Two design approaches to consider
The essence pattern
The fluent interface pattern
These are both similar in intent, in that we slowly build up an intermediate object, and then create our target object in a single step.
An example of the fluent interface in action would be:
public class CustomerBuilder {
String surname;
String firstName;
String ssn;
public static CustomerBuilder customer() {
return new CustomerBuilder();
}
public CustomerBuilder withSurname(String surname) {
this.surname = surname;
return this;
}
public CustomerBuilder withFirstName(String firstName) {
this.firstName = firstName;
return this;
}
public CustomerBuilder withSsn(String ssn) {
this.ssn = ssn;
return this;
}
// client doesn't get to instantiate Customer directly
public Customer build() {
return new Customer(this);
}
}
public class Customer {
private final String firstName;
private final String surname;
private final String ssn;
Customer(CustomerBuilder builder) {
if (builder.firstName == null) throw new NullPointerException("firstName");
if (builder.surname == null) throw new NullPointerException("surname");
if (builder.ssn == null) throw new NullPointerException("ssn");
this.firstName = builder.firstName;
this.surname = builder.surname;
this.ssn = builder.ssn;
}
public String getFirstName() { return firstName; }
public String getSurname() { return surname; }
public String getSsn() { return ssn; }
}
import static com.acme.CustomerBuilder.customer;
public class Client {
public void doSomething() {
Customer customer = customer()
.withSurname("Smith")
.withFirstName("Fred")
.withSsn("123XS1")
.build();
}
}
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.
Best Solution
First, one of Perlis's epigrams:
Some of the 10 arguments are presumably related. Group them into an object, and pass that instead.
Making an example up, because there's not enough information in the question to answer directly:
Then your 10 argument function:
becomes:
and the caller changes to: