如何在Python的堆栈/上下文中放置变量

我继续做了一些可以做你想做的事情。它可以同时用作修饰器和上下文管理器:

from __future__ import with_statement
try:
    import cPickle as pickle
except ImportError:
    import pickle


class cached(object):
    """Decorator/context manager for caching function call results.
    All results are cached in one dictionary that is shared by all cached
    functions.

    To use this as a decorator:
        @cached
        def function(...):
            ...

    The results returned by a decorated function are not cleared from the
    cache until decorated_function.clear_my_cache() or cached.clear_cache()
    is called

    To use this as a context manager:

        with cached(function) as function:
            ...
            function(...)
            ...

    The function's return values will be cleared from the cache when the
    with block ends

    To clear all cached results, call the cached.clear_cache() class method
    """

    _CACHE = {}

    def __init__(self, fn):
        self._fn = fn

    def __call__(self, *args, **kwds):
        key = self._cache_key(*args, **kwds)
        function_cache = self._CACHE.setdefault(self._fn, {})
        try:
            return function_cache[key]
        except KeyError:
            function_cache[key] = result = self._fn(*args, **kwds)
            return result

    def clear_my_cache(self):
        """Clear the cache for a decorated function
        """
        try:
            del self._CACHE[self._fn]
        except KeyError:
            pass # no cached results

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):
        self.clear_my_cache()

    def _cache_key(self, *args, **kwds):
        """Create a cache key for the given positional and keyword
        arguments. pickle.dumps() is used because there could be
        unhashable objects in the arguments, but passing them to 
        pickle.dumps() will result in a string, which is always hashable.

        I used this to make the cached class as generic as possible. Depending
        on your requirements, other key generating techniques may be more
        efficient
        """
        return pickle.dumps((args, sorted(kwds.items())), pickle.HIGHEST_PROTOCOL)

    @classmethod
    def clear_cache(cls):
        """Clear everything from all functions from the cache
        """
        cls._CACHE = {}


if __name__ == '__main__':
    # used as decorator
    @cached
    def fibonacci(n):
        print "calculating fibonacci(%d)" % n
        if n == 0:
            return 0
        if n == 1:
            return 1
        return fibonacci(n - 1) + fibonacci(n - 2)

    for n in xrange(10):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))


    def lucas(n):
        print "calculating lucas(%d)" % n
        if n == 0:
            return 2
        if n == 1:
            return 1
        return lucas(n - 1) + lucas(n - 2)

    # used as context manager
    with cached(lucas) as lucas:
        for i in xrange(10):
            print 'lucas(%d) = %d' % (i, lucas(i))

    for n in xrange(9, -1, -1):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))

    cached.clear_cache()

    for n in xrange(9, -1, -1):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))

这个问题似乎是两个问题

• a)共享数据库连接
• b)缓存/记忆

b)你自己回答了

A)我似乎不明白你为什么要把它叠起来? 你可以做这些

1. 您可以使用类和连接 可能是它的属性
2. 你可以装饰你所有的功能 这样他们就可以从 中心位置
3. 每个函数都可以显式使用 全局连接方法
4. 您可以创建连接并传递 或者创建一个上下文 物体和传递 上下文,连接可以是 语境

等

可以使用包装在getter函数中的全局变量:

def getConnection():
    global connection
    if connection:
        return connection
    connection=createConnection()
    return connection

“您收到一个请求,然后打开一个数据库连接….关闭数据库连接。“

这就是对象的用途。创建连接对象,将其传递给其他对象,然后在完成后将其关闭。全局不合适。只需将该值作为参数传递给正在执行该工作的其他对象即可。

“每个报告由多个部分组成,每个部分可以依赖于不同的计算,有时不同的部分部分部分部分依赖于相同的计算….我要把它们藏起来”

这就是对象的用途。创建一个包含有用计算结果的字典,并将其从报表部件传递到报表部件。

您不需要处理“栈变量”、“静态线程局部”或类似的事情。 只需将普通变量参数传递给普通方法函数即可。你会更快乐的。

class MemoizedCalculation( object ):
    pass

class Fibonacci( MemoizedCalculation ):
    def __init__( self ):
       self.cache= { 0: 1, 1: 1 }
    def __call__( self, arg ):
       if arg not in self.cache:
           self.cache[arg]= self(arg-1) + self(arg-2)
       return self.cache[arg]

class MathContext( object ):
    def __init__( self ):
        self.fibonacci = Fibonacci()

你可以这样用

>>> mc= MathContext()
>>> mc.fibonacci( 4 )
5

您可以定义任意数量的计算,并将它们全部折叠到一个容器对象中。

如果需要,可以将MathContext设置为正式的上下文管理器,以便它与 具有 语句。将这两个方法添加到MathContext。

def __enter__( self ):
    print "Initialize"
    return self
def __exit__( self, type_, value, traceback ):
    print "Release"

然后你可以这样做。

with  MathContext() as mc:
    print mc.fibonacci( 4 )

在 具有 声明,你可以保证 __exit__ 方法被调用。