Metaclass, and double reflection method

Moto类

print(type('abc'))
print(type(True))
print(type(100))
print(type([1, 2, 3]))
print(type({'name': '太白金星'}))
print(type((1,2,3)))
print(type(object))

class A:
    pass

print(isinstance(object,type))
print(isinstance(A, type))

type metaclass is to obtain the object belongs to a class, and type special class, Python principle is: everything is an object, in fact, classes can also be understood as 'objects', also known as class and type yuan to build classes, python majority built-in classes (including object) as well as their own definition of the class are created by the type metaclass.

And the relationship between the type classes and more unique object class: object is an instance of the class type, and the type is a subclass of the class of the object class, comparing the relationship expressed python magic can not be used in the code, which is defined as a must before further presence. So this only as understanding.

reflection

Reflecting means can access the program, its ability to detect and modify the state itself or behavior (introspection).

python reflective object-oriented: the object-related properties operated by a string. Everything is an object in python (could use reflection)

Reflecting commonly used four functions:

class Student:
    f = '类的静态变量'
    def __init__(self,name,age):
        self.name=name
        self.age=age

    def say_hi(self):
        print('hi,%s'%self.name)
obj=Student('alex',16)

# hasatter 检测是否含有某属性,方法
print(hasattr(obj,'name'))
print(hasattr(obj,'say_hi'))
结果：
True
True

# 获取属性，方法  # 获取不到直接报错
n=getattr(obj,'name')
print(n)
func=getattr(obj,'say_hi')
func()
结果：
'alex'
'hi,alex'

# 设置属性
setattr(obj,'sb',True)
setattr(obj,'show_name',lambda self:self.name+'sb')
print(obj.__dict__)
print(obj.show_name(obj))
结果：
{'name': 'alex', 'age': 16, 'sb': True, 'show_name': <function <lambda> at 0x00000180E591BB70>}
alexsb

# 删除属性
delattr(obj,'age')
# delattr(obj,'show_name')
# delattr(obj,'show_name111')#不存在,则报错

print(obj.__dict__)
结果：
{'name': 'alex'}

Class reflection:

class Foo(object):
 
    staticField = "old boy"
 
    def __init__(self):
        self.name = 'wupeiqi'
 
    def func(self):
        return 'func'
 
    @staticmethod
    def bar():
        return 'bar'
 
print(getattr(Foo, 'staticField'))
print(getattr(Foo, 'func'))
print(getattr(Foo, 'bar'))
# 结果：
old boy
<function Foo.func at 0x00000212D21FBC80>  # 获取func对象方法地址
<function Foo.bar at 0x00000212D21FBD08>  # 获取bar静态方法地址

It reflected current module:

import sys


def s1():
    print('s1')


def s2():
    print('s2') 


this_module = sys.modules[__name__]

print(hasattr(this_module, 's1'))
print(getattr(this_module, 's2'))
# 结果：
True
<function s2 at 0x000002000247BBF8>

Reflecting other modules:

import time
print(hasattr(time,'ctime'))
print(getattr(time,'ctime'))
print(getattr(time,'ctime')())
# 结果：
True
<built-in function ctime>
'Fri Aug  9 08:12:54 2019'

Reflected Application

# 没学反射之前的解决方式:
class User:
    def login(self):
        print('欢迎来到登录页面')
    
    def register(self):
        print('欢迎来到注册页面')
    
    def save(self):
        print('欢迎来到存储页面')


while 1:
    choose = input('>>>').strip()
    if choose == 'login':
        obj = User()
        obj.login()
    
    elif choose == 'register':
        obj = User()
        obj.register()
        
    elif choose == 'save':
        obj = User()
        obj.save()

# 学了反射之后解决方式
class User:
    def login(self):
        print('欢迎来到登录页面')
    
    def register(self):
        print('欢迎来到注册页面')
    
    def save(self):
        print('欢迎来到存储页面')

user = User()
while 1:
    choose = input('>>>').strip()
    if hasattr(user,choose):
        func = getattr(user,choose)
        func()
    else:
        print('输入错误。。。。')

