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Python simple lazy loading

I'm trying to clean up some logic and remove duplicate values in some code and am looking for a way to introduce some very simple lazy-loading to handle settings variables. Something that would work like this:
FOO = {'foo': 1}
BAR = {'test': FOO['foo'] }
# ...complex logic here which ultimately updates the value of Foo['foo']...
FOO['foo'] = 2
print(BAR['test']) # Outputs 1 but would like to get 2
Update:
My question may not have been clear based on the initial responses. I'm looking to replace the value being set for test in BAR with a lazy-loaded substitute. I know a way I can do this but it seems unnecessarily complex for what it is, I'm wondering if there's a simpler approach.
Update #2:
Okay, here's a solution that works. Is there any built-in type that can do this out of the box:
FOO = {'foo': 1}
import types
class LazyDict(dict):
def __getitem__(self, item):
value = super().__getitem__(item)
return value if not isinstance(value, types.LambdaType) else value()
BAR = LazyDict({ 'test': lambda: FOO['foo'] })
# ...complex logic here which ultimately updates the value of Foo['foo']...
FOO['foo'] = 2
print(BAR['test']) # Outputs 2

As I stated in the comment above, what you are seeking is some of the facilities of reactive programming paradigm. (not to be confounded with the JavaScript library which borrows its name from there).
It is possible to instrument objects in Python to do so - I think the minimum setup here would be a specialized target mapping, and a special object type you set as the values in it, that would fetch the target value.
Python can do this in more straightforward ways with direct attribute access (using the dot notation: myinstance.value) than by using the key-retrieving notation used in dictionaries mydata['value'] due to the fact a class is already a template to a certain data group, and class attributes can define mechanisms to access each instance's attribute value. That is called the "descriptor protocol" and is bound into the language model itself.
Nonetheless a minimalist Mapping based version can be implemented as such:
FOO = {'foo': 1}
from collections.abc import MutableMapping
class LazyValue:
def __init__(self, source, key):
self.source = source
self.key = key
def get(self):
return self.source[self.key]
def __repr__(self):
return f"<LazyValue {self.get()!r}>"
class LazyDict(MutableMapping):
def __init__(self, *args, **kw):
self.data = dict(*args, **kw)
def __getitem__(self, key):
value = self.data[key]
if isinstance(value, LazyValue):
value = value.get()
return value
def __setitem__(self, key, value):
self.data[key] = value
def __delitem__(key):
del self.data[key]
def __iter__(self):
return iter(self.data)
def __len__():
return len(self.data)
def __repr__():
return repr({key: value} for key, value in self.items())
BAR = LazyDict({'test': LazyValue(FOO, 'foo')})
# ...complex logic here which ultimately updates the value of Foo['foo']...
FOO['foo'] = 2
print(BAR['test']) # Outputs 2
The reason this much code is needed is that there are several ways to retrieve data from a dictionary or mapping (.values, .items, .get, .setdefault) and simply inheriting from dict and implementing __getitem__ would "leak" the special lazy object in any of the other methods. Going through this MutableMapping approach ensure a single point of reading of the value in the __getitem__ method - and the resulting instance can be used reliably anywhere a mapping is expected.
However, notice that if you are using normal classes and instances rather than dictionaries, this can be much simpler - you can just use plain Python "property" and have a getter that will fetch the value. The main factor you should ponder is whether your referenced data keys are fixed, and can be hard-coded when writting the source code, or if they are dynamic, and which keys will work as lazy-references are only known at runtime. In this last case, the custom mapping approach, as above, will be usually better:
FOO = {'foo': 1}
class LazyStuff:
def __init__(self, source):
self.source = source
#property
def test(self):
return self.source["foo"]
BAR = LazyStuff(FOO)
FOO["foo"] = 2
print(BAR.test)
Perceive that in this way you have to hardcode the key "foo" and "test" in the class body, but it is just plaincode, and no need for the intermediary "LazyValue" class. Also, if you need this data as a dictionary, you could add an .as_dict method to LazyStuff that would collect all attributes in the moment it were called and yield a snapshot of those values as a dictionary..

