Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

An extension to PEAK-Rules to prioritize methods in order to to avoid AmbiguousMethods situations

Project description

This module provides four decorators:





These behave like their peak.rules counterparts except that they accept an optional prio argument which can be used to provide a comparable object (usually an integer) that will be used to disambiguate situations in which more than rule applies to the given arguments and no rule is more specific than another. That is, situations in which an peak.rules.AmbiguousMethods would have been raised.

This is useful for libraries which want to be extensible via generic functions but want their users to easily override a method without figuring out how to write a more specific rule or when it is not feasible.

For example, TurboJson provides a jsonify function that looks like this:

>>> def jsonify(obj):
...     "jsonify an object"

And extends it so it can handle SqlAlchemy mapped classes in a way similar to this one:

>>> from peak.rules import when

>>> def jsonify_sa(obj):
...     print "You're a SA object and I'm going to jsonify you!"

>>> when(jsonify, "hasattr(obj, 'c')")(jsonify_sa) # doctest: +ELLIPSIS
<function jsonify_sa at ...>

>>> class Person(object):
...     def __init__(self):
...         self.c = "im a stub"

>>> jsonify(Person())
You're a SA object and I'm going to jsonify you!

So far so good, however, when a user of the library wants to override the built in implementation it can become quite hard since they have to write a more specific rule which can be tedious, for example:

hasattr(self, 'c') and isinstance(obj, Person)

Notice the hasattr test, even though isinstance(obj, Person) implies it, just to make it more specific than the built in, this gets more cumbersome the more complicated the expression becomes.

Else this is what happens:

>>> def jsonify_Person(obj):
...     print "No way, I'm going to jsonify you!"

>>> when(jsonify, (Person,))(jsonify_Person) # doctest: +ELLIPSIS
<function jsonify_Person at ...>

>>> try:
...     jsonify(Person())
... except AmbiguousMethods:
...     print "I told you, gfs can sometimes be a pain"
I told you, gfs can sometimes be a pain

To remedy this situation prioritized_when can be used to provide an implementation that will override the one declared with when:

>>> def jsonify_Person2(obj):
...     print "No way, I'm going to jsonify you!"

>>> prioritized_when(jsonify, (Person,))(jsonify_Person2) # doctest: +ELLIPSIS
<function jsonify_Person2 at ...>

>>> jsonify(Person())
No way, I'm going to jsonify you!

Notice that we didn’t need a prio argument. This is because methods decorated with prioritized_when always override those that have been decorated with peak.rules.when.

Methods decorated with prioritized_when can also override other methods that have been decorated by the same decorator using the prio parameter, the one which compares greater wins, if both are equal AmbiguousMethods will be raised as usual.

>>> def jsonify_Person3(obj):
...     print "Don't be so smart, I am, my prio is higher!"
>>> prioritized_when(jsonify, (Person,), prio=1)(jsonify_Person3) # doctest: +ELLIPSIS
<function jsonify_Person3 at ...>
>>> jsonify(Person())
Don't be so smart, I am, my prio is higher!

For convenience, a generic decorator is provided too which behaves like peak.rules.dispatch.generic except that the when,…,``after`` decorators that will be bound as attributes of the decorated function will be prioritized:

>>> @generic
... def f(n): pass

>>> f(5)
Traceback (most recent call last):
NoApplicableMethods: ((5,), {})

Add a default rule:

>>> @f.when()
... def default_f(n):
...     return n
>>> f(5)

Add a default rule that overrides the former:

>>> @f.when(prio=1)
... def new_default_f(n):
...     return n+1
>>> f(5)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for prioritized_methods, version 0.2.1
Filename, size File type Python version Upload date Hashes
Filename, size prioritized_methods-0.2.1-py2.5.egg (9.8 kB) File type Egg Python version 2.5 Upload date Hashes View hashes
Filename, size prioritized_methods-0.2.1.tar.gz (4.7 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page