File size: 12,762 Bytes
b29ed17 031d4db b29ed17 031d4db b29ed17 031d4db b29ed17 031d4db b29ed17 031d4db 002ceab 031d4db b29ed17 031d4db b29ed17 031d4db |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 |
import pandas as pd
from utils.kpi_analysis_utils import (
analyze_fails_kpi,
cell_availability_analysis,
combine_comments,
create_daily_date,
create_dfs_per_kpi,
kpi_naming_cleaning,
summarize_fails_comments,
)
from utils.utils_vars import get_physical_db
tx_comments_mapping = {
"iub_frameloss exceeded threshold": "iub frameloss",
"iub_frameloss exceeded threshold, hsdpa_congestion_rate_iub exceeded threshold": "iub frameloss and hsdpa iub congestion",
"hsdpa_congestion_rate_iub exceeded threshold": "hsdpa iub congestion",
}
operational_comments_mapping = {
"Down Site": "Down Cell",
"iub frameloss, instability": "Availability and TX issues",
"iub frameloss and hsdpa iub congestion, Availability OK": "TX issues",
"iub frameloss, Availability OK": "TX issues",
"critical instability": "Availability issues",
"iub frameloss, critical instability": "Availability and TX issues",
"iub frameloss and hsdpa iub congestion, instability": "Availability and TX issues",
"Availability OK": "Site OK",
"hsdpa iub congestion, instability": "Availability and TX issues",
"instability": "Availability issues",
"hsdpa iub congestion, Availability OK": "TX issues",
"iub frameloss and hsdpa iub congestion, critical instability": "Availability and TX issues",
"hsdpa iub congestion, critical instability": "Availability and TX issues",
}
fails_comments_mapping = {
"ac, ac_dl, bts, code fails": "Power, Bts and Code fails",
"bts fails": "Bts fails",
"ac, bts, code fails": "Power and Code fails",
"ac, code fails": "Power fails",
"ac fails": "Power fails",
"ac, ac_dl fails": "Power fails",
"ac, bts fails": "Power and Bts fails",
"ac, ac_dl, bts fails": "Power and Bts fails",
"ac, ac_dl, code fails": "Power and Code fails",
"ac, ac_ul, bts, code fails": "Power, Bts and Code fails",
"ac, ac_dl, ac_ul, bts, code fails": "Power, Bts and Code fails",
}
KPI_COLUMNS = [
"WCEL_name",
"date",
"Cell_Availability_excluding_blocked_by_user_state_BLU",
"Total_CS_traffic_Erl",
"HSDPA_TRAFFIC_VOLUME",
"HSDPA_USER_THROUGHPUT",
"Max_simult_HSDPA_users",
"IUB_LOSS_CC_FRAME_LOSS_IND_M1022C71",
"HSDPA_congestion_rate_in_Iub",
"rrc_conn_stp_fail_ac_M1001C3",
"RRC_CONN_STP_FAIL_AC_UL_M1001C731",
"RRC_CONN_STP_FAIL_AC_DL_M1001C732",
"RRC_CONN_STP_FAIL_AC_COD_M1001C733",
"rrc_conn_stp_fail_bts_M1001C4",
]
WCEL_ANALYSIS_COLUMNS = [
"WCEL_name",
"Average_cell_availability_daily",
"number_of_days_exceeding_availability_threshold_daily",
"availability_comment_daily",
"sum_traffic_cs",
"sum_traffic_dl",
"max_dl_throughput",
"avg_dl_throughput",
"max_users",
"max_iub_frameloss",
"number_of_days_with_iub_frameloss_exceeded",
"max_hsdpa_congestion_rate_iub",
"number_of_days_with_hsdpa_congestion_rate_iub_exceeded",
"max_rrc_fail_ac",
"number_of_days_with_rrc_fail_ac_exceeded",
"max_rrc_fail_ac_ul",
"number_of_days_with_rrc_fail_ac_ul_exceeded",
"max_rrc_fail_ac_dl",
"number_of_days_with_rrc_fail_ac_dl_exceeded",
"max_rrc_fail_code",
"number_of_days_with_rrc_fail_code_exceeded",
"max_rrc_fail_bts",
"number_of_days_with_rrc_fail_bts_exceeded",
"tx_congestion_comments",
"operational_comments",
"fails_comments",
"final_comments",
]
class WcelCapacity:
final_results: pd.DataFrame = None
def wcel_kpi_analysis(
df: pd.DataFrame,
num_last_days: int,
num_threshold_days: int,
availability_threshold: int,
iub_frameloss_threshold: int,
hsdpa_congestion_rate_iub_threshold: int,
fails_treshold: int,
) -> pd.DataFrame:
pivoted_kpi_dfs = create_dfs_per_kpi(