You can try using lambdas and calling the value on return. Like this:
FOO = {'foo': 1}
BAR = {'test': lambda: FOO['foo'] }
FOO['foo'] = 2
print(BAR['test']()) # Outputs 2

If you're only one level deep, you may wish to try ChainMap, E.g.,
>>> from collections import ChainMap
>>> defaults = {'foo': 42}
>>> myvalues = {}
>>> result = ChainMap(myvalues, defaults)
>>> result['foo']
42
>>> defaults['foo'] = 99
>>> result['foo']
99

How to access a dictionary value from within the same dictionary in Python? [duplicate]

I'm new to Python, and am sort of surprised I cannot do this.
dictionary = {
'a' : '123',
'b' : dictionary['a'] + '456'
}
I'm wondering what the Pythonic way to correctly do this in my script, because I feel like I'm not the only one that has tried to do this.
EDIT: Enough people were wondering what I'm doing with this, so here are more details for my use cases. Lets say I want to keep dictionary objects to hold file system paths. The paths are relative to other values in the dictionary. For example, this is what one of my dictionaries may look like.
dictionary = {
'user': 'sholsapp',
'home': '/home/' + dictionary['user']
}
It is important that at any point in time I may change dictionary['user'] and have all of the dictionaries values reflect the change. Again, this is an example of what I'm using it for, so I hope that it conveys my goal.
From my own research I think I will need to implement a class to do this.

No fear of creating new classes -
You can take advantage of Python's string formating capabilities
and simply do:
class MyDict(dict):
def __getitem__(self, item):
return dict.__getitem__(self, item) % self
dictionary = MyDict({
'user' : 'gnucom',
'home' : '/home/%(user)s',
'bin' : '%(home)s/bin'
})
print dictionary["home"]
print dictionary["bin"]

Nearest I came up without doing object:
dictionary = {
'user' : 'gnucom',
'home' : lambda:'/home/'+dictionary['user']
}
print dictionary['home']()
dictionary['user']='tony'
print dictionary['home']()

>>> dictionary = {
... 'a':'123'
... }
>>> dictionary['b'] = dictionary['a'] + '456'
>>> dictionary
{'a': '123', 'b': '123456'}
It works fine but when you're trying to use dictionary it hasn't been defined yet (because it has to evaluate that literal dictionary first).
But be careful because this assigns to the key of 'b' the value referenced by the key of 'a' at the time of assignment and is not going to do the lookup every time. If that is what you are looking for, it's possible but with more work.

What you're describing in your edit is how an INI config file works. Python does have a built in library called ConfigParser which should work for what you're describing.

