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import numpy as np
import pandas as pd
from utils.config_band import config_band
from utils.convert_to_excel import convert_dfs, save_dataframe
from utils.kml_creator import generate_kml_from_df
from utils.utils_vars import (
LteFddAnalysisData,
LteTddAnalysisData,
UtilsVars,
get_band,
get_physical_db,
)
LNCEL_COLUMNS = [
"ID_LNBTS",
"ID_LNCEL",
"MRBTS",
"LNBTS",
"LNCEL",
"final_name",
"name",
"cellName",
"code",
"SectorId",
"Code_Sector",
"actModulationSchemeDl",
"actModulationSchemeUL",
"administrativeState",
"eutraCelId",
"lcrId",
"pMax",
"phyCellId",
"tac",
"Region",
"band",
"band_type",
]
LNCEL_MOBILITY_COLUMNS = [
"ID_LNBTS",
"ID_LNCEL",
"MRBTS",
"LNBTS",
"LNCEL",
"final_name",
"name",
"cellName",
"code",
"SectorId",
"Code_Sector",
"administrativeState",
"lcrId",
"band",
"band_type",
"a3Offset",
"enableBetterCellHo",
"enableCovHo",
"threshold3",
"threshold3a",
"threshold4",
"threshold2InterFreq",
"threshold2Wcdma",
"threshold2a",
"threshold1",
"hysThreshold2InterFreq",
"hysThreshold2Wcdma",
"hysThreshold2a",
"hysThreshold3",
"hysThreshold4",
]
LNCEL_FDD_COLUMNS = [
"ID_LNCEL",
"dlChBw",
"dlMimoMode",
"dlRsBoost",
"earfcnDL",
"earfcnUL",
"prachCS",
"rootSeqIndex",
"ulChBw",
]
LNCEL_TDD_COLUMNS = [
"ID_LNCEL",
"chBw",
"dlMimoMode",
"dlRsBoost",
"earfcn",
"prachCS",
"rootSeqIndex",
]
LTE_KML_COLUMNS = [
"code",
"final_name",
"Longitude",
"Latitude",
"Azimut",
"Hauteur",
"lcrId",
"pMax",
"phyCellId",
"tac",
"rootSeqIndex",
"band",
]
def process_lncel(file_path: str):
"""
Process data from the specified file path.
Args:
file_path (str): The path to the file.
"""
# Read excel sheets into dataframes
dfs = pd.read_excel(
file_path,
sheet_name=["LNCEL"],
engine="calamine",
skiprows=[0],
)
# Process LNCEL data
df_lncel = dfs["LNCEL"]
df_lncel.columns = df_lncel.columns.str.replace(r"[ ]", "", regex=True)
df_lncel["final_name"] = df_lncel["name"].fillna(df_lncel["cellName"])
df_lncel["code"] = df_lncel["final_name"].str.split("_").str[0]
df_lncel["code"] = (
pd.to_numeric(df_lncel["code"], errors="coerce").fillna(0).astype(int)
)
df_lncel["SectorId"] = (
df_lncel["lcrId"].map(UtilsVars.sector_mapping).fillna(df_lncel["lcrId"])
)
df_lncel["Code_Sector"] = (
df_lncel[["code", "SectorId"]]
.astype(str)
.apply("_".join, axis=1)
.str.replace(".0", "")
.str.lstrip("0")
)
df_lncel["ID_LNCEL"] = (
df_lncel[["MRBTS", "LNBTS", "LNCEL"]].astype(str).apply("_".join, axis=1)
)
df_lncel["ID_LNBTS"] = (
df_lncel[["MRBTS", "LNBTS"]].astype(str).apply("_".join, axis=1)
)
df_lncel["Region"] = df_lncel["final_name"].str.split("_").str[1]
df_lncel["band"] = df_lncel["final_name"].apply(get_band)
df_lncel["band_type"] = np.where(df_lncel["band"] == "L2300", "TDD", "FDD")
return df_lncel
def process_lte_data(file_path: str):
"""
Process data from the specified file path.
Args:
file_path (str): The path to the file.
