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import numpy as np
import pandas as pd
from queries.process_lte import process_lte_data
from utils.convert_to_excel import save_dataframe
from utils.kpi_analysis_utils import (
LteCapacity,
analyze_prb_usage,
cell_availability_analysis,
create_dfs_per_kpi,
create_hourly_date,
kpi_naming_cleaning,
)
LTE_ANALYSIS_COLUMNS = [
"code",
"code_sector",
"Region",
"site_config_band",
"Longitude",
"Latitude",
"LNCEL_name_l800",
"LNCEL_name_l1800",
"LNCEL_name_l2300",
"LNCEL_name_l2600",
"LNCEL_name_l1800s",
"avg_prb_usage_bh_l800",
"avg_prb_usage_bh_l1800",
"avg_prb_usage_bh_l2300",
"avg_prb_usage_bh_l2600",
"avg_prb_usage_bh_l1800s",
"avg_prb_usage_bh_l800_2nd",
"avg_prb_usage_bh_l1800_2nd",
"avg_prb_usage_bh_l2300_2nd",
"avg_prb_usage_bh_l2600_2nd",
"avg_prb_usage_bh_l1800s_2nd",
"avg_act_ues_l800",
"avg_act_ues_l1800",
"avg_act_ues_l2300",
"avg_act_ues_l2600",
"avg_act_ues_l1800s",
"avg_dl_thp_l800",
"avg_dl_thp_l1800",
"avg_dl_thp_l2300",
"avg_dl_thp_l2600",
"avg_dl_thp_l1800s",
"avg_ul_thp_l800",
"avg_ul_thp_l1800",
"avg_ul_thp_l2300",
"avg_ul_thp_l2600",
"avg_ul_thp_l1800s",
"num_congested_cells",
"num_cells",
"num_cell_with_kpi",
"num_down_or_no_kpi_cells",
"prb_diff_between_cells",
"load_balance_required",
"congestion_comment",
"final_comments",
]
LTE_DATABASE_COLUMNS = [
"code",
"Region",
"site_config_band",
"final_name",
"Longitude",
"Latitude",
]
KPI_COLUMNS = [
"date",
"LNCEL_name",
"Cell_Avail_excl_BLU",
"E_UTRAN_Avg_PRB_usage_per_TTI_DL",
"DL_PRB_Util_p_TTI_Lev_10",
"Avg_PDCP_cell_thp_UL",
"Avg_PDCP_cell_thp_DL",
"Avg_act_UEs_DL",
]
PRB_COLUMNS = [
"LNCEL_name",
"avg_prb_usage_bh",
"avg_prb_usage_bh_2nd",
"avg_act_ues",
"avg_dl_thp",
"avg_ul_thp",
]
def lte_analysis_logic(
df: pd.DataFrame,
prb_usage_threshold: int,
prb_diff_between_cells_threshold: int,
) -> pd.DataFrame:
lte_analysis_logic_df = df.copy()
lte_analysis_logic_df["num_congested_cells"] = (
lte_analysis_logic_df[
[
"avg_prb_usage_bh_l800",
"avg_prb_usage_bh_l1800",
"avg_prb_usage_bh_l2300",
"avg_prb_usage_bh_l2600",
"avg_prb_usage_bh_l1800s",
]
]
>= prb_usage_threshold
).sum(axis=1)
# Add Number of cells LNCEL_name_l800 LNCEL_name_l1800 LNCEL_name_l2300 LNCEL_name_l2600 LNCEL_name_l1800s
lte_analysis_logic_df["num_cells"] = lte_analysis_logic_df[
[
"LNCEL_name_l800",
"LNCEL_name_l1800",
"LNCEL_name_l2300",
"LNCEL_name_l2600",
"LNCEL_name_l1800s",
]
].count(axis=1)
# Add Number of cell with KPI
lte_analysis_logic_df["num_cell_with_kpi"] = lte_analysis_logic_df[
[
"avg_prb_usage_bh_l800",
"avg_prb_usage_bh_l1800",
"avg_prb_usage_bh_l2300",
"avg_prb_usage_bh_l2600",
"avg_prb_usage_bh_l1800s",
]
].count(axis=1)
# Number of Down or No KPI cells = num_cells -num_cell_with_kpi
lte_analysis_logic_df["num_down_or_no_kpi_cells"] = (
lte_analysis_logic_df["num_cells"] - lte_analysis_logic_df["num_cell_with_kpi"]
)
# Check Max difference between avg_prb_usage_bh_l800 avg_prb_usage_bh_l1800 avg_prb_usage_bh_l2300 avg_prb_usage_bh_l2600 avg_prb_usage_bh_l1800s
lte_analysis_logic_df["prb_diff_between_cells"] = lte_analysis_logic_df[
[
"avg_prb_usage_bh_l800",
"avg_prb_usage_bh_l1800",
"avg_prb_usage_bh_l2300",
"avg_prb_usage_bh_l2600",
"avg_prb_usage_bh_l1800s",
]
].apply(lambda row: max(row) - min(row), axis=1)
# Add Load balance required column = Yes if prb_diff_between_cells > prb_diff_between_cells_threshold else No
lte_analysis_logic_df["load_balance_required"] = lte_analysis_logic_df[
"prb_diff_between_cells"
].apply(lambda x: "Yes" if x > prb_diff_between_cells_threshold else "No")
# Add Next band column
lte_analysis_logic_df["next_band"] = lte_analysis_logic_df["site_config_band"].map(
LteCapacity.next_band_mapping
)
# Add congestion comments
# if num_congested_cells == 0 and num_down_or_no_kpi_cells == 0 = " No Congestion"
# if num_congested_cells == 0 and num_down_or_no_kpi_cells > 0 = "No congestion but Down cell"
# if num_congested_cells > 0 and num_down_or_no_kpi_cells > 0 = "Congestion but Colocated Down Cell"
# Else Need Action
conditions = [
(lte_analysis_logic_df["num_congested_cells"] == 0)
& (lte_analysis_logic_df["num_down_or_no_kpi_cells"] == 0),
(lte_analysis_logic_df["num_congested_cells"] == 0)
& (lte_analysis_logic_df["num_down_or_no_kpi_cells"] > 0),
(lte_analysis_logic_df["num_congested_cells"] > 0)
& (lte_analysis_logic_df["num_down_or_no_kpi_cells"] > 0),
]
choices = [
"No Congestion",
"No congestion but Down cell",
"Congestion but Colocated Down Cell",
]
lte_analysis_logic_df["congestion_comment"] = np.select(
conditions, choices, default="Need Action"
)
# Add "Actions" column
# if load_balance_required = "Yes" and congestion_comment = "Need Action" then "Load Balancing parameter tuning required"
# if load_balance_required = "Yes" and congestion_comment = "Need Action" then "Add Layer"
# Else keep congestion_comment
conditions = [
(lte_analysis_logic_df["load_balance_required"] == "Yes")
& (lte_analysis_logic_df["congestion_comment"] == "Need Action"),
(lte_analysis_logic_df["load_balance_required"] == "No")
& (lte_analysis_logic_df["congestion_comment"] == "Need Action"),
]
choices = [
"Load Balancing parameter tuning required",
"Add Layer",
]
lte_analysis_logic_df["actions"] = np.select(
conditions, choices, default=lte_analysis_logic_df["congestion_comment"]
)
# Add Final Comments
# if "actions" = "Add Layer" then "'Add' + 'next_band''
# Else keep "actions" as it is
lte_analysis_logic_df["final_comments"] = lte_analysis_logic_df.apply(
lambda row: (
f"Add {row['next_band']}"
if row["actions"] == "Add Layer"
else row["actions"]
),
axis=1,
)
# create column "sector" equal to conteent of "LNCEL_name_l800" if not empty else "LNCEL_name_l1800" if not empty else "LNCEL_name_l2300"
lte_analysis_logic_df["sector"] = (
lte_analysis_logic_df["LNCEL_name_l800"]
.combine_first(lte_analysis_logic_df["LNCEL_name_l1800"])
.combine_first(lte_analysis_logic_df["LNCEL_name_l2300"])
.combine_first(lte_analysis_logic_df["LNCEL_name_l2600"])
.combine_first(lte_analysis_logic_df["LNCEL_name_l1800s"])
)
# remove rows where sector is empty
lte_analysis_logic_df = lte_analysis_logic_df[
lte_analysis_logic_df["sector"].notna()
]
# Add sector_id column if sector contains : '_1_" then 1 elif sector contains : '_2_" then 2 elif sector contains : '_3_" then 3
lte_analysis_logic_df["sector_id"] = np.where(
lte_analysis_logic_df["sector"].str.contains("_1_"),
1,
np.where(
lte_analysis_logic_df["sector"].str.contains("_2_"),
2,
np.where(lte_analysis_logic_df["sector"].str.contains("_3_"), 3, np.nan),
),
)
# add code_sector column by combine code and sector_id
lte_analysis_logic_df["code_sector"] = (
lte_analysis_logic_df["code"].astype(str)
+ "_"
+ lte_analysis_logic_df["sector_id"].astype(str)
)
# remove '.0' from code_sector
lte_analysis_logic_df["code_sector"] = lte_analysis_logic_df[
"code_sector"
].str.replace(".0", "")
# lte_analysis_logic_df = lte_analysis_logic_df[LTE_ANALYSIS_COLUMNS]
return lte_analysis_logic_df
def dfs_per_band_cell(df: pd.DataFrame) -> pd.DataFrame:
# Base DataFrame with unique codes, Region, and site_config_band
all_codes_df = df[
["code", "Region", "site_config_band", "Longitude", "Latitude"]
].drop_duplicates()
# Configuration for sector groups and their respective LNCEL patterns and column suffixes
# Format: { "group_key": [(lncel_name_pattern_part, column_suffix), ...] }
# lncel_name_pattern_part will be combined with "_<group_key>" or similar
# Example: for group "1", pattern "_1_L800" gives suffix "l800"
sector_groups_config = {
"1": [
("_1_L800", "l800"),
("_1_L1800", "l1800"),
("_1_L2300", "l2300"),
("_1_L2600", "l2600"),
("_1S_L1800", "l1800s"),
],
"2": [
("_2_L800", "l800"),
("_2_L1800", "l1800"),
("_2_L2300", "l2300"),
("_2_L2600", "l2600"),
("_2S_L1800", "l1800s"),
],
"3": [
("_3_L800", "l800"),
("_3_L1800", "l1800"),
("_3_L2300", "l2300"),
("_3_L2600", "l2600"),
("_3S_L1800", "l1800s"),
],
}
all_processed_sectors_dfs = []
for sector_group_key, band_configurations in sector_groups_config.items():
# Start with the base DataFrame for the current sector group
current_sector_group_df = all_codes_df.copy()
for lncel_name_pattern, column_suffix in band_configurations:
# Filter the original DataFrame for the current LNCEL pattern
# The pattern assumes LNCEL_name contains something like "SITENAME<lncel_name_pattern>"
filtered_band_df = df[df["LNCEL_name"].str.contains(lncel_name_pattern)]
# Select relevant columns and rename them for the merge
# This avoids pandas automatically adding _x, _y suffixes and then needing to rename them
df_to_merge = filtered_band_df[
[
"code",
"LNCEL_name",
"avg_prb_usage_bh",
"avg_prb_usage_bh_2nd",
"avg_act_ues",
"avg_dl_thp",
"avg_ul_thp",
]
].rename(
columns={
"LNCEL_name": f"LNCEL_name_{column_suffix}",
"avg_prb_usage_bh": f"avg_prb_usage_bh_{column_suffix}",
"avg_prb_usage_bh_2nd": f"avg_prb_usage_bh_{column_suffix}_2nd",
"avg_act_ues": f"avg_act_ues_{column_suffix}",
"avg_dl_thp": f"avg_dl_thp_{column_suffix}",
"avg_ul_thp": f"avg_ul_thp_{column_suffix}",
}
)
# Perform a left merge
current_sector_group_df = pd.merge(
current_sector_group_df, df_to_merge, on="code", how="left"
)
all_processed_sectors_dfs.append(current_sector_group_df)
