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import io
import re
from typing import Optional
import pandas as pd
def _parse_int(value: object) -> Optional[int]:
v = pd.to_numeric(value, errors="coerce")
if pd.isna(v):
return None
return int(v)
def _strip_suffix(value: str, suffix: str) -> str:
if value.endswith(suffix):
return value[: -len(suffix)]
return value
def _base_site_name_from_enb_name(enb_name: object) -> str:
if not isinstance(enb_name, str):
return ""
s = enb_name.strip()
s = _strip_suffix(s, "_4G")
s = _strip_suffix(s, "_4g")
return s
def _band_from_cell_name(cell_name: object) -> str:
if not isinstance(cell_name, str):
return ""
s = cell_name.upper()
for band in ["L800", "L1800", "L2600", "L2300", "L700"]:
if f"_{band}" in s or s.endswith(band):
return band
return ""
def _sector_from_cell_name(cell_name: object) -> Optional[int]:
if not isinstance(cell_name, str):
return None
m = re.search(r"_(\d+)_L\d+\b", cell_name.upper())
if not m:
return None
try:
return int(m.group(1))
except ValueError:
return None
def _dl_mimo_mode_for_band(band: str) -> str:
if band in {"L2600"}:
return "4x4"
return "2x2"
def _ul_earfcn_from_dl(dl_earfcn: int) -> int:
return int(dl_earfcn) + 18000
def _build_output_columns(band_blocks: list[str]) -> list[str]:
cols = [
"Unique Site ID",
"Config",
"Site Name",
"$mrbtsid",
"$lnbtsid",
"$enbname",
"$mcc",
"$mnc",
]
cell_idx = 1
for band_block_idx, _ in enumerate(band_blocks, start=1):
for _slot in range(4):
cols.extend(
[
f"$lncelname{cell_idx}",
f"$Eutracelid{cell_idx}",
f"$pci{cell_idx}",
f"$rsi{cell_idx}",
f"$ltemaxpower{cell_idx}",
]
)
cell_idx += 1
cols.extend(
[
f"$tac{band_block_idx}",
f"$dlMimoMode{band_block_idx}",
f"$ChBw{band_block_idx}",
f"$dlearfcnlte{band_block_idx}",
f"$ulearfcnlte{band_block_idx}",
]
)
return cols
def read_ciq_4g_brut(ciq_file) -> pd.DataFrame:
if hasattr(ciq_file, "seek"):
ciq_file.seek(0)
df = pd.read_excel(ciq_file, engine="calamine")
df.columns = df.columns.astype(str).str.strip()
required = [
"eNodeBName",
"CellName",
"DlEarfcn",
"eNodeB Id",
"Local Cell ID",
"TAC",
"Physical cell ID",
"Root sequence index",
]
missing = [c for c in required if c not in df.columns]
if missing:
raise ValueError(f"CIQ 4G brut is missing required columns: {missing}")
return df
def generate_ciq_4g_sheet(
ciq_df: pd.DataFrame,
year_suffix: str,
bands: str,
mcc: int,
mnc: int,
band_blocks: Optional[list[str]] = None,
ch_bw: int = 20,
lte_max_power: int = 460,
) -> pd.DataFrame:
if band_blocks is None:
band_blocks = ["L800", "L1800", "L2600"]
output_cols = _build_output_columns(band_blocks)
rows_out: list[list[object]] = []
for enb_id_raw, site_rows in ciq_df.groupby("eNodeB Id", dropna=False):
enb_id = _parse_int(enb_id_raw)
if enb_id is None:
continue
enb_name = str(site_rows["eNodeBName"].dropna().iloc[0]).strip()
base_site = _base_site_name_from_enb_name(enb_name)
site_name = f"{base_site}_{year_suffix}_{bands}_NA"
enbname_out = f"{enb_name}_NA"
row_map: dict[str, object] = {
"Unique Site ID": enb_id,
"Config": bands,
"Site Name": site_name,
"$mrbtsid": int(10000 + enb_id),
"$lnbtsid": enb_id,
"$enbname": enbname_out,
"$mcc": int(mcc),
"$mnc": str(int(mnc)).zfill(2),
}
cell_slot_idx = 1
for block_idx, band in enumerate(band_blocks, start=1):
sub = site_rows[
site_rows["CellName"].apply(_band_from_cell_name) == band
].copy()
sub["_sector"] = sub["CellName"].apply(_sector_from_cell_name)
sub = sub.sort_values(by=["_sector", "Local Cell ID"], na_position="last")
for slot in range(4):
if slot < len(sub):
r = sub.iloc[slot]
cell_name = str(r.get("CellName")).strip()
dl_earfcn = _parse_int(r.get("DlEarfcn"))
local_cell_id = _parse_int(r.get("Local Cell ID"))
pci = _parse_int(r.get("Physical cell ID"))
rsi = _parse_int(r.get("Root sequence index"))
row_map[f"$lncelname{cell_slot_idx}"] = f"{cell_name}_NA"
row_map[f"$Eutracelid{cell_slot_idx}"] = local_cell_id
row_map[f"$pci{cell_slot_idx}"] = pci
row_map[f"$rsi{cell_slot_idx}"] = rsi
row_map[f"$ltemaxpower{cell_slot_idx}"] = int(lte_max_power)
cell_slot_idx += 1
if not sub.empty:
tac = _parse_int(sub.iloc[0].get("TAC"))
dl_earfcn = _parse_int(sub.iloc[0].get("DlEarfcn"))
row_map[f"$tac{block_idx}"] = tac
row_map[f"$dlMimoMode{block_idx}"] = _dl_mimo_mode_for_band(band)
row_map[f"$ChBw{block_idx}"] = int(ch_bw)
row_map[f"$dlearfcnlte{block_idx}"] = dl_earfcn
row_map[f"$ulearfcnlte{block_idx}"] = (
_ul_earfcn_from_dl(dl_earfcn) if dl_earfcn is not None else None
)
rows_out.append([row_map.get(c) for c in output_cols])
return pd.DataFrame(rows_out, columns=output_cols)
def generate_ciq_4g_excel(
ciq_file,
year_suffix: str = "25",
bands: str = "G9G18U9U21L8L18L26",
mcc: int = 610,
mnc: int = 2,
) -> tuple[dict[str, pd.DataFrame], bytes]:
df_in = read_ciq_4g_brut(ciq_file)
df_out = generate_ciq_4g_sheet(
df_in,
year_suffix=year_suffix,
bands=bands,
mcc=mcc,
mnc=mnc,
)
sheets: dict[str, pd.DataFrame] = {"CIQ_4G": df_out}
bytes_io = io.BytesIO()
with pd.ExcelWriter(bytes_io, engine="xlsxwriter") as writer:
for sheet_name, df in sheets.items():
df.to_excel(writer, sheet_name=sheet_name, index=False)
return sheets, bytes_io.getvalue()
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