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Question: How can I Intercept __getitem__ calls on an object attribute?
Explanation:
So, the scenario is the following. I have an object that stores a dict-like object as an attribute. Every time the __getitem__ method of this attribute gets called, I want to intercept that call and do some special processing on the fetched item depending on the key. What I want would look something like this:
class Test:
def __init__(self):
self._d = {'a': 1, 'b': 2}
#property
def d(self, key):
val = self._d[key]
if key == 'a':
val += 2
return val
t = Test()
assert(t.d['a'] == 3) # Should not throw AssertionError
The problem is that the #property method doesn't actually have access to the key in the __getitem__ call, so I can't check for it at all to do my special postprocessing step.
Important Note: I can't just subclass a MutableMapping, override the __getitem__ method of my subclass to do this special processing, and store an instance of the subclass in self._d. In my actual code self._d is already a subclass of MutableMapping and other clients of this subclass need access to the unmodified data.
Thanks for any and all help!
One solution would be a Mapping that proxies the underlying mapping. The d property would wrap the underlying self._d mapping in the proxy wrapper and return it, and use of that proxy would exhibit the necessary behaviors. Example:
from collections.abc import Mapping
class DProxy(Mapping):
__slots__ = ('proxymap',)
def __init__(self, proxymap):
self.proxymap = proxymap
def __getitem__(self, key):
val = self.proxymap[key]
if key == 'a':
val += 2
return val
def __iter__(self):
return iter(self.proxymap)
def __len__(self):
return len(self.proxymap)
Once you've made that, your original class can be:
class Test:
def __init__(self):
self._d = {'a': 1, 'b': 2}
#property
def d(self):
return DProxy(self._d)
Users would then access instances of Test with test.d[somekey]; test.d would return the proxy, which would then modify the result of __getitem__ as needed for somekey. They could even store off references with locald = test.d and then use locald while preserving the necessary proxy behaviors. You can make it a MutableMapping if needed, but a plain Mapping-based proxy avoids complexity when the goal is reading the values, never modifying them through the proxy.
Yes, this makes a new DProxy instance on each access to d; you could cache it if you like, but given how simple the DProxy class's __init__ is, the cost is only meaningful if qualified access via the d attribute is performed frequently on the hottest of code paths.
Here's a fairly similar approach to ShadowRanger's. It's a bit shorter, as it inherits from dict directly, so there's less explicit delegation to define.
class DictProxy(dict):
def __getitem__(self, item):
val = super().__getitem__(item)
if item == 'a':
val += 2
return val
class Test:
def __init__(self):
self._d = {'a': 1, 'b': 2}
#property
def d(self):
return DictProxy(self._d)
t = Test()
assert(t.d['a'] == 3) # Does not throw AssertionError anymore :)
In terms of behavior, it really comes down to taste. There's nothing wrong with either approach.
EDIT: Thanks to ShadowRanger for pointing out that this solution actually copies the dictionary every time. Therefore, it's probably better to use his explicit delegation solution, which uses the same internal dictionary representation. It'll be more efficient that way, and if you ever want to change your proxy in the future so that it actually affects the original data structure, his approach will make it a lot easier to make those future changes.
No shallow copying, shortest, and with modification possibilities:
from collections import UserDict
class DictProxy(UserDict):
def __init__(self, d):
self.data = d
def __getitem__(self, item):
val = super().__getitem__(item)
if item == 'a':
val += 2
return val
A frozen set is a frozenset.
A frozen list could be a tuple.
What would a frozen dict be? An immutable, hashable dict.
I guess it could be something like collections.namedtuple, but that is more like a frozen-keys dict (a half-frozen dict). Isn't it?
A "frozendict" should be a frozen dictionary, it should have keys, values, get, etc., and support in, for, etc.
update :
* there it is : https://www.python.org/dev/peps/pep-0603
Python doesn't have a builtin frozendict type. It turns out this wouldn't be useful too often (though it would still probably be useful more often than frozenset is).
The most common reason to want such a type is when memoizing function calls for functions with unknown arguments. The most common solution to store a hashable equivalent of a dict (where the values are hashable) is something like tuple(sorted(kwargs.items())).
This depends on the sorting not being a bit insane. Python cannot positively promise sorting will result in something reasonable here. (But it can't promise much else, so don't sweat it too much.)
