--- /dev/null
+#!/usr/bin/env python
+import new
+
+# Make the environment more like Python 3.0
+__metaclass__ = type
+from itertools import izip as zip
+import textwrap
+import inspect
+
+
+__all__ = [
+ "AnyType",
+ "overloaded"
+]
+
+
+AnyType = object
+
+
+class overloaded:
+ """
+ Dynamically overloaded functions.
+
+ This is an implementation of (dynamically, or run-time) overloaded
+ functions; also known as generic functions or multi-methods.
+
+ The dispatch algorithm uses the types of all argument for dispatch,
+ similar to (compile-time) overloaded functions or methods in C++ and
+ Java.
+
+ Most of the complexity in the algorithm comes from the need to support
+ subclasses in call signatures. For example, if an function is
+ registered for a signature (T1, T2), then a call with a signature (S1,
+ S2) is acceptable, assuming that S1 is a subclass of T1, S2 a subclass
+ of T2, and there are no other more specific matches (see below).
+
+ If there are multiple matches and one of those doesn't *dominate* all
+ others, the match is deemed ambiguous and an exception is raised. A
+ subtlety here: if, after removing the dominated matches, there are
+ still multiple matches left, but they all map to the same function,
+ then the match is not deemed ambiguous and that function is used.
+ Read the method find_func() below for details.
+
+ @note Python 2.5 is required due to the use of predicates any() and all().
+ @note only supports positional arguments
+
+ @author http://www.artima.com/weblogs/viewpost.jsp?thread=155514
+
+ >>> import misc
+ >>> misc.validate_decorator (overloaded)
+ >>>
+ >>>
+ >>>
+ >>>
+ >>> #################
+ >>> #Basics, with reusing names and without
+ >>> @overloaded
+ ... def foo(x):
+ ... "prints x"
+ ... print x
+ ...
+ >>> @foo.register(int)
+ ... def foo(x):
+ ... "prints the hex representation of x"
+ ... print hex(x)
+ ...
+ >>> from types import DictType
+ >>> @foo.register(DictType)
+ ... def foo_dict(x):
+ ... "prints the keys of x"
+ ... print [k for k in x.iterkeys()]
+ ...
+ >>> #combines all of the doc strings to help keep track of the specializations
+ >>> foo.__doc__ # doctest: +ELLIPSIS
+ "prints x\\n\\n...overloading.foo (<type 'int'>):\\n\\tprints the hex representation of x\\n\\n...overloading.foo_dict (<type 'dict'>):\\n\\tprints the keys of x"
+ >>> foo ("text")
+ text
+ >>> foo (10) #calling the specialized foo
+ 0xa
+ >>> foo ({3:5, 6:7}) #calling the specialization foo_dict
+ [3, 6]
+ >>> foo_dict ({3:5, 6:7}) #with using a unique name, you still have the option of calling the function directly
+ [3, 6]
+ >>>
+ >>>
+ >>>
+ >>>
+ >>> #################
+ >>> #Multiple arguments, accessing the default, and function finding
+ >>> @overloaded
+ ... def two_arg (x, y):
+ ... print x,y
+ ...
+ >>> @two_arg.register(int, int)
+ ... def two_arg_int_int (x, y):
+ ... print hex(x), hex(y)
+ ...
+ >>> @two_arg.register(float, int)
+ ... def two_arg_float_int (x, y):
+ ... print x, hex(y)
+ ...
+ >>> @two_arg.register(int, float)
+ ... def two_arg_int_float (x, y):
+ ... print hex(x), y
+ ...
+ >>> two_arg.__doc__ # doctest: +ELLIPSIS
+ "...overloading.two_arg_int_int (<type 'int'>, <type 'int'>):\\n\\n...overloading.two_arg_float_int (<type 'float'>, <type 'int'>):\\n\\n...overloading.two_arg_int_float (<type 'int'>, <type 'float'>):"
+ >>> two_arg(9, 10)
+ 0x9 0xa
+ >>> two_arg(9.0, 10)
+ 9.0 0xa
+ >>> two_arg(15, 16.0)
+ 0xf 16.0
+ >>> two_arg.default_func(9, 10)
+ 9 10
+ >>> two_arg.find_func ((int, float)) == two_arg_int_float
+ True
+ >>> (int, float) in two_arg
+ True
+ >>> (str, int) in two_arg
+ False
+ >>>
+ >>>
+ >>>
+ >>> #################
+ >>> #wildcard
+ >>> @two_arg.register(AnyType, str)
+ ... def two_arg_any_str (x, y):
+ ... print x, y.lower()
+ ...
