Support conversion AND logic rules

main.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import re
 import concurrent.futures
 import os
 import json
@@ -6,20 +7,49 @@ import requests
 import yaml
 import ipaddress
 
+# 映射字典
+MAP_DICT = {'DOMAIN-SUFFIX': 'domain_suffix', 'HOST-SUFFIX': 'domain_suffix', 'DOMAIN': 'domain', 'HOST': 'domain', 'host': 'domain',
+            'DOMAIN-KEYWORD':'domain_keyword', 'HOST-KEYWORD': 'domain_keyword', 'host-keyword': 'domain_keyword', 'IP-CIDR': 'ip_cidr',
+            'ip-cidr': 'ip_cidr', 'IP-CIDR6': 'ip_cidr', 
+            'IP6-CIDR': 'ip_cidr','SRC-IP-CIDR': 'source_ip_cidr', 'GEOIP': 'geoip', 'DST-PORT': 'port',
+            'SRC-PORT': 'source_port', "URL-REGEX": "domain_regex"}
+
 def read_yaml_from_url(url):
     response = requests.get(url)
-    response.raise_for_status()  # Raise an HTTPError for bad responses
+    response.raise_for_status()
     yaml_data = yaml.safe_load(response.text)
     return yaml_data
 
 def read_list_from_url(url):
     df = pd.read_csv(url, header=None, names=['pattern', 'address', 'other', 'other2', 'other3'])
     filtered_rows = []
+    rules = []
+    # 处理逻辑规则
+    if 'AND' in df['pattern'].values:
+        and_rows = df[df['pattern'].str.contains('AND', na=False)]
+        for _, row in and_rows.iterrows():
+            rule = {
+                "type": "logical",
+                "mode": "and",
+                "rules": []
+            }
+            pattern = ",".join(row.values.astype(str))
+            components = re.findall(r'\((.*?)\)', pattern)
+            for component in components:
+                for keyword in MAP_DICT.keys():
+                    if keyword in component:
+                        match = re.search(f'{keyword},(.*)', component)
+                        if match:
+                            value = match.group(1)
+                            rule["rules"].append({
+                                MAP_DICT[keyword]: value
+                            })
+            rules.append(rule)
     for index, row in df.iterrows():
         if 'AND' not in row['pattern']:
             filtered_rows.append(row)
     df_filtered = pd.DataFrame(filtered_rows, columns=['pattern', 'address', 'other', 'other2', 'other3'])
-    return df_filtered
+    return df_filtered, rules
 
 def is_ipv4_or_ipv6(address):
     try:
@@ -62,9 +92,9 @@ def parse_and_convert_to_dataframe(link):
                 rows.append({'pattern': pattern.strip(), 'address': address.strip(), 'other': None})
             df = pd.DataFrame(rows, columns=['pattern', 'address', 'other'])
         except:
-            df = read_list_from_url(link)
+            df, rules_from_url = read_list_from_url(link)
     else:
-        df = read_list_from_url(link)
+        df, rules_from_url = read_list_from_url(link)
     return df
 
 # 对字典进行排序，含list of dict
@@ -80,40 +110,21 @@ def sort_dict(obj):
 
 def parse_list_file(link, output_directory):
     with concurrent.futures.ThreadPoolExecutor() as executor:
-        # 使用executor.map并行处理链接
+        results= list(executor.map(parse_and_convert_to_dataframe, [link]))  # 使用executor.map并行处理链接
-        results = list(executor.map(parse_and_convert_to_dataframe, [link]))
+        df = pd.concat(results, ignore_index=True)  # 拼接为一个DataFrame
-        # 拼接为一个DataFrame
+    df = df[~df['pattern'].str.contains('#')].reset_index(drop=True)  # 删除pattern中包含#号的行
-        df = pd.concat(results, ignore_index=True)
+    df = df[df['pattern'].isin(MAP_DICT.keys())].reset_index(drop=True)  # 删除不在字典中的pattern
-
+    df = df.drop_duplicates().reset_index(drop=True)  # 删除重复行
-    # 删除pattern中包含#号的行
+    df['pattern'] = df['pattern'].replace(MAP_DICT)  # 替换pattern为字典中的值
-    df = df[~df['pattern'].str.contains('#')].reset_index(drop=True)
+    os.makedirs(output_directory, exist_ok=True)  # 创建自定义文件夹
-
-    # 映射字典
-    map_dict = {'DOMAIN-SUFFIX': 'domain_suffix', 'HOST-SUFFIX': 'domain_suffix', 'DOMAIN': 'domain', 'HOST': 'domain', 'host': 'domain',
-                'DOMAIN-KEYWORD':'domain_keyword', 'HOST-KEYWORD': 'domain_keyword', 'host-keyword': 'domain_keyword', 'IP-CIDR': 'ip_cidr',
-                'ip-cidr': 'ip_cidr', 'IP-CIDR6': 'ip_cidr', 
-                'IP6-CIDR': 'ip_cidr','SRC-IP-CIDR': 'source_ip_cidr', 'GEOIP': 'geoip', 'DST-PORT': 'port',
-                'SRC-PORT': 'source_port', "URL-REGEX": "domain_regex"}
-
-    # 删除不在字典中的pattern
-    df = df[df['pattern'].isin(map_dict.keys())].reset_index(drop=True)
-
-    # 删除重复行
-    df = df.drop_duplicates().reset_index(drop=True)
-    # 替换pattern为字典中的值
-    df['pattern'] = df['pattern'].replace(map_dict)
-
-    # 创建自定义文件夹
-    os.makedirs(output_directory, exist_ok=True)
 
     result_rules = {"version": 1, "rules": []}
     domain_entries = []
-
     for pattern, addresses in df.groupby('pattern')['address'].apply(list).to_dict().items():
         if pattern == 'domain_suffix':
             rule_entry = {pattern: ['.' + address.strip() for address in addresses]}
             result_rules["rules"].append(rule_entry)
-            # domain_entries.extend([address.strip() for address in addresses])
+            # domain_entries.extend([address.strip() for address in addresses])  # 1.9以下的版本需要额外处理 domain_suffix
         elif pattern == 'domain':
             domain_entries.extend([address.strip() for address in addresses])
         else:
@@ -124,6 +135,11 @@ def parse_list_file(link, output_directory):
     if domain_entries:
         result_rules["rules"].insert(0, {'domain': domain_entries})
 
+    # 处理逻辑规则
+    _, rules_from_url = read_list_from_url(link)
+    if rules_from_url:
+        result_rules["rules"].extend(rules_from_url)
+
     # 使用 output_directory 拼接完整路径
     file_name = os.path.join(output_directory, f"{os.path.basename(link).split('.')[0]}.json")
     with open(file_name, 'w', encoding='utf-8') as output_file: