File size: 5,886 Bytes
263b2ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
import pandas as pd
from utils.convert_to_excel import convert_dfs, save_dataframe
from utils.utils_vars import UtilsVars
ADCE_INITIAL_COLUMNS = [
"ID_BTS",
"lac_id",
]
ADJS_INITIAL_COLUMNS = [
"ID_WCEL",
"lac_id",
]
BTS_SOURCE = [
"ID_BTS",
"name",
]
BTS_TARGET = [
"lac_id",
"name",
]
WCEL_SOURCE = [
"ID_WCEL",
"name",
]
WCEL_TARGET = [
"lac_id",
"name",
]
def process_neighbors_data(file_path: str):
"""
Process data from the specified file path.
Args:
file_path (str): The path to the file.
"""
# Read the specific sheet into a DataFrame
dfs = pd.read_excel(
file_path,
sheet_name=["ADCE", "ADJS", "ADJI", "ADJG", "ADJW", "BTS", "WCEL"],
engine="calamine",
skiprows=[0],
)
# # Process ADCE data
df_adce = dfs["ADCE"]
df_adce.columns = df_adce.columns.str.replace(r"[ ]", "", regex=True)
df_adce["ID_BTS"] = (
df_adce[["BSC", "BCF", "BTS"]].astype(str).apply("_".join, axis=1)
)
df_adce["lac_id"] = (
df_adce[["adjacentCellIdLac", "adjacentCellIdCI"]]
.astype(str)
.apply("_".join, axis=1)
)
df_adce["lac_id"] = df_adce["lac_id"].str.replace(".0", "")
df_adce = df_adce[ADCE_INITIAL_COLUMNS]
# Process BTS data
df_bts = dfs["BTS"]
df_bts.columns = df_bts.columns.str.replace(r"[ ]", "", regex=True)
df_bts["ID_BTS"] = df_bts[["BSC", "BCF", "BTS"]].astype(str).apply("_".join, axis=1)
df_bts["lac_id"] = (
df_bts[["locationAreaIdLAC", "cellId"]].astype(str).apply("_".join, axis=1)
)
df_bts_source = df_bts[BTS_SOURCE]
df_bts_source.rename(columns={"name": "SOURCE_NAME"}, inplace=True)
df_bts_target = df_bts[BTS_TARGET]
df_bts_target.rename(columns={"name": "TARGET_NAME"}, inplace=True)
# #create final adce
df_adce_final = pd.merge(df_adce, df_bts_source, on="ID_BTS", how="left")
df_adce_final = pd.merge(
df_adce_final, df_bts_target, on="lac_id", how="left"
).dropna()
df_adce_final.rename(
columns={"ID_BTS": "SOURCE_ID", "lac_id": "TARGET_LAC_ID"}, inplace=True
)
# process ADJS data
df_adjs = dfs["ADJS"]
df_adjs.columns = df_adjs.columns.str.replace(r"[ ]", "", regex=True)
df_adjs["ID_WCEL"] = (
df_adjs[["RNC", "WBTS", "WCEL"]].astype(str).apply("_".join, axis=1)
)
df_adjs["lac_id"] = (
df_adjs[["AdjsLAC", "AdjsCI"]].astype(str).apply("_".join, axis=1)
)
df_adjs = df_adjs[ADJS_INITIAL_COLUMNS]
# process WCEL DATA
df_wcel = dfs["WCEL"]
df_wcel.columns = df_wcel.columns.str.replace(r"[ ]", "", regex=True)
df_wcel["ID_WCEL"] = (
df_wcel[["RNC", "WBTS", "WCEL"]].astype(str).apply("_".join, axis=1)
)
df_wcel["lac_id"] = df_wcel[["LAC", "CId"]].astype(str).apply("_".join, axis=1)
df_wcel = df_wcel[["ID_WCEL", "lac_id", "name"]]
