import numpy as np
import pandas as pd

# url = "https://raw.githubusercontent.com/DavMelchi/STORAGE/refs/heads/main/physical_db/physical_database.csv"
url = r"./physical_db/physical_database.csv"


def get_physical_db():
    """
    Reads the physical_database.csv file from the physical_db directory and
    returns a pandas DataFrame containing only the columns 'Code_Sector',
    'Azimut', 'Longitude', 'Latitude', and 'Hauteur'.

    Returns:
        pd.DataFrame: A DataFrame containing the filtered columns.
    """
    physical = pd.read_csv(url)
    physical = physical[
        [
            "Code_Sector",
            "Azimut",
            "Longitude",
            "Latitude",
            "Hauteur",
            "City",
            "Adresse",
            "Commune",
            "Cercle",
        ]
    ]
    return physical


class UtilsVars:
    sector_mapping = {4: 1, 5: 2, 6: 3, 11: 1, 12: 2, 13: 3, 81: 1, 82: 2, 83: 3}
    type_cellule = {1: "Macro Cell 1800", 0: "Macro Cell 900"}
    oml_band_frequence = {1: "OML BAND GSM 1800", 0: "OML BAND GSM 900"}
    gsm_band = {1: "G1800", 0: "G900"}
    configuration_schema = {1: "EGPRS 1800", 0: "EGPRS 900"}
    channeltype_mapping = {4: "BCCH", 3: "TRX_TCH"}
    oml_lte_freq_band = {
        "L1800": "OML E-UTRA Band 3 - 20MHz",
        "L800": "OML E-UTRA Band 20 - 20MHz",
        "L2300": "OML E-UTRA Band 43 - 20MHz",
        "L2600": "OML E-UTRA Band 7 - 20MHz",
    }
    porteuse_mapping = {
        3004: "OML UTRA Band VIII",
        3006: "OML UTRA Band VIII",
        10812: "OML UTRA Band I",
        10787: "OML UTRA Band I",
        10837: "OML UTRA Band I",
    }
    color_mapping = {
        "U900": "7fff0000",
        "U2100": "7f00ff00",
        "G900": "7fff0000",
        "G1800": "7f00ff00",
        "L800": "7fff0000",
        "L1800": "7f00ff00",
        "L2300": "7f00ffff",
        "L2600": "7f0000ff",
    }
    size_mapping = {
        "U900": 100,
        "U2100": 120,
        "G900": 100,
        "G1800": 120,
        "L800": 100,
        "L1800": 120,
        "L2300": 90,
        "L2600": 80,
    }
    wcdma_band = {
        3004: "U900",
        3006: "U900",
        10787: "U2100",
        10837: "U2100",
        10812: "U2100",
    }
    bsc_name = {
        403698: "MBSCTST",
        403699: "MBSC01",
        403701: "MBSC04",
        403702: "MBSC03",
        403703: "MBSC02",
        406283: "MBSKTL01",
        406284: "MBSSEG01",
        406308: "MBSSK0S1",
    }
    final_lte_database = ""
    final_gsm_database = ""
    final_wcdma_database = ""
    final_trx_database = ""
    final_mrbts_database = ""
    final_invunit_database = ""
    final_mal_database = ""
    gsm_dfs = []
    wcdma_dfs = []
    lte_dfs = []
    all_db_dfs = []
    all_db_dfs_names = []
    final_all_database = None
    atoll_dfs = []
    final_atoll_database = None
    final_nice_database = None
    neighbors_database = ""
    file_path = ""
    gsm_kml_file = None
    wcdma_kml_file = None
    lte_kml_file = None
    # physisal_db = get_physical_db()


def get_band(text):
    """
    Extract the band from the given string.

    Parameters
    ----------
    text : str
        The string to extract the band from.

    Returns
    -------
    str or np.nan
        The extracted band, or NaN if the text was not a string or did not contain
        any of the recognized bands (L1800, L2300, L800).
    """
    if isinstance(text, str):  # Check if text is a string
        if "L1800" in text:
            return "L1800"
        elif "L2300" in text:
            return "L2300"
        elif "L800" in text:
            return "L800"
        elif "L2600" in text:
            return "L2600"
    return np.nan  # or return None


class GsmAnalysisData:
    total_number_of_bsc = 0
    total_number_of_cell = 0
    number_of_site = 0
    number_of_cell_per_bsc = pd.DataFrame()
    number_of_site_per_bsc = pd.DataFrame()
    number_of_bts_name_empty = 0
    number_of_bcf_name_empty = 0
    number_of_bcch_empty = 0
    bts_administate_distribution = pd.DataFrame()
    trx_administate_distribution = pd.DataFrame()
    number_of_trx_per_bsc = pd.DataFrame()
    number_of_cell_per_lac = pd.DataFrame()
    number_of_site_per_lac = pd.DataFrame()
    trx_frequency_distribution = pd.DataFrame()


class WcdmaAnalysisData:
    total_number_of_rnc = 0
    total_number_of_wcel = 0
    number_of_site = 0
    number_of_site_per_rnc = 0
    number_of_cell_per_rnc = pd.DataFrame()
    number_of_empty_wbts_name = 0
    number_of_empty_wcel_name = 0
    wcel_administate_distribution = pd.DataFrame()
    psc_distribution = pd.DataFrame()
    number_of_cell_per_lac = pd.DataFrame()
    number_of_site_per_lac = pd.DataFrame()


class LteFddAnalysisData:
    total_number_of_lncel = 0
    total_number_of_site = 0
    number_of_empty_lncel_name = 0
    number_of_empty_lncel_cellname = 0
    number_of_empty_lnbts_name = 0
    number_of_cell_per_band = pd.DataFrame()
    phycellid_distribution = pd.DataFrame()
    rootsequenceindex_distribution = pd.DataFrame()
    lncel_administate_distribution = pd.DataFrame()
    number_of_cell_per_tac = pd.DataFrame()


class LteTddAnalysisData:
    total_number_of_lncel = 0
    total_number_of_site = 0
    number_of_empty_lncel_name = 0
    number_of_empty_lncel_cellname = 0
    number_of_empty_lnbts_name = 0
    number_of_cell_per_band = pd.DataFrame()
    phycellid_distribution = pd.DataFrame()
    rootsequenceindex_distribution = pd.DataFrame()
    lncel_administate_distribution = pd.DataFrame()
    number_of_cell_per_tac = pd.DataFrame()


class SiteAnalysisData:
    total_number_of_site = 0
    total_munber_of_gsm_site = 0
    total_number_of_wcdma_site = 0
    total_number_of_lte_site = 0
    gsm_bands_distribution = pd.DataFrame()
    wcdma_bands_distribution = pd.DataFrame()
    lte_bands_distribution = pd.DataFrame()
    all_bands_distribution = pd.DataFrame()
    number_of_trx_per_site_distribution = pd.DataFrame()