"""
# Read excel sheets into dataframes
dfs = pd.read_excel(
file_path,
sheet_name=["LNBTS", "LNCEL_FDD", "LNCEL_TDD"],
engine="calamine",
skiprows=[0],
)
# Get LNCEL data
df_lncel = process_lncel(file_path)
df_lncel = df_lncel[LNCEL_COLUMNS]
# create band dataframe
df_band = config_band(df_lncel)
# Process LNBTS data
df_lnbts = dfs["LNBTS"]
df_lnbts.columns = df_lnbts.columns.str.replace(r"[ ]", "", regex=True)
df_lnbts["ID_LNBTS"] = (
df_lnbts[["MRBTS", "LNBTS"]].astype(str).apply("_".join, axis=1)
)
df_lnbts.rename(columns={"name": "lnbts_name"}, inplace=True)
df_lnbts = df_lnbts[["ID_LNBTS", "lnbts_name"]]
# Merge dataframes
df_lncel_lnbts = pd.merge(df_lncel, df_lnbts, on="ID_LNBTS", how="left")
df_lncel_lnbts = pd.merge(df_lncel_lnbts, df_band, on="code", how="left")
df_physical_db = get_physical_db()
df_lncel_lnbts = pd.merge(
df_lncel_lnbts, df_physical_db, on="Code_Sector", how="left"
)
# Process LNCEL_FDD and LNCEL_TDD data
df_lncel_fdd = dfs["LNCEL_FDD"]
df_lncel_fdd.columns = df_lncel_fdd.columns.str.replace(r"[ ]", "", regex=True)
df_lncel_fdd["ID_LNCEL"] = (
df_lncel_fdd[["MRBTS", "LNBTS", "LNCEL"]].astype(str).apply("_".join, axis=1)
)
df_lncel_fdd = df_lncel_fdd[LNCEL_FDD_COLUMNS]
df_lncel_tdd = dfs["LNCEL_TDD"]
df_lncel_tdd.columns = df_lncel_tdd.columns.str.replace(r"[ ]", "", regex=True)
df_lncel_tdd["ID_LNCEL"] = (
df_lncel_tdd[["MRBTS", "LNBTS", "LNCEL"]].astype(str).apply("_".join, axis=1)
)
df_lncel_tdd = df_lncel_tdd[LNCEL_TDD_COLUMNS]
# Create df_fdd and df_tdd base on "band"
df_fdd = df_lncel_lnbts[df_lncel_lnbts["band"] != "L2300"]
df_tdd = df_lncel_lnbts[df_lncel_lnbts["band"] == "L2300"]
df_fdd_final = pd.merge(df_fdd, df_lncel_fdd, on="ID_LNCEL", how="left")
df_tdd_final = pd.merge(df_tdd, df_lncel_tdd, on="ID_LNCEL", how="left")
# Save dataframes
# save_dataframe(df_fdd_final, "fdd")
# save_dataframe(df_tdd_final, "tdd")
UtilsVars.all_db_dfs.extend([df_fdd_final, df_tdd_final])
UtilsVars.lte_dfs.extend([df_fdd_final, df_tdd_final])
UtilsVars.all_db_dfs_names.extend(["LTE_FDD", "LTE_TDD"])
return [df_fdd_final, df_tdd_final]
# add the fdd and tdd to the list
# UtilsVars.final_lte_database = [df_fdd_final, df_tdd_final]
def process_lte_data_to_excel(file_path: str):
lte_dfs = process_lte_data(file_path)
UtilsVars.final_lte_database = convert_dfs(lte_dfs, ["LTE_FDD", "LTE_TDD"])
############################# KML CREATION #################################
def process_lte_data_to_kml(file_path: str):
lte_kml_dfs = process_lte_data(file_path)
lte_fdd_klm_df = lte_kml_dfs[0]
lte_fdd_klm_df = lte_fdd_klm_df[LTE_KML_COLUMNS]
lte_tdd_klm_df = lte_kml_dfs[1]
lte_tdd_klm_df = lte_tdd_klm_df[LTE_KML_COLUMNS]
# Merge FDD and TDD dataframes
lte_kml_df = pd.concat([lte_fdd_klm_df, lte_tdd_klm_df], ignore_index=True)
# Rename "final_name" to "name"
lte_kml_df.rename(columns={"final_name": "name"}, inplace=True)
# Add colors column base on "band" column
lte_kml_df["color"] = lte_kml_df["band"].map(UtilsVars.color_mapping)
# Add size column base on "band" column
lte_kml_df["size"] = lte_kml_df["band"].map(UtilsVars.size_mapping)
# Remove empty rows
lte_kml_df = lte_kml_df.dropna(subset=["Longitude", "Latitude", "Azimut"])
# Generate kml
UtilsVars.lte_kml_file = generate_kml_from_df(lte_kml_df)
#############################LTE ANALYSIS#################################
def lte_fdd_analaysis(file_path: str):
# df_fdd = process_lte_data(file_path)[0]
df_fdd: pd.DataFrame = UtilsVars.lte_dfs[0]
LteFddAnalysisData.total_number_of_lncel = len(df_fdd["ID_LNCEL"].unique())
LteFddAnalysisData.total_number_of_site = len(df_fdd["code"].unique())
LteFddAnalysisData.number_of_empty_lncel_name = df_fdd["name"].isna().sum()
LteFddAnalysisData.number_of_empty_lncel_cellname = df_fdd["cellName"].isna().sum()
LteFddAnalysisData.number_of_empty_lnbts_name = df_fdd["lnbts_name"].isna().sum()
LteFddAnalysisData.number_of_cell_per_band = df_fdd["band"].value_counts()
LteFddAnalysisData.phycellid_distribution = df_fdd["phyCellId"].value_counts()
LteFddAnalysisData.rootsequenceindex_distribution = df_fdd[
"rootSeqIndex"
].value_counts()
LteFddAnalysisData.lncel_administate_distribution = df_fdd[
"administrativeState"
].value_counts()
LteFddAnalysisData.number_of_cell_per_tac = df_fdd["tac"].value_counts()
def lte_tdd_analaysis(file_path: str):
# df_tdd = process_lte_data(file_path)[1]
df_tdd: pd.DataFrame = UtilsVars.lte_dfs[1]
LteTddAnalysisData.total_number_of_lncel = len(df_tdd["ID_LNCEL"].unique())
LteTddAnalysisData.total_number_of_site = len(df_tdd["code"].unique())
LteTddAnalysisData.number_of_empty_lncel_name = df_tdd["name"].isna().sum()
LteTddAnalysisData.number_of_empty_lncel_cellname = df_tdd["cellName"].isna().sum()
LteTddAnalysisData.number_of_empty_lnbts_name = df_tdd["lnbts_name"].isna().sum()
LteTddAnalysisData.number_of_cell_per_band = df_tdd["band"].value_counts()
LteTddAnalysisData.phycellid_distribution = df_tdd["phyCellId"].value_counts()
LteTddAnalysisData.rootsequenceindex_distribution = df_tdd[
"rootSeqIndex"
].value_counts()
LteTddAnalysisData.lncel_administate_distribution = df_tdd[
"administrativeState"
].value_counts()
LteTddAnalysisData.number_of_cell_per_tac = df_tdd["tac"].value_counts()
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