如何在Python中创建类(即静态)变量或方法?
当前回答
python中的静态方法称为classmethods。查看以下代码
class MyClass:
def myInstanceMethod(self):
print 'output from an instance method'
@classmethod
def myStaticMethod(cls):
print 'output from a static method'
>>> MyClass.myInstanceMethod()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unbound method myInstanceMethod() must be called [...]
>>> MyClass.myStaticMethod()
output from a static method
注意,当我们调用方法myInstanceMethod时,会得到一个错误。这是因为它要求在此类的实例上调用该方法。方法myStaticMethod使用decorator@classmethod设置为类方法。
为了好玩,我们可以通过传入类的实例来调用类上的myInstanceMethod,如下所示:
>>> MyClass.myInstanceMethod(MyClass())
output from an instance method
其他回答
总结其他人的回答并补充,在python中声明静态方法或变量有很多种方法。
1.使用staticmethod()作为装饰符:
可以简单地在声明的方法(函数)上方放置一个修饰符,使其成为静态方法。例如。
class Calculator:
@staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
print(Calculator.multiply(1, 2, 3, 4)) # 24
2.使用staticmethod()作为参数函数:
此方法可以接收函数类型的参数,并返回传递函数的静态版本。例如。
class Calculator:
def add(n1, n2, *args):
return n1 + n2 + sum(args)
Calculator.add = staticmethod(Calculator.add)
print(Calculator.add(1, 2, 3, 4)) # 10
3.使用classmethod()作为装饰符:
@classmethod对函数的影响与@staticmethod类似,但是这一次,需要在函数中接受一个额外的参数(类似于实例变量的self参数)。例如。
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
@classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
print(Calculator.get_digits(314159)) # 314159
4.使用classmethod()作为参数函数:
@classmethod也可以用作参数函数,以防不想修改类定义。例如。
class Calculator:
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.divide(15, 3, 5)) # 1.0
5.直接申报
在所有其他方法外部但在类内部声明的方法/变量自动是静态的。
class Calculator:
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
print(Calculator.subtract(10, 2, 3, 4)) # 1
整个计划
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
@staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
def add(n1, n2, *args):
return n1 + n2 + sum(args)
@classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
Calculator.add = staticmethod(Calculator.add)
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.multiply(1, 2, 3, 4)) # 24
print(Calculator.add(1, 2, 3, 4)) # 10
print(Calculator.get_digits(314159)) # 314159
print(Calculator.divide(15, 3, 5)) # 1.0
print(Calculator.subtract(10, 2, 3, 4)) # 1
有关掌握Python中的OOP,请参阅Python文档。
@数据类定义提供用于定义实例变量和初始化方法__init__()的类级名称。如果要在@dataclass中使用类级变量,则应使用typeing.ClassVar类型提示。ClassVar类型的参数定义类级别变量的类型。
from typing import ClassVar
from dataclasses import dataclass
@dataclass
class Test:
i: ClassVar[int] = 10
x: int
y: int
def __repr__(self):
return f"Test({self.x=}, {self.y=}, {Test.i=})"
用法示例:
> test1 = Test(5, 6)
> test2 = Test(10, 11)
> test1
Test(self.x=5, self.y=6, Test.i=10)
> test2
Test(self.x=10, self.y=11, Test.i=10)
所以这可能是一个黑客,但我一直在使用eval(str)来获取一个静态对象,这有点矛盾,在python 3中。
有一个Records.py文件,除了用保存一些参数的静态方法和构造函数定义的类对象外,它什么都没有。然后从另一个.py文件导入Records,但我需要动态选择每个对象,然后根据读入的数据类型按需实例化它。
因此,在object_name=“RecordOne”或类名的情况下,我调用cur_type=eval(object_name),然后要实例化它,请执行cur_inst=cur_type(args)然而,在实例化之前,您可以从cur_type.getName()调用静态方法,例如,类似于抽象基类实现或任何目标。然而,在后端,它可能是在python中实例化的,并不是真正静态的,因为eval返回一个对象。。。。必须已实例化。。。。这会产生类似静态的行为。
类变量并允许子类化
假设你不是在寻找一个真正的静态变量,而是一个类似于蟒蛇的东西,它可以为同意的成年人做同样的工作,那么就使用一个类变量。这将为您提供一个所有实例都可以访问(和更新)的变量
注意:其他许多使用类变量的答案都会破坏子类化。应避免直接按名称引用类。
from contextlib import contextmanager
class Sheldon(object):
foo = 73
def __init__(self, n):
self.n = n
def times(self):
cls = self.__class__
return cls.foo * self.n
#self.foo * self.n would give the same result here but is less readable
# it will also create a local variable which will make it easier to break your code
def updatefoo(self):
cls = self.__class__
cls.foo *= self.n
#self.foo *= self.n will not work here
# assignment will try to create a instance variable foo
@classmethod
@contextmanager
def reset_after_test(cls):
originalfoo = cls.foo
