标签：lag adc ISE undo __name__ 上传 cin global 字符
详细见python文档，猛击这里
操作文件时，一般需要经历如下步骤：
一、打开文件
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文件句柄 = file(‘文件路径‘, ‘模式‘) |
注：python中打开文件有两种方式，即：open(...) 和 file(...) ，本质上前者在内部会调用后者来进行文件操作，推荐使用 open。
打开文件时，需要指定文件路径和以何等方式打开文件，打开后，即可获取该文件句柄，日后通过此文件句柄对该文件操作。
打开文件的模式有：
"+" 表示可以同时读写某个文件
"U"表示在读取时，可以将 \r \n \r\n自动转换成 \n （与 r 或 r+ 模式同使用）
"b"表示处理二进制文件（如：FTP发送上传ISO镜像文件，linux可忽略，windows处理二进制文件时需标注）
二、操作操作
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class file(object): def close(self): # real signature unknown; restored from __doc__ 关闭文件 """ close() -> None or (perhaps) an integer. Close the file. Sets data attribute .closed to True. A closed file cannot be used for further I/O operations. close() may be called more than once without error. Some kinds of file objects (for example, opened by popen()) may return an exit status upon closing. """ def fileno(self): # real signature unknown; restored from __doc__ 文件描述符 """ fileno() -> integer "file descriptor". This is needed for lower-level file interfaces, such os.read(). """ return 0 def flush(self): # real signature unknown; restored from __doc__ 刷新文件内部缓冲区 """ flush() -> None. Flush the internal I/O buffer. """ pass def isatty(self): # real signature unknown; restored from __doc__ 判断文件是否是同意tty设备 """ isatty() -> true or false. True if the file is connected to a tty device. """ return False def next(self): # real signature unknown; restored from __doc__ 获取下一行数据，不存在，则报错 """ x.next() -> the next value, or raise StopIteration """ pass def read(self, size=None): # real signature unknown; restored from __doc__ 读取指定字节数据 """ read([size]) -> read at most size bytes, returned as a string. If the size argument is negative or omitted, read until EOF is reached. Notice that when in non-blocking mode, less data than what was requested may be returned, even if no size parameter was given. """ pass def readinto(self): # real signature unknown; restored from __doc__ 读取到缓冲区，不要用，将被遗弃 """ readinto() -> Undocumented. Don‘t use this; it may go away. """ pass def readline(self, size=None): # real signature unknown; restored from __doc__ 仅读取一行数据 """ readline([size]) -> next line from the file, as a string. Retain newline. A non-negative size argument limits the maximum number of bytes to return (an incomplete line may be returned then). Return an empty string at EOF. """ pass def readlines(self, size=None): # real signature unknown; restored from __doc__ 读取所有数据，并根据换行保存值列表 """ readlines([size]) -> list of strings, each a line from the file. Call readline() repeatedly and return a list of the lines so read. The optional size argument, if given, is an approximate bound on the total number of bytes in the lines returned. """ return [] def seek(self, offset, whence=None): # real signature unknown; restored from __doc__ 指定文件中指针位置 """ seek(offset[, whence]) -> None. Move to new file position. Argument offset is a byte count. Optional argument whence defaults to 0 (offset from start of file, offset should be >= 0); other values are 1 (move relative to current position, positive or negative), and 2 (move relative to end of file, usually negative, although many platforms allow seeking beyond the end of a file). If the file is opened in text mode, only offsets returned by tell() are legal. Use of other offsets causes undefined behavior. Note that not all file objects are seekable. """ pass def tell(self): # real signature unknown; restored from __doc__ 获取当前指针位置 """ tell() -> current file position, an integer (may be a long integer). """ pass def truncate(self, size=None): # real signature unknown; restored from __doc__ 截断数据，仅保留指定之前数据 """ truncate([size]) -> None. Truncate the file to at most size bytes. Size defaults to the current file position, as returned by tell(). """ pass def write(self, p_str): # real signature unknown; restored from __doc__ 写内容 """ write(str) -> None. Write string str to file. Note that due to buffering, flush() or close() may be needed before the file on disk reflects the data written. """ pass def writelines(self, sequence_of_strings): # real signature unknown; restored from __doc__ 将一个字符串列表写入文件 """ writelines(sequence_of_strings) -> None. Write the strings to the file. Note that newlines are not added. The sequence can be any iterable object producing strings. This is equivalent to calling write() for each string. """ pass def xreadlines(self): # real signature unknown; restored from __doc__ 可用于逐行读取文件，非全部 """ xreadlines() -> returns self. For backward compatibility. File objects now include the performance optimizations previously implemented in the xreadlines module. """ pass |
三、with
为了避免打开文件后忘记关闭，可以通过管理上下文，即：
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with open(‘log‘,‘r‘) as f: ... |
如此方式，当with代码块执行完毕时，内部会自动关闭并释放文件资源。
在Python 2.7 后，with又支持同时对多个文件的上下文进行管理，即：
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with open(‘log1‘) as obj1, open(‘log2‘) as obj2: pass |
四、那么问题来了...