You could easily enough make some sort of wrapper that works much like a dict. It might look something like
import collections
class FrozenDict(collections.Mapping):
"""Don't forget the docstrings!!"""
def __init__(self, *args, **kwargs):
self._d = dict(*args, **kwargs)
self._hash = None
def __iter__(self):
return iter(self._d)
def __len__(self):
return len(self._d)
def __getitem__(self, key):
return self._d[key]
def __hash__(self):
# It would have been simpler and maybe more obvious to
# use hash(tuple(sorted(self._d.iteritems()))) from this discussion
# so far, but this solution is O(n). I don't know what kind of
# n we are going to run into, but sometimes it's hard to resist the
# urge to optimize when it will gain improved algorithmic performance.
if self._hash is None:
hash_ = 0
for pair in self.items():
hash_ ^= hash(pair)
self._hash = hash_
return self._hash
It should work great:
>>> x = FrozenDict(a=1, b=2)
>>> y = FrozenDict(a=1, b=2)
>>> x is y
False
>>> x == y
True
>>> x == {'a': 1, 'b': 2}
True
>>> d = {x: 'foo'}
>>> d[y]
'foo'
Curiously, although we have the seldom useful frozenset, there's still no frozen mapping. The idea was rejected in PEP 416 -- Add a frozendict builtin type. This idea may be revisited in a later Python release, see PEP 603 -- Adding a frozenmap type to collections.
So the Python 2 solution to this:
def foo(config={'a': 1}):
...
Still seems to be the usual:
def foo(config=None):
if config is None:
config = {'a': 1} # default config
...
In Python 3 you have the option of this:
from types import MappingProxyType
default_config = {'a': 1}
DEFAULTS = MappingProxyType(default_config)
def foo(config=DEFAULTS):
...
Now the default config can be updated dynamically, but remain immutable where you want it to be immutable by passing around the proxy instead.
So changes in the default_config will update DEFAULTS as expected, but you can't write to the mapping proxy object itself.
Admittedly it's not really the same thing as an "immutable, hashable dict", but it might be a decent substitute for some use cases of a frozendict.
Assuming the keys and values of the dictionary are themselves immutable (e.g. strings) then:
>>> d
{'forever': 'atones', 'minks': 'cards', 'overhands': 'warranted',
'hardhearted': 'tartly', 'gradations': 'snorkeled'}
>>> t = tuple((k, d[k]) for k in sorted(d.keys()))
>>> hash(t)
1524953596
There is no fronzedict, but you can use MappingProxyType that was added to the standard library with Python 3.3:
>>> from types import MappingProxyType
>>> foo = MappingProxyType({'a': 1})
>>> foo
mappingproxy({'a': 1})
>>> foo['a'] = 2
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'mappingproxy' object does not support item assignment
>>> foo
mappingproxy({'a': 1})
I think of frozendict everytime I write a function like this:
def do_something(blah, optional_dict_parm=None):
if optional_dict_parm is None:
optional_dict_parm = {}
Install frozendict
pip install frozendict
Use it!
from frozendict import frozendict
def smth(param = frozendict({})):
pass
Here is the code I've been using. I subclassed frozenset. The advantages of this are the following.
This is a truly immutable object. No relying on the good behavior of future users and developers.
It's easy to convert back and forth between a regular dictionary and a frozen dictionary. FrozenDict(orig_dict) --> frozen dictionary. dict(frozen_dict) --> regular dict.
Update Jan 21 2015: The original piece of code I posted in 2014 used a for-loop to find a key that matched. That was incredibly slow. Now I've put together an implementation which takes advantage of frozenset's hashing features. Key-value pairs are stored in special containers where the __hash__ and __eq__ functions are based on the key only. This code has also been formally unit-tested, unlike what I posted here in August 2014.
MIT-style license.
if 3 / 2 == 1:
version = 2
elif 3 / 2 == 1.5:
version = 3
def col(i):
''' For binding named attributes to spots inside subclasses of tuple.'''
g = tuple.__getitem__
#property
def _col(self):
return g(self,i)
return _col
class Item(tuple):
''' Designed for storing key-value pairs inside
a FrozenDict, which itself is a subclass of frozenset.
The __hash__ is overloaded to return the hash of only the key.
__eq__ is overloaded so that normally it only checks whether the Item's
key is equal to the other object, HOWEVER, if the other object itself
is an instance of Item, it checks BOTH the key and value for equality.
WARNING: Do not use this class for any purpose other than to contain
key value pairs inside FrozenDict!!!!
The __eq__ operator is overloaded in such a way that it violates a
fundamental property of mathematics. That property, which says that
a == b and b == c implies a == c, does not hold for this object.
