When we instantiate a SimpleMessage and then pass it to the various decorators, we now get new behavior. Moreover, since both the concrete component and the concrete decorators all implement / descend from IMessage, they are interchangeable as far as the program is concerned, meaning that we can loop over them together. Further, rather than having to create a new ExcitedAndQuizzicalMessageDecorator class, we were able to achieve the same effect by double wrapping a SimpleMessage object (first in an ExcitedMessageDecorator and then in a QuizzicalMessageDecorator). Finally, note that despite having been passed into various decorators, our simpleMsg object remains unchanged at the end of the program.
Writing a class decorator is very similar to writing a function decorator. The only difference is that the decorator will receive a class and not a function as an argument. In fact, all the decorators you saw above will work as class decorators. When you are using them on a class instead of a function, their effect might not be what you want. In the following example, the @timer decorator is applied to a class:
The .__init__() method must store a reference to the function and can do any other necessary initialization. The .__call__() method will be called instead of the decorated function. It does essentially the same thing as the wrapper() function in our earlier examples. Note that you need to use the functools.update_wrapper() function instead of @functools.wraps.
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Overall unfamiliarity with the concept. For people who have a passing acquaintance with algebra (or even basic arithmetic) or have used at least one other programming language, much of Python is intuitive. Very few people will have had any experience with the decorator concept before encountering it in Python. There's just no strong preexisting meme that captures the concept.
This decorator works by storing the time just before the function starts running (at the line marked # 1) and just after the function finishes (at # 2). The time the function takes is then the difference between the two (at # 3). We use the time.perf_counter() function, which does a good job of measuring time intervals. Here are some examples of timings:
Now we have our logit decorator in production, but when some parts of our application are considered critical, failure might be something that needs more immediate attention. Let’s say sometimes you want to just log to a file. Other times you want an email sent, so the problem is brought to your attention, and still keep a log for your own records. This is a case for using inheritence, but so far we’ve only seen functions being used to build decorators.
In general, functions in Python may also have side effects rather than just turning an input into an output. The print() function is a basic example of this: it returns None while having the side effect of outputting something to the console. However, to understand decorators, it is enough to think about functions as something that turns given arguments into a value.
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