import pandas as pd
from pathlib import Path
import argparse


def concat_pair(r_file: Path, s_file: Path) -> pd.DataFrame:
    crank_df = pd.read_csv(r_file)
    cam_df = pd.read_csv(s_file)

    crank_values = crank_df.iloc[:, 0]
    cam_values = cam_df.iloc[:, 0]

    start_time = min(crank_values.iloc[0], cam_values.iloc[0])

    crank_values = crank_values - start_time
    cam_values = cam_values - start_time

    rows = max(crank_values.max(), cam_values.max()) + 1

    final_df = pd.DataFrame(index=range(rows), columns=["crank", "cam"])

    final_df["crank"] = final_df.index.isin(crank_values).astype(int)
    final_df["cam"] = final_df.index.isin(cam_values).astype(int)
    final_df.index.name = "time_us"

    return final_df


parser = argparse.ArgumentParser()
parser.add_argument("directory", type=Path, help="Source data directory")

args = parser.parse_args()

directory: Path = args.directory

if not directory.is_dir():
    parser.error(f"{directory} is not a valid directory")

print(f"Processing data in: {directory}")

file_groups: dict[str, dict[str, Path]] = {}

for path in directory.glob("*.csv"):
    stem = path.stem

    try:
        base_name, channel = stem.rsplit("_", 1)
    except ValueError:
        print(f"Skipping badly named file: {path}")
        continue

    if channel not in ("R", "S"):
        print(f"Skipping unknown file: {path}")
        continue

    file_groups.setdefault(base_name, {})[channel] = path

file_pairs: list[tuple[Path, Path]] = []

for base_name, files in sorted(file_groups.items()):
    if "R" not in files:
        print(f"Missing R file for {base_name}")
        continue

    if "S" not in files:
        print(f"Missing S file for {base_name}")
        continue

    file_pairs.append((files["R"], files["S"]))

for r_file, s_file in file_pairs:
    df = concat_pair(r_file, s_file)

    base_name, _ = r_file.stem.rsplit("_", 1)
    output = r_file.parent / f"{base_name}_concat.csv"

    df.to_csv(output)


exit(0)