Related
I know that Python does not support method overloading, but I've run into a problem that I can't seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
# Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
# And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can't create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we'll 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 it's 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
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.
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
You can use "roll-your-own" solution for function overloading. This one is copied from Guido van Rossum's 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
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
#dispatch(int, int)
def add(x, y):
return x + y
#dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
In Python 3.4 PEP-0443. Single-dispatch generic functions was added.
Here is a short API description from PEP.
To define a generic function, decorate it with the #singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
#singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
#fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
#fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
The #overload decorator was added with type hints (PEP 484).
While this doesn't change the behaviour of Python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
This type of behaviour is typically solved (in OOP languages) using polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, and then do the job of determining which c function to call based on the values in the initial c function. So, Python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You're essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. It doesn't sound too bad I guess.
It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
#overload
def f():
pass
#f.args(int, int)
def f(x, y):
print('two integers')
#f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. "overloading"). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume that we define, in a hypothetical static language, the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see "number called" twice, because x has been declared as Number, and that's all overloading cares about. With dynamic dispatch you will see "integer called, float called", because those are the actual types of x at the time the function is called.
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the #singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(self, arg: int):
return -arg
#neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
#singledispatchmethod supports nesting with other decorators such as
#classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
#staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(arg: int) -> int:
return -arg
#neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
I think your basic requirement is to have a C/C++-like syntax in Python with the least headache possible. Although I liked Alexander Poluektov's answer it doesn't work for classes.
The following should work for classes. It works by distinguishing by the number of non-keyword arguments (but it doesn't support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I'm a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I'm another Sprite
Script:
while x == True: print 'hi'
You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python's explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I'd add two functions: add_bullet_straight and add_bullet_curved.
Overloading methods is tricky in Python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, and this could help here to understand people to overload the methods.
Let's take your example:
A class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
Pass the arguments from the remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
Or
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
Try it out for your code.
Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)
I'm having some problems. How we can define a function outside of a function that can be used in a class property? Also, how we can insert the self parameter into the function signature? I would like to visualize it like this:
>>> def a(self, x): #I thought maybe class will give "self" to this property function
... print(self)
...
>>> class aa:
... def __init__(self):
... pass
... #a
... def p():
... print('in it')
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 4, in aa
TypeError: a() missing 1 required positional argument: 'x'
I want to define a function outside but to use inside of a class. Like a class's method as a property. How can I do this?
It's not really clear what you want your out-of-class function to do. There are a bunch of possibilities, but you may not know the terminology yet to describe it to us.
Here's the three I think are most likely:
You may want your function to be a decorator. That means you can apply it to a method with #decorator syntax to other functions, including methods in a class.
For this to work, your function needs to be written to accept a function object as its only argument. Whatever it returns is what will replace the function or method it was being called on, so usually you want to return a callable, but you could instead return a descriptor like property does. Try something like this:
def decorator(func):
def wrapper(self, *args, **kwargs):
print("in the wrapper")
result = func(self, *args, **kwargs)
print("wrapper is done")
return result
return wrapper
class Foo:
#decorator
def foo(self, x):
print("in foo(), x is", x)
f = Foo()
f.foo(1) # prints three messages
When you call the foo method, you're actually going to be calling the wrapper method that the decorator returned after it was applied to the original method (func). Because of how we wrote the wrapper, it will call func so the original method prints out its message too.
You may want to use property (a descriptor type) to call your out-of-class function. This is a less common way of using property than applying it as a decorator on a method, but it's not impossible. You could even have two different functions, one to be called when requesting the attribute, the other than will be called when setting it (but I'll demonstrate with just the getter):
def getter(obj):
print("in the getter")
return 1
class Foo2:
foo = property(getter)
f2 = Foo2()
print(f2.foo) # prints a message from the getter function first, then prints 1
Note that you can't use #decorator syntax when building a property this way. That is only legal syntax immediately before a function definition, and we're not defining any functions that way inside our class.
