# Copyright (C) 2026 Hector van der Aa <hector@h3cx.dev>
# Copyright (C) 2026 Pierre Barbier <pierrebarbier741@gmail.com>
# Copyright (C) 2026 Association Exergie <association.exergie@gmail.com>
# SPDX-License-Identifier: GPL-3.0-or-later

import pandas as pd
from pathlib import Path
import argparse
from tqdm import tqdm


def find_last_crank(df: pd.DataFrame, time_us: int) -> int | None:
    previous_crank_hits = df.loc[: time_us - 1]
    previous_crank_hits = previous_crank_hits[previous_crank_hits["crank"] == 1]

    if previous_crank_hits.empty:
        return None

    return previous_crank_hits.index[-1]


def find_next_crank(df: pd.DataFrame, time_us: int) -> int | None:
    next_crank_hits = df.loc[time_us + 1 :]
    next_crank_hits = next_crank_hits[next_crank_hits["crank"] == 1]

    if next_crank_hits.empty:
        return None

    return next_crank_hits.index[0]




def filter_data(file: Path) -> pd.DataFrame:
    df = pd.read_csv(file).set_index("time_us", drop=False)
    rows = []
    last_crank = -1
    last_crank_delta = -1
    previous_crank = -1
    last_cam = -1
    cam_flag = 0
    crank_flag = False

    for _, row in tqdm(df.iterrows(), total=len(df), desc="Derivative"):
        time_us: int = row["time_us"]
        crank: int = row["crank"]
        cam: int = row["cam"]
        c1 = 0
        c2 = 0
        if crank==1:
            d1 = time_us-c1
            d2 = d1-(c1-c2)
            if crank_flag:
                rows.append({
                    "time_us": time_us,
                    "d1": d1,
                    "d2": d2,
                    "ratio": d2/d1
                })
            else:
                crank_flag = True
            c2=c1
            c1=time_us
        output = pd.DataFrame(rows)
    return output


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}")

concat_files: list[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 != "trimmed":
        print(f"Skipping unknown file: {path}")
        continue

    concat_files.append(path)

for file in concat_files:
    base_name, _ = file.stem.rsplit("_", 1)
    output = file.parent / f"{base_name}_derivative.csv"
    out_df = filter_data(file)
    out_df.to_csv(output)