Here's a demonstration:
[in] >>> x = Item(('a',4))
[in] >>> y = Item(('a',5))
[in] >>> hash('a')
[out] >>> 194817700
[in] >>> hash(x)
[out] >>> 194817700
[in] >>> hash(y)
[out] >>> 194817700
[in] >>> 'a' == x
[out] >>> True
[in] >>> 'a' == y
[out] >>> True
[in] >>> x == y
[out] >>> False
'''
__slots__ = ()
key, value = col(0), col(1)
def __hash__(self):
return hash(self.key)
def __eq__(self, other):
if isinstance(other, Item):
return tuple.__eq__(self, other)
return self.key == other
def __ne__(self, other):
return not self.__eq__(other)
def __str__(self):
return '%r: %r' % self
def __repr__(self):
return 'Item((%r, %r))' % self
class FrozenDict(frozenset):
''' Behaves in most ways like a regular dictionary, except that it's immutable.
It differs from other implementations because it doesn't subclass "dict".
Instead it subclasses "frozenset" which guarantees immutability.
FrozenDict instances are created with the same arguments used to initialize
regular dictionaries, and has all the same methods.
[in] >>> f = FrozenDict(x=3,y=4,z=5)
[in] >>> f['x']
[out] >>> 3
[in] >>> f['a'] = 0
[out] >>> TypeError: 'FrozenDict' object does not support item assignment
FrozenDict can accept un-hashable values, but FrozenDict is only hashable if its values are hashable.
[in] >>> f = FrozenDict(x=3,y=4,z=5)
[in] >>> hash(f)
[out] >>> 646626455
[in] >>> g = FrozenDict(x=3,y=4,z=[])
[in] >>> hash(g)
[out] >>> TypeError: unhashable type: 'list'
FrozenDict interacts with dictionary objects as though it were a dict itself.
[in] >>> original = dict(x=3,y=4,z=5)
[in] >>> frozen = FrozenDict(x=3,y=4,z=5)
[in] >>> original == frozen
[out] >>> True
FrozenDict supports bi-directional conversions with regular dictionaries.
[in] >>> original = {'x': 3, 'y': 4, 'z': 5}
[in] >>> FrozenDict(original)
[out] >>> FrozenDict({'x': 3, 'y': 4, 'z': 5})
[in] >>> dict(FrozenDict(original))
[out] >>> {'x': 3, 'y': 4, 'z': 5} '''
__slots__ = ()
def __new__(cls, orig={}, **kw):
if kw:
d = dict(orig, **kw)
items = map(Item, d.items())
else:
try:
items = map(Item, orig.items())
except AttributeError:
items = map(Item, orig)
return frozenset.__new__(cls, items)
def __repr__(self):
cls = self.__class__.__name__
items = frozenset.__iter__(self)
_repr = ', '.join(map(str,items))
return '%s({%s})' % (cls, _repr)
def __getitem__(self, key):
if key not in self:
raise KeyError(key)
diff = self.difference
item = diff(diff({key}))
key, value = set(item).pop()
return value
def get(self, key, default=None):
if key not in self:
return default
return self[key]
def __iter__(self):
items = frozenset.__iter__(self)
return map(lambda i: i.key, items)
def keys(self):
items = frozenset.__iter__(self)
return map(lambda i: i.key, items)
def values(self):
items = frozenset.__iter__(self)
return map(lambda i: i.value, items)
def items(self):
items = frozenset.__iter__(self)
return map(tuple, items)
def copy(self):
cls = self.__class__
items = frozenset.copy(self)
dupl = frozenset.__new__(cls, items)
return dupl
#classmethod
def fromkeys(cls, keys, value):
d = dict.fromkeys(keys,value)
return cls(d)
def __hash__(self):
kv = tuple.__hash__
items = frozenset.__iter__(self)
return hash(frozenset(map(kv, items)))
def __eq__(self, other):
if not isinstance(other, FrozenDict):
try:
other = FrozenDict(other)
except Exception:
return False
return frozenset.__eq__(self, other)
def __ne__(self, other):
return not self.__eq__(other)
if version == 2:
#Here are the Python2 modifications
class Python2(FrozenDict):
def __iter__(self):
items = frozenset.__iter__(self)
for i in items:
yield i.key
def iterkeys(self):
items = frozenset.__iter__(self)
for i in items:
yield i.key
def itervalues(self):
items = frozenset.__iter__(self)
for i in items:
yield i.value
def iteritems(self):
items = frozenset.__iter__(self)
for i in items:
yield (i.key, i.value)
def has_key(self, key):
return key in self
def viewkeys(self):
return dict(self).viewkeys()
def viewvalues(self):
return dict(self).viewvalues()