You may just want to copy a function defined outside of the class into it, without any decorator or property nonsense. This is the easiest one to do, it's just a simple assignment:
def func(self, x):
print("x is", x)
class Foo3:
method = func # just assign the global to a name in the class body
func = func # you can even use the same name if you don't mind confusing people
f3 = Foo3()
f3.method(1)
f3.func(2)
If you want to create a property that uses a function defined outside your class, it would be something like this:
def myfunc(self):
return self._p
class Foo:
def __init__(self, p):
self._p = p
p = property(myfunc)
f = Foo("Alpha")
f.p # gives "Alpha"
property accepts a function as its (first) argument. The function should have self as a parameter, and should return the value that you want the property to evaluate to.
I want to write a function which returns the calling function:
def foo():
return get_calling_function() #should return 'foo' function object
There's numerous examples online how to get the calling function's name, but not how to get the actual object. I've come up with the following solution which gets the name, then looks it up in the calling function's global namespace. However this doesn't work for class functions since there you need the class name as well, and I image there's a bunch of other edge cases as well.
from inspect import stack
def get_calling_function():
return stack()[2][0].f_globals[stack()[1][3]]
So any advice how or if its possible to write this function so that it works generically (on Python 3, btw)? Thanks.
Calling can happen from any code object (and from an extension module/builtin): from exec, execfile, from module name space (during import), from within a class definition, from within a method / classmethod / staticmethod, from a decorated function/method, from within a nested function, ... - so there is no "calling function" in general, and the difficulty to do anything good with that.
The stack frames and their code objects are the most general you can get - and examine the attributes.
This one finds the calling function in many cases:
import sys, inspect
def get_calling_function():
"""finds the calling function in many decent cases."""
fr = sys._getframe(1) # inspect.stack()[1][0]
co = fr.f_code
for get in (
lambda:fr.f_globals[co.co_name],
lambda:getattr(fr.f_locals['self'], co.co_name),
lambda:getattr(fr.f_locals['cls'], co.co_name),
lambda:fr.f_back.f_locals[co.co_name], # nested
lambda:fr.f_back.f_locals['func'], # decorators
lambda:fr.f_back.f_locals['meth'],
lambda:fr.f_back.f_locals['f'],
):
try:
func = get()
except (KeyError, AttributeError):
pass
else:
if func.__code__ == co:
return func
raise AttributeError("func not found")
# Usage
def f():
def nested_func():
print get_calling_function()
print get_calling_function()
nested_func()
class Y:
def meth(self, a, b=10, c=11):
print get_calling_function()
class Z:
def methz(self):
print get_calling_function()
z = Z()
z.methz()
return z
#classmethod
def clsmeth(cls):
print get_calling_function()
#staticmethod
def staticmeth():
print get_calling_function()
f()
y = Y()
z = y.meth(7)
z.methz()
y.clsmeth()
##y.staticmeth() # would fail
It finds:
<function f at 0x012E5670>
<function nested_func at 0x012E51F0>
<bound method Y.meth of <__main__.Y instance at 0x01E41580>>
<bound method Z.methz of <__main__.Z instance at 0x01E63EE0>>
<bound method Z.methz of <__main__.Z instance at 0x01E63EE0>>
<bound method classobj.clsmeth of <class __main__.Y at 0x01F3CF10>>
However, it may fail to find the function, or find the wrong function, for example, finding the wrong decorator-generated wrapper when the same decorator is used on multiple functions with the same name in different scopes.
Is it possible to have a decorator that makes a method work like an attribute if values are assigned to it using class.something = 2 and work like a method if it is called like class.something(2, True)?
