| import numpy as np |
| import pandas as pd |
|
|
| |
| 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, |
| } |
| lte_band = { |
| 1786: "L1800", |
| 6350: "L800", |
| 3050: "L2600", |
| 38750: "L2300", |
| 1761: "L1800", |
| } |
| 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 |
| adjl_database = None |
| |
|
|
|
|
| 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): |
| 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 |
|
|
|
|
| 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 |
|
|
|
|
| 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() |
|
|