This is an interesting problem. It seems like Greg has a good solution. But that's no fun ;)
jsbueno as a very elegant solution but that only applies to strings (as you requested).
The trick to a 'general' self referential dictionary is to use a surrogate object. It takes a few (understatement) lines of code to pull off, but the usage is about what you want:
S = SurrogateDict(AdditionSurrogateDictEntry)
d = S.resolve({'user': 'gnucom',
'home': '/home/' + S['user'],
'config': [S['home'] + '/.emacs', S['home'] + '/.bashrc']})
The code to make that happen is not nearly so short. It lives in three classes:
import abc
class SurrogateDictEntry(object):
__metaclass__ = abc.ABCMeta
def __init__(self, key):
"""record the key on the real dictionary that this will resolve to a
value for
"""
self.key = key
def resolve(self, d):
""" return the actual value"""
if hasattr(self, 'op'):
# any operation done on self will store it's name in self.op.
# if this is set, resolve it by calling the appropriate method
# now that we can get self.value out of d
self.value = d[self.key]
return getattr(self, self.op + 'resolve__')()
else:
return d[self.key]
#staticmethod
def make_op(opname):
"""A convience class. This will be the form of all op hooks for subclasses
The actual logic for the op is in __op__resolve__ (e.g. __add__resolve__)
"""
def op(self, other):
self.stored_value = other
self.op = opname
return self
op.__name__ = opname
return op
Next, comes the concrete class. simple enough.
class AdditionSurrogateDictEntry(SurrogateDictEntry):
__add__ = SurrogateDictEntry.make_op('__add__')
__radd__ = SurrogateDictEntry.make_op('__radd__')
def __add__resolve__(self):
return self.value + self.stored_value
def __radd__resolve__(self):
return self.stored_value + self.value
Here's the final class
class SurrogateDict(object):
def __init__(self, EntryClass):
self.EntryClass = EntryClass
def __getitem__(self, key):
"""record the key and return"""
return self.EntryClass(key)
#staticmethod
def resolve(d):
"""I eat generators resolve self references"""
stack = [d]
while stack:
cur = stack.pop()
# This just tries to set it to an appropriate iterable
it = xrange(len(cur)) if not hasattr(cur, 'keys') else cur.keys()
for key in it:
# sorry for being a duche. Just register your class with
# SurrogateDictEntry and you can pass whatever.
while isinstance(cur[key], SurrogateDictEntry):
cur[key] = cur[key].resolve(d)
# I'm just going to check for iter but you can add other
# checks here for items that we should loop over.
if hasattr(cur[key], '__iter__'):
stack.append(cur[key])
return d
In response to gnucoms's question about why I named the classes the way that I did.
The word surrogate is generally associated with standing in for something else so it seemed appropriate because that's what the SurrogateDict class does: an instance replaces the 'self' references in a dictionary literal. That being said, (other than just being straight up stupid sometimes) naming is probably one of the hardest things for me about coding. If you (or anyone else) can suggest a better name, I'm all ears.
I'll provide a brief explanation. Throughout S refers to an instance of SurrogateDict and d is the real dictionary.
A reference S[key] triggers S.__getitem__ and SurrogateDictEntry(key) to be placed in the d.
When S[key] = SurrogateDictEntry(key) is constructed, it stores key. This will be the key into d for the value that this entry of SurrogateDictEntry is acting as a surrogate for.
After S[key] is returned, it is either entered into the d, or has some operation(s) performed on it. If an operation is performed on it, it triggers the relative __op__ method which simple stores the value that the operation is performed on and the name of the operation and then returns itself. We can't actually resolve the operation because d hasn't been constructed yet.
After d is constructed, it is passed to S.resolve. This method loops through d finding any instances of SurrogateDictEntry and replacing them with the result of calling the resolve method on the instance.
The SurrogateDictEntry.resolve method receives the now constructed d as an argument and can use the value of key that it stored at construction time to get the value that it is acting as a surrogate for. If an operation was performed on it after creation, the op attribute will have been set with the name of the operation that was performed. If the class has a __op__ method, then it has a __op__resolve__ method with the actual logic that would normally be in the __op__ method. So now we have the logic (self.op__resolve) and all necessary values (self.value, self.stored_value) to finally get the real value of d[key]. So we return that which step 4 places in the dictionary.
finally the SurrogateDict.resolve method returns d with all references resolved.
That'a a rough sketch. If you have any more questions, feel free to ask.

If you, just like me wandering how to make #jsbueno snippet work with {} style substitutions, below is the example code (which is probably not much efficient though):
import string
class MyDict(dict):
def __init__(self, *args, **kw):
super(MyDict,self).__init__(*args, **kw)
self.itemlist = super(MyDict,self).keys()
self.fmt = string.Formatter()
def __getitem__(self, item):
return self.fmt.vformat(dict.__getitem__(self, item), {}, self)
xs = MyDict({
'user' : 'gnucom',
'home' : '/home/{user}',
'bin' : '{home}/bin'
})
>>> xs["home"]
'/home/gnucom'
>>> xs["bin"]
'/home/gnucom/bin'
I tried to make it work with the simple replacement of % self with .format(**self) but it turns out it wouldn't work for nested expressions (like 'bin' in above listing, which references 'home', which has it's own reference to 'user') because of the evaluation order (** expansion is done before actual format call and it's not delayed like in original % version).

