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+import sys
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+import pandas as pd
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+import re
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+
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+# Ensure its easier to use for normies
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+if len(sys.argv) < 2:
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+ print("Usage: python rdb-to-sheets.py <input_file> [output_file]")
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+ print("Example: python rdb-to-sheets.py RDBv2.txt RDB.xlsx")
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+ print("If no output file is defined RDB.xlsx will be used per default make sure such doesn't exist already")
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+ sys.exit(1) # Committ da sus side for script!
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+
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+# Set input and output file
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+input_file = sys.argv[1]
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+output_path = sys.argv[2] if len(sys.argv) > 2 else "RDB.xlsx" # Default to RDB.xlsx if user of script doesnt define it lmao
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+
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+
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+with open(input_file, "r") as file:
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+ lines = file.readlines()
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+
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+# Look at the data
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+def parse_rdb_data(lines):
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+ entries = []
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+ current_entry = {}
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+ current_tns = []
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+
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+ for line in lines:
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+ line = line.strip()
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+ if not line:
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+ continue
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+
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+ # New DN entry starts
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+ if line.startswith("DN"):
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+ if current_entry: # How about we save the previous entry before we look at a new one omg
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+ if current_tns:
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+ current_entry["TNs"] = current_tns
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+ entries.append(current_entry)
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+ current_tns = []
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+ current_entry = {"DN": line.split()[1]}
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+
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+ elif "CPND" in line:
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+ current_entry["NAME"] = None
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+
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+ elif "NAME" in line and current_entry.get("NAME") is None:
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+ current_entry["NAME"] = line.replace("NAME", "").strip()
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+
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+ elif line.startswith("TYPE"):
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+ current_entry["TYPE"] = line.split()[1]
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+
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+ elif line.startswith("ROUT"):
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+ current_entry["ROUT"] = line.split()[1]
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+
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+ elif line.startswith("STCD"):
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+ current_entry["STCD"] = line.split()[1]
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+
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+ elif line.startswith("FEAT"):
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+ current_entry["FEAT"] = " ".join(line.split()[1:])
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+
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+ elif line.startswith("TN"):
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+ tn_match = re.search(r"(\d{3} \d \d{2} \d{2})", line)
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+ if tn_match:
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+ tn_value = tn_match.group(1)
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+ tn_entry = {"TN_1_TN": tn_value}
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+
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+ key_match = re.search(r"KEY (\d{2})", line)
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+ if key_match:
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+ tn_entry["KEY"] = key_match.group(1)
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+
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+ des_match = re.search(r"DES\s+(\S+)", line)
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+ if des_match:
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+ tn_entry["DES"] = des_match.group(1)
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+
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+ date_match = re.search(r"(\d{1,2} \w{3} \d{4})", line)
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+ if date_match:
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+ tn_entry["DATE"] = date_match.group(0)
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+
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+ tn_entry["MARP"] = "YES" if "MARP" in line else "NO"
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+ current_tns.append(tn_entry)
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+
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+ if current_entry:
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+ if current_tns:
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+ current_entry["TNs"] = current_tns
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+ entries.append(current_entry)
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+
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+ return entries
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+
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+parsed_entries = parse_rdb_data(lines)
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+
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+# Categorize them and shit
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+def categorize_entries(entries):
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+ main_entries = []
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+ multiple_tns_entries = []
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+ route_entries = []
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+ cdp_entries = []
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+ feature_code_entries = []
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+ att_ldn_entries = []
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+ other_entries = []
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+ unexpected_data = []
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+
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+ for entry in entries:
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+ entry_type = entry.get("TYPE")
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+
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+ if entry_type in ["SL1", "500"]:
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+ if "TNs" in entry and len(entry["TNs"]) > 1:
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+ for tn in entry["TNs"]:
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+ multi_entry = entry.copy()
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+ multi_entry.update(tn)
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+ multiple_tns_entries.append(multi_entry)
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+ elif "TNs" in entry:
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+ for tn in entry["TNs"]:
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+ main_entry = entry.copy()
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+ main_entry.update(tn)
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+ main_entries.append(main_entry)
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+ else:
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+ main_entries.append(entry)
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+
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+ elif entry_type in ["ATT", "LDN"]:
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+ if "TNs" in entry:
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+ for tn in entry["TNs"]:
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+ att_ldn_entry = entry.copy()
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+ att_ldn_entry.update(tn)
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+ att_ldn_entries.append(att_ldn_entry)
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+ else:
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+ att_ldn_entries.append(entry)
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+
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+ elif entry_type == "RDB":
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+ route_entries.append(entry)
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+
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+ elif entry_type == "CDP":
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+ cdp_entries.append({"DN": entry.get("DN", ""), "TYPE": entry.get("TYPE", ""), "STCD": entry.get("STCD", "DSC")})
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+
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+ elif entry_type == "FFC":
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+ feature_code_entries.append(entry)
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+
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+ elif entry_type is None:
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+ unexpected_data.append(entry)
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+
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+ else:
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+ other_entries.append(entry)
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+
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+ unexpected_sheet_name = "No Unexpected Data" if not unexpected_data else "Unexpected Data"
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+
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+ return main_entries, multiple_tns_entries, route_entries, cdp_entries, feature_code_entries, att_ldn_entries, other_entries, unexpected_data, unexpected_sheet_name
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+
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+# Categorize entries further
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+main_entries, multiple_tns_entries, route_entries, cdp_entries, feature_code_entries, att_ldn_entries, other_entries, unexpected_data, unexpected_sheet_name = categorize_entries(parsed_entries)
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+
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+
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+# Save all the data to excel but dropping the raw unprocessed TNs tab that gets made for some reason I dont know why I hate coding and python
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+with pd.ExcelWriter(output_path) as writer:
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+ for sheet_name, data in {
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+ "Main": main_entries,
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+ "Multiple TNs": multiple_tns_entries,
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+ "Routes": route_entries,
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+ "CDP": cdp_entries,
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+ "Feature Codes": feature_code_entries,
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+ "ATT_LDN": att_ldn_entries,
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+ "Other": other_entries,
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+ unexpected_sheet_name: unexpected_data
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+ }.items():
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+ df = pd.DataFrame(data)
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+ if "TNs" in df.columns:
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+ df = df.drop(columns=["TNs"]) # Remove TNs column if present
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+ df.to_excel(writer, sheet_name=sheet_name, index=False)
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+
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+print("Processing complete. Output saved to", output_path)
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Kablersalat
9 kuukautta sitten