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
|
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
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 stuff
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 dont like 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 for some reason
df.to_excel(writer, sheet_name=sheet_name, index=False)
print("Processing complete. Output saved to", output_path)
|