# Concatenate all the processed sector DataFrames
all_sectors_dfs = pd.concat(all_processed_sectors_dfs, axis=0, ignore_index=True)
# save_dataframe(all_sectors_dfs, "all_sectors_dfs.csv")
return all_sectors_dfs
def lte_database_for_capacity(dump_path: str):
dfs = process_lte_data(dump_path)
lte_fdd = dfs[0]
lte_tdd = dfs[1]
lte_fdd = lte_fdd[LTE_DATABASE_COLUMNS]
lte_tdd = lte_tdd[LTE_DATABASE_COLUMNS]
lte_db = pd.concat([lte_fdd, lte_tdd], axis=0)
# rename final_name to LNCEL_name
lte_db = lte_db.rename(columns={"final_name": "LNCEL_name"})
# save_dataframe(lte_db, "LTE_Database.csv")
return lte_db
def lte_bh_dfs_per_kpi(
dump_path: str,
df: pd.DataFrame,
number_of_kpi_days: int = 7,
availability_threshold: int = 95,
prb_usage_threshold: int = 80,
prb_diff_between_cells_threshold: int = 20,
number_of_threshold_days: int = 3,
main_prb_to_use: str = "",
) -> pd.DataFrame:
# print(df.columns)
pivoted_kpi_dfs = create_dfs_per_kpi(
df=df,
pivot_date_column="date",
pivot_name_column="LNCEL_name",
kpi_columns_from=2,
)
cell_availability_df = cell_availability_analysis(
df=pivoted_kpi_dfs["Cell_Avail_excl_BLU"],
days=number_of_kpi_days,
availability_threshold=availability_threshold,
)
prb_usage_df = analyze_prb_usage(
df=pivoted_kpi_dfs["E_UTRAN_Avg_PRB_usage_per_TTI_DL"],
number_of_kpi_days=number_of_kpi_days,
prb_usage_threshold=prb_usage_threshold,
analysis_type="BH",
number_of_threshold_days=number_of_threshold_days,
suffix="" if main_prb_to_use == "E-UTRAN Avg PRB usage per TTI DL" else "_2nd",
)
prb_lev10_usage_df = analyze_prb_usage(
df=pivoted_kpi_dfs["DL_PRB_Util_p_TTI_Lev_10"],
number_of_kpi_days=number_of_kpi_days,
prb_usage_threshold=prb_usage_threshold,
analysis_type="BH",
number_of_threshold_days=number_of_threshold_days,
suffix="" if main_prb_to_use == "DL PRB Util p TTI Lev_10" else "_2nd",
)
act_ues_df = pivoted_kpi_dfs["Avg_act_UEs_DL"]
# Add Max and avg columns for act_ues_df
act_ues_df["max_act_ues"] = act_ues_df.max(axis=1)
act_ues_df["avg_act_ues"] = act_ues_df.mean(axis=1)
dl_thp_df = pivoted_kpi_dfs["Avg_PDCP_cell_thp_DL"]
# Add Max and avg columns for dl_thp_df
dl_thp_df["max_dl_thp"] = dl_thp_df.max(axis=1)
dl_thp_df["avg_dl_thp"] = dl_thp_df.mean(axis=1)
ul_thp_df = pivoted_kpi_dfs["Avg_PDCP_cell_thp_UL"]
# Add Max and avg columns for ul_thp_df
ul_thp_df["max_ul_thp"] = ul_thp_df.max(axis=1)
ul_thp_df["avg_ul_thp"] = ul_thp_df.mean(axis=1)
bh_kpi_df = pd.concat(
[
cell_availability_df,
prb_lev10_usage_df,
prb_usage_df,
act_ues_df,
dl_thp_df,
ul_thp_df,
],
axis=1,
)
bh_kpi_df = bh_kpi_df.reset_index()
prb_df = bh_kpi_df[PRB_COLUMNS]
# drop row if lnCEL_name is empty or 1
prb_df = prb_df[prb_df["LNCEL_name"].str.len() > 3]
# prb_df = prb_df.reset_index()
prb_df = prb_df.droplevel(level=1, axis=1) # Drop the first level (date)
# prb_df = prb_df.reset_index()
# prb_df["code"] = prb_df["LNCEL_name"].str.split("_").str[0]
lte_db = lte_database_for_capacity(dump_path)
db_and_prb = pd.merge(lte_db, prb_df, on="LNCEL_name", how="left")
# if avg_prb_usage_bh is "" then set it to "cell exists in dump but not in BH report"
# db_and_prb.loc[db_and_prb["avg_prb_usage_bh"].isnull(), "avg_prb_usage_bh"] = (
# "cell exists in dump but not in BH report"
# )
# drop row if lnCEL_name is empty or 1
db_and_prb = db_and_prb[db_and_prb["LNCEL_name"].str.len() > 3]
lte_analysis_df = dfs_per_band_cell(db_and_prb)
lte_analysis_df = lte_analysis_logic(
lte_analysis_df,
prb_usage_threshold,
prb_diff_between_cells_threshold,
)
lte_analysis_df = lte_analysis_df[LTE_ANALYSIS_COLUMNS]
# Rename columns
lte_analysis_df = lte_analysis_df.rename(
columns={
"LNCEL_name_l800": "name_l800",
"LNCEL_name_l1800": "name_l1800",
"LNCEL_name_l2300": "name_l2300",
"LNCEL_name_l2600": "name_l2600",
"LNCEL_name_l1800s": "name_l1800s",
"avg_prb_usage_bh_l800": "prb_l800",
"avg_prb_usage_bh_l1800": "prb_l1800",
"avg_prb_usage_bh_l2300": "prb_l2300",
"avg_prb_usage_bh_l2600": "prb_l2600",
"avg_prb_usage_bh_l1800s": "prb_l1800s",
"avg_prb_usage_bh_l800_2nd": "prb_l800_2nd",
"avg_prb_usage_bh_l1800_2nd": "prb_l1800_2nd",
"avg_prb_usage_bh_l2300_2nd": "prb_l2300_2nd",
"avg_prb_usage_bh_l2600_2nd": "prb_l2600_2nd",
"avg_prb_usage_bh_l1800s_2nd": "prb_l1800s_2nd",
"avg_act_ues_l800": "act_ues_l800",
"avg_act_ues_l1800": "act_ues_l1800",
"avg_act_ues_l2300": "act_ues_l2300",
"avg_act_ues_l2600": "act_ues_l2600",
"avg_act_ues_l1800s": "act_ues_l1800s",
"avg_dl_thp_l800": "dl_thp_l800",
"avg_dl_thp_l1800": "dl_thp_l1800",
"avg_dl_thp_l2300": "dl_thp_l2300",
"avg_dl_thp_l2600": "dl_thp_l2600",
"avg_dl_thp_l1800s": "dl_thp_l1800s",
"avg_ul_thp_l800": "ul_thp_l800",
"avg_ul_thp_l1800": "ul_thp_l1800",
"avg_ul_thp_l2300": "ul_thp_l2300",
"avg_ul_thp_l2600": "ul_thp_l2600",
"avg_ul_thp_l1800s": "ul_thp_l1800s",
}
)
return [bh_kpi_df, lte_analysis_df]
def process_lte_bh_report(
dump_path: str,
bh_report_path: str,
num_last_days: int,
num_threshold_days: int,
availability_threshold: float,
prb_usage_threshold: float,
prb_diff_between_cells_threshold: float,
main_prb_to_use: str,
) -> dict:
"""
Process LTE Busy Hour report and perform capacity analysis
Args:
bh_report_path: Path to BH report CSV file
num_last_days: Number of last days for analysis
num_threshold_days: Number of days for threshold calculation
availability_threshold: Minimum required availability
prb_usage_threshold: Maximum allowed PRB usage
prb_diff_between_cells_threshold: Maximum allowed PRB usage difference between cells
Returns:
Dictionary containing analysis results and DataFrames
"""
LteCapacity.final_results = None
# lte_db_dfs = lte_database_for_capacity(dump_path)
# Read BH report
df = pd.read_csv(bh_report_path, delimiter=";")
df = kpi_naming_cleaning(df)
# print(df.columns)
df = create_hourly_date(df)
df = df[KPI_COLUMNS]
pivoted_kpi_dfs = lte_bh_dfs_per_kpi(
dump_path=dump_path,
df=df,
number_of_kpi_days=num_last_days,
availability_threshold=availability_threshold,
prb_usage_threshold=prb_usage_threshold,
prb_diff_between_cells_threshold=prb_diff_between_cells_threshold,
number_of_threshold_days=num_threshold_days,
main_prb_to_use=main_prb_to_use,
)
# save_dataframe(pivoted_kpi_dfs, "LTE_BH_Report.csv")
return pivoted_kpi_dfs
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