df=df,
pivot_date_column="date",
pivot_name_column="WCEL_name",
kpi_columns_from=2,
)
cell_availability_df = cell_availability_analysis(
df=pivoted_kpi_dfs["Cell_Availability_excluding_blocked_by_user_state_BLU"],
days=num_last_days,
availability_threshold=availability_threshold,
)
# Trafics, throughput and max users
trafic_cs_df = pivoted_kpi_dfs["Total_CS_traffic_Erl"]
hsdpa_traffic_df = pivoted_kpi_dfs["HSDPA_TRAFFIC_VOLUME"]
hsdpa_user_throughput_df = pivoted_kpi_dfs["HSDPA_USER_THROUGHPUT"]
max_simult_hsdpa_users_df = pivoted_kpi_dfs["Max_simult_HSDPA_users"]
# Add Max of Trafics, throughput and max users
trafic_cs_df["sum_traffic_cs"] = trafic_cs_df.sum(axis=1)
hsdpa_traffic_df["sum_traffic_dl"] = hsdpa_traffic_df.sum(axis=1)
hsdpa_user_throughput_df["max_dl_throughput"] = hsdpa_user_throughput_df.max(axis=1)
max_simult_hsdpa_users_df["max_users"] = max_simult_hsdpa_users_df.max(axis=1)
# add average of Trafics, throughput and max users
hsdpa_user_throughput_df["avg_dl_throughput"] = hsdpa_user_throughput_df.mean(
axis=1
)
max_simult_hsdpa_users_df["avg_users"] = max_simult_hsdpa_users_df.mean(axis=1)
# TX Congestion
iub_frameloss_df = pivoted_kpi_dfs["IUB_LOSS_CC_FRAME_LOSS_IND_M1022C71"]
hsdpa_congestion_rate_iub_df = pivoted_kpi_dfs["HSDPA_congestion_rate_in_Iub"]
iub_frameloss_df = analyze_fails_kpi(
df=iub_frameloss_df,
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=iub_frameloss_threshold,
kpi_column_name="iub_frameloss",
)
hsdpa_congestion_rate_iub_df = analyze_fails_kpi(
df=hsdpa_congestion_rate_iub_df,
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=hsdpa_congestion_rate_iub_threshold,
kpi_column_name="hsdpa_congestion_rate_iub",
)
# Fails
rrc_conn_stp_fail_ac_df = analyze_fails_kpi(
df=pivoted_kpi_dfs["rrc_conn_stp_fail_ac_M1001C3"],
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=fails_treshold,
kpi_column_name="rrc_fail_ac",
)
rrc_conn_stp_fail_ac_ul_df = analyze_fails_kpi(
df=pivoted_kpi_dfs["RRC_CONN_STP_FAIL_AC_UL_M1001C731"],
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=fails_treshold,
kpi_column_name="rrc_fail_ac_ul",
)
rrc_conn_stp_fail_ac_dl_df = analyze_fails_kpi(
df=pivoted_kpi_dfs["RRC_CONN_STP_FAIL_AC_DL_M1001C732"],
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=fails_treshold,
kpi_column_name="rrc_fail_ac_dl",
)
rrc_conn_stp_fail_ac_cod_df = analyze_fails_kpi(
df=pivoted_kpi_dfs["RRC_CONN_STP_FAIL_AC_COD_M1001C733"],
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=fails_treshold,
kpi_column_name="rrc_fail_code",
)
rrc_conn_stp_fail_bts_df = analyze_fails_kpi(
df=pivoted_kpi_dfs["rrc_conn_stp_fail_bts_M1001C4"],
number_of_kpi_days=num_last_days,
number_of_threshold_days=num_threshold_days,
kpi_threshold=fails_treshold,
kpi_column_name="rrc_fail_bts",
)
kpi_df = pd.concat(
[
cell_availability_df,
trafic_cs_df,
hsdpa_traffic_df,
hsdpa_user_throughput_df,
max_simult_hsdpa_users_df,
iub_frameloss_df,
hsdpa_congestion_rate_iub_df,
rrc_conn_stp_fail_ac_df,
rrc_conn_stp_fail_ac_ul_df,
rrc_conn_stp_fail_ac_dl_df,
rrc_conn_stp_fail_ac_cod_df,
rrc_conn_stp_fail_bts_df,
],
axis=1,
)
kpi_df = kpi_df.reset_index()
kpi_df = combine_comments(
kpi_df,
"iub_frameloss_comment",
"hsdpa_congestion_rate_iub_comment",
new_column="tx_congestion_comments",
)
kpi_df["tx_congestion_comments"] = kpi_df["tx_congestion_comments"].apply(
lambda x: tx_comments_mapping.get(x, x)
)
kpi_df = combine_comments(
kpi_df,
"tx_congestion_comments",
"availability_comment_daily",
new_column="operational_comments",
)
kpi_df["operational_comments"] = kpi_df["operational_comments"].apply(
lambda x: operational_comments_mapping.get(x, x)
)
kpi_df = combine_comments(
kpi_df,
"rrc_fail_ac_comment",
"rrc_fail_ac_ul_comment",
"rrc_fail_ac_dl_comment",
"rrc_fail_code_comment",
"rrc_fail_bts_comment",
new_column="fails_comments",
)
kpi_df["fails_comments"] = kpi_df["fails_comments"].apply(summarize_fails_comments)
kpi_df["fails_comments"] = kpi_df["fails_comments"].apply(
lambda x: fails_comments_mapping.get(x, x)
)
kpi_df = combine_comments(
kpi_df,
"operational_comments",
"fails_comments",
new_column="final_comments",
)
wcel_analysis_df = kpi_df[WCEL_ANALYSIS_COLUMNS]
wcel_analysis_df = wcel_analysis_df.droplevel(level=1, axis=1)
# Rename
wcel_analysis_df = wcel_analysis_df.rename(
columns={
"WCEL_name": "name",
"Average_cell_availability_daily": "Avg_availability",
"number_of_days_exceeding_availability_threshold_daily": "Avail_exceed_days",
"availability_comment_daily": "availability_comment",
"number_of_days_with_iub_frameloss_exceeded": "iub_frameloss_exceed_days",
"number_of_days_with_hsdpa_congestion_rate_iub_exceeded": "hsdpa_iub_exceed_days",
"number_of_days_with_rrc_fail_ac_exceeded": "ac_fail_exceed_days",
"number_of_days_with_rrc_fail_ac_ul_exceeded": "ac_ul_fail_exceed_days",
"number_of_days_with_rrc_fail_ac_dl_exceeded": "ac_dl_fail_exceed_days",
"number_of_days_with_rrc_fail_code_exceeded": "code_fail_exceed_days",
"number_of_days_with_rrc_fail_bts_exceeded": "bts_fail_exceed_days",
}
)
# remove row if name less than 5 characters
wcel_analysis_df = wcel_analysis_df[wcel_analysis_df["name"].str.len() >= 5]
wcel_analysis_df["code"] = wcel_analysis_df["name"].str.split("_").str[0]
wcel_analysis_df["code"] = (
pd.to_numeric(wcel_analysis_df["code"], errors="coerce").fillna(0).astype(int)
)
wcel_analysis_df["Region"] = wcel_analysis_df["name"].str.split("_").str[1]
# move code to the first column
wcel_analysis_df = wcel_analysis_df[
["code", "Region"]
+ [col for col in wcel_analysis_df if col != "code" and col != "Region"]
]
# Load physical database
physical_db: pd.DataFrame = get_physical_db()
# Convert code_sector to code
physical_db["code"] = physical_db["Code_Sector"].str.split("_").str[0]
# remove duplicates
physical_db = physical_db.drop_duplicates(subset="code")
# keep only code and longitude and latitude
physical_db = physical_db[["code", "Longitude", "Latitude", "City"]]
physical_db["code"] = (
pd.to_numeric(physical_db["code"], errors="coerce").fillna(0).astype(int)
)
wcel_analysis_df = pd.merge(
wcel_analysis_df,
physical_db,
on="code",
how="left",
)
return [wcel_analysis_df, kpi_df]
def load_and_process_wcel_capacity_data(
uploaded_file: pd.DataFrame,
num_last_days: int,
num_threshold_days: int,
availability_threshold: int,
iub_frameloss_threshold: int,
hsdpa_congestion_rate_iub_threshold: int,
fails_treshold: int,
) -> pd.DataFrame:
"""
Load and process data for WCEL capacity analysis.
Args:
uploaded_file: Uploaded CSV file containing WCEL capacity data
num_last_days: Number of days for analysis
num_threshold_days: Minimum days above threshold to flag for upgrade
availability_threshold: Utilization threshold percentage for flagging
iub_frameloss_threshold: Utilization threshold percentage for flagging
hsdpa_congestion_rate_iub_threshold: Utilization threshold percentage for flagging
fails_treshold: Utilization threshold percentage for flagging
Returns:
Processed DataFrame with WCEL capacity analysis results
"""
# Load data
df = pd.read_csv(uploaded_file, delimiter=";")
df = kpi_naming_cleaning(df)
df = create_daily_date(df)
df = df[KPI_COLUMNS]
dfs = wcel_kpi_analysis(
df,
num_last_days,
num_threshold_days,
availability_threshold,
iub_frameloss_threshold,
hsdpa_congestion_rate_iub_threshold,
fails_treshold,
)
return dfs
|