Python:用一些不可擦的东西来腌制dict

我将使用pickler对持久对象引用的文档化支持。持久对象引用是由pickle引用但不存储在pickle中的对象。

http://docs.python.org/library/pickle.html#pickling-and-unpickling-external-objects

你可以这样开始(未经测试):

import cPickle

def persistent_id(obj):
    if isinstance(obj, wxObject):
        return "filtered:wxObject"
    else:
        return None

class FilteredObject:
    def __init__(self, about):
        self.about = about
    def __repr__(self):
        return 'FilteredObject(%s)' % repr(self.about)

def persistent_load(obj_id):
    if obj_id.startswith('filtered:'):
        return FilteredObject(obj_id[9:])
    else:
        raise cPickle.UnpicklingError('Invalid persistent id')

def dump_filtered(obj, file):
    p = cPickle.Pickler(file)
    p.persistent_id = persistent_id
    p.dump(obj)

def load_filtered(file)
    u = cPickle.Unpickler(file)
    u.persistent_load = persistent_load
    return u.load()

然后只调用dump_filtered()和load_filtered(),而不是pickle.dump()和pickle.load()。wxPython对象将被pickle为持久id,在取消pickling时将被FilteredObjects替换。

__getstate__ 方法。

更新

from cPickle import Pickler, Unpickler, UnpicklingError


class FilteredObject:
    def __init__(self, about):
        self.about = about
    def __repr__(self):
        return 'FilteredObject(%s)' % repr(self.about)


class MyPickler(object):

    def __init__(self, file, protocol=0):
        pickler = Pickler(file, protocol)
        pickler.persistent_id = self.persistent_id
        self.dump = pickler.dump
        self.clear_memo = pickler.clear_memo

    def persistent_id(self, obj):
        if not hasattr(obj, '__getstate__') and not isinstance(obj,
            (basestring, int, long, float, tuple, list, set, dict)):
            return "filtered:%s" % type(obj)
        else:
            return None


class MyUnpickler(object):

    def __init__(self, file):
        unpickler = Unpickler(file)
        unpickler.persistent_load = self.persistent_load
        self.load = unpickler.load
        self.noload = unpickler.noload

    def persistent_load(self, obj_id):
        if obj_id.startswith('filtered:'):
            return FilteredObject(obj_id[9:])
        else:
            raise UnpicklingError('Invalid persistent id')


if __name__ == '__main__':
    from cStringIO import StringIO

    class UnpickleableThing(object):
        pass

    f = StringIO()
    p = MyPickler(f)
    p.dump({'a': 1, 'b': UnpickleableThing()})

    f.seek(0)
    u = MyUnpickler(f)
    obj = u.load()
    print obj

    assert obj['a'] == 1
    assert isinstance(obj['b'], FilteredObject)
    assert obj['b'].about

我就是这样做的(我以前也做过类似的事情,而且成功了):

2. 根据上面的函数,列出所有可pickle变量
3. 创建一个新的字典(称为D),存储所有不可pickle的变量
4. 对于D中的每个变量(只有在D中有非常相似的变量时才有效) 列一个字符串列表,其中每个字符串都是合法的python代码,这样 当所有这些字符串都按顺序执行时,您将获得所需的变量

希望这有帮助

最后我用Shane Hathaway的方法编写了自己的解决方案。

Here's the code CutePickler 和 CuteUnpickler .) Here are the tests GarlicSim ,所以你可以通过 installing garlicsim 和做 from garlicsim.general_misc import pickle_tools

如果要在Python3代码中使用它,请使用 Python 3 fork of garlicsim .

一种方法是继承 pickle.Pickler save_dict() 方法。从基类复制它,如下所示:

def save_dict(self, obj):
    write = self.write

    if self.bin:
        write(EMPTY_DICT)
    else:   # proto 0 -- can't use EMPTY_DICT
        write(MARK + DICT)

    self.memoize(obj)
    self._batch_setitems(obj.iteritems())

但是,在批处理setitems中,传递一个迭代器,该迭代器过滤掉所有不希望转储的项,例如

def save_dict(self, obj):
    write = self.write

    if self.bin:
        write(EMPTY_DICT)
    else:   # proto 0 -- can't use EMPTY_DICT
        write(MARK + DICT)

    self.memoize(obj)
    self._batch_setitems(item for item in obj.iteritems() 
                         if not isinstance(item[1], bad_type))

