feat(lab): complete E30 evidence review gate
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DCCONSTRUCTIONS
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from __future__ import annotations
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import hashlib
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import json
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from pathlib import Path
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from typing import Any
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import pytest
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from k1link.compute.e30_review_pack import (
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E30_REASON_TAXONOMY,
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E30ReviewPackError,
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E30ReviewSelectionProfile,
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build_e30_review_pack,
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)
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from k1link.compute.semantic_geometry_fusion import (
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CAMERA_GEOMETRY_FRAME_SCHEMA,
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CAMERA_GEOMETRY_FUSION_SCHEMA,
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CAMERA_GEOMETRY_REPORT_SCHEMA,
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)
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def _canonical(value: object) -> bytes:
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return json.dumps(
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value,
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sort_keys=True,
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separators=(",", ":"),
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allow_nan=False,
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).encode()
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def _sha256(path: Path) -> str:
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return hashlib.sha256(path.read_bytes()).hexdigest()
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def _observation(status: str, index: int) -> dict[str, object]:
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return {
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"track_id": index,
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"label": "car" if index % 2 else "person",
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"association_group": "vehicle" if index % 2 else "person",
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"geometry_status": status,
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"geometry_reason": f"reason-{status}",
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"range_m": None if status in {"single-source-camera", "unknown"} else 2.5 + index,
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"support": {"connected_occupied_points": 0},
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}
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def _source_result(root: Path) -> Path:
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result_id = "e29-camera-geometry-" + "a" * 64
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result = root / result_id
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result.mkdir()
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frames = [
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{
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"schema_version": CAMERA_GEOMETRY_FRAME_SCHEMA,
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"frame_index": 0,
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"source_frame_index": 10,
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"session_seconds": 1.0,
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"semantic_observations": [
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_observation("agree", 1),
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_observation("single-source-camera", 2),
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_observation("conflict", 3),
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_observation("unknown", 4),
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],
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"geometry_only_occupied": [
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{
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"geometry_status": "single-source-geometry",
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"nearest_range_m": 8.0,
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"point_count": 10,
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}
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],
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},
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{
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"schema_version": CAMERA_GEOMETRY_FRAME_SCHEMA,
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"frame_index": 1,
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"source_frame_index": 11,
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"session_seconds": 2.0,
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"semantic_observations": [
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_observation("agree", 5),
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_observation("single-source-camera", 6),
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_observation("conflict", 7),
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_observation("unknown", 8),
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],
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"geometry_only_occupied": [
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{
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"geometry_status": "single-source-geometry",
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"nearest_range_m": 2.0,
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"point_count": 12,
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}
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],
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},
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]
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frames_path = result / "camera-geometry-frames.jsonl"
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frames_path.write_bytes(b"".join(_canonical(frame) + b"\n" for frame in frames))
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identity: dict[str, Any] = {
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"frame_count": 2,
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"timeline_start_seconds": 1.0,
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"timeline_end_seconds": 2.0,
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"source_result_id": "e10-integrated-perception-" + "b" * 64,
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"source_pack_id": "e10-lidar-pack-" + "c" * 64,
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"local_surface_model_id": "k1-local-surface-" + "d" * 64,
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"profile": {"profile_id": "camera-first-local-surface-validation/v1"},
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"authority": {
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"commands_enabled": False,
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"navigation_or_safety_accepted": False,
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},
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}
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report = {
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"schema_version": CAMERA_GEOMETRY_REPORT_SCHEMA,
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"result_id": result_id,
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"status": "diagnostic-replay-complete",
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"ground_truth": False,
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"identity": identity,
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"metrics": {
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"semantic_observations": {
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"geometry_status": {
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"agree": 2,
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"single-source-camera": 2,
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"conflict": 2,
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"unknown": 2,
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}
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},
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"geometry_only_occupied": {"cluster_count": 2},
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},
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"authority": {
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"commands_enabled": False,
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"navigation_or_safety_accepted": False,
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},
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}
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report_path = result / "camera-geometry-report.json"
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report_path.write_bytes(_canonical(report))
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identity_sha256 = hashlib.sha256(_canonical(identity)).hexdigest()
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result_id = f"e29-camera-geometry-{identity_sha256}"
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result_with_identity = root / result_id
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result.rename(result_with_identity)
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result = result_with_identity
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frames_path = result / frames_path.name
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report_path = result / report_path.name
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report["result_id"] = result_id
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report_path.write_bytes(_canonical(report))
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manifest = {
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"schema_version": CAMERA_GEOMETRY_FUSION_SCHEMA,
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"result_id": result_id,
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"identity_sha256": identity_sha256,
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"identity": identity,
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"ground_truth": False,
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"artifacts": [
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{
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"role": "camera-geometry-frames",
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"path": frames_path.name,
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"byte_length": frames_path.stat().st_size,
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"sha256": _sha256(frames_path),
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},
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{
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"role": "camera-geometry-report",
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"path": report_path.name,
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"byte_length": report_path.stat().st_size,
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"sha256": _sha256(report_path),
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},
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],
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}
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(result / "manifest.json").write_bytes(_canonical(manifest))
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return result
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def test_review_pack_binds_all_strata_and_keeps_human_decision_open(
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tmp_path: Path,
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) -> None:
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source = _source_result(tmp_path)
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profile = E30ReviewSelectionProfile(
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agree_maximum=1,
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camera_only_maximum=1,
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unknown_maximum=1,
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geometry_only_maximum=1,
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temporal_bins=2,
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)
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first = build_e30_review_pack(
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e29_result_root=source,
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output_root=tmp_path / "review-packs",
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profile=profile,
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)
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second = build_e30_review_pack(
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e29_result_root=source,
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output_root=tmp_path / "review-packs",
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profile=profile,
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)
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assert first.result_id == second.result_id
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assert first.manifest["human_review_complete"] is False
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assert first.manifest["lab_published"] is False
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assert first.manifest["source_counts"] == {
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"agree": 2,
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"camera-only": 2,
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"conflict": 2,
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"geometry-only": 2,
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"unknown": 2,
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}
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assert first.manifest["selected_counts"] == {
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"agree": 1,
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"camera-only": 1,
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"conflict": 2,
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"geometry-only": 1,
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"unknown": 1,
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}
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lines = (first.result_root / "review-items.jsonl").read_text().splitlines()
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items = [json.loads(line) for line in lines]
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assert len(items) == 6
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assert all(item["review"]["state"] == "unreviewed" for item in items)
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assert all(item["materialization"]["source_reprojection_required"] for item in items)
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assert len({item["item_id"] for item in items}) == 6
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def test_review_pack_taxonomy_is_fixed_and_source_tampering_rejects(
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tmp_path: Path,
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) -> None:
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assert E30_REASON_TAXONOMY[0] == "no_lidar_observation"
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assert E30_REASON_TAXONOMY[-1] == "unknown"
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assert len(E30_REASON_TAXONOMY) == 19
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source = _source_result(tmp_path)
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frames = source / "camera-geometry-frames.jsonl"
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frames.write_bytes(frames.read_bytes() + b"\n")
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with pytest.raises(E30ReviewPackError, match="byte length changed"):
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build_e30_review_pack(
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e29_result_root=source,
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output_root=tmp_path / "review-packs",
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)
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