| |
| """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() |
|
|
