Datasets:

maxwellinked
/

time-lapse-artifacts-derived

#!/usr/bin/env python3
"""Build a deterministic engineering validation set from pinned source metadata."""

from __future__ import annotations

import argparse
import csv
import hashlib
from pathlib import Path
from typing import Iterable


SEED = "time-lapse-artifacts-derivative-validation-v1"
DEFAULT_SIZE = 18


KNOWN_COMPLETENESS = {
    "tla-3f49a8917fe22676": {
        "source_master_status": "active_derivative",
        "visible_start_state": "mid_process",
        "visible_end_state": "completed_signed",
        "capture_extent": "starts_mid_process",
        "completeness_evidence": "creator-confirmed source note",
    },
    "tla-9d7a3463d2902dcd": {
        "source_master_status": "active_derivative",
        "visible_start_state": "blank_or_unmarked",
        "visible_end_state": "unknown",
        "capture_extent": "unknown",
        "completeness_evidence": "creator-confirmed source note",
    },
    "tla-c043b4f55b583c4e": {
        "source_master_status": "active_derivative",
        "visible_start_state": "preliminary_work_present",
        "visible_end_state": "active_work",
        "capture_extent": "starts_and_ends_mid_process",
        "completeness_evidence": "creator-confirmed source note",
    },
}


OUTPUT_FIELDS = [
    "record_id",
    "source_file_path",
    "collection_tier",
    "duration_seconds",
    "size_bytes",
    "video_codec",
    "orientation",
    "source_has_audio",
    "source_hub_xet_hash",
    "source_revision",
    "selection_reasons",
    "pilot_wave",
    "work_id",
    "work_id_status",
    "source_master_status",
    "visible_start_state",
    "visible_end_state",
    "capture_extent",
    "completeness_evidence",
]


RELATIONSHIP_FIELDS = [
    "record_id",
    "source_file_path",
    "filename_sequence_key",
    "filename_sequence_token",
    "work_id",
    "relationship_status",
    "sequence_status",
    "evidence_method",
    "notes",
]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--standard-metadata", required=True, type=Path)
    parser.add_argument("--prestandard-metadata", required=True, type=Path)
    parser.add_argument("--source-revision", required=True)
    parser.add_argument("--output", required=True, type=Path)
    parser.add_argument("--relationship-output", required=True, type=Path)
    parser.add_argument("--size", type=int, default=DEFAULT_SIZE)
    return parser.parse_args()


def stable_key(record_id: str) -> str:
    return hashlib.sha256(f"{SEED}:{record_id}".encode("utf-8")).hexdigest()


def load_csv(path: Path, tier: str) -> list[dict[str, str]]:
    with path.open("r", encoding="utf-8-sig", newline="") as handle:
        rows = list(csv.DictReader(handle))

    for row in rows:
        row["collection_tier"] = tier
        file_name = row["file_name"]
        if tier == "pre_standard" and not file_name.startswith(
            "Pre_Standard_Time_Lapses/"
        ):
            file_name = f"Pre_Standard_Time_Lapses/{file_name}"
        row["source_file_path"] = file_name
        row["_duration"] = float(row["duration_seconds"])
        row["_size"] = int(row["size_bytes"])
    return rows


def add(
    selected: dict[str, set[str]], rows: Iterable[dict[str, str]], reason: str
) -> None:
    for row in rows:
        selected.setdefault(row["record_id"], set()).add(reason)


def extremes(
    rows: list[dict[str, str]], key: str, count: int = 2
) -> tuple[list[dict[str, str]], list[dict[str, str]]]:
    ordered = sorted(rows, key=lambda row: (row[key], stable_key(row["record_id"])))
    return ordered[:count], list(reversed(ordered[-count:]))


def median_record(rows: list[dict[str, str]], key: str) -> dict[str, str]:
    ordered = sorted(rows, key=lambda row: (row[key], stable_key(row["record_id"])))
    return ordered[(len(ordered) - 1) // 2]


def one_per_value(
    rows: list[dict[str, str]], field: str
) -> list[tuple[str, dict[str, str]]]:
    values = sorted({row[field] for row in rows})
    chosen = []
    for value in values:
        candidates = [row for row in rows if row[field] == value]
        # Categorical coverage should be inexpensive in the first pilot.  Size
        # is therefore the primary key; the seeded hash is only a stable tie
        # breaker.  A single small record may cover several rare conditions.
        chosen.append(
            (
                value,
                min(
                    candidates,
                    key=lambda row: (row["_size"], stable_key(row["record_id"])),
                ),
            )
        )
    return chosen


def filename_sequence_parts(source_path: str) -> tuple[str, str] | None:
    name = Path(source_path).name
    marker = ".Rec"
    if marker not in name or not name.endswith(".mp4"):
        return None
    prefix, suffix = name.rsplit(marker, 1)
    token = f"Rec{suffix[:-4]}"
    return prefix, token


def choose(rows: list[dict[str, str]], target_size: int) -> dict[str, set[str]]:
    if target_size < 12:
        raise ValueError("validation set size must be at least 12")