def viewitems(self):
return dict(self).viewitems()
#If this is Python2, rebuild the class
#from scratch rather than use a subclass
py3 = FrozenDict.__dict__
py3 = {k: py3[k] for k in py3}
py2 = {}
py2.update(py3)
dct = Python2.__dict__
py2.update({k: dct[k] for k in dct})
FrozenDict = type('FrozenDict', (frozenset,), py2)
You may use frozendict from utilspie package as:
>>> from utilspie.collectionsutils import frozendict
>>> my_dict = frozendict({1: 3, 4: 5})
>>> my_dict # object of `frozendict` type
frozendict({1: 3, 4: 5})
# Hashable
>>> {my_dict: 4}
{frozendict({1: 3, 4: 5}): 4}
# Immutable
>>> my_dict[1] = 5
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/mquadri/workspace/utilspie/utilspie/collectionsutils/collections_utils.py", line 44, in __setitem__
self.__setitem__.__name__, type(self).__name__))
AttributeError: You can not call '__setitem__()' for 'frozendict' object
As per the document:
frozendict(dict_obj): Accepts obj of dict type and returns a hashable and immutable dict
Subclassing dict
i see this pattern in the wild (github) and wanted to mention it:
class FrozenDict(dict):
def __init__(self, *args, **kwargs):
self._hash = None
super(FrozenDict, self).__init__(*args, **kwargs)
def __hash__(self):
if self._hash is None:
self._hash = hash(tuple(sorted(self.items()))) # iteritems() on py2
return self._hash
def _immutable(self, *args, **kws):
raise TypeError('cannot change object - object is immutable')
# makes (deep)copy alot more efficient
def __copy__(self):
return self
def __deepcopy__(self, memo=None):
if memo is not None:
memo[id(self)] = self
return self
__setitem__ = _immutable
__delitem__ = _immutable
pop = _immutable
popitem = _immutable
clear = _immutable
update = _immutable
setdefault = _immutable
example usage:
d1 = FrozenDict({'a': 1, 'b': 2})
d2 = FrozenDict({'a': 1, 'b': 2})
d1.keys()
assert isinstance(d1, dict)
assert len(set([d1, d2])) == 1 # hashable
Pros
support for get(), keys(), items() (iteritems() on py2) and all the goodies from dict out of the box without explicitly implementing them
uses internally dict which means performance (dict is written in c in CPython)
elegant simple and no black magic
isinstance(my_frozen_dict, dict) returns True - although python encourages duck-typing many packages uses isinstance(), this can save many tweaks and customizations
Cons
any subclass can override this or access it internally (you cant really 100% protect something in python, you should trust your users and provide good documentation).
if you care for speed, you might want to make __hash__ a bit faster.
Yes, this is my second answer, but it is a completely different approach. The first implementation was in pure python. This one is in Cython. If you know how to use and compile Cython modules, this is just as fast as a regular dictionary. Roughly .04 to .06 micro-sec to retrieve a single value.
This is the file "frozen_dict.pyx"
import cython
from collections import Mapping
cdef class dict_wrapper:
cdef object d
cdef int h
def __init__(self, *args, **kw):
self.d = dict(*args, **kw)
self.h = -1
def __len__(self):
return len(self.d)
def __iter__(self):
return iter(self.d)
def __getitem__(self, key):
return self.d[key]
def __hash__(self):
if self.h == -1:
self.h = hash(frozenset(self.d.iteritems()))
return self.h
class FrozenDict(dict_wrapper, Mapping):
def __repr__(self):
c = type(self).__name__
r = ', '.join('%r: %r' % (k,self[k]) for k in self)
return '%s({%s})' % (c, r)
__all__ = ['FrozenDict']
Here's the file "setup.py"
from distutils.core import setup
from Cython.Build import cythonize
setup(
ext_modules = cythonize('frozen_dict.pyx')
)
If you have Cython installed, save the two files above into the same directory. Move to that directory in the command line.
python setup.py build_ext --inplace
python setup.py install
And you should be done.
The main disadvantage of namedtuple is that it needs to be specified before it is used, so it's less convenient for single-use cases.