What I currently have
To be more concrete, I currently have the following
class attributeSetter(object):
''' Makes functions appear as attributes. Takes care of autologging.'''
def __init__(self, func):
self.func = func
def __set__(self, obj, value):
return self.func(obj, value)
def __repr__(self):
return repr(self.__getattribute__)
So with this class with a decorated method:
class myClass(object):
#attributeSetter # decorated!
def myAttrib(self, value, add=False, subtract=False):
if add:
value += 2
if subtract:
value -= 2
self.__dict__['myAttrib'] = value
I can do this:
instance = myClass()
instance.myAttrib = 2 # sets the value to 2
instance.myAttrib *= 4 # now 8
print instance.myAttrib # 8
What I want
But I want, in addition, to be able to do this:
instance.myAttrib(3, add=True) # now 8 + 3 + 2 = 13
instance.myAttrib(0, subtact=True) # now 13 + 0 - 2 = 11
print instance.myAttrib # 11
My hunch is that I just have to add a __call__ to the attributeSetter decorator like this:
def __call__(self, *args, **kwargs):
self.func(*args, **kwargs)
but then it won't let me set values using the "=" syntax.
Why?
I'm co-developing PsychoPy (a python module for stimulus delivery in psychphysics) and we want do to some processing when each stimulus parameter is set, hence the decorator to avoid setter/getter methods. We log each attribute change by default but in some cases, users want to disable logging of this particular setting with an extra log=False argument, or provide more arguments. Hence it would be nice in those cases if could just use the attribute like a function/method, which it really is in the first place.
You'll need to implement a __get__ method returning a callable; your decorator already provides a descriptor object, simply make it work when accessing as an attribute.
This can be as simple as turning around and binding the wrapped function (so you get a bound method):
class attributeSetter(object):
''' Makes functions appear as attributes. Takes care of autologging.'''
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
return self.func.__get__(instance, owner)
def __set__(self, obj, value):
return self.func(obj, value)
However, this makes it incompatible with simply accessing the attribute! instance.myAttrib now returns a bound method! You cannot have it both ways here; methods are simply bound attributes (so attributes that passed through the descriptor protocol), that happen to be callable.
You could of course return a proxy object from __get__; one that implements a __call__ method and otherwise tries to act as much as possible as the underlying managed instance attribute (what your function stored in self.__dict__['myAttrib']); but this path is fraught with problems as a proxy object can never truly be the underlying attribute value.
I know that Python does not support method overloading, but I've run into a problem that I can't seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
# Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
# And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can't create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we'll 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 it's 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
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.
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
You can use "roll-your-own" solution for function overloading. This one is copied from Guido van Rossum's 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
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
#dispatch(int, int)
def add(x, y):
return x + y
#dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
In Python 3.4 PEP-0443. Single-dispatch generic functions was added.
Here is a short API description from PEP.
To define a generic function, decorate it with the #singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
#singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
#fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
#fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
The #overload decorator was added with type hints (PEP 484).
While this doesn't change the behaviour of Python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
This type of behaviour is typically solved (in OOP languages) using polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, and then do the job of determining which c function to call based on the values in the initial c function. So, Python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You're essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. It doesn't sound too bad I guess.
It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
#overload
def f():
pass
#f.args(int, int)
def f(x, y):
print('two integers')
#f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. "overloading"). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume that we define, in a hypothetical static language, the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see "number called" twice, because x has been declared as Number, and that's all overloading cares about. With dynamic dispatch you will see "integer called, float called", because those are the actual types of x at the time the function is called.
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the #singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(self, arg: int):
return -arg
#neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
#singledispatchmethod supports nesting with other decorators such as
#classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
#staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(arg: int) -> int:
return -arg
#neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
I think your basic requirement is to have a C/C++-like syntax in Python with the least headache possible. Although I liked Alexander Poluektov's answer it doesn't work for classes.
The following should work for classes. It works by distinguishing by the number of non-keyword arguments (but it doesn't support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I'm a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I'm another Sprite
Script:
while x == True: print 'hi'
You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python's explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I'd add two functions: add_bullet_straight and add_bullet_curved.
Overloading methods is tricky in Python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, and this could help here to understand people to overload the methods.
Let's take your example:
A class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
Pass the arguments from the remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
Or
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
Try it out for your code.
Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)