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import numpy as np |
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import pandas as pd |
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def get_physical_db(): |
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""" |
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Reads the physical_database.csv file from the physical_db directory and |
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returns a pandas DataFrame containing only the columns 'Code_Sector', |
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'Azimut', 'Longitude', 'Latitude', and 'Hauteur'. |
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Returns: |
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pd.DataFrame: A DataFrame containing the filtered columns. |
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""" |
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physical = pd.read_csv(r"./physical_db/physical_database.csv") |
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physical = physical[["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]] |
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return physical |
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class UtilsVars: |
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sector_mapping = {4: 1, 5: 2, 6: 3, 11: 1, 12: 2, 13: 3} |
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type_cellule = {1: "Macro Cell 1800", 0: "Macro Cell 900"} |
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oml_band_frequence = {1: "OML BAND GSM 1800", 0: "OML BAND GSM 900"} |
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gsm_band = {1: "G1800", 0: "G900"} |
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configuration_schema = {1: "EGPRS 1800", 0: "EGPRS 900"} |
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channeltype_mapping = {4: "BCCH", 3: "TRX_TCH"} |
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porteuse_mapping = { |
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3004: "OML UTRA Band VIII", |
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3006: "OML UTRA Band VIII", |
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10812: "OML UTRA Band I", |
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10787: "OML UTRA Band I", |
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10837: "OML UTRA Band I", |
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} |
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wcdma_band = { |
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3004: "U900", |
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3006: "U900", |
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10787: "U2100", |
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10837: "U2100", |
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10812: "U2100", |
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} |
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bsc_name = { |
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403698: "MBSCTST", |
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403699: "MBSC01", |
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403701: "MBSC04", |
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403702: "MBSC03", |
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403703: "MBSC02", |
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406283: "MBSKTL01", |
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406284: "MBSSEG01", |
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406308: "MBSSK0S1", |
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} |
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final_lte_database = "" |
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final_gsm_database = "" |
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final_wcdma_database = "" |
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final_trx_database = "" |
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final_mrbts_database = "" |
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final_mal_database = "" |
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gsm_dfs = [] |
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wcdma_dfs = [] |
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lte_dfs = [] |
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all_db_dfs = [] |
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all_db_dfs_names = [] |
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final_all_database = None |
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neighbors_database = "" |
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file_path = "" |
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physisal_db = get_physical_db() |
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def get_band(text): |
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""" |
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Extract the band from the given string. |
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Parameters |
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---------- |
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text : str |
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The string to extract the band from. |
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Returns |
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------- |
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str or np.nan |
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The extracted band, or NaN if the text was not a string or did not contain |
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any of the recognized bands (L1800, L2300, L800). |
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""" |
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if isinstance(text, str): |
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if "L1800" in text: |
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return "L1800" |
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elif "L2300" in text: |
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return "L2300" |
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elif "L800" in text: |
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return "L800" |
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return np.nan |
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class GsmAnalysisData: |
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total_number_of_bsc = 0 |
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total_number_of_cell = 0 |
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number_of_site = 0 |
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number_of_cell_per_bsc = pd.DataFrame() |
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number_of_site_per_bsc = pd.DataFrame() |
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number_of_bts_name_empty = 0 |
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number_of_bcf_name_empty = 0 |
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number_of_bcch_empty = 0 |
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bts_administate_distribution = pd.DataFrame() |
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trx_administate_distribution = pd.DataFrame() |
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number_of_trx_per_bsc = pd.DataFrame() |
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number_of_cell_per_lac = pd.DataFrame() |
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class WcdmaAnalysisData: |
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total_number_of_rnc = 0 |
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total_number_of_wcel = 0 |
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number_of_site = 0 |
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number_of_site_per_rnc = 0 |
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number_of_cell_per_rnc = pd.DataFrame() |
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number_of_empty_wbts_name = 0 |
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number_of_empty_wcel_name = 0 |
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wcel_administate_distribution = pd.DataFrame() |
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psc_distribution = pd.DataFrame() |
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number_of_cell_per_lac = pd.DataFrame() |
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class LteFddAnalysisData: |
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total_number_of_lncel = 0 |
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total_number_of_site = 0 |
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number_of_empty_lncel_name = 0 |
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number_of_empty_lncel_cellname = 0 |
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number_of_empty_lnbts_name = 0 |
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number_of_cell_per_band = pd.DataFrame() |
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phycellid_distribution = pd.DataFrame() |
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rootsequenceindex_distribution = pd.DataFrame() |
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lncel_administate_distribution = pd.DataFrame() |
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number_of_cell_per_tac = pd.DataFrame() |
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class LteTddAnalysisData: |
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total_number_of_lncel = 0 |
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total_number_of_site = 0 |
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number_of_empty_lncel_name = 0 |
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number_of_empty_lncel_cellname = 0 |
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number_of_empty_lnbts_name = 0 |
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number_of_cell_per_band = pd.DataFrame() |
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phycellid_distribution = pd.DataFrame() |
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rootsequenceindex_distribution = pd.DataFrame() |
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lncel_administate_distribution = pd.DataFrame() |
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number_of_cell_per_tac = pd.DataFrame() |
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