However, there is a practical workaround that can be used to handle many such cases. Let's say that you want to have an immutable equivalent of the following dict:
MY_CONSTANT = {
'something': 123,
'something_else': 456
}
This can be emulated like this:
from collections import namedtuple
MY_CONSTANT = namedtuple('MyConstant', 'something something_else')(123, 456)
It's even possible to write an auxiliary function to automate this:
def freeze_dict(data):
from collections import namedtuple
keys = sorted(data.keys())
frozen_type = namedtuple(''.join(keys), keys)
return frozen_type(**data)
a = {'foo':'bar', 'x':'y'}
fa = freeze_dict(data)
assert a['foo'] == fa.foo
Of course this works only for flat dicts, but it shouldn't be too difficult to implement a recursive version.
freeze implements frozen collections (dict, list and set) that are hashable, type-hinted and will recursively freeze the data you give them (when possible) for you.
pip install frz
Usage:
from freeze import FDict
a_mutable_dict = {
"list": [1, 2],
"set": {3, 4},
}
a_frozen_dict = FDict(a_mutable_dict)
print(repr(a_frozen_dict))
# FDict: {'list': FList: (1, 2), 'set': FSet: {3, 4}}
In the absence of native language support, you can either do it yourself or use an existing solution. Fortunately Python makes it dead simple to extend off of their base implementations.
class frozen_dict(dict):
def __setitem__(self, key, value):
raise Exception('Frozen dictionaries cannot be mutated')
frozen_dict = frozen_dict({'foo': 'FOO' })
print(frozen['foo']) # FOO
frozen['foo'] = 'NEWFOO' # Exception: Frozen dictionaries cannot be mutated
# OR
from types import MappingProxyType
frozen_dict = MappingProxyType({'foo': 'FOO'})
print(frozen_dict['foo']) # FOO
frozen_dict['foo'] = 'NEWFOO' # TypeError: 'mappingproxy' object does not support item assignment
I needed to access fixed keys for something at one point for something that was a sort of globally-constanty kind of thing and I settled on something like this:
class MyFrozenDict:
def __getitem__(self, key):
if key == 'mykey1':
return 0
if key == 'mykey2':
return "another value"
raise KeyError(key)
Use it like
a = MyFrozenDict()
print(a['mykey1'])
WARNING: I don't recommend this for most use cases as it makes some pretty severe tradeoffs.
I had the need to implement a hashable dict so I could use a dictionary as a key for another dictionary.
A few months ago I used this implementation: Python hashable dicts
However I got a notice from a colleague saying 'it is not really immutable, thus it is not safe. You can use it, but it does make me feel like a sad Panda'.
So I started looking around to create one that is immutable. I have no need to compare the 'key-dict' to another 'key-dict'. Its only use is as a key for another dictionary.
I have come up with the following:
class HashableDict(dict):
"""Hashable dict that can be used as a key in other dictionaries"""
def __new__(self, *args, **kwargs):
# create a new local dict, that will be used by the HashableDictBase closure class
immutableDict = dict(*args, **kwargs)
class HashableDictBase(object):
"""Hashable dict that can be used as a key in other dictionaries. This is now immutable"""
def __key(self):
"""Return a tuple of the current keys"""
return tuple((k, immutableDict[k]) for k in sorted(immutableDict))
def __hash__(self):
"""Return a hash of __key"""
return hash(self.__key())
def __eq__(self, other):
"""Compare two __keys"""
return self.__key() == other.__key() # pylint: disable-msg=W0212
def __repr__(self):
"""#see: dict.__repr__"""
return immutableDict.__repr__()
def __str__(self):
"""#see: dict.__str__"""
return immutableDict.__str__()
def __setattr__(self, *args):
raise TypeError("can't modify immutable instance")
__delattr__ = __setattr__
return HashableDictBase()
I used the following to test the functionality:
d = {"a" : 1}
a = HashableDict(d)
b = HashableDict({"b" : 2})
print a
d["b"] = 2
print a
c = HashableDict({"a" : 1})
test = {a : "value with a dict as key (key a)",
b : "value with a dict as key (key b)"}
print test[a]
print test[b]
print test[c]
which gives:
{'a': 1}
{'a': 1}
value with a dict as key (key a)
value with a dict as key (key b)
value with a dict as key (key a)
as output
Is this the 'best possible' immutable dictionary that I can use that satisfies my requirements? If not, what would be a better solution?
If you are only using it as a key for another dict, you could go for frozenset(mutabledict.items()). If you need to access the underlying mappings, you could then use that as the parameter to dict.
mutabledict = dict(zip('abc', range(3)))
immutable = frozenset(mutabledict.items())
read_frozen = dict(immutable)
read_frozen['a'] # => 1
Note that you could also combine this with a class derived from dict, and use the frozenset as the source of the hash, while disabling __setitem__, as suggested in another answer. (#RaymondHettinger's answer for code which does just that).
The Mapping abstract base class makes this easy to implement:
import collections
class ImmutableDict(collections.Mapping):
def __init__(self, somedict):
self._dict = dict(somedict) # make a copy
self._hash = None
def __getitem__(self, key):
return self._dict[key]
def __len__(self):
return len(self._dict)
def __iter__(self):
return iter(self._dict)
def __hash__(self):
if self._hash is None:
self._hash = hash(frozenset(self._dict.items()))
return self._hash
def __eq__(self, other):
return self._dict == other._dict
I realize this has already been answered, but types.MappingProxyType is an analogous implementation for Python 3.3. Regarding the original question of safety, there is a discussion in PEP 416 -- Add a frozendict builtin type on why the idea of a frozendict was rejected.