Write a class, maybe something with properties:
class PathInfo(object):
def __init__(self, user):
self.user = user
#property
def home(self):
return '/home/' + self.user
p = PathInfo('thc')
print p.home # /home/thc

As sort of an extended version of #Tony's answer, you could build a dictionary subclass that calls its values if they are callables:
class CallingDict(dict):
"""Returns the result rather than the value of referenced callables.
>>> cd = CallingDict({1: "One", 2: "Two", 'fsh': "Fish",
... "rhyme": lambda d: ' '.join((d[1], d['fsh'],
... d[2], d['fsh']))})
>>> cd["rhyme"]
'One Fish Two Fish'
>>> cd[1] = 'Red'
>>> cd[2] = 'Blue'
>>> cd["rhyme"]
'Red Fish Blue Fish'
"""
def __getitem__(self, item):
it = super(CallingDict, self).__getitem__(item)
if callable(it):
return it(self)
else:
return it
Of course this would only be usable if you're not actually going to store callables as values. If you need to be able to do that, you could wrap the lambda declaration in a function that adds some attribute to the resulting lambda, and check for it in CallingDict.__getitem__, but at that point it's getting complex, and long-winded, enough that it might just be easier to use a class for your data in the first place.

This is very easy in a lazily evaluated language (haskell).
Since Python is strictly evaluated, we can do a little trick to turn things lazy:
Y = lambda f: (lambda x: x(x))(lambda y: f(lambda *args: y(y)(*args)))
d1 = lambda self: lambda: {
'a': lambda: 3,
'b': lambda: self()['a']()
}
# fix the d1, and evaluate it
d2 = Y(d1)()
# to get a
d2['a']() # 3
# to get b
d2['b']() # 3
Syntax wise this is not very nice. That's because of us needing to explicitly construct lazy expressions with lambda: ... and explicitly evaluate lazy expression with ...(). It's the opposite problem in lazy languages needing strictness annotations, here in Python we end up needing lazy annotations.
I think with some more meta-programmming and some more tricks, the above could be made more easy to use.
Note that this is basically how let-rec works in some functional languages.

The jsbueno answer in Python 3 :
class MyDict(dict):
def __getitem__(self, item):
return dict.__getitem__(self, item).format(self)
dictionary = MyDict({
'user' : 'gnucom',
'home' : '/home/{0[user]}',
'bin' : '{0[home]}/bin'
})
print(dictionary["home"])
print(dictionary["bin"])
Her ewe use the python 3 string formatting with curly braces {} and the .format() method.
Documentation : https://docs.python.org/3/library/string.html

Serializing namedtuples via PyYAML

I'm looking for some reasonable way to serialize namedtuples in YAML using PyYAML.
A few things I don't want to do:
Rely on a dynamic call to add a constructor/representor/resolver upon instantiation of the namedtuple. These YAML files may be stored and re-loaded later, so I cannot rely on the same runtime environment existing when they are restored.
Register the namedtuples in global.
Rely on the namedtuples having unique names
I was thinking of something along these lines:
class namedtuple(object):
def __new__(cls, *args, **kwargs):
x = collections.namedtuple(*args, **kwargs)
class New(x):
def __getstate__(self):
return {
"name": self.__class__.__name__,
"_fields": self._fields,
"values": self._asdict().values()
}
return New
def namedtuple_constructor(loader, node):
import IPython; IPython.embed()
value = loader.construct_scalar(node)
import re
pattern = re.compile(r'!!python/object/new:myapp.util\.')
yaml.add_implicit_resolver(u'!!myapp.util.namedtuple', pattern)
yaml.add_constructor(u'!!myapp.util.namedtuple', namedtuple_constructor)
Assuming this was in an application module at the path myapp/util.py
I'm not getting into the constructor, however, when I try to load:
from myapp.util import namedtuple
x = namedtuple('test', ['a', 'b'])
t = x(1,2)
dump = yaml.dump(t)
load = yaml.load(dump)
It will fail to find New in myapp.util.
I tried a variety of other approaches as well, this was just one that I thought might work best.
Disclaimer: Even once I get into the proper constructor I'm aware my spec will need further work regarding what arguments get saved how they are passed into the resulting object, but the first step for me is to get the YAML representation into my constructor function, then the rest should be easy.