    selected: dict[str, set[str]] = {}

    documented_shortened = [
        row for row in rows if row["record_id"] in KNOWN_COMPLETENESS
    ]
    add(selected, documented_shortened, "documented_shortened_derivative")

    duration_low, duration_high = extremes(rows, "_duration")
    add(selected, duration_low, "duration_low_extreme")
    add(selected, duration_high, "duration_high_extreme")
    add(selected, [median_record(rows, "_duration")], "duration_median")

    size_low, size_high = extremes(rows, "_size")
    add(selected, size_low, "size_low_extreme")
    add(selected, size_high, "size_high_extreme")
    add(selected, [median_record(rows, "_size")], "size_median")

    for field, label in (
        ("collection_tier", "tier"),
        ("video_codec", "codec"),
        ("orientation", "orientation"),
        ("has_audio", "audio_stream_presence"),
    ):
        for value, row in one_per_value(rows, field):
            add(selected, [row], f"{label}:{value}")

    sequence_rows = sorted(
        [row for row in rows if filename_sequence_parts(row["source_file_path"])],
        key=lambda row: (row["source_file_path"], stable_key(row["record_id"])),
    )
    add(selected, sequence_rows[:2], "filename_multi_recording_case")

    if len(selected) > target_size:
        raise ValueError(
            f"required deterministic coverage selects {len(selected)} records, "
            f"which exceeds target size {target_size}"
        )

    remaining = sorted(
        [row for row in rows if row["record_id"] not in selected],
        key=lambda row: stable_key(row["record_id"]),
    )
    for row in remaining[: target_size - len(selected)]:
        add(selected, [row], "deterministic_fill")

    return selected


def make_output_row(
    row: dict[str, str], reasons: set[str], source_revision: str
) -> dict[str, str]:
    completeness = KNOWN_COMPLETENESS.get(row["record_id"], {})
    wave_one = (
        "documented_shortened_derivative" in reasons
        or "duration_low_extreme" in reasons
        or row["video_codec"] != "h264"
        or row["orientation"] != "portrait"
        or row["has_audio"].lower() != "true"
    )
    return {
        "record_id": row["record_id"],
        "source_file_path": row["source_file_path"],
        "collection_tier": row["collection_tier"],
        "duration_seconds": row["duration_seconds"],
        "size_bytes": row["size_bytes"],
        "video_codec": row["video_codec"],
        "orientation": row["orientation"],
        "source_has_audio": row["has_audio"].lower(),
        "source_hub_xet_hash": row["hub_xet_hash"],
        "source_revision": source_revision,
        "selection_reasons": ";".join(sorted(reasons)),
        "pilot_wave": "1" if wave_one else "2",
        "work_id": "",
        "work_id_status": "unassigned",
        "source_master_status": completeness.get("source_master_status", "unknown"),
        "visible_start_state": completeness.get("visible_start_state", "unknown"),
        "visible_end_state": completeness.get("visible_end_state", "unknown"),
        "capture_extent": completeness.get("capture_extent", "unknown"),
        "completeness_evidence": completeness.get("completeness_evidence", ""),
    }


def write_csv(path: Path, fields: list[str], rows: list[dict[str, str]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", encoding="utf-8", newline="") as handle:
        writer = csv.DictWriter(handle, fieldnames=fields, lineterminator="\n")
        writer.writeheader()
        writer.writerows(rows)


def relationship_rows(rows: list[dict[str, str]]) -> list[dict[str, str]]:
    output = []
    for row in sorted(rows, key=lambda item: item["source_file_path"]):
        parts = filename_sequence_parts(row["source_file_path"])
        if not parts:
            continue
        key, token = parts
        output.append(
            {
                "record_id": row["record_id"],
                "source_file_path": row["source_file_path"],
                "filename_sequence_key": key,
                "filename_sequence_token": token,
                "work_id": "",
                "relationship_status": "unreviewed",
                "sequence_status": "unverified",
                "evidence_method": "",
                "notes": (
                    "Filename-level review candidate only; shared key and Rec token "
                    "do not establish shared physical-work identity or sequence."
                ),
            }
        )
    return output


def main() -> None:
    args = parse_args()
    rows = load_csv(args.standard_metadata, "standard") + load_csv(
        args.prestandard_metadata, "pre_standard"
    )
    ids = [row["record_id"] for row in rows]
    if len(ids) != len(set(ids)):
        raise ValueError("record_id values are not unique across source metadata")

    selected = choose(rows, args.size)
    by_id = {row["record_id"]: row for row in rows}
    output_rows = [
        make_output_row(by_id[record_id], reasons, args.source_revision)
        for record_id, reasons in sorted(selected.items())
    ]
    write_csv(args.output, OUTPUT_FIELDS, output_rows)
    write_csv(
        args.relationship_output,
        RELATIONSHIP_FIELDS,
        relationship_rows(rows),
    )

    wave_one = sum(row["pilot_wave"] == "1" for row in output_rows)
    print(
        f"selected={len(output_rows)} wave1={wave_one} wave2={len(output_rows)-wave_one} "
        f"source_rows={len(rows)}"
    )


if __name__ == "__main__":
    main()