import pandas as pd
from functions import *


def calculate_anomaly(gsm_file, base_file):

    df_gsm = pd.read_csv(
        gsm_file,
        delimiter=" ",
        skiprows=1,
        names=["time", "lon", "lat", "f", "grad", "_", "quality"]
     )

    df_gsm["lon"] = df_gsm["lon"].apply(convert_deg)
    df_gsm["lat"] = df_gsm["lat"].apply(convert_deg)
    df_gsm["time"] = df_gsm["time"].apply(time_to_seconds)
    df_gsm = df_gsm[["time", "lon", "lat", "f", "grad", "quality"]]
    df_gsm["time"] = df_gsm["time"].apply(round).astype(float)

    df_base = pd.read_csv(
        base_file,
        sep=r"\s+",
        skiprows=22,
        names=["date", "time", "doy", "x", "y", "z", "f"]
    )

    df_base = df_base[df_base["f"] < 88000]
    df_base["time"] = df_base["time"].apply(datetime_to_seconds)
    result_df = pd.merge_asof(
        df_gsm,
        df_base[['time', 'f']],  # берем только нужные колонки
        left_on='time',
        right_on='time',
        direction='nearest',
        suffixes=('_grad', '_base')
    )

    result_df["delta_f"] = result_df["f_grad"] - result_df["f_base"]
    result_df = result_df[["lon", "lat", "time", "f_grad", "delta_f", "grad"]]
    result_df = result_df.rename(columns={"f_grad": "f", "grad": "grad_f"})

    return result_df