I was able to solve my problem, though in a slightly less than ideal way.
My application now uses its own namedtuple implementation; I copied the collections.namedtuple source, created a base class for all new namedtuple types to inherit, and modified the template (excerpts below for brevity, simply highlighting whats change from the namedtuple source).
class namedtupleBase(tuple):
pass
_class_template = '''\
class {typename}(namedtupleBase):
'{typename}({arg_list})'
One little change to the namedtuple function itself to add the new class into the namespace:
namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,
OrderedDict=OrderedDict, _property=property, _tuple=tuple,
namedtupleBase=namedtupleBase)
Now registering a multi_representer solves the problem:
def repr_namedtuples(dumper, data):
return dumper.represent_mapping(u"!namedtupleBase", {
"__name__": data.__class__.__name__,
"__dict__": collections.OrderedDict(
[(k, v) for k, v in data._asdict().items()])
})
def consruct_namedtuples(loader, node):
value = loader.construct_mapping(node)
cls_ = namedtuple(value['__name__'], value['__dict__'].keys())
return cls_(*value['__dict__'].values())
yaml.add_multi_representer(namedtupleBase, repr_namedtuples)
yaml.add_constructor("!namedtupleBase", consruct_namedtuples)
Hattip to Represent instance of different classes with the same base class in pyyaml for the inspiration behind the solution.
Would love an idea that doesn't require re-creating the namedtuple function, but this accomplished my goals.

Would love an idea that doesn't require re-creating the namedtuple function, but this accomplished my goals.
Here you go.
TL;DR
Proof of concept using PyAML 3.12.
import yaml
def named_tuple(self, data):
if hasattr(data, '_asdict'):
return self.represent_dict(data._asdict())
return self.represent_list(data)
yaml.SafeDumper.yaml_multi_representers[tuple] = named_tuple
Note: To be clean you should use one of the add_multi_representer() methods at your disposition and a custom representer/loader, like you did.
This gives you:
>>> import collections
>>> Foo = collections.namedtuple('Foo', 'x y z')
>>> yaml.safe_dump({'foo': Foo(1,2,3), 'bar':(4,5,6)})
'bar: [4, 5, 6]\nfoo: {x: 1, y: 2, z: 3}\n'
>>> print yaml.safe_dump({'foo': Foo(1,2,3), 'bar':(4,5,6)})
bar: [4, 5, 6]
foo: {x: 1, y: 2, z: 3}
How does this work
As you discovered by yourself, a namedtuple does not have a special class; exploring it gives:
>>> collections.namedtuple('Bar', '').mro()
[<class '__main__.Bar'>, <type 'tuple'>, <type 'object'>]
So the instances of the Python named tuples are tuple instances with an additional _asdict() method.

How to create a new unknown or dynamic/expando object in Python

In python how can we create a new object without having a predefined Class and later dynamically add properties to it ?
example:
dynamic_object = Dynamic()
dynamic_object.dynamic_property_a = "abc"
dynamic_object.dynamic_property_b = "abcdefg"
What is the best way to do it?
EDIT Because many people advised in comments that I might not need this.
The thing is that I have a function that serializes an object's properties. For that reason, I don't want to create an object of the expected class due to some constructor restrictions, but instead create a similar one, let's say like a mock, add any "custom" properties I need, then feed it back to the function.

