singledispatch 3.4.0.0

This library brings functools.singledispatch from Python 3.4 to Python 2.6-3.3.

PEP 443 proposed to expose a mechanism in the functools standard library module in Python 3.4 that provides a simple form of generic programming known as single-dispatch generic functions.

This library is a backport of this functionality to Python 2.6 - 3.3.

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. It takes a type parameter:

>>> @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)

To enable registering lambdas and pre-existing functions, the register() attribute can be used in a functional form:

>>> def nothing(arg, verbose=False):
...     print("Nothing.")
...
>>> fun.register(type(None), nothing)

The register() attribute returns the undecorated function which enables decorator stacking, pickling, as well as creating unit tests for each variant independently:

>>> @fun.register(float)
... @fun.register(Decimal)
... def fun_num(arg, verbose=False):
...     if verbose:
...         print("Half of your number:", end=" ")
...     print(arg / 2)
...
>>> fun_num is fun
False

When called, the generic function dispatches on the first argument:

>>> fun("Hello, world.")
Hello, world.
>>> fun("test.", verbose=True)
Let me just say, test.
>>> fun(42, verbose=True)
Strength in numbers, eh? 42
>>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
Enumerate this:
0 spam
1 spam
2 eggs
3 spam
>>> fun(None)
Nothing.
>>> fun(1.23)
0.615

To get the implementation for a specific type, use the dispatch() attribute:

>>> fun.dispatch(float)
<function fun_num at 0x104319058>
>>> fun.dispatch(dict)
<function fun at 0x103fe4788>

To access all registered overloads, use the read-only registry attribute:

>>> fun.registry.keys()
dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
          <class 'decimal.Decimal'>, <class 'list'>,
          <class 'float'>])
>>> fun.registry[float]
<function fun_num at 0x1035a2840>
>>> fun.registry[object]
<function fun at 0x103170788>

The vanilla documentation is available at http://docs.python.org/3/library/functools.html#functools.singledispatch.

Versioning

This backport is intended to keep 100% compatibility with the vanilla release in Python 3.4+. To help maintaining a version you want and expect, a versioning scheme is used where:

• the first three numbers indicate the version of Python 3.x from which the backport is done

• a backport release number is provided after the last dot

For example, 3.4.0.0 is the first release of singledispatch compatible with the library found in Python 3.4.0.

A single exception from the 100% compatibility principle is that bugs fixed before releasing another minor Python 3.x.y version will be included in the backport releases done in the mean time. This rule applies to bugs only.

Maintenance

This backport is maintained on BitBucket by Łukasz Langa, one of the members of the core CPython team:

• singledispatch Mercurial repository

• singledispatch issue tracker

Change Log

3.4.0.0

• the first public release compatible with 3.4.0

Conversion Process

This section is technical and should bother you only if you are wondering how this backport is produced. If the implementation details of this backport are not important for you, feel free to ignore the following content.

singledispatch is converted using six so that a single codebase can be used for all compatible Python versions. Because a fully automatic conversion was not doable, I took the following branching approach:

• the upstream branch holds unchanged files synchronized from the upstream CPython repository. The synchronization is currently done by manually copying the required code parts and stating from which CPython changeset they come from. The tests should pass on Python 3.4 on this branch.

• the default branch holds the manually translated version and this is where all tests are run for all supported Python versions using Tox.