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And the last section showed how to combine class decorators with descriptors to provide a powerful and exible mechanism for creating validated attributes This chapter completes our coverage of the Python language itself