Decorator for overloading in Python
Quick answer: there is an overload package on PyPI which implements this more robustly than what I describe below, although using a slightly different syntax. It's declared to work only with Python 3 but it looks like only slight modifications (if any, I haven't tried) would be needed to make it work with Python 2.
Long answer: In languages where you can overload functions, the name of a function is (either literally or effectively) augmented by information about its type signature, both when the function is defined and when it is called. When a compiler or interpreter looks up the function definition, then, it uses both the declared name and the types of the parameters to resolve which function to access. So the logical way to implement overloading in Python is to implement a wrapper that uses both the declared name and the parameter types to resolve the function.
Here's a simple implementation:
from collections import defaultdict
def determine_types(args, kwargs):
return tuple([type(a) for a in args]), \
tuple([(k, type(v)) for k,v in kwargs.iteritems()])
function_table = defaultdict(dict)
def overload(arg_types=(), kwarg_types=()):
def wrap(func):
named_func = function_table[func.__name__]
named_func[arg_types, kwarg_types] = func
def call_function_by_signature(*args, **kwargs):
return named_func[determine_types(args, kwargs)](*args, **kwargs)
return call_function_by_signature
return wrap
overload
should be called with two optional arguments, a tuple representing the types of all positional arguments and a tuple of tuples representing the name-type mappings of all keyword arguments. Here's a usage example:
>>> @overload((str, int))
... def f(a, b):
... return a * b
>>> @overload((int, int))
... def f(a, b):
... return a + b
>>> print f('a', 2)
aa
>>> print f(4, 2)
6
>>> @overload((str,), (('foo', int), ('bar', float)))
... def g(a, foo, bar):
... return foo*a + str(bar)
>>> @overload((str,), (('foo', float), ('bar', float)))
... def g(a, foo, bar):
... return a + str(foo*bar)
>>> print g('a', foo=7, bar=4.4)
aaaaaaa4.4
>>> print g('b', foo=7., bar=4.4)
b30.8
Shortcomings of this include
It doesn't actually check that the function the decorator is applied to is even compatible with the arguments given to the decorator. You could write
@overload((str, int)) def h(): return 0
and you'd get an error when the function was called.
It doesn't gracefully handle the case where no overloaded version exists corresponding to the types of the arguments passed (it would help to raise a more descriptive error)
It distinguishes between named and positional arguments, so something like
g('a', 7, bar=4.4)
doesn't work.
- There are a lot of nested parentheses involved in using this, as in the definitions for
g
. - As mentioned in the comments, this doesn't deal with functions having the same name in different modules.
All of these could be remedied with enough fiddling, I think. In particular, the issue of name collisions is easily resolved by storing the dispatch table as an attribute of the function returned from the decorator. But as I said, this is just a simple example to demonstrate the basics of how to do it.
Since Python 3.4 the functools
module now supports a @singledispatch
decorator. It works like this:
from functools import singledispatch
@singledispatch
def func(val):
raise NotImplementedError
@func.register
def _(val: str):
print('This is a string')
@func.register
def _(val: int):
print('This is an int')
Usage
func("test") --> "This is a string"
func(1) --> "This is an int"
func(None) --> NotImplementedError