import pandas as pd

from utils.utils_vars import SiteAnalysisData, UtilsVars

GSM_COLUMNS = [
    "code",
    "site_name",
    "site_config_band",
    "Region",
    "number_trx_per_site",
    "Longitude",
    "Latitude",
    "Hauteur",
    "City",
    "Adresse",
    "Commune",
    "Cercle",
]

WCDMA_COLUMNS = [
    "code",
    "site_name",
    "Region",
    "site_config_band",
    "Longitude",
    "Latitude",
    "Hauteur",
    "City",
    "Adresse",
    "Commune",
    "Cercle",
]
LTE_COLUMNS = [
    "code",
    "lnbts_name",
    "site_config_band",
    "Region",
    "Longitude",
    "Latitude",
    "Hauteur",
    "City",
    "Adresse",
    "Commune",
    "Cercle",
]

CODE_COLUMNS = [
    "code",
    "Region",
    "Longitude",
    "Latitude",
    "Hauteur",
    "City",
    "Adresse",
    "Commune",
    "Cercle",
]


def clean_bands(bands):
    if pd.isna(bands):
        return None
    parts = [p for p in bands.split("/") if p != "nan"]
    return "/".join(parts) if parts else None


def site_db():
    gsm_df: pd.DataFrame = UtilsVars.all_db_dfs[0]
    wcdma_df: pd.DataFrame = UtilsVars.all_db_dfs[3]
    lte_fdd_df: pd.DataFrame = UtilsVars.all_db_dfs[4]
    lte_tdd_df: pd.DataFrame = UtilsVars.all_db_dfs[5]

    gsm_df = gsm_df[GSM_COLUMNS]
    gsm_df = gsm_df.rename(
        columns={
            "code": "code",
            "site_name": "gsm_name",
            "site_config_band": "2G_Bands",
        }
    )
    gsm_df.drop_duplicates(subset=["code"], keep="first", inplace=True)

    wcdma_df = wcdma_df[WCDMA_COLUMNS]
    wcdma_df = wcdma_df.rename(
        columns={
            "code": "code",
            "site_name": "wcdma_name",
            "site_config_band": "3G_Bands",
        }
    )

    wcdma_df.drop_duplicates(subset=["code"], keep="first", inplace=True)

    lte_fdd_df = lte_fdd_df[LTE_COLUMNS]
    lte_tdd_df = lte_tdd_df[LTE_COLUMNS]
    lte_df: pd.DataFrame = pd.concat([lte_fdd_df, lte_tdd_df], ignore_index=False)

    lte_df = lte_df.rename(
        columns={
            "code": "code",
            "lnbts_name": "lte_name",
            "site_config_band": "4G_Bands",
        }
    )

    lte_df.drop_duplicates(subset=["code"], keep="first", inplace=True)

    ################################# CODE DATAFRAME#############################

    gsm_code_df: pd.DataFrame = (
        gsm_df[CODE_COLUMNS].copy() if gsm_df is not None else pd.DataFrame()
    )
    wcdma_code_df: pd.DataFrame = (
        wcdma_df[CODE_COLUMNS].copy() if wcdma_df is not None else pd.DataFrame()
    )
    lte_code_df: pd.DataFrame = (
        lte_df[CODE_COLUMNS].copy() if lte_df is not None else pd.DataFrame()
    )

    code_df: pd.DataFrame = pd.concat(
        [gsm_code_df, wcdma_code_df, lte_code_df], ignore_index=True
    )
    code_df.drop_duplicates(subset=["code"], keep="first", inplace=True)
    code_df.dropna(subset=["code"], inplace=True)
    # order by code
    code_df.sort_values(by=["code"], inplace=True)

    # print(code_df)
    # ################################# SITE DATAFRAME#############################
    gsm_df_final = gsm_df[
        [
            "code",
            "gsm_name",
            "2G_Bands",
            "number_trx_per_site",
        ]
    ].copy()
    wcdma_df_final = wcdma_df[["code", "wcdma_name", "3G_Bands"]].copy()
    lte_df_final = lte_df[["code", "lte_name", "4G_Bands"]].copy()

    site_df = pd.merge(code_df, gsm_df_final, how="left", on="code")
    site_df = pd.merge(site_df, wcdma_df_final, how="left", on="code")
    site_df = pd.merge(site_df, lte_df_final, how="left", on="code")
    # order by code
    site_df["site_name"] = (
        site_df["gsm_name"].fillna(site_df["wcdma_name"]).fillna(site_df["lte_name"])
    )

    site_df["all_bands"] = (
        (site_df[["2G_Bands", "3G_Bands", "4G_Bands"]])
        .astype(str)
        .apply("/".join, axis=1)
    )
    site_df["all_bands"] = site_df["all_bands"].apply(clean_bands)

    site_df = site_df[
        [
            "code",
            "site_name",
            "Region",
            "2G_Bands",
            "3G_Bands",
            "4G_Bands",
            "all_bands",
            "number_trx_per_site",
            "Longitude",
            "Latitude",
            "Hauteur",
            "City",
            "Adresse",
            "Commune",
            "Cercle",
        ]
    ]

    site_df.sort_values(by=["code"], inplace=True)

    UtilsVars.all_db_dfs.append(site_df)
    UtilsVars.all_db_dfs_names.append("SITE")

    ####################### SITE ANALYSIS ###################################################

    SiteAnalysisData.total_number_of_site = len(site_df["code"].unique())
    SiteAnalysisData.total_munber_of_gsm_site = site_df["2G_Bands"].notna().sum()
    SiteAnalysisData.total_number_of_wcdma_site = site_df["3G_Bands"].notna().sum()
    SiteAnalysisData.total_number_of_lte_site = site_df["4G_Bands"].notna().sum()

    SiteAnalysisData.gsm_bands_distribution = site_df["2G_Bands"].value_counts(
        ascending=True
    )
    SiteAnalysisData.wcdma_bands_distribution = site_df["3G_Bands"].value_counts(
        ascending=True
    )
    SiteAnalysisData.lte_bands_distribution = site_df["4G_Bands"].value_counts(
        ascending=True
    )
    SiteAnalysisData.all_bands_distribution = site_df["all_bands"].value_counts(
        ascending=True
    )
    SiteAnalysisData.number_of_trx_per_site_distribution = site_df[
        "number_trx_per_site"
    ].value_counts()