1、如何在线上环境优雅的修改配置文件?
global log 127.0.0.1 local2 daemon maxconn 256 log 127.0.0.1 local2 info defaults log global mode http timeout connect 5000ms timeout client 50000ms timeout server 50000ms option dontlognull listen stats :8888 stats enable stats uri /admin stats auth admin:1234 frontend oldboy.org bind 0.0.0.0:80 option httplog option httpclose option forwardfor log global acl www hdr_reg(host) -i www.oldboy.org use_backend www.oldboy.org if www backend www.oldboy.org server 100.1.7.9 100.1.7.9 weight 20 maxconn 3000 原配置文件
1、查 输入：www.oldboy.org 获取当前backend下的所有记录 2、新建 输入： arg = { ‘bakend‘: ‘www.oldboy.org‘, ‘record‘:{ ‘server‘: ‘100.1.7.9‘, ‘weight‘: 20, ‘maxconn‘: 30 } } 3、删除 输入： arg = { ‘bakend‘: ‘www.oldboy.org‘, ‘record‘:{ ‘server‘: ‘100.1.7.9‘, ‘weight‘: 20, ‘maxconn‘: 30 } } 需求
1 #!/usr/bin/env python 2 # -*- coding:utf-8 -*- 3 import json 4 import os 5 6 7 def fetch(backend): 8 backend_title = ‘backend %s‘ % backend 9 record_list = [] 10 with open(‘ha‘) as obj: 11 flag = False 12 for line in obj: 13 line = line.strip() 14 if line == backend_title: 15 flag = True 16 continue 17 if flag and line.startswith(‘backend‘): 18 flag = False 19 break 20 21 if flag and line: 22 record_list.append(line) 23 24 return record_list 25 26 27 def add(dict_info): 28 backend = dict_info.get(‘backend‘) 29 record_list = fetch(backend) 30 backend_title = "backend %s" % backend 31 current_record = "server %s %s weight %d maxconn %d" % (dict_info[‘record‘][‘server‘], dict_info[‘record‘][‘server‘], dict_info[‘record‘][‘weight‘], dict_info[‘record‘][‘maxconn‘]) 32 if not record_list: 33 record_list.append(backend_title) 34 record_list.append(current_record) 35 with open(‘ha‘) as read_file, open(‘ha.new‘, ‘w‘) as write_file: 36 flag = False 37 for line in read_file: 38 write_file.write(line) 39 for i in record_list: 40 if i.startswith(‘backend‘): 41 write_file.write(i+‘\n‘) 42 else: 43 write_file.write("%s%s\n" % (8*" ", i)) 44 else: 45 record_list.insert(0, backend_title) 46 if current_record not in record_list: 47 record_list.append(current_record) 48 49 with open(‘ha‘) as read_file, open(‘ha.new‘, ‘w‘) as write_file: 50 flag = False 51 has_write = False 52 for line in read_file: 53 line_strip = line.strip() 54 if line_strip == backend_title: 55 flag = True 56 continue 57 if flag and line_strip.startswith(‘backend‘): 58 flag = False 59 if not flag: 60 write_file.write(line) 61 else: 62 if not has_write: 63 for i in record_list: 64 if i.startswith(‘backend‘): 65 write_file.write(i+‘\n‘) 66 else: 67 write_file.write("%s%s\n" % (8*" ", i)) 68 has_write = True 69 os.rename(‘ha‘,‘ha.bak‘) 70 os.rename(‘ha.new‘,‘ha‘) 71 72 73 def remove(dict_info): 74 backend = dict_info.get(‘backend‘) 75 record_list = fetch(backend) 76 backend_title = "backend %s" % backend 77 current_record = "server %s %s weight %d maxconn %d" % (dict_info[‘record‘][‘server‘], dict_info[‘record‘][‘server‘], dict_info[‘record‘][‘weight‘], dict_info[‘record‘][‘maxconn‘]) 78 if not record_list: 79 return 80 else: 81 if current_record not in record_list: 82 return 83 else: 84 del record_list[record_list.index(current_record)] 85 if len(record_list) > 0: 86 record_list.insert(0, backend_title) 87 with open(‘ha‘) as read_file, open(‘ha.new‘, ‘w‘) as write_file: 88 flag = False 89 has_write = False 90 for line in read_file: 91 line_strip = line.strip() 92 if line_strip == backend_title: 93 flag = True 94 continue 95 if flag and line_strip.startswith(‘backend‘): 96 flag = False 97 if not flag: 98 write_file.write(line) 99 else: 100 if not has_write: 101 for i in record_list: 102 if i.startswith(‘backend‘): 103 write_file.write(i+‘\n‘) 104 else: 105 write_file.write("%s%s\n" % (8*" ", i)) 106 has_write = True 107 os.rename(‘ha‘,‘ha.bak‘) 108 os.rename(‘ha.new‘,‘ha‘) 109 110 if __name__ == ‘__main__‘: 111 """ 112 print ‘1、获取；2、添加；3、删除‘ 113 num = raw_input(‘请输入序号：‘) 114 data = raw_input(‘请输入内容：‘) 115 if num == ‘1‘: 116 fetch(data) 117 else: 118 dict_data = json.loads(data) 119 if num == ‘2‘: 120 add(dict_data) 121 elif num == ‘3‘: 122 remove(dict_data) 123 else: 124 pass 125 """ 126 #data = "www.oldboy.org" 127 #fetch(data) 128 #data = ‘{"backend": "tettst.oldboy.org","record":{"server": "100.1.7.90","weight": 20,"maxconn": 30}}‘ 129 #dict_data = json.loads(data) 130 #add(dict_data) 131 #remove(dict_data) 132 133 demo
2、文件处理中xreadlines的内部是如何实现的呢？
一、背景
在学习函数之前，一直遵循：面向过程编程，即：根据业务逻辑从上到下实现功能，其往往用一长段代码来实现指定功能，开发过程中最常见的操作就是粘贴复制，也就是将之前实现的代码块复制到现需功能处，如下
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while True： if cpu利用率 > 90%: #发送邮件提醒 连接邮箱服务器 发送邮件 关闭连接 if 硬盘使用空间 > 90%: #发送邮件提醒 连接邮箱服务器 发送邮件 关闭连接 if 内存占用 > 80%: #发送邮件提醒 连接邮箱服务器 发送邮件 关闭连接 |
腚眼一看上述代码，if条件语句下的内容可以被提取出来公用，如下：
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def 发送邮件(内容) #发送邮件提醒 连接邮箱服务器 发送邮件 关闭连接 while True： if cpu利用率 > 90%: 发送邮件(‘CPU报警‘) if 硬盘使用空间 > 90%: 发送邮件(‘硬盘报警‘) if 内存占用 > 80%: |
对于上述的两种实现方式，第二次必然比第一次的重用性和可读性要好，其实这就是函数式编程和面向过程编程的区别：
函数式编程最重要的是增强代码的重用性和可读性
二、 函数的定义和使用
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def 函数名(参数): ... 函数体 ... |
函数的定义主要有如下要点：
以上要点中，比较重要有参数和返回值：
1、返回值
函数是一个功能块，该功能到底执行成功与否，需要通过返回值来告知调用者。
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def 发送短信(): 发送短信的代码... if 发送成功: return True else: return Falsewhile True: # 每次执行发送短信函数，都会将返回值自动赋值给result # 之后，可以根据result来写日志，或重发等操作 result = 发送短信() if result == False: 记录日志，短信发送失败... |
2、参数
为什么要有参数？
1 def CPU报警邮件() 2 #发送邮件提醒 3 连接邮箱服务器 4 发送邮件 5 关闭连接 6 7 def 硬盘报警邮件() 8 #发送邮件提醒 9 连接邮箱服务器 10 发送邮件 11 关闭连接 12 13 def 内存报警邮件() 14 #发送邮件提醒 15 连接邮箱服务器 16 发送邮件 17 关闭连接 18 19 while True： 20 21 if cpu利用率 > 90%: 22 CPU报警邮件（） 23 24 if 硬盘使用空间 > 90%: 25 硬盘报警邮件（） 26 27 if 内存占用 > 80%: 28 内存报警邮件（） 29 30 上例，无参数实现
1 def 发送邮件(邮件内容) 2 3 #发送邮件提醒 4 连接邮箱服务器 5 发送邮件 6 关闭连接 7 8 9 while True： 10 11 if cpu利用率 > 90%: 12 发送邮件("CPU报警了。") 13 14 if 硬盘使用空间 > 90%: 15 发送邮件("硬盘报警了。") 16 17 if 内存占用 > 80%: 18 发送邮件("内存报警了。") 19 20 上列，有参数实现
函数的有三中不同的参数：
1 # ######### 定义函数 ######### 2 3 # name 叫做函数func的形式参数，简称：形参 4 def func(name): 5 print name 6 7 # ######### 执行函数 ######### 8 # ‘wupeiqi‘ 叫做函数func的实际参数，简称：实参 9 func(‘wupeiqi‘) 10 11 普通参数
1 def func(name, age = 18): 2 3 print "%s:%s" %(name,age) 4 5 # 指定参数 6 func(‘wupeiqi‘, 19) 7 # 使用默认参数 8 func(‘alex‘) 9 10 注：默认参数需要放在参数列表最后 11 12 默认参数
1 def func(*args): 2 3 print args 4 5 6 # 执行方式一 7 func(11,33,4,4454,5) 8 9 # 执行方式二 10 li = [11,2,2,3,3,4,54] 11 func(*li) 12 13 动态参数一
1 def func(**kwargs): 2 3 print args 4 5 6 # 执行方式一 7 func(name＝‘wupeiqi‘,age=18) 8 9 # 执行方式二 10 li = {‘name‘:‘wupeiqi‘, age:18, ‘gender‘:‘male‘} 11 func(**li) 12 13 动态参数二
1 def func(*args, **kwargs): 2 3 print args 4 print kwargs
扩展：发送邮件实例
1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 import smtplib 16 from email.mime.text import MIMEText 17 from email.utils import formataddr 18 19 20 msg = MIMEText(‘邮件内容‘, ‘plain‘, ‘utf-8‘) 21 msg[‘From‘] = formataddr(["武沛齐",‘wptawy@126.com‘]) 22 msg[‘To‘] = formataddr(["走人",‘424662508@qq.com‘]) 23 msg[‘Subject‘] = "主题" 24 25 server = smtplib.SMTP("smtp.126.com", 25) 26 server.login("wptawy@126.com", "邮箱密码") 27 server.sendmail(‘wptawy@126.com‘, [‘424662508@qq.com‘,], msg.as_string()) 28 server.quit()
学习条件运算时，对于简单的 if else 语句，可以使用三元运算来表示，即：
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# 普通条件语句if 1 == 1: name = ‘wupeiqi‘else: name = ‘alex‘ # 三元运算name = ‘wupeiqi‘ if 1 == 1 else ‘alex‘ |
对于简单的函数，也存在一种简便的表示方式，即：lambda表达式
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# ###################### 普通函数 ####################### 定义函数（普通方式）def func(arg): return arg + 1 # 执行函数result = func(123) # ###################### lambda ###################### # 定义函数（lambda表达式）my_lambda = lambda arg : arg + 1 # 执行函数result = my_lambda(123) |
lambda存在意义就是对简单函数的简洁表示
一、map
遍历序列，对序列中每个元素进行操作，最终获取新的序列。
1 #每个元素增加100 2 li = [11, 22, 33] 3 4 new_list = map(lambda a: a + 100, li)
1 #两个列表对应元素相加 2 li = [11, 22, 33] 3 sl = [1, 2, 3] 4 new_list = map(lambda a, b: a + b, li, sl)
二、filter
对于序列中的元素进行筛选，最终获取符合条件的序列
1 #获取列表中大于12的所用元素集合 2 li = [11, 22, 33] 3 4 new_list = filter(lambda arg: arg > 22, li) 5 6 #filter第一个参数为空，将获取原来序列
三、reduce
对于序列内所有元素进行累计操作
1 #获取序列所有元素的和 2 li = [11, 22, 33] 3 4 result = reduce(lambda arg1, arg2: arg1 + arg2, li) 5 6 # reduce的第一个参数，函数必须要有两个参数 7 # reduce的第二个参数，要循环的序列 8 # reduce的第三个参数，初始值 9 10 获取序列所有元素的和
1、对比range 和 xrange 的区别
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>>> print range(10)[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]>>> print xrange(10)xrange(10) |
如上代码所示，range会在内存中创建所有指定的数字，而xrange不会立即创建，只有在迭代循环时，才去创建每个数组。
1 def nrange(num): 2 temp = -1 3 while True: 4 temp = temp + 1 5 if temp >= num: 6 return 7 else: 8 yield temp 9 10 自定义生成器nrange
2、文件操作的 read 和 xreadlinex 的的区别
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read会读取所有内容到内存xreadlines则只有在循环迭代时才获取 |
