import numpy as np import pandas as pd 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(r"./physical_db/physical_database.csv") physical = physical[["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]] return physical class UtilsVars: sector_mapping = {4: 1, 5: 2, 6: 3, 11: 1, 12: 2, 13: 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"} 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", } 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_mal_database = "" gsm_dfs = [] wcdma_dfs = [] lte_dfs = [] all_db_dfs = [] all_db_dfs_names = [] final_all_database = None neighbors_database = "" file_path = "" physisal_db = get_physical_db() # BSC name # 403698 MBSCTST # 403699 MBSC01 # 403701 MBSC04 # 403702 MBSC03 # 403703 MBSC02 # 406283 MBSKTL01 # 406284 MBSSEG01 # 406308 MBSSK0S1 # print(UtilsVars.physisal_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" return np.nan # or return None ##############################STATISTICS############################ 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() 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() 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()