由于save-dict不是一个正式的API,您需要检查每个新的Python版本是否仍然正确。

过滤部分确实很棘手。使用简单的技巧,你可以很容易地得到腌菜的工作。但是,你可能会过滤掉太多的内容,并且在过滤器看起来更深一点的时候丢失了你可以保留的信息。但最终可能发生的事情 .namespace

但是,我们可以利用已经是Python一部分的部分,比如 deepcopy 在 copy 模块。

我复印了一份股票 模块,并执行以下操作:

1. 创建名为 LostObject 表示将在酸洗过程中丢失的物体。
2. _deepcopy_atomic 以确保 x 是可挑选的。如果不是,则返回
3. 对象可以定义方法 __reduce__ 和/或 __reduce_ex__ 提供是否以及如何腌制的提示。我们确保这些方法不会抛出异常,以提供无法对其进行pickle的提示。
4. 洛杉矶 实际的deepcopy),我们递归地检查对象是否可拾取,并且只生成不可拾取的部分。例如,对于一个可选取列表和一个不可选取对象的元组,我们将复制一个元组-只是容器-而不是它的成员列表。

[~/Development/scratch/] $ diff -uN  /System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/copy.py mcopy.py
--- /System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/copy.py  2010-01-09 00:18:38.000000000 -0800
+++ mcopy.py    2010-11-10 08:50:26.000000000 -0800
@@ -157,6 +157,13 @@

     cls = type(x)

+    # if x is picklable, there is no need to make a new copy, just ref it
+    try:
+        dumps(x)
+        return x
+    except TypeError:
+        pass
+
     copier = _deepcopy_dispatch.get(cls)
     if copier:
         y = copier(x, memo)
@@ -179,10 +186,18 @@
                     reductor = getattr(x, "__reduce_ex__", None)
                     if reductor:
                         rv = reductor(2)
+                        try:
+                            x.__reduce_ex__()
+                        except TypeError:
+                            rv = LostObject, tuple()
                     else:
                         reductor = getattr(x, "__reduce__", None)
                         if reductor:
                             rv = reductor()
+                            try:
+                                x.__reduce__()
+                            except TypeError:
+                                rv = LostObject, tuple()
                         else:
                             raise Error(
                                 "un(deep)copyable object of type %s" % cls)
@@ -194,7 +209,12 @@

 _deepcopy_dispatch = d = {}

+from pickle import dumps
+class LostObject(object): pass
 def _deepcopy_atomic(x, memo):
+    try:
+        dumps(x)
+    except TypeError: return LostObject()
     return x
 d[type(None)] = _deepcopy_atomic
 d[type(Ellipsis)] = _deepcopy_atomic

现在回到酸洗部分。你只要用这个新的 深度复制

x = dict(a=1)
xx = dict(x=x)
x['xx'] = xx
x['f'] = file('/tmp/1', 'w')
class List():
    def __init__(self, *args, **kwargs):
        print 'making a copy of a list'
        self.data = list(*args, **kwargs)
x['large'] = List(range(1000))
# now x contains a loop and a unpickable file object
# the following line will throw
from pickle import dumps, loads
try:
    dumps(x)
except TypeError:
    print 'yes, it throws'

def check_picklable(x):
    try:
        dumps(x)
    except TypeError:
        return False
    return True

class LostObject(object): pass

from mcopy import deepcopy

# though x has a big List object, this deepcopy will not make a new copy of it
c = deepcopy(x)
dumps(c)
cc = loads(dumps(c))
# check loop refrence
if cc['xx']['x'] == cc:
    print 'yes, loop reference is preserved'
# check unpickable part
if isinstance(cc['f'], LostObject):
    print 'unpicklable part is now an instance of LostObject'
# check large object
if loads(dumps(c))['large'].data[999] == x['large'].data[999]:
    print 'large object is ok'

输出如下:

making a copy of a list
yes, it throws
yes, loop reference is preserved
unpicklable part is now an instance of LostObject
large object is ok

你看到1)相互的指针 十 和 xx )是保留的,我们不会进入无限循环;2)不可拾取的文件对象被转换为 实例;和3)不会创建大对象的新副本,因为它是可拾取的。