import pandas as pd


INPUT_XLSX = "data.xlsx"
OUTPUT_XLSX = "task1/01_clean.xlsx"
SHEET_NAME = "addresses2019 updated"


def main() -> None:
    df_raw = pd.read_excel(INPUT_XLSX, sheet_name=SHEET_NAME)

    required = [
        "Site Name",
        "latitude",
        "longitude",
        "Number of Visits in 2019",
        "Average Demand per Visit",
        "StDev(Demand per Visit)",
    ]
    missing = [c for c in required if c not in df_raw.columns]
    if missing:
        raise ValueError(f"Missing required columns: {missing}")

    df = df_raw[required].copy()
    df = df.rename(
        columns={
            "Site Name": "site_name",
            "latitude": "lat",
            "longitude": "lon",
            "Number of Visits in 2019": "visits_2019",
            "Average Demand per Visit": "mu_clients_per_visit",
            "StDev(Demand per Visit)": "sd_clients_per_visit",
        }
    )

    df.insert(0, "site_id", range(1, len(df) + 1))

    numeric_cols = ["lat", "lon", "visits_2019", "mu_clients_per_visit", "sd_clients_per_visit"]
    for col in numeric_cols:
        df[col] = pd.to_numeric(df[col], errors="coerce")

    if df["site_name"].isna().any():
        raise ValueError("Found missing site_name values.")
    if df[numeric_cols].isna().any().any():
        bad = df[df[numeric_cols].isna().any(axis=1)][["site_id", "site_name"] + numeric_cols]
        raise ValueError(f"Found missing numeric values:\n{bad}")
    if (df["mu_clients_per_visit"] < 0).any() or (df["sd_clients_per_visit"] < 0).any():
        raise ValueError("Found negative mu/sd values; expected nonnegative.")
    if (df["visits_2019"] <= 0).any():
        raise ValueError("Found non-positive visits_2019; expected >0 for all 70 regular sites.")

    with pd.ExcelWriter(OUTPUT_XLSX, engine="openpyxl") as writer:
        df.to_excel(writer, index=False, sheet_name="sites")


if __name__ == "__main__":
    main()