Just define your own class to do it:
class Expando(object):
pass
ex = Expando()
ex.foo = 17
ex.bar = "Hello"

If you take metaclassing approach from #Martijn's answer, #Ned's answer can be rewritten shorter (though it's obviously less readable, but does the same thing).
obj = type('Expando', (object,), {})()
obj.foo = 71
obj.bar = 'World'
Or just, which does the same as above using dict argument:
obj = type('Expando', (object,), {'foo': 71, 'bar': 'World'})()
For Python 3, passing object to bases argument is not necessary (see type documentation).
But for simple cases instantiation doesn't have any benefit, so is okay to do:
ns = type('Expando', (object,), {'foo': 71, 'bar': 'World'})
At the same time, personally I prefer a plain class (i.e. without instantiation) for ad-hoc test configuration cases as simplest and readable:
class ns:
foo = 71
bar = 'World'
Update
In Python 3.3+ there is exactly what OP asks for, types.SimpleNamespace. It's just:
A simple object subclass that provides attribute access to its namespace, as well as a meaningful repr.
Unlike object, with SimpleNamespace you can add and remove attributes. If a SimpleNamespace object is initialized with keyword arguments, those are directly added to the underlying namespace.
import types
obj = types.SimpleNamespace()
obj.a = 123
print(obj.a) # 123
print(repr(obj)) # namespace(a=123)
However, in stdlib of both Python 2 and Python 3 there's argparse.Namespace, which has the same purpose:
Simple object for storing attributes.
Implements equality by attribute names and values, and provides a simple string representation.
import argparse
obj = argparse.Namespace()
obj.a = 123
print(obj.a) # 123
print(repr(obj)) # Namespace(a=123)
Note that both can be initialised with keyword arguments:
types.SimpleNamespace(a = 'foo',b = 123)
argparse.Namespace(a = 'foo',b = 123)

Using an object just to hold values isn't the most Pythonic style of programming. It's common in programming languages that don't have good associative containers, but in Python, you can use use a dictionary:
my_dict = {} # empty dict instance
my_dict["foo"] = "bar"
my_dict["num"] = 42
You can also use a "dictionary literal" to define the dictionary's contents all at once:
my_dict = {"foo":"bar", "num":42}
Or, if your keys are all legal identifiers (and they will be, if you were planning on them being attribute names), you can use the dict constructor with keyword arguments as key-value pairs:
my_dict = dict(foo="bar", num=42) # note, no quotation marks needed around keys
Filling out a dictionary is in fact what Python is doing behind the scenes when you do use an object, such as in Ned Batchelder's answer. The attributes of his ex object get stored in a dictionary, ex.__dict__, which should end up being equal to an equivalent dict created directly.
Unless attribute syntax (e.g. ex.foo) is absolutely necessary, you may as well skip the object entirely and use a dictionary directly.

Use the collections.namedtuple() class factory to create a custom class for your return value:
from collections import namedtuple
return namedtuple('Expando', ('dynamic_property_a', 'dynamic_property_b'))('abc', 'abcdefg')
The returned value can be used both as a tuple and by attribute access:
print retval[0] # prints 'abc'
print retval.dynamic_property_b # prints 'abcdefg'

One way that I found is also by creating a lambda. It can have sideeffects and comes with some properties that are not wanted. Just posting for the interest.
dynamic_object = lambda:expando
dynamic_object.dynamic_property_a = "abc"
dynamic_object.dynamic_property_b = "abcdefg"

I define a dictionary first because it's easy to define. Then I use namedtuple to convert it to an object:
from collections import namedtuple
def dict_to_obj(dict):
return namedtuple("ObjectName", dict.keys())(*dict.values())
my_dict = {
'name': 'The mighty object',
'description': 'Yep! Thats me',
'prop3': 1234
}
my_obj = dict_to_obj(my_dict)

Ned Batchelder's answer is the best. I just wanted to record a slightly different answer here, which avoids the use of the class keyword (in case that's useful for instructive reasons, demonstration of closure, etc.)
Just define your own class to do it:
def Expando():
def inst():
None
return inst
ex = Expando()
ex.foo = 17
ex.bar = "Hello"

Can I use a dynamic mapping to unpack keyword arguments in Python?

