#!/usr/bin/env python3
"""Create the PhaseFlow missing-count CSV views next to a source phase table."""

from __future__ import annotations

import argparse
import json
from pathlib import Path

import pandas as pd


PHASE_COLUMNS = [f"group_{row}{column}" for row in range(1, 5) for column in range(1, 5)]
PACKAGE_ROOT = Path(__file__).resolve().parents[1]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--input", type=Path, default=PACKAGE_ROOT / "data/raw/phase_diagram_original_scale.csv")
    parser.add_argument("--output-dir", type=Path, default=PACKAGE_ROOT / "data/raw/by_missing")
    return parser.parse_args()


def main() -> None:
    args = parse_args()
    frame = pd.read_csv(args.input)
    required = {"AminoAcidSequence", *PHASE_COLUMNS}
    missing = sorted(required.difference(frame.columns))
    if missing:
        raise ValueError(f"Missing required columns: {missing}")

    args.output_dir.mkdir(parents=True, exist_ok=True)
    missing_count = frame[PHASE_COLUMNS].isna().sum(axis=1)
    counts: dict[str, int] = {}
    for count in range(16):
        subset = frame.loc[missing_count == count]
        subset.to_csv(args.output_dir / f"missing_{count}.csv", index=False)
        counts[str(count)] = int(len(subset))

    report = {
        "input": str(args.input),
        "rows": int(len(frame)),
        "phase_columns": PHASE_COLUMNS,
        "missing_count_rows": counts,
    }
    (args.output_dir / "missing_split_report.json").write_text(
        json.dumps(report, indent=2, sort_keys=True) + "\n"
    )
    print(json.dumps(report, sort_keys=True))


if __name__ == "__main__":
    main()