This module adds multimethod support to the Python programming language. In contrast to other multiple dispatch implementations, this one doesn't strictly dispatch on argument types, but on a user-provided dispatch function that can differ for each multimethod. This design is inspired the Clojure programming language's multimethod implementation.
Multimethods provide a mechanism to dispatch function execution to different implementations of this function. It works similarly to the well-known concept of "instance methods" in OO languages like Python, which in a call to obj.method() would look up a member called "method" in obj's class.
However, multimethod methods are NOT neccessarily associated with a single class. Instead, they belong to a MultiMethod instance. Calls on the MultiMethod will be dispatched to its corresponding methods using a custom, user-defined dispatch function.
The dispatch function can be any callable. Once a MultiMethod is called, the dispatch function will receive the exact arguments the MultiMethod call received, and is expected to return a value that will be dispatched on. This return value is then used to select a 'method', which is basically just a function that has been associated with this multimethod and dispatch value using the @method decorator.
Note that in the dispatch function lies the real power of this whole concept. For example, you can use it to dispatch on the type of the arguments like in Java/C, on their exact values, or whether they evaluate to True in a boolean context. If the arguments are dictionaries, you can dispatch on whether they contain certain keys. Or, if you're going really wild, you could even send them over the network to a remote service and let that decide which method to call.
Of course, not every possible application of multimethods is actually useful, but your creativity is the only limit to what you can do.
To use multimethods, a MultiMethod instance must be created first. Each MultiMethod instance takes a name and a dispatch function, as discussed above.
Methods are associated with MultiMethods by decorating a function with the @method decorator. The function needs to have the same name as the MultiMethod. The @method decorator takes a dispatch value which signals to the MultiMethod that whenever its dispatch function returns this value, this method should be selected.
Okay, that was dry enough. Let's put this concept to work with a small example:
Without multimethods, naively implementing a function that has two different behaviours based on a the types of the arguments could look like this:
def combine(x, y): if isinstance(x, int) and isinstance(y, int): return x * y elif isinstance(x, basestring) and isinstance(y, basestring): return x + ', ' + y else: return None
However, this is ugly and becomes unwieldy fast as we add more elif cases for additional types. Fortunately, implementing dispatch on function arguments' types is easy using multimethods. Let's implement a multimethod version of combine() with exactly the same signature.
First, we have to define a dispatch function. It will take the same arguments as the multimethod, and return a value which is then used to select the correct method implementation:
def combine_dispatch(x, y): return (type(x), type(y))
Thus, we are going to dispatch on a tuple of types, namely the types of our arguments. The next step is to instantiate the MultiMethod itself:
from multimethods import MultiMethod, method, Default combine = MultiMethod('combine', combine_dispatch)
A multimethod by itself does almost nothing. It is dependent on being given methods in order to implement its functionality for different dispatch values. Let's define methods for all-integer and all-string cases as above:
@method((int, int)) def combine(x, y): return x*y @method((str, str)) def combine(x, y): return x + '&' + y @method(Default) def combine(x, y): return 'Eh?'
The behaviour for ints and strings is straightforward:
>>> combine(21, 2) 42 >>> combine('foo', 'bar') 'foo&bar'
However, notice the last method definition above. Instead of specifying a tuple of types, we have given it the special multimethods.Default object. This is a marker which simply tells the multimethod: "In case we don't have a method implementation for some dispatch value, just use this method instead."
>>> combine(21, 'bar') 'Eh?'
Default methods are completely optional, you are free not to provide one at all. An Exception will be raised for unknown dispatch values instead.
Now would be a good time to show that the dispatch function's signature doesn't have to match methods' signature bit-by-bit. Let's refactor the dispatch function and make it more generic:
def dispatch_on_arg_type(*args): return tuple(type(x) for x in args)
This version will support all possible (non-variadic, non-keyword) signatures at no additional cost, and makes it easy to re-use the dispatch function for other multimethods with different numbers of arguments.
A small stumbling block remains when dispatching on argument type: Comparing dispatch values is done via ==, not via isinstance(). This is best explained using the string-concatenating combine() implementation from above:
@method((basestring, basestring)) def combine(x, y): return x + '&' + y combine('foo', 'bar') # BREAKS!
This fails because type('foo') returns str, not basestring. I haven't found a way yet to allow this to work, short of checking all dispatch values for isinstance-ness in linear time or adding special cases to the code. If you have an idea how to implement this, great -- please contact me, or better yet, attach a patch :-)
What follows is a horribly inefficient algorithm to determine a list's length. It is often used as an example to teach basic recursion, and also goes to show how the edge case can be modeled using simple pattern matching.
from multimethods import MultiMethod, method, Default identity = lambda x: x len2 = MultiMethod('len2', identity) @method() def len2(l): return 0 @method(Default) def len2(l): return 1 + len2(l[1:])
Here's a slightly more involved example. Let's say ACME Corporation has standard billing procedures that apply to most of its customers, but some of the bigger customers receive wildly different conditions. How do we express this as code without resorting to heaps of if statements?
from multimethods import MultiMethod, method, Default def sum_amounts(purchase): return sum(product.price for product in purchase) def get_customer(purchase): return purchase.customer.company_name calc_total = MultiMethod('calc_total', get_customer) @method(Default) def calc_total(purchase): # Normal customer pricing return sum_amounts(purchase) @method("Wile E.") def calc_total(purchase): # Always gets 20% off return sum_amounts(purchase) * 0.8 @method("Wolfram & Hart") def calc_total(purchase): # Has already paid an annual flat fee in advance; also receives # a token of enduring friendship with every order purchase.append(champagne) return 0.0
This work has been created by and is copyrighted by Daniel Werner. All rights reserved, and that kind of stuff. You may freely use this work under the terms of the simplified (2-clause) version of the BSD license, a copy of which is included in this distribution.
While this Python module is new, the idea of multimethods is definitely not. Common Lisp has its generic functions, which only dispatch on type (and eql). There has also been a prior Python implementation by Guido Rossum, which is even more limited.
This module however is really a near-faithful implementation of multimethods as found in the Clojure programming language (http://clojure.org), sans beautiful macro-based syntax. I'd like to give credit to the principal author of Clojure, Rich Hickey, for coming up with the idea to generalize multimethods to use a custom dispatch function, and for publishing his implementation for the world to use (and port to different languages). Thanks, Rich!
Thanks to Matthew von Rocketstein for providing me with a setup.py.