1 def NReadlines(): 2 with open(‘log‘,‘r‘) as f: 3 while True: 4 line = f.next() 5 if line: 6 yield line 7 else: 8 return 9 10 for i in NReadlines(): 11 print i 12 13 基于next自定义生成器NReadlines
1 def NReadlines(): 2 with open(‘log‘,‘r‘) as f: 3 seek = 0 4 while True: 5 f.seek(seek) 6 data = f.readline() 7 if data: 8 seek = f.tell() 9 yield data 10 else: 11 return 12 13 for item in NReadlines(): 14 print item 15 16 基于seek和tell自定义生成器NReadlines
装饰器是函数，只不过该函数可以具有特殊的含义，装饰器用来装饰函数或类，使用装饰器可以在函数执行前和执行后添加相应操作。
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def wrapper(func): def result(): print ‘before‘ func() print ‘after‘ return result@wrapperdef foo(): print ‘foo‘ |
1 import functools 2 3 4 def wrapper(func): 5 @functools.wraps(func) 6 def wrapper(): 7 print ‘before‘ 8 func() 9 print ‘after‘ 10 return wrapper 11 12 @wrapper 13 def foo(): 14 print ‘foo‘
1 #!/usr/bin/env python 2 #coding:utf-8 3 4 def Before(request,kargs): 5 print ‘before‘ 6 7 def After(request,kargs): 8 print ‘after‘ 9 10 11 def Filter(before_func,after_func): 12 def outer(main_func): 13 def wrapper(request,kargs): 14 15 before_result = before_func(request,kargs) 16 if(before_result != None): 17 return before_result; 18 19 main_result = main_func(request,kargs) 20 if(main_result != None): 21 return main_result; 22 23 after_result = after_func(request,kargs) 24 if(after_result != None): 25 return after_result; 26 27 return wrapper 28 return outer 29 30 @Filter(Before, After) 31 def Index(request,kargs): 32 print ‘index‘ 33 34 35 if __name__ == ‘__main__‘: 36 Index(1,2)
需求：请按照从小到大对列表 [13, 22, 6, 99, 11] 进行排序
思路：相邻两个值进行比较，将较大的值放在右侧，依次比较！
1 li = [13, 22, 6, 99, 11] 2 3 for m in range(4): # 等价于 #for m in range(len(li)-1): 4 if li[m]> li[m+1]: 5 temp = li[m+1] 6 li[m+1] = li[m] 7 li[m] = temp
1 li = [13, 22, 6, 99, 11] 2 3 for m in range(4): # 等价于 #for m in range(len(li)-1): 4 if li[m]> li[m+1]: 5 temp = li[m+1] 6 li[m+1] = li[m] 7 li[m] = temp 8 9 for m in range(3): # 等价于 #for m in range(len(li)-2): 10 if li[m]> li[m+1]: 11 temp = li[m+1] 12 li[m+1] = li[m] 13 li[m] = temp 14 15 for m in range(2): # 等价于 #for m in range(len(li)-3): 16 if li[m]> li[m+1]: 17 temp = li[m+1] 18 li[m+1] = li[m] 19 li[m] = temp 20 21 for m in range(1): # 等价于 #for m in range(len(li)-4): 22 if li[m]> li[m+1]: 23 temp = li[m+1] 24 li[m+1] = li[m] 25 li[m] = temp 26 print li
1 li = [13, 22, 6, 99, 11] 2 3 for i in range(1,5): 4 for m in range(len(li)-i): 5 if li[m] > li[m+1]: 6 temp = li[m+1] 7 li[m+1] = li[m] 8 li[m] = temp
利用函数编写如下数列：
斐波那契数列指的是这样一个数列 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233，377，610，987，1597，2584，4181，6765，10946，17711，28657，46368
1 def func(arg1,arg2): 2 if arg1 == 0: 3 print arg1, arg2 4 arg3 = arg1 + arg2 5 print arg3 6 func(arg2, arg3) 7 8 func(0,1) 9 10 demo
标签：lag adc ISE undo __name__ 上传 cin global 字符
原文地址：https://www.cnblogs.com/kingo/p/9106184.html
