Archived from the original on 2008-06-04. But all this doable in Python. Importing fetches an object from the dictionary. This is because it isn't actually equivalent to doing the same thing within the body of the class when it is defined. The primary pattern for this was: import wrapt import inspect wrapt. I tried digging into the source code, but it seems fairly complex for what I'm trying to do. For this reason monkey patches are often made conditional, and only applied if appropriate.
See StackOverflow questions to get an idea. The word , homophonous with or nearly so , became monkey, possibly to make the patch sound less intimidating. When one starts to talk about monkey patching arbitrary code, rather than simply applying decorators to your own code, both of these issues become even more important as you could quite easily interfere with the behaviour of the existing code you are monkey patching in unexpected ways. In other words, it is possible to create a single decorator implementation that can be used across functions, methods and classes and you can tell at the time of the call the scenario and adjust the behaviour of the decorator accordingly. Note With it matters that you patch objects in the namespace where they are looked up. This is essentially monkey patching the value of pi to 3.
In lines 18-19, I patch the square and cube functions in their module because they are used in the main function. Usually, the descriptor protocol is triggered implicitly when an object is accessed as an attribute. If you want a validator, this could never work anyway, since you're proposing to modify either a particular integer or all integers. Delayed patching is bad Now a very big warning is required at this point. In contrast more arbitrary monkey patching involves coming in some time later after the class definition has been created and applying the function wrapper at that point. All examples can be found in this.
Importing a module for the first time results in a module object being put in sys. Anyway, I hope you found this interesting. In simple words, monkey patching is making changes to a module or class while the program is running. This would only be practical in very limited circumstances because in reality you are not going to have any idea where the function might be getting used if it is a common function. Monkey patching has its uses well beyond just patching bugs though. That aside there is a way to use mock to affect the results of an import. If the arguments are mutated by the code under test then you can no longer make assertions about what the values were when the mock was called.
Now let us see how to completely change the behavior of a method. It is often used to replace a method on the module or class level with a custom implementation. The two most commonly used forms of monkey patching in Python which you might not even equate with monkey patching are decorators and the use of mocking libraries to assist in performing unit testing. Note that it fetches an object, which need not be a module. When the patch is complete the decorated function exits, the with statement body is complete or patcher. Monkey patching in Python In Python, modules or classes are just like any other mutable objects like lists, i.
This is normally straightforward, but for a quick guide read. This is especially the case when monkey patching methods of a class. Suppose we expect some object to be passed to a mock that by default compares equal based on object identity which is the Python default for user defined classes. You could also use a testing framework like to get more info on failing asserts in your tests. I had to modify one method of GenerallyHelpfulClass. This code can be used whenever you need it.
Another common use case is to pass an object into a method or some part of the system under test and then check that it is used in the correct way. The primary problem there was decorators which aren't implemented in a way which preserve proper introspection capabilities, and which don't preserve the correct semantics of the Python descriptor protocol when applied to methods of classes. In both cases these are separated from the 'args' and 'kwargs' argument so you do not need to fiddle around with extracting it yourself. Both these packages do delayed patching of functions and so are sensitive to the order in which modules are imported. What is an important distinction though with decorators is that the wrapper function is being applied at the time the class containing the method is being defined. This can also be solved in better ways than an unconditional local import store the module as a class or module attribute and only do the import on first use. AssertionError: Expected to be called once.
Some do regard it as a useful necessity you can't avoid in order to patch bugs in third party code. Since a class is also just an object, we can monkey patch it with any other object. You no longer have to create multiple implementations of a decorator and ensure that you are using the correct one in each scenario. Sometimes a mock may have several calls made to it, and you are only interested in asserting about some of those calls. Monkey patching is a technique used to dynamically update the behavior of a piece of code at run-time. . Now let us take a look at how to monkey patch a class attribute.
As this chain of calls is made from an instance attribute we can monkey patch the backend attribute on a Something instance. Giving a structure was helpful, I just hadn't understood that the point was to show that the different cases can be processed differently. We will take a look at how to update the builtin print method in Python3 to include a timestamp. Monkey patching can only be done in dynamic languages, of which python is a good example. I'm trying to learn some advanced decorator usage.