+ >>> two_arg("Hello", "World")
+ Hello world
+ >>> two_arg(500, "World")
+ 500 world
+ """
+
+ def __init__(self, default_func):
+ # Decorator to declare new overloaded function.
+ self.registry = {}
+ self.cache = {}
+ self.default_func = default_func
+ self.__name__ = self.default_func.__name__
+ self.__doc__ = self.default_func.__doc__
+ self.__dict__.update (self.default_func.__dict__)
+
+ def __get__(self, obj, type=None):
+ if obj is None:
+ return self
+ return new.instancemethod(self, obj)
+
+ def register(self, *types):
+ """
+ Decorator to register an implementation for a specific set of types.
+
+ .register(t1, t2)(f) is equivalent to .register_func((t1, t2), f).
+ """
+
+ def helper(func):
+ self.register_func(types, func)
+
+ originalDoc = self.__doc__ if self.__doc__ is not None else ""
+ typeNames = ", ".join ([str(type) for type in types])
+ typeNames = "".join ([func.__module__+".", func.__name__, " (", typeNames, "):"])
+ overloadedDoc = ""
+ if func.__doc__ is not None:
+ overloadedDoc = textwrap.fill (func.__doc__, width=60, initial_indent="\t", subsequent_indent="\t")
+ self.__doc__ = "\n".join ([originalDoc, "", typeNames, overloadedDoc]).strip()
+
+ new_func = func
+
+ #Masking the function, so we want to take on its traits
+ if func.__name__ == self.__name__:
+ self.__dict__.update (func.__dict__)
+ new_func = self
+ return new_func
+
+ return helper
+
+ def register_func(self, types, func):
+ """Helper to register an implementation."""
+ self.registry[tuple(types)] = func
+ self.cache = {} # Clear the cache (later we can optimize this).
+
+ def __call__(self, *args):
+ """Call the overloaded function."""
+ types = tuple(map(type, args))
+ func = self.cache.get(types)
+ if func is None:
+ self.cache[types] = func = self.find_func(types)
+ return func(*args)
+
+ def __contains__ (self, types):
+ return self.find_func(types) is not self.default_func
+
+ def find_func(self, types):
+ """Find the appropriate overloaded function; don't call it.
+
+ @note This won't work for old-style classes or classes without __mro__
+ """
+ func = self.registry.get(types)
+ if func is not None:
+ # Easy case -- direct hit in registry.
+ return func
+
+ # Phillip Eby suggests to use issubclass() instead of __mro__.
+ # There are advantages and disadvantages.
+
+ # I can't help myself -- this is going to be intense functional code.
+ # Find all possible candidate signatures.
+ mros = tuple(inspect.getmro(t) for t in types)
+ n = len(mros)
+ candidates = [sig for sig in self.registry
+ if len(sig) == n and
+ all(t in mro for t, mro in zip(sig, mros))]
+
+ if not candidates:
+ # No match at all -- use the default function.
+ return self.default_func
+ elif len(candidates) == 1:
+ # Unique match -- that's an easy case.
+ return self.registry[candidates[0]]
+
+ # More than one match -- weed out the subordinate ones.
+
+ def dominates(dom, sub,
+ orders=tuple(dict((t, i) for i, t in enumerate(mro))
+ for mro in mros)):
+ # Predicate to decide whether dom strictly dominates sub.
+ # Strict domination is defined as domination without equality.
+ # The arguments dom and sub are type tuples of equal length.
+ # The orders argument is a precomputed auxiliary data structure
+ # giving dicts of ordering information corresponding to the
+ # positions in the type tuples.
+ # A type d dominates a type s iff order[d] <= order[s].
+ # A type tuple (d1, d2, ...) dominates a type tuple of equal length
+ # (s1, s2, ...) iff d1 dominates s1, d2 dominates s2, etc.
+ if dom is sub:
+ return False
+ return all(order[d] <= order[s] for d, s, order in zip(dom, sub, orders))
+
+ # I suppose I could inline dominates() but it wouldn't get any clearer.
+ candidates = [cand
+ for cand in candidates
+ if not any(dominates(dom, cand) for dom in candidates)]
+ if len(candidates) == 1:
+ # There's exactly one candidate left.
+ return self.registry[candidates[0]]
+
+ # Perhaps these multiple candidates all have the same implementation?
+ funcs = set(self.registry[cand] for cand in candidates)
+ if len(funcs) == 1:
+ return funcs.pop()
+
+ # No, the situation is irreducibly ambiguous.
+ raise TypeError("ambigous call; types=%r; candidates=%r" %
+ (types, candidates))
projects / gonvert / blobdiff