yield
cls.foo = originalfoo
#if you don't do this then running a full test suite will fail
#updates to foo in one test will be kept for later tests
将为您提供与使用Sheldon.foo处理变量相同的功能,并将通过以下测试:
def test_times():
with Sheldon.reset_after_test():
s = Sheldon(2)
assert s.times() == 146
def test_update():
with Sheldon.reset_after_test():
s = Sheldon(2)
s.updatefoo()
assert Sheldon.foo == 146
def test_two_instances():
with Sheldon.reset_after_test():
s = Sheldon(2)
s3 = Sheldon(3)
assert s.times() == 146
assert s3.times() == 219
s3.updatefoo()
assert s.times() == 438
它还允许其他人简单地:
class Douglas(Sheldon):
foo = 42
这也将起作用:
def test_subclassing():
with Sheldon.reset_after_test(), Douglas.reset_after_test():
s = Sheldon(2)
d = Douglas(2)
assert d.times() == 84
assert s.times() == 146
d.updatefoo()
assert d.times() == 168 #Douglas.Foo was updated
assert s.times() == 146 #Seldon.Foo is still 73
def test_subclassing_reset():
with Sheldon.reset_after_test(), Douglas.reset_after_test():
s = Sheldon(2)
d = Douglas(2)
assert d.times() == 84 #Douglas.foo was reset after the last test
assert s.times() == 146 #and so was Sheldon.foo
有关创建课程时要注意的事项的最佳建议,请查看Raymond Hettinger的视频https://www.youtube.com/watch?v=HTLu2DFOdTg
可以使用静态类变量,但可能不值得这样做。
这里有一个用Python 3编写的概念证明——如果任何确切的细节都是错误的,那么可以对代码进行调整,以匹配静态变量所指的任何内容:
class Static:
def __init__(self, value, doc=None):
self.deleted = False
self.value = value
self.__doc__ = doc
def __get__(self, inst, cls=None):
if self.deleted:
raise AttributeError('Attribute not set')
return self.value
def __set__(self, inst, value):
self.deleted = False
self.value = value
def __delete__(self, inst):
self.deleted = True
class StaticType(type):
def __delattr__(cls, name):
obj = cls.__dict__.get(name)
if isinstance(obj, Static):
obj.__delete__(name)
else:
super(StaticType, cls).__delattr__(name)
def __getattribute__(cls, *args):
obj = super(StaticType, cls).__getattribute__(*args)
if isinstance(obj, Static):
obj = obj.__get__(cls, cls.__class__)
return obj
def __setattr__(cls, name, val):
# check if object already exists
obj = cls.__dict__.get(name)
if isinstance(obj, Static):
obj.__set__(name, val)
else:
super(StaticType, cls).__setattr__(name, val)
使用中:
class MyStatic(metaclass=StaticType):
"""
Testing static vars
"""
a = Static(9)
b = Static(12)
c = 3
class YourStatic(MyStatic):
d = Static('woo hoo')
e = Static('doo wop')
以及一些测试:
ms1 = MyStatic()
ms2 = MyStatic()
ms3 = MyStatic()
assert ms1.a == ms2.a == ms3.a == MyStatic.a
assert ms1.b == ms2.b == ms3.b == MyStatic.b
assert ms1.c == ms2.c == ms3.c == MyStatic.c
ms1.a = 77
assert ms1.a == ms2.a == ms3.a == MyStatic.a
ms2.b = 99
assert ms1.b == ms2.b == ms3.b == MyStatic.b
MyStatic.a = 101
assert ms1.a == ms2.a == ms3.a == MyStatic.a
MyStatic.b = 139
assert ms1.b == ms2.b == ms3.b == MyStatic.b
del MyStatic.b
for inst in (ms1, ms2, ms3):
try:
getattr(inst, 'b')
except AttributeError:
pass
else:
print('AttributeError not raised on %r' % attr)
ms1.c = 13
ms2.c = 17
ms3.c = 19
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19
MyStatic.c = 43
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19
ys1 = YourStatic()
ys2 = YourStatic()
ys3 = YourStatic()
MyStatic.b = 'burgler'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
assert ys1.d == ys2.d == ys3.d == YourStatic.d
assert ys1.e == ys2.e == ys3.e == YourStatic.e
ys1.a = 'blah'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
ys2.b = 'kelp'
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
ys1.d = 'fee'
assert ys1.d == ys2.d == ys3.d == YourStatic.d
ys2.e = 'fie'
assert ys1.e == ys2.e == ys3.e == YourStatic.e
MyStatic.a = 'aargh'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
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