计算结果如下：
- IPv6 数据巨细：0.01kb
- 总数据巨细：0.02kb
- IPv6 数据占比：47.37%
- ⚠️ 未支撑IPv6的域名信息：
- 域名：ipv4.lookup.test-ipv6.com，数据巨细：0.00kb，占比：5.26%
- 域名：test4.vyncke.org，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.master.test-ipv6.com，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.test-ipv6.go6.si，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.test-ipv6.sin.vr.org，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.test-ipv6.hu，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.test-ipv6.is，数据巨细：0.00kb，占比：5.26%
- 域名：v4-only.steffann.nl，数据巨细：0.00kb，占比：5.26%
- 域名：ipv4.ipv6-test.pl，数据巨细：0.00kb，占比：5.26%
最近在做IPV6晋级支撑改造，发现一篇文章中有计算IPV6浓度，计算代码有点小问题，以下是我修正往后的：
计算代码 analysis_ipv6.py：
import argparse
import json
import os
import glob
import ipaddress
# 获取url的domain
from urllib.parse import urlparse
# 导出表格数据
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Alignment
# IPv6占比基线
ipv6_baseline = 1.0
def get_domain(url):
return urlparse(url).netloc
def check_ipv4_address(ip):
try:
ip_obj = ipaddress.ip_address(ip)
print("ip_obj",ip_obj.version)
return ip_obj.version == 4
except ValueError:
print("无效的IP地址")
return False
def check_ipv6_address(ip):
try:
ip_obj = ipaddress.ip_address(ip)
print("ip_obj",ip_obj.version)
return ip_obj.version == 6
except ValueError:
print("无效的IP地址")
return False
def calculate_sizes(entries):
domains = {}
totalsizes = 0
ipv6sizes = 0
for entry in entries:
url = entry['request']['url']
response = entry['response']
size = response.get('_transferSize', response.get(
'bodySize', 0) + response.get('headersSize', 0))
if 'serverIPAddress' in entry:
serverIPAddress = entry['serverIPAddress']
totalsizes += 1
if check_ipv6_address(serverIPAddress):
ipv6sizes += 1
else:
domain = get_domain(url)
domains[domain] = domains.get(domain, 0) + 1
return domains, totalsizes, ipv6sizes
def print_results(domains, sizes, ipv6sizes):
ipv6point = ipv6sizes / sizes * 100
print("IPv6 数据巨细：{:.2f}kb".format(ipv6sizes / 1024))
print("总数据巨细：{:.2f}kb".format(sizes / 1024))
print("IPv6 数据占比：{:.2f}%".format(ipv6point))
print("⚠️ 未支撑IPv6的域名信息：")
ipv4info = ''
for key, value in domains.items():
info = "域名：{}，数据巨细：{:.2f}kb，占比：{:.2f}%".format(
key, value / 1024, value / sizes * 100)
ipv4info += info + 'n'
print(info)
print("====================")
print("")
return ipv6point, ipv4info
def analysis_request(entries):
domains, sizes, ipv6sizes = calculate_sizes(entries)
ipv6point, ipv4info = print_results(domains, sizes, ipv6sizes)
results = {}
if ipv6sizes / sizes < ipv6_baseline:
results['ipv6point'] = '{:.2f}%'.format(ipv6point)
results['ipv4list'] = ipv4info
return results
def read_jsonfile(file):
json_data = json.load(file)
log = json_data['log']
pages = log.get('pages', [])
if not pages:
print('har无页面数据')
page_title = "未知页面"
else:
page = pages[0]
page_title = page['title']
print("")
print("加载的页面：", page_title)
entries = log.get('entries', [])
if 'entries' in log:
results = analysis_request(entries)
if results:
results['page'] = page_title
return results
def export_excel(data):
# 创立一个数据字典
# data = {'Name': ['Tom', 'Nick', 'John'],'Age': [20, 21, 19]}
# 将数据字典转换为pandas DataFrame
df = pd.DataFrame(data)
# 将DataFrame导出为Excel文件
df.to_excel(output_file, index=False)
def format_excel():
# 加载Excel文件
wb = load_workbook(output_file)
# 选择要修正的作业表
ws = wb.active
# 设置第一列的宽度为特定值
ws.column_dimensions['A'].width = 40
ws.column_dimensions['B'].width = 15
ws.column_dimensions['C'].width = 80
# 遍历一切单元格，设置其对齐方法为上下居中
for row in ws.iter_rows():
for cell in row:
cell.alignment = Alignment(vertical='center', wrap_text=True)
# 获取C列的一切单元格
# c_column_cells = ws['C']
# for cell in c_column_cells:
# # 设置其对齐方法为主动换行
# cell.alignment = Alignment(wrap_text=True)
# 保存修正后的Excel文件
wb.save(output_file)
def traverse_files(directory):
# 指定当前目录途径
# directory = os.getcwd()
print(directory)
# 指定文件格局
file_format = '*.har'
# 运用glob模块获取匹配的文件途径列表
file_list = glob.glob(os.path.join(directory, file_format))
pages = []
ipv6points = []
ipv4lists = []
# 遍历文件列表
for file_path in file_list:
# 打开文件进行处理
with open(file_path, 'r') as file:
result = read_jsonfile(file)
if result:
pages.append(result['page'])
ipv6points.append(result['ipv6point'])
ipv4lists.append(result['ipv4list'])
if pages:
data = {
'页面': pages,
'IPv6占比': ipv6points,
'IPv4域名列表': ipv4lists
}
export_excel(data)
format_excel()
# 创立一个解析器
parser = argparse.ArgumentParser(description='剖析IPv6数据')
# 增加一个命令行参数
parser.add_argument('--directory', type=str, help='要剖析的文件的目录')
parser.add_argument('--d', type=str, help='要剖析的文件的目录')
# 解析命令行参数
args = parser.parse_args()
directory = os.getcwd()
if args.directory:
directory = args.directory
elif args.d:
directory = args.d
output_file = directory + '/output.xlsx'
# 运用命令行参数
traverse_files(directory)
履行代码：
python3 analysis_ipv6.py --directory '/Users/mac/Desktop/IPV6/other'
如何获取抓包内容，请参阅原文。
原文在知乎：zhuanlan.zhihu.com/p/665965753