The difference between the functions and methods

Determined by the print function (method) name

def func():
    pass

print(func)  # <function func at 0x00000260A2E690D0>


class A:
    def func(self):
        pass
    
print(A.func)  # <function A.func at 0x0000026E65AE9C80>
obj = A()
print(obj.func)  # <bound method A.func of <__main__.A object at 0x00000230BAD4C9E8>>

Module types verified by

from types import FunctionType
from types import MethodType

def func():
    pass

class A:
    def func(self):
        pass

obj = A()
print(isinstance(func,FunctionType))  # True
print(isinstance(A.func,FunctionType))  # True
print(isinstance(obj.func,FunctionType))  # False
print(isinstance(obj.func,MethodType))  # True

Static method is a function

from types import FunctionType
from types import MethodType

class A:
    
    def func(self):
        pass
    
    @classmethod
    def func1(self):
        pass
    
    @staticmethod
    def func2(self):
        pass
obj = A()

# 静态方法其实是函数
print(isinstance(A.func2,FunctionType))  # True
print(isinstance(obj.func2,FunctionType))  # True

Several differences between functions and methods:

(1) function is a dominant parameter passing, a method is implicit parameter passing.

(2) function has nothing to do with the object.

(3) The method of operating data of the internal classes.

(4) method is associated with the object.

Wire method

Definition: The Double Down is a special method, he is an interpreter provided by the method name plus a double underscore double underlined __ method name has special significance __ method, the method is to double down python-source programmers, in try not to use development under the dual method, double underline known methods help us study the source code

** 1 ** __ __ only

class B:
    def __len__(self):
        print(666)

b = B()
len(b) # len 一个对象就会触发 __len__方法。

class A:
    def __init__(self):
        self.a = 1
        self.b = 2

    def __len__(self):
        return len(self.__dict__)
a = A()
print(len(a))

**2、__hash__**

class A:
    def __init__(self):
        self.a = 1
        self.b = 2

    def __hash__(self):
        return hash(str(self.a)+str(self.b))
a = A()
print(hash(a))

**3、__str__**

If a class is defined __str__ method, then when the print target, the method returns the default output value.

class A:
    def __init__(self):
        pass
    def __str__(self):
        return '太白'
a = A()
print(a)
print('%s' % a)

**4、__repr__**

If a class is defined __repr__ method, then at repr (object), the method returns a default output value.

class A:
    def __init__(self):
        pass
    def __repr__(self):
        return '太白'
a = A()
print(repr(a))
print('%r'%a)

**5、__call__**

Brackets behind the object, trigger the execution.

Note: the constructor is executed by the __new__ is triggered to create an object, namely: class name = Object (); For the call performed by the method of the object triggers the brackets, namely: Object () or class () ( )

class Foo:

    def __init__(self):
        pass
    
    def __call__(self, *args, **kwargs):

        print('__call__')


obj = Foo() # 执行 __init__
obj()       # 执行 __call__

**6、__eq__**

class A:
    def __init__(self):
        self.a = 1
        self.b = 2

    def __eq__(self,obj):
        if  self.a == obj.a and self.b == obj.b:
            return True
a = A()
b = A()
print(a == b)

** 7 ** __ of __

Destructor, when the object is released in the memory, automatically trigger the execution.

Note: This method is generally no need to define, because Python is a high-level language, programmers used without concern allocate and free memory, because this work is to the Python interpreter to execute, so call the destructor is It triggered automatically performed at the time garbage collector by an interpreter.

class A:
    def __init__(self):
        self.x = 1
        print('in init function')
    def __new__(cls, *args, **kwargs):
        print('in new function')
        return object.__new__(A, *args, **kwargs)

a = A()
print(a.x)

**8、__new__**

class A:
    def __init__(self):
        self.x = 1
        print('in init function')
    def __new__(cls, *args, **kwargs):
        print('in new function')
        return object.__new__(A, *args, **kwargs)

a = A()
print(a.x)

Singleton:

class A:
    __instance = None
    def __new__(cls, *args, **kwargs):
        if cls.__instance is None:
            obj = object.__new__(cls)
            cls.__instance = obj
        return cls.__instance

