Decorator Abstractions: Our decorator abstraction takes the form of the abstract MessageDecorator class, which also implements IMessage. The MessageDecorator class has a constructor that accepts an IMessage object as a parameter and then assigns it to a private variable. For its part, MessageDecorator doesn’t have any special behaviors and simply delegates GetMessage and PrintMessage calls to whichever IMessage object was injected into it.
This wrapper_repeat() function takes arbitrary arguments and returns the value of the decorated function, func(). This wrapper function also contains the loop that calls the decorated function num_times times. This is no different from the earlier wrapper functions you have seen, except that it is using the num_times parameter that must be supplied from the outside.
Did you get it? We just applied the previously learned principles. This is exactly what the decorators do in Python! They wrap a function and modify its behaviour in one way or the another. Now you might be wondering that we did not use the @ anywhere in our code? That is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.
There have been a number of objections raised to this location -- the primary one is that it's the first real Python case where a line of code has an effect on a following line. The syntax available in 2.4a3 requires one decorator per line (in a2, multiple decorators could be specified on the same line), and the final decision for 2.4 final stayed one decorator per line.
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Two decorators (classmethod() and staticmethod()) have been available in Python since version 2.2. It's been assumed since approximately that time that some syntactic support for them would eventually be added to the language. Given this assumption, one might wonder why it's been so difficult to arrive at a consensus. Discussions have raged off-and-on at times in both comp.lang.python and the python-dev mailing list about how best to implement function decorators. There is no one clear reason why this should be so, but a few problems seem to be most divisive.
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The discussion continued on and off on python-dev from February 2002 through July 2004. Hundreds and hundreds of posts were made, with people proposing many possible syntax variations. Guido took a list of proposals to EuroPython 2004 [7], where a discussion took place. Subsequent to this, he decided that we'd have the Java-style [10] @decorator syntax, and this appeared for the first time in 2.4a2. Barry Warsaw named this the 'pie-decorator' syntax, in honor of the Pie-thon Parrot shootout which occurred around the same time as the decorator syntax, and because the @ looks a little like a pie. Guido outlined his case [8] on Python-dev, including this piece [9] on some of the (many) rejected forms.
This difference becomes most important when there are several independent ways of extending functionality. In some object-oriented programming languages, classes cannot be created at runtime, and it is typically not possible to predict, at design time, what combinations of extensions will be needed. This would mean that a new class would have to be made for every possible combination. By contrast, decorators are objects, created at runtime, and can be combined on a per-use basis. The I/O Streams implementations of both Java and the .NET Framework incorporate the decorator pattern.
This difference becomes most important when there are several independent ways of extending functionality. In some object-oriented programming languages, classes cannot be created at runtime, and it is typically not possible to predict, at design time, what combinations of extensions will be needed. This would mean that a new class would have to be made for every possible combination. By contrast, decorators are objects, created at runtime, and can be combined on a per-use basis. The I/O Streams implementations of both Java and the .NET Framework incorporate the decorator pattern.
Did you get it? We just applied the previously learned principles. This is exactly what the decorators do in Python! They wrap a function and modify its behaviour in one way or the another. Now you might be wondering that we did not use the @ anywhere in our code? That is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.
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The delegate injection point must be an injected field, initializer method parameter or bean constructor method parameter. If an injection point that is not an injected field, initializer method parameter or bean constructor method parameter is annotated @Delegate, the container automatically detects the problem and treats it as a definition error.
Now, assume one also desires the ability to add borders to windows. Again, the original Window class has no support. The ScrollingWindow subclass now poses a problem, because it has effectively created a new kind of window. If one wishes to add border support to many but not all windows, one must create subclasses WindowWithBorder and ScrollingWindowWithBorder etc. This problem gets worse with every new feature or window subtype to be added. For the decorator solution, we simply create a new BorderedWindowDecorator—at runtime, we can decorate existing windows with the ScrollingWindowDecorator or the BorderedWindowDecorator or both, as we see fit. Notice that if the functionality needs to be added to all Windows, you could modify the base class and that will do. On the other hand, sometimes (e.g., using external frameworks) it is not possible, legal, or convenient to modify the base class.
In object-oriented programming, the decorator pattern is a design pattern that allows behavior to be added to an individual object, dynamically, without affecting the behavior of other objects from the same class.[1] The decorator pattern is often useful for adhering to the Single Responsibility Principle, as it allows functionality to be divided between classes with unique areas of concern.[2] The decorator pattern is structurally nearly identical to the chain of responsibility pattern, the difference being that in a chain of responsibility, exactly one of the classes handles the request, while for the decorator, all classes handle the request.
... the keyword starting the line that heads a block draws a lot of attention to it. This is true for "if", "while", "for", "try", "def" and "class". But the "using" keyword (or any other keyword in its place) doesn't deserve that attention; the emphasis should be on the decorator or decorators inside the suite, since those are the important modifiers to the function definition that follows. ...
Some commonly used decorators that are even built-ins in Python are @classmethod, @staticmethod, and @property. The @classmethod and @staticmethod decorators are used to define methods inside a class namespace that are not connected to a particular instance of that class. The @property decorator is used to customize getters and setters for class attributes. Expand the box below for an example using these decorators.
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