for name, section, key in stage_keys})
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stage_totals = {name: median([item[name] for item in profile_stage_sums])
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for name, _, _ in stage_keys}
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strategy_row = {
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"strate": strate,
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"tiles": EXPECTED_TILES[strate],
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"clean_wall_median_s": round(median([row["whole_view_ready_s"] for row in clean]), 3),
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"clean_worker_median_s": round(median([row["worker_seconds"] for row in clean]), 3),
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"profile_wall_median_s": round(median([row["whole_view_ready_s"] for row in profile]), 3),
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"profile_worker_median_s": round(median([row["worker_seconds"] for row in profile]), 3),
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"paired_profile_wall_delta_median_s": round(median(paired_wall_delta), 3),
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"paired_profile_worker_delta_median_s": round(median(paired_worker_delta), 3),
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"profile_all_air_tiles_median": 0,
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"profile_all_solid_tiles_median": 0,
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"profile_whole_tile_skips_median": 0,
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"profile_density_point_calls_median": 0,
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"profile_tile_lod_visits_median": 0,
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"profile_density_points_per_tile": 0.0,
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**{name: round(value, 3) for name, value in stage_totals.items()},
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}
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for lod in LODS:
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cls = [row["details"]["classification_by_lod"].get(str(lod), {})
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for row in profile]
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cost = [row["details"]["cost_by_lod"].get(str(lod), {}) for row in profile]
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worker = [row["details"]["worker_by_lod"].get(str(lod), {}) for row in profile]
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stages = [row["details"]["stages_by_lod"].get(str(lod), {}) for row in profile]
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island = [row["details"]["floating_island_attribution_by_lod"].get(str(lod), {})
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for row in profile]
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passage = [row["details"]["passage_composer_by_lod"].get(str(lod), {})
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for row in profile]
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first = cls[0] if cls else {}
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if not first:
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continue
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row = {
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"strate": strate,
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"lod": lod,
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"tiles": int(median([float(item.get("tiles", 0)) for item in cls])),
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"all_air_tiles": int(median([float(item.get("all_air", 0)) for item in cls])),
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"all_solid_tiles": int(median([float(item.get("all_solid", 0)) for item in cls])),
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"mixed_tiles": int(median([float(item.get("mixed", 0)) for item in cls])),
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"unclassified_tiles": int(median([float(item.get("unclassified", 0)) for item in cls])),
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"whole_tile_skips": int(median([float(item.get("tile_skips", 0)) for item in cls])),
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"density_grid_tiles": int(median([float(item.get("density_grid_tiles", 0)) for item in cls])),
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"density_grid_sample_points": int(median([float(item.get("density_samples", 0))
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for item in cost])),
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"density_point_calls": int(median([float(item.get("density_point_calls", 0)) for item in cls])),
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"density_points_per_tile": round(median([float(item.get("density_points_per_tile", 0))
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for item in cls]), 1),
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"field_eval_sample_points": int(median([float(item.get("field_eval_sample_points", 0))
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for item in cls])),
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"proof_air_cells": int(median([float(item.get("proof_air_cells", 0)) for item in cls])),
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"proof_solid_cells": int(median([float(item.get("proof_solid_cells", 0)) for item in cls])),
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"classify_worker_ms": round(median([float(item.get("classify_ms", 0))
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for item in stages]), 3),
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"field_eval_worker_ms_est": round(median([float(item.get("field_eval_ms_est", 0))
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for item in cls]), 3),
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"mc_total_worker_ms": round(median([float(item.get("mc_exclusive_ms", 0))
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for item in worker]), 3),
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"mc_grid_worker_ms": round(median([float(item.get("mc_grid_ms", 0))
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for item in cost]), 3),
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"mc_cell_class_worker_ms": round(median([float(item.get("mc_cell_class_ms", 0))
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for item in cost]), 3),
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"mc_normals_worker_ms": round(median([float(item.get("mc_gradient_ms", 0))
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for item in cost]), 3),
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"mc_interpolation_worker_ms": round(median([float(item.get("mc_interpolation_ms", 0))
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for item in cost]), 3),
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"mc_dedup_worker_ms": round(median([float(item.get("mc_dedup_stream_ms", 0))
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for item in cost]), 3),
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"mc_other_worker_ms": round(median([float(item.get("mc_other_ms", 0))
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for item in cost]), 3),
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"skirt_worker_ms": round(median([float(item.get("skirt_ms", 0))
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for item in worker]), 3),
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"ground_refine_worker_ms": round(median([float(item.get("ground_refine_exclusive_ms", 0))
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for item in worker]), 3),
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"cap_refine_worker_ms": round(median([float(item.get("cap_refine_exclusive_ms", 0))
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for item in worker]), 3),
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"connector_shell_worker_ms": round(median([float(item.get("connector_shell_ms", 0))
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for item in cost]), 3),
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"mesh_assembly_worker_ms": round(median([float(item.get("mesh_assembly_ms", 0))
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for item in worker]), 3),
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"payload_build_worker_ms": round(median([float(item.get("payload_build_ms", 0))
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for item in cls]), 3),
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"passage_queries": int(median([float(item.get("queries", 0)) for item in passage])),
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"passage_relief_field_calls": int(median([float(item.get("relief_field_calls", 0))
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for item in passage])),
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"passage_carve_segment_visits": int(median([float(item.get("carve_segment_visits", 0))
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for item in passage])),
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"passage_support_segment_visits": int(median([float(item.get("support_segment_visits", 0))
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for item in passage])),
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"passage_mouth_route_checks": int(median([float(item.get("mouth_route_segment_checks", 0))
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for item in passage])),
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"passage_smoothstep_evaluations": int(median([float(item.get("smoothstep_evaluations", 0))
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for item in passage])),
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"fi_component_field_ns_est": int(median([float(item.get("comp_field_ns_est", 0))
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for item in island])),
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"fi_component_island_ns_est": int(median([float(item.get("comp_island_ns_est", 0))
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for item in island])),
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"fi_component_roots_ns_est": int(median([float(item.get("comp_roots_ns_est", 0))
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for item in island])),
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}
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lod_rows.append(row)
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for name in ("all_air_tiles", "all_solid_tiles", "whole_tile_skips",
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"density_point_calls"):
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strategy_row["profile_" + name + "_median"] += row[name]
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strategy_row["profile_tile_lod_visits_median"] += row["tiles"]
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strategy_row["profile_density_points_per_tile"] = round(
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strategy_row["profile_density_point_calls_median"] /
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strategy_row["profile_tile_lod_visits_median"], 1)
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strategy_rows.append(strategy_row)
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write_csv(OUT / "lod-breakdown.csv", lod_rows, list(lod_rows[0]))
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write_csv(OUT / "strate-summary.csv", strategy_rows, list(strategy_rows[0]))
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print(f"[L73Analysis] lod_rows={len(lod_rows)} strategy_rows={len(strategy_rows)} "
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f"plan_hash={next(iter(signatures))[0]} layout={next(iter(signatures))[1]}")
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for row in strategy_rows:
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print("[L73Summary] " + json.dumps(row, sort_keys=True))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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