需要 Python 3.4+,一个参数用来选择测试搜索服务还是 GAE 服务。测试 GAE 服务的话需要先修改开头的两个变量。从标准输入读取 IP 地址或者 IP 段(形如 192.168.0.0/16)列表,每行一个。可用 IP 输出到标准输出。实时测试结果输出到标准错误。50 线程并发。
checkgoogleip
#!/usr/bin/env python3 import sys from ipaddress import IPv4Network import http.client as client from concurrent.futures import ThreadPoolExecutor import argparse import ssl import socket # 先按自己的情况修改以下几行 APP_ID = 'your_id_here' APP_PATH = '/fetch.py' context = ssl.SSLContext(ssl.PROTOCOL_TLSv1) context.verify_mode = ssl.CERT_REQUIRED context.load_verify_locations('/etc/ssl/certs/ca-certificates.crt') class HTTPSConnection(client.HTTPSConnection): def __init__(self, *args, hostname=None, **kwargs): self._hostname = hostname super().__init__(*args, **kwargs) def connect(self): super(client.HTTPSConnection, self).connect() if self._tunnel_host: server_hostname = self._tunnel_host else: server_hostname = self._hostname or self.host sni_hostname = server_hostname if ssl.HAS_SNI else None self.sock = self._context.wrap_socket(self.sock, server_hostname=sni_hostname) if not self._context.check_hostname and self._check_hostname: try: ssl.match_hostname(self.sock.getpeercert(), server_hostname) except Exception: self.sock.shutdown(socket.SHUT_RDWR) self.sock.close() raise def check_ip_p(ip, func): if func(ip): print(ip, flush=True) def check_for_gae(ip): return _check(APP_ID + '.appspot.com', APP_PATH, ip) def check_for_search(ip): return _check('www.google.com', '/', ip) def _check(host, path, ip): for chance in range(1,-1,-1): try: conn = HTTPSConnection( ip, timeout = 5, context = context, hostname = host, ) conn.request('GET', path, headers = { 'Host': host, }) response = conn.getresponse() if response.status < 400: print('GOOD:', ip, file=sys.stderr) else: raise Exception('HTTP Error %s %s' % ( response.status, response.reason)) return True except KeyboardInterrupt: raise except Exception as e: if isinstance(e, ssl.CertificateError): print('WARN: %s is not Google\'s!' % ip, file=sys.stderr) chance = 0 if chance == 0: print('BAD :', ip, e, file=sys.stderr) return False else: print('RE :', ip, e, file=sys.stderr) def main(): parser = argparse.ArgumentParser(description='Check Google IPs') parser.add_argument('service', choices=['search', 'gae'], help='service to check') args = parser.parse_args() func = globals()['check_for_' + args.service] count = 0 with ThreadPoolExecutor(max_workers=50) as executor: for l in sys.stdin: l = l.strip() if '/' in l: for ip in IPv4Network(l).hosts(): executor.submit(check_ip_p, str(ip), func) count += 1 else: executor.submit(check_ip_p, l, func) count += 1 print('%d IP checked.' % count) if __name__ == '__main__': main()

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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