Python是一门弱类型语言，很多从C/C++转过来的朋友起初不是很适应。比如，在声明一个函数时，不能指定参数的类型。用C做类比，那就是所有参数都是void*类型！void类型强制转换在C++中被广泛地认为是个坏习惯，不到万不得已是不会使用的。

Python自然没有类型强制转换一说了，因为它是动态语言。首先，所有对象都从Object继承而来，其次，它有强大的内省，如果调用某个不存在的方法会有异常抛出。大多数情况，我们都不需要做参数类型栓查，除了一些特殊情况。例如，某个函数接受一个str类型，结果在实际调用时传入的是unicode，测试过程中又没有代码覆盖到，这样问题就比较严重了。解决方法也很简单，借助Python的内省，很容易就能判断出参数的类型。但是每个地方都写检查代码会很累赘，何况它带来的实际价值并不高。一个好的解决方法是使用装饰器。

'''

>>> NONE, MEDIUM, STRONG = 0, 1, 2

>>>

>>> @accepts(int, int, int)

... def average(x, y, z):

... return (x + y + z) / 2

...

>>> average(5.5, 10, 15.0)

TypeWarning: 'average' method accepts (int, int, int), but was given

(float, int, float)

15.25

'''

def accepts(*types, **kw):

""" Function decorator. Checks that inputs given to decorated function

are of the expected type.

Parameters:

types -- The expected types of the inputs to the decorated function.

Must specify type for each parameter.

kw -- Optional specification of 'debug' level (this is the only valid

keyword argument, no other should be given).

debug = ( 0 | 1 | 2 )

"""

if not kw:

# default level: MEDIUM

debug = 1

else:

debug = kw['debug']

try:

def decorator(f):

def newf(*args):

if debug == 0:

return f(*args)

assert len(args) == len(types)

argtypes = tuple(map(type, args))

if argtypes != types:

msg = info(f.__name__, types, argtypes, 0)

if debug == 1:

print >> sys.stderr, 'TypeWarning: ', msg

elif debug == 2:

raise TypeError, msg

return f(*args)

newf.__name__ = f.__name__

return newf

return decorator

except KeyError, key:

raise KeyError, key + "is not a valid keyword argument"

except TypeError, msg:

raise TypeError, msg

def info(fname, expected, actual, flag):

""" Convenience function returns nicely formatted error/warning msg. """

format = lambda types: ', '.join([str(t).split("'")[1] for t in types])

expected, actual = format(expected), format(actual)

msg = "'%s' method " % fname \

+ ("accepts", "returns")[flag] + " (%s), but " % expected\

+ ("was given", "result is")[flag] + " (%s)" % actual

return msg