单例模式是一种常用的软件设计模式。在它的核心结构中只包含一个被称为单例类的特殊类。通过单例模式可以保证系统中一个类只有一个实例而且该实例易于外界访问，从而方便对实例个数的控制并节约系统资源。如果希望在系统中某个类的对象只能存在一个，单例模式是最好的解决方案。
【采用单例模式动机、原因】
对于系统中的某些类来说，只有一个实例很重要，例如，一个系统中可以存在多个打印任务，但是只能有一个正在工作的任务；一个系统只能有一个窗口管理器或文件系统；一个系统只能有一个计时工具或ID(序号)生成器。如在Windows中就只能打开一个任务管理器。如果不使用机制对窗口对象进行唯一化，将弹出多个窗口，如果这些窗口显示的内容完全一致，则是重复对象，浪费内存资源；如果这些窗口显示的内容不一致，则意味着在某一瞬间系统有多个状态，与实际不符，也会给用户带来误解，不知道哪一个才是真实的状态。因此有时确保系统中某个对象的唯一性即一个类只能有一个实例非常重要。
如何保证一个类只有一个实例并且这个实例易于被访问呢？定义一个全局变量可以确保对象随时都可以被访问，但不能防止我们实例化多个对象。一个更好的解决办法是让类自身负责保存它的唯一实例。这个类可以保证没有其他实例被创建，并且它可以提供一个访问该实例的方法。这就是单例模式的模式动机。
【单例模式优缺点】
【优点】
一、实例控制
单例模式会阻止其他对象实例化其自己的单例对象的副本，从而确保所有对象都访问唯一实例。
二、灵活性
因为类控制了实例化过程，所以类可以灵活更改实例化过程。
【缺点】
一、开销
虽然数量很少，但如果每次对象请求引用时都要检查是否存在类的实例，将仍然需要一些开销。可以通过使用静态初始化解决此问题。
二、可能的开发混淆
使用单例对象（尤其在类库中定义的对象）时，开发人员必须记住自己不能使用new关键字实例化对象。因为可能无法访问库源代码，因此应用程序开发人员可能会意外发现自己无法直接实例化此类。
三、对象生存期
不能解决删除单个对象的问题。在提供内存管理的语言中（例如基于.NET Framework的语言），只有单例类能够导致实例被取消分配，因为它包含对该实例的私有引用。在某些语言中（如 C++），其他类可以删除对象实例，但这样会导致单例类中出现悬浮引用

** 9, __item__ series **

class Foo:
    def __init__(self,name):
        self.name=name

    def __getitem__(self, item):
        print(self.__dict__[item])

    def __setitem__(self, key, value):
        self.__dict__[key]=value
    def __delitem__(self, key):
        print('del obj[key]时,我执行')
        self.__dict__.pop(key)
    def __delattr__(self, item):
        print('del obj.key时,我执行')
        self.__dict__.pop(item)

f1=Foo('sb')
f1['age']=18
f1['age1']=19
del f1.age1
del f1['age']
f1['name']='alex'
print(f1.__dict__)

10 related context manager

__enter__ __exit__

# 如果想要对一个类的对象进行with  as 的操作 不行。
class A:
    def __init__(self, text):
        self.text = text

with A('大爷') as f1:
    print(f1.text)

class A:
    
    def __init__(self, text):
        self.text = text
    
    def __enter__(self):  # 开启上下文管理器对象时触发此方法
        self.text = self.text + '您来啦'
        return self  # 将实例化的对象返回f1
    
    def __exit__(self, exc_type, exc_val, exc_tb):  # 执行完上下文管理器对象f1时触发此方法
        self.text = self.text + '这就走啦'
        
with A('大爷') as f1:
    print(f1.text)
print(f1.text)

Custom File Manager

class Diycontextor:
    def __init__(self,name,mode):
        self.name = name
        self.mode = mode
 
    def __enter__(self):
        print("Hi enter here!!")
        self.filehander = open(self.name,self.mode)
        return self.filehander
 
    def __exit__(self,*para):
        print("Hi exit here")
        self.filehander.close()
 
 
with Diycontextor('py_ana.py','r') as f:
    for i in f:
        print(i)