class – Python function overloading

What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.

However, before looking at that, well first tackle why overloading is not really what you want in Python.

Why Not Overloading?

First, one needs to understand the concept of overloading and why its not applicable to Python.

When working with languages that can discriminate data types at
compile-time, selecting among the alternatives can occur at
compile-time. The act of creating such alternative functions for
compile-time selection is usually referred to as overloading a
function. (Wikipedia)

Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:

In programming languages that defer data type identification until
run-time the selection among alternative
functions must occur at run-time, based on the dynamically determined
types of function arguments. Functions whose alternative
implementations are selected in this manner are referred to most
generally as multimethods. (Wikipedia)

So we should be able to do multimethods in Python—or, as it is alternatively called: multiple dispatch.

Multiple dispatch

The multimethods are also called multiple dispatch:

Multiple dispatch or multimethods is the feature of some
object-oriented programming languages in which a function or method
can be dynamically dispatched based on the run time (dynamic) type of
more than one of its arguments. (Wikipedia)

Python does not support this out of the box¹, but, as it happens, there is an excellent Python package called multipledispatch that does exactly that.

Solution

Here is how we might use multipledispatch² package to implement your methods:

>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import *  # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True

>>> Sprite = namedtuple(Sprite, [name])
>>> Point = namedtuple(Point, [x, y])
>>> Curve = namedtuple(Curve, [x, y, z])
>>> Vector = namedtuple(Vector, [x,y,z])

>>> @dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
...     print(Called Version 1)
...
>>> @dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
...     print(Called version 2)
...
>>> @dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
...     print(Called version 3)
...
>>> @dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
...     print(Called version 4)
...

>>> sprite = Sprite(Turtle)
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s**2
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away

>>> add_bullet(sprite, start, direction, speed)
Called Version 1

>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2

>>> add_bullet(sprite, script)
Called version 3

>>> add_bullet(sprite, curve, speed)
Called version 4

<sub>1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.</sub>

Python does support method overloading as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, sprite=default, start=default, 
                 direction=default, speed=default, accel=default, 
                  curve=default):
        # do stuff with your arguments

In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.

You could also do something like this:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, **kwargs):
        # here you can unpack kwargs as (key, values) and
        # do stuff with them, and use some global dictionary
        # to provide default values and ensure that ``key``
        # is a valid argument...

        # do stuff with your arguments

Another alternative is to directly hook the desired function directly to the class or instance:

def some_implementation(self, arg1, arg2, arg3):
  # implementation
my_class.add_bullet = some_implementation_of_add_bullet

Yet another way is to use an abstract factory pattern:

class Character(object):
   def __init__(self, bfactory, *args, **kwargs):
       self.bfactory = bfactory
   def add_bullet(self):
       sprite = self.bfactory.sprite()
       speed = self.bfactory.speed()
       # do stuff with your sprite and speed

class pretty_and_fast_factory(object):
    def sprite(self):
       return pretty_sprite
    def speed(self):
       return 10000000000.0

my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory

# now, if you have another factory called ugly_and_slow_factory 
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()

# In the last example you can see abstract factory and method
# overloading (as you call it) in action 

class – Python function overloading

You can use roll-your-own solution for function overloading. This one is copied from Guido van Rossums article about multimethods (because there is little difference between multimethods and overloading in Python):

registry = {}

class MultiMethod(object):
    def __init__(self, name):
        self.name = name
        self.typemap = {}
    def __call__(self, *args):
        types = tuple(arg.__class__ for arg in args) # a generator expression!
        function = self.typemap.get(types)
        if function is None:
            raise TypeError(no match)
        return function(*args)
    def register(self, types, function):
        if types in self.typemap:
            raise TypeError(duplicate registration)
        self.typemap[types] = function


def multimethod(*types):
    def register(function):
        name = function.__name__
        mm = registry.get(name)
        if mm is None:
            mm = registry[name] = MultiMethod(name)
        mm.register(types, function)
        return mm
    return register

The usage would be

from multimethods import multimethod
import unittest

# overload makes more sense in this case
overload = multimethod

class Sprite(object):
    pass

class Point(object):
    pass

class Curve(object):
    pass

@overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
    # ...

@overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
    # ...

@overload(Sprite, str)
def add_bullet(sprite, script):
    # ...

@overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
    # ...

Most restrictive limitations at the moment are:

• methods are not supported, only functions that are not class members;
• inheritance is not handled;
• kwargs are not supported;
• registering new functions should be done at import time thing is not thread-safe