df_wcel_source = df_wcel[WCEL_SOURCE]
df_wcel_source.rename(columns={"name": "SOURCE_NAME"}, inplace=True)
df_wcel_target = df_wcel[WCEL_TARGET]
df_wcel_target.rename(columns={"name": "TARGET_NAME"}, inplace=True)
# create final adjs
df_adjs_final = pd.merge(df_adjs, df_wcel_source, on="ID_WCEL", how="left")
df_adjs_final = pd.merge(
df_adjs_final, df_wcel_target, on="lac_id", how="left"
).dropna()
df_adjs_final.rename(
columns={"ID_WCEL": "SOURCE_ID", "lac_id": "TARGET_LAC_ID"}, inplace=True
)
# process ADJI DATA
df_adji = dfs["ADJI"]
df_adji.columns = df_adji.columns.str.replace(r"[ ]", "", regex=True)
df_adji["ID_WCEL"] = (
df_adji[["RNC", "WBTS", "WCEL"]].astype(str).apply("_".join, axis=1)
)
df_adji["lac_id"] = (
df_adji[["AdjiLAC", "AdjiCI"]].astype(str).apply("_".join, axis=1)
)
df_adji = df_adji[["ID_WCEL", "lac_id"]]
df_adji_final = pd.merge(df_adji, df_wcel_source, on="ID_WCEL", how="left")
df_adji_final = pd.merge(
df_adji_final, df_wcel_target, on="lac_id", how="left"
).dropna()
df_adji_final.rename(
columns={"ID_WCEL": "SOURCE_ID", "lac_id": "TARGET_LAC_ID"}, inplace=True
)
# process ADJG DATA
df_adjg = dfs["ADJG"]
df_adjg.columns = df_adjg.columns.str.replace(r"[ ]", "", regex=True)
df_adjg["ID_WCEL"] = (
df_adjg[["RNC", "WBTS", "WCEL"]].astype(str).apply("_".join, axis=1)
)
df_adjg["lac_id"] = (
df_adjg[["AdjgLAC", "AdjgCI"]].astype(str).apply("_".join, axis=1)
)
df_adjg = df_adjg[["ID_WCEL", "lac_id"]]
df_adjg_final = pd.merge(df_adjg, df_wcel_source, on="ID_WCEL", how="left")
df_adjg_final = pd.merge(
df_adjg_final, df_bts_target, on="lac_id", how="left"
).dropna()
df_adjg_final.rename(
columns={"ID_WCEL": "SOURCE_ID", "lac_id": "TARGET_LAC_ID"}, inplace=True
)
# process ADJW DATA
df_adjw = dfs["ADJW"]
df_adjw.columns = df_adjw.columns.str.replace(r"[ ]", "", regex=True)
df_adjw["ID_BTS"] = (
df_adjw[["BSC", "BCF", "BTS"]].astype(str).apply("_".join, axis=1)
)
df_adjw["lac_id"] = df_adjw[["lac", "AdjwCId"]].astype(str).apply("_".join, axis=1)
df_adjw = df_adjw[["ID_BTS", "lac_id"]]
df_adjw_final = pd.merge(df_adjw, df_bts_source, on="ID_BTS", how="left")
df_adjw_final = pd.merge(
df_adjw_final, df_wcel_target, on="lac_id", how="left"
).dropna()
df_adjw_final.rename(
columns={"ID_BTS": "SOURCE_ID", "lac_id": "TARGET_LAC_ID"}, inplace=True
)
# save_dataframe(df_adjw_final, "ADJW")
return [df_adjw_final, df_adjg_final, df_adji_final, df_adjs_final, df_adce_final]
def process_neighbors_data_to_excel(file_path: str):
neighbors_dfs = process_neighbors_data(file_path)
UtilsVars.neighbors_database = convert_dfs(
neighbors_dfs, ["ADJW", "ADJG", "ADJI", "ADJS", "ADCE"]
)
|