In order for your immutable dictionary to be safe, all it needs to do is never change its hash. Why don't you just disable __setitem__ as follows:
class ImmutableDict(dict):
def __setitem__(self, key, value):
raise Exception("Can't touch this")
def __hash__(self):
return hash(tuple(sorted(self.items())))
a = ImmutableDict({'a':1})
b = {a:1}
print b
print b[a]
a['a'] = 0
The output of the script is:
{{'a': 1}: 1}
1
Traceback (most recent call last):
File "ex.py", line 11, in <module>
a['a'] = 0
File "ex.py", line 3, in __setitem__
raise Exception("Can't touch this")
Exception: Can't touch this
Here is a link to pip install-able implementation of #RaymondHettinger's answer: https://github.com/pcattori/icicle
Simply pip install icicle and you can from icicle import FrozenDict!
Update: icicle has been deprecated in favor of maps: https://github.com/pcattori/maps (documentation, PyPI).
It appears I am late to post. Not sure if anyone else has come up with ideas. But here is my take on it. The Dict is immutable and hashable. I made it immutable by overriding all the methods, magic and otherwise, with a custom '_readonly' function that raises an Exception. This is done when the object is instantiated. To get around the problem of not being able to apply the values I set the 'hash' under '__new__'. I then I override the '__hash__'function. Thats it!
class ImmutableDict(dict):
_HASH = None
def __new__(cls, *args, **kwargs):
ImmutableDict._HASH = hash(frozenset(args[0].items()))
return super(ImmutableDict, cls).__new__(cls, args)
def __hash__(self):
return self._HASH
def _readonly(self, *args, **kwards):
raise TypeError("Cannot modify Immutable Instance")
__delattr__ = __setattr__ = __setitem__ = pop = update = setdefault = clear = popitem = _readonly
Test:
immutabled1 = ImmutableDict({"This": "That", "Cheese": "Blarg"})
dict1 = {immutabled1: "Yay"}
dict1[immutabled1]
"Yay"
dict1
{{'Cheese': 'Blarg', 'This': 'That'}: 'Yay'}
Variation of Raymond Hettinger's answer by wrapping the self._dict with types.MappingProxyType.
class ImmutableDict(collections.Mapping):
"""
Copies a dict and proxies it via types.MappingProxyType to make it immutable.
"""
def __init__(self, somedict):
dictcopy = dict(somedict) # make a copy
self._dict = MappingProxyType(dictcopy) # lock it
self._hash = None
def __getitem__(self, key):
return self._dict[key]
def __len__(self):
return len(self._dict)
def __iter__(self):
return iter(self._dict)
def __hash__(self):
if self._hash is None:
self._hash = hash(frozenset(self._dict.items()))
return self._hash
def __eq__(self, other):
return self._dict == other._dict
def __repr__(self):
return str(self._dict)
You can use an enum:
import enum
KeyDict1 = enum.Enum('KeyDict1', {'InnerDictKey1':'bla', 'InnerDictKey2 ':2})
d = { KeyDict1: 'whatever', KeyDict2: 1, ...}
You can access the enums like you would a dictionary:
KeyDict1['InnerDictKey2'].value # This is 2
You can iterate over the names, and get their values... It does everything you'd expect.
You can try using https://github.com/Lightricks/freeze
It provides recursively immutable and hashable dictionaries
from freeze import FDict
a_mutable_dict = {
"list": [1, 2],
"set": {3, 4},
}
a_frozen_dict = FDict(a_mutable_dict)
print(a_frozen_dict)
print(hash(a_frozen_dict))
# FDict: {'list': FList: (1, 2), 'set': FSet: {3, 4}}
# -4855611361973338606
Is there any equivalent to KeyedCollection in Python, i.e. a set where the elements have (or dynamically generate) their own keys?
i.e. the goal here is to avoid storing the key in two places, and therefore dictionaries are less than ideal (hence the question).
You can simulate that very easily:
class KeyedObject(object):
def get_key(self):
raise NotImplementedError("You must subclass this before you can use it.")
class KeyedDict(dict):
def append(self, obj):
self[obj.get_key()] = obj
Now you can use a KeyedDict instead of dict with subclasses of KeyedObject (where get_key return a valid key based on some object property).