Long story short, I want to call format with arbitrarily named arguments, which will preform a lookup.
'{Thing1} and {other_thing}'.format(**my_mapping)
I've tried implementing my_mapping like this:
class Mapping(object):
def __getitem__(self, key):
return 'Proxied: %s' % key
my_mapping = Mapping()
Which works as expected when calling my_mapping['anything']. But when passed to format() as shown above I get:
TypeError: format() argument after ** must be a mapping, not Mapping
I tried subclassing dict instead of object, but now calling format() as shown raises KeyError. I even implemented __contains__ as return True, but still KeyError.
So it seems that ** is not just calling __getitem__ on the object passed in. Does anyone know how to get around this?

In Python 2 you can do this using string.Formatter class.
>>> class Mapping(object):
... def __getitem__(self, key):
... return 'Proxied: %s' % key
...
>>> my_mapping = Mapping()
>>> from string import Formatter
>>> Formatter().vformat('{Thing1} and {other_thing}', (), my_mapping)
'Proxied: Thing1 and Proxied: other_thing'
>>>
vformat takes 3 args: the format string, sequence of positional fields and mapping of keyword fields. Since positional fields weren't needed, I used an empty tuple ().

Python 3.2+:
'{Thing1} and {other_thing}'.format_map(my_mapping)

This may be a bit of necromancy, but I recently came across this problem, and this SO question was the first result. I wasn't happy with using string.Formatter, and wanted it to Just Work (TM).
If you implement a keys() function for your class as well as __getitem__(), then **my_mapping will work.
I.e:
class Mapping(object):
def __getitem__(self, key):
return 'Proxied: %s' % key
def keys(self):
return proxy.keys()
where
>>> my_mapping = Mapping()
>>> my_mapping.keys()
['Thing1','other_thing',...,'Thing2']
will result in a successful mapping that will work with .format.
Apparently (though I haven't actually looked at the source for str.format), it appears to use keys() to get a list of keys, then map the identifiers given in the string to those keys, then use __getitem__() to retrieve the specified values.
Hope this helps.
EDIT:
If you are in #aaron-mcmillin's position, and the key set is large, then a possible approach is to not generate a full set of keys, but generate a smaller subset. This only works of course if you know you will only need to format a small subset.
I.e:
class Mapping(object):
...
def keys(self):
return ['Thing1','other_thing', 'Thing2']

This is the best I could come up with:
If you have a custom mapping object that you want to pass to a func taking key-word arguments, then it must have a set of keys (which may be dynamically generated, but it must be a finite set), and it must be able to map those keys somehow. So, if you can assume that it will have an __iter__ to get the keys, and a __getitem__ that will succeed for each of those keys, e.g.:
class Me(object):
def __init__(self):
pass
def __iter__(self):
return iter(['a', 'b', 'c'])
def __getitem__(self, key):
return 12
Say the function is:
def myfunc(**kwargs):
print kwargs, type(kwargs)
Then we can pass it along by making a dict:
m = Me()
myfunc(**dict((k, m[k]) for k in m))
Resulting in:
{'a': 12, 'c': 12, 'b': 12} <type 'dict'>
Apparently this must be the way it's done... even if you pass in an object derived from dict, the function will still have a dict for the kwargs:
class Me(dict): pass
m = Me()
print type(m) #prints <class '__Main__.Me'>
def myfunc(**kwargs):
print type(kwargs)
myfunc(**m) #prints <type 'dict'>
Since it sounds like you wanted to do something like return a value based on what the key was, without having a particular set of keys in mind, it seems like you can't use the format function.