Advanced Programming Techniques in Java

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need to change level s value, and just as we need to tell Python that we want to change global variables using the global statement (to prevent a new local variable from being created rather than the global variable updated), the same applies to variables that we want to change but which belong to an outer scope Although it is often best to avoid using global altogether, it is also best to use nonlocal with care
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def update_indented_list(entry): indented_listappend(entry[ITEM]) for subentry in sorted(entry[CHILDREN]): update_indented_list(subentry)
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In the algorithm s rst stage we build up a list of entries, each a (key, item, children) 3-tuple, in the same order as they are in the original list In the algorithm s second stage we begin with a new empty indented list and iterate over the sorted entries, calling update_indented_list() for each one to build up the new indented list The update_indented_list() function is recursive For each top-level entry it adds an item to the indented_list, and then calls itself for each of the item s child entries Each child is added to the indented_list, and then the function calls itself for each child s children and so on The base case (when the recursion stops) is when an item, or child, or child of a child, and so on has no children of its own Python looks for indented_list in the local (inner function) scope and doesn t nd it, so it then looks in the enclosing scope and nds it there But notice that inside the function we append items to the indented_list even though we have not used nonlocal This works because nonlocal (and global) are concerned with object references, not with the objects they refer to In the second version of add_entry() we had to use nonlocal for level because the += operator applied to a number rebinds the object reference to a new object what really happens is level = level + 1, so level is set to refer to a new integer object But when we call listappend() on the indented_list, it modi es the list itself and no rebinding takes place, and therefore nonlocal is not necessary (For the same reason, if we have a dictionary, list, or other global collection, we can add or remove items from it without using a global statement)
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Function and Method Decorators
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A decorator is a function that takes a function or method as its sole argument and returns a new function or method that incorporates the decorated function or method with some additional functionality added We have already made use of some prede ned decorators, for example, @property and @classmethod In this subsection we will learn how to create our own function decorators, and later in this chapter we will see how to create class decorators
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Class decorators
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Further Procedural Programming
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For our rst decorator example, let us suppose that we have many functions that perform calculations, and that some of these must always produce a positive result We could add an assertion to each of these, but using a decorator is easier and clearer Here s a function decorated with the @positive_result decorator that we will create in a moment:
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@positive_result def discriminant(a, b, c): return (b ** 2) - (4 * a * c)
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Thanks to the decorator, if the result is ever less than 0, an AssertionError exception will be raised and the program will terminate And of course, we can use the decorator on as many functions as we like Here s the decorator s implementation:
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def positive_result(function): def wrapper(*args, **kwargs): result = function(*args, **kwargs) assert result >= 0, function__name__ + "() result isn't >= 0" return result wrapper__name__ = function__name__ wrapper__doc__ = function__doc__ return wrapper
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Decorators de ne a new local function that calls the original function Here, the local function is wrapper(); it calls the original function and stores the result, and it uses an assertion to guarantee that the result is positive (or that the program will terminate) The wrapper nishes by returning the result computed by the wrapped function After creating the wrapper, we set its name and docstring to those of the original function This helps with introspection, since we want error messages to mention the name of the original function, not the wrapper Finally, we return the wrapper function it is this function that will be used in place of the original
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def positive_result(function): @functoolswraps(function) def wrapper(*args, **kwargs): result = function(*args, **kwargs) assert result >= 0, function__name__ + "() result isn't >= 0" return result return wrapper
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Here is a slightly cleaner version of the @positive_result decorator The wrapper itself is wrapped using the functools module s @functoolswraps decorator, which ensures that the wrapper() function has the name and docstring of the original function
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