Given your constraints, everyone trying to implement what you're looking for using a dict is barking up the wrong tree. Instead, you should write a list subclass that overrides __getitem__ to provide the behavior you want. I've written it so it tries to get the desired item by index first, then falls back to searching for the item by the key attribute of the contained objects. (This could be a property if the object needs to determine this dynamically.)
There's no way to avoid a linear search if you don't want to duplicate something somewhere; I am sure the C# implementation does exactly the same thing if you don't allow it to use a dictionary to store the keys.
class KeyedCollection(list):
def __getitem__(self, key):
if isinstance(key, int) or isinstance(key, slice):
return list.__getitem__(key)
for item in self:
if getattr(item, "key", 0) == key:
return item
raise KeyError('item with key `%s` not found' % key)
You would probably also want to override __contains__ in a similar manner so you could say if "key" in kc.... If you want to make it even more like a dict, you could also implement keys() and so on. They will be equally inefficient, but you will have an API like a dict, that also works like a list.
#Mehrdad said:
Because semantically, it doesn't make as much sense. When an object
knows its key, it doesn't make sense to put it in a dictionary -- it's
not a key-value pair. It's more of a semantic issue than anything
else.
With this constraint, there is nothing in Python that does what you want. I suggest you use a dict and not worry about this level of detail on the semantics. #Gabi Purcaru's answer shows how you can create an object with the interface you want. Why get bothered about how it's working internally?
It could be that C#'s KeyedCollection is doing the same thing under the covers: asking the object for its key and then storing the key for fast access. In fact, from the docs:
By default, the KeyedCollection(Of TKey, TItem) includes a lookup
dictionary that you can obtain with the Dictionary property. When an
item is added to the KeyedCollection(Of TKey, TItem), the item's key
is extracted once and saved in the lookup dictionary for faster
searches. This behavior is overridden by specifying a dictionary
creation threshold when you create the KeyedCollection(Of TKey,
TItem). The lookup dictionary is created the first time the number of
elements exceeds that threshold. If you specify –1 as the threshold,
the lookup dictionary is never created.
I'm not much of a C#'er, but I think dictionaries is what you need.
http://docs.python.org/tutorial/datastructures.html#dictionaries
http://docs.python.org/tutorial/datastructures.html
Or maybe lists:
http://docs.python.org/library/functions.html#list
Why not simply use a dict? If the key already exists, a reference to the key will be used in the dict; it won't be senselessly duplicated.
class MyExample(object):
def __init__(self, key, value):
self.key = key
self.value = value
m = MyExample("foo", "bar")
d = {}
d[m.key] = m
first_key = d.keys()[0]
first_key is m.key # returns True
If the key doesn't already exist, a copy of it will be saved, but I don't see that as a problem.
def lame_hash(s):
h = 0
for ch in s:
h ^= ord(ch)
return h
d = {}
d[lame_hash(m.key)] = m
print d # key value is 102 which is stored in the dict
lame_hash(m.key) in d # returns True
I'm not sure if this is what you meant, but this dictionary will create it's own keys as you add to it...
class KeyedCollection(dict):
def __init__(self):
self.current_key = 0
def add(self, item):
self[self.current_key] = item
abc = KeyedCollection()
abc.add('bob')
abc.add('jane')
>>> abc
{0: 'bob', 1: 'jane'}
How about a set()? The elements can have their own k
To go a little more in detail that the already correct answer from #Gabi Purcaru's answer, here a class that do the same as gabi one's but that also check for correct given type on key and value (as the TKey and TValue of the .net KeyedCollection).
class KeyedCollection(MutableMapping):
"""
Provides the abstract base class for a collection (:class:`MutableMappinp`) whose keys are embedded in the values.
"""
__metaclass__ = abc.ABCMeta
_dict = None # type: dict
def __init__(self, seq={}):
self._dict = dict(seq)
#abc.abstractmethod
def __is_type_key_correct__(self, key):
"""
Returns: The type of keys in the collection
"""
pass
#abc.abstractmethod
def __is_type_value_correct__(self, value):
"""
Returns: The type of values in the collection
"""
pass
#abc.abstractmethod
def get_key_for_item(self, value):
"""
When implemented in a derivated class, extracts the key from the specified element.
Args:
value: the element from which to extract the key (of type specified by :meth:`type_value`)
Returns: The key of specified element (of type specified by :meth:`type_key`)
"""
pass
def __assert_type_key(self, key, arg_name='key'):
if not self.__is_type_key_correct__(key) :
raise ValueError("{} type is not correct".format(arg_name))
def __assert_type_value(self, value, arg_name='value'):
if not self.__is_type_value_correct__(value) :
raise ValueError("{} type is not correct".format(arg_name))
def add(self, value):
"""
Adds an object to the KeyedCollection.
Args:
value: The object to be added to the KeyedCollection (of type specified by :meth:`type_value`).
"""
key = self.get_key_for_item(value)
self._dict[key] = value
# Implements abstract method __setitem__ from MutableMapping parent class
def __setitem__(self, key, value):
self.__assert_type_key(key)
self.__assert_type_value(value)
if value.get_key() != key:
raise ValueError("provided key does not correspond to the given KeyedObject value")
self._dict[key] = value
# Implements abstract method __delitem__ from MutableMapping parent class
def __delitem__(self, key):
self.__assert_type_key(key)
self._dict.pop(key)
# Implements abstract method __getitem__ from MutableMapping parent class (Mapping base class)
def __getitem__(self, key):
self.__assert_type_key(key)
return self._dict[key]
# Implements abstract method __len__ from MutableMapping parent class (Sized mixin on Mapping base class)
def __len__(self):
return len(self._dict)
# Implements abstract method __iter__ from MutableMapping parent class (Iterable mixin on Mapping base class)
def __iter__(self):
return iter(self._dict)
pass
# Implements abstract method __contains__ from MutableMapping parent class (Container mixin on Mapping base class)
def __contains__(self, x):
self.__assert_type_key(x, 'x')
return x in self._dict
I need to "flatten" objects into nested dicts of the object's properties. The objects I want to do this with are generally just containers for basic types or other objects which act in a similar way. For example:
class foo(object):
bar = None
baz = None
class spam(object):
eggs = []
x = spam()
y = foo()
y.bar = True
y.baz = u"boz"
x.eggs.append(y)
What I need to "flatten" this to is:
{ 'eggs': [ { 'bar': True, 'baz': u'boz' } ] }
Is there anything in the stdlib which can do this for me? If not, would I have to test isinstance against all known base-types to ensure I don't try to convert an object which can't be converted (eg: bool)?
Edit:
These are objects are being returned to my code from an external library and therefore I have no control over them. I could use them as-is in my methods, but it would be easier (safer?) to convert them to dicts - especially for unit testing.
Code: You may need to handle other iterable types though:
def flatten(obj):
if obj is None:
return None
elif hasattr(obj, '__dict__') and obj.__dict__:
return dict([(k, flatten(v)) for (k, v) in obj.__dict__.items()])
elif isinstance(obj, (dict,)):
return dict([(k, flatten(v)) for (k, v) in obj.items()])
elif isinstance(obj, (list,)):
return [flatten(x) for x in obj]
elif isinstance(obj, (tuple,)):
return tuple([flatten(x) for x in obj])
else:
return obj
Bug?
In your code instead of:
class spam(object):
eggs = []
x = spam()
x.eggs.add(...)
please do:
class spam(object):
eggs = None #// if you need this line at all though
x = spam()
x.eggs = []
x.eggs.add(...)
If you do not, then all instances of spam will share the same eggs list.
No, there is nothing in the standardlib. Yes, you would have to somehow test that the types are basic types like str, unicode, bool, int, float, long...
You could probably make a registry of methods to "serialize" different types, but that would only be useful if you have some types that should not have all it's attributes serialized, for example, or if you also need to flatten class attributes, etc.
Almost every object has a dictionary (called __dict__), with all its methods and members.
With some type checking, you can then write a function that filters out only the members you are interested in.
It is not a big task, but as chrispy said, it could worth to try looking at your problem from a completely different perspective.
Well, I'm not very proud of this, but is possible to do the following:
Create a super class that has the serialization method and loop through its properties.
At runtime extend your classes using bases at runtime
Execute the class from the new super class. It should be able to access the dict data from the children and work.
Here is an example:
class foo(object):
def __init__(self):
self.bar = None
self.baz = None
class spam(object):
delf __init__(self):
self.eggs = []
class Serializable():
def serialize(self):
result = {}
for property in self.__dict__.keys():
result[property] = self.__dict__[property]
return result
foo.__bases__ += (Serializable,)
spam.__bases__ += (Serializable,)
x = spam()
y = foo()
y.bar = True
y.baz = u"boz"
x.eggs.append(y)
y.serialize()
Things to point out. If you do not set the var is init the dict willnot work 'cause it is accessing the instance variables not the class variables (I suppose you meant instance ones). Second, make sure Serializable DOES NOT inherit from object, it is does you will have a
TypeError: Error when calling the metaclass bases
Cannot create a consistent method resolution
Hope it helps!
Edit: If you are just copying the dict use the deepcopy module, this is just an example :P