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| -rw-r--r-- | README.md | 16 | ||||
| -rw-r--r-- | rdb-to-sheets.py | 165 | ||||
| -rw-r--r-- | requirements.txt | 8 | ||||
| -rw-r--r-- | setup.sh | 5 |
4 files changed, 194 insertions, 0 deletions
diff --git a/README.md b/README.md new file mode 100644 index 0000000..3e14c6a --- /dev/null +++ b/README.md @@ -0,0 +1,16 @@ +# rdb-to-sheets +A very simple python script to convert the TXT output from RDB to an Excel Sheet + +## How to use +1. On Meridian 1 do : LD 21; REQ PRT; TYPE RDB; +1. Copy the output from the first DN up to the last DN listed (see example below) +1. Save to somewhere as a raw txt +1. Use Bash script to set up Python3 VENV, or install requirements.txt things with pip on your system however you please. +1. Run : python3 ./rdb-to-sheets.py /your/rdb/file.txt +1. Enjoy your now converted rdb excel sheet (made in same directory as rdb-to-sheets.py by default, can also be anywhere you desire python3 like this ./rdb-to-sheets.py /your/rdb/file.txt /your/output/rdb/sheet.xslx) + +## Consideration +1. I know little coding and even less python, I've tested it on my Meridian 1 Option 11e only as thats the only system I own. +1. If you have suggestions on how to improve this feel free to submit those here. +1. If I made something completely wrong in the script its not my responsibility. +1. Have fun ! diff --git a/rdb-to-sheets.py b/rdb-to-sheets.py new file mode 100644 index 0000000..b715284 --- /dev/null +++ b/rdb-to-sheets.py @@ -0,0 +1,165 @@ +import sys +import pandas as pd +import re + +# Ensure its easier to use for normies +if len(sys.argv) < 2: + print("Usage: python rdb-to-sheets.py <input_file> [output_file]") + print("Example: python rdb-to-sheets.py RDBv2.txt RDB.xlsx") + print("If no output file is defined RDB.xlsx will be used per default make sure such doesn't exist already") + sys.exit(1) # Committ da sus side for script! + +# Set input and output file +input_file = sys.argv[1] +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 + + +with open(input_file, "r") as file: + lines = file.readlines() + +# Look at the data +def parse_rdb_data(lines): + entries = [] + current_entry = {} + current_tns = [] + + for line in lines: + line = line.strip() + if not line: + continue + + # New DN entry starts + if line.startswith("DN"): + if current_entry: # How about we save the previous entry before we look at a new one omg + if current_tns: + current_entry["TNs"] = current_tns + entries.append(current_entry) + current_tns = [] + current_entry = {"DN": line.split()[1]} + + elif "CPND" in line: + current_entry["NAME"] = None + + elif "NAME" in line and current_entry.get("NAME") is None: + current_entry["NAME"] = line.replace("NAME", "").strip() + + elif line.startswith("TYPE"): + current_entry["TYPE"] = line.split()[1] + + elif line.startswith("ROUT"): + current_entry["ROUT"] = line.split()[1] + + elif line.startswith("STCD"): + current_entry["STCD"] = line.split()[1] + + elif line.startswith("FEAT"): + current_entry["FEAT"] = " ".join(line.split()[1:]) + + elif line.startswith("TN"): + tn_match = re.search(r"(\d{3} \d \d{2} \d{2})", line) + if tn_match: + tn_value = tn_match.group(1) + tn_entry = {"TN_1_TN": tn_value} + + key_match = re.search(r"KEY (\d{2})", line) + if key_match: + tn_entry["KEY"] = key_match.group(1) + + des_match = re.search(r"DES\s+(\S+)", line) + if des_match: + tn_entry["DES"] = des_match.group(1) + + date_match = re.search(r"(\d{1,2} \w{3} \d{4})", line) + if date_match: + tn_entry["DATE"] = date_match.group(0) + + tn_entry["MARP"] = "YES" if "MARP" in line else "NO" + current_tns.append(tn_entry) + + if current_entry: + if current_tns: + current_entry["TNs"] = current_tns + entries.append(current_entry) + + return entries + +parsed_entries = parse_rdb_data(lines) + +# Categorize them and shit +def categorize_entries(entries): + main_entries = [] + multiple_tns_entries = [] + route_entries = [] + cdp_entries = [] + feature_code_entries = [] + att_ldn_entries = [] + other_entries = [] + unexpected_data = [] + + for entry in entries: + entry_type = entry.get("TYPE") + + if entry_type in ["SL1", "500"]: + if "TNs" in entry and len(entry["TNs"]) > 1: + for tn in entry["TNs"]: + multi_entry = entry.copy() + multi_entry.update(tn) + multiple_tns_entries.append(multi_entry) + elif "TNs" in entry: + for tn in entry["TNs"]: + main_entry = entry.copy() + main_entry.update(tn) + main_entries.append(main_entry) + else: + main_entries.append(entry) + + elif entry_type in ["ATT", "LDN"]: + if "TNs" in entry: + for tn in entry["TNs"]: + att_ldn_entry = entry.copy() + att_ldn_entry.update(tn) + att_ldn_entries.append(att_ldn_entry) + else: + att_ldn_entries.append(entry) + + elif entry_type == "RDB": + route_entries.append(entry) + + elif entry_type == "CDP": + cdp_entries.append({"DN": entry.get("DN", ""), "TYPE": entry.get("TYPE", ""), "STCD": entry.get("STCD", "DSC")}) + + elif entry_type == "FFC": + feature_code_entries.append(entry) + + elif entry_type is None: + unexpected_data.append(entry) + + else: + other_entries.append(entry) + + unexpected_sheet_name = "No Unexpected Data" if not unexpected_data else "Unexpected Data" + + return main_entries, multiple_tns_entries, route_entries, cdp_entries, feature_code_entries, att_ldn_entries, other_entries, unexpected_data, unexpected_sheet_name + +# Categorize entries further +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) + + +# 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 +with pd.ExcelWriter(output_path) as writer: + for sheet_name, data in { + "Main": main_entries, + "Multiple TNs": multiple_tns_entries, + "Routes": route_entries, + "CDP": cdp_entries, + "Feature Codes": feature_code_entries, + "ATT_LDN": att_ldn_entries, + "Other": other_entries, + unexpected_sheet_name: unexpected_data + }.items(): + df = pd.DataFrame(data) + if "TNs" in df.columns: + df = df.drop(columns=["TNs"]) # Remove TNs column if present + df.to_excel(writer, sheet_name=sheet_name, index=False) + +print("Processing complete. Output saved to", output_path) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..358d115 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,8 @@ +et_xmlfile==2.0.0 +numpy==2.2.2 +openpyxl==3.1.5 +pandas==2.2.3 +python-dateutil==2.9.0.post0 +pytz==2025.1 +six==1.17.0 +tzdata==2025.1 diff --git a/setup.sh b/setup.sh new file mode 100644 index 0000000..26fc88f --- /dev/null +++ b/setup.sh @@ -0,0 +1,5 @@ +#!/bin/bash +python3 -m venv .venv +source .venv/bin/activate +pip3 install -r requirements.txt +echo "venv made and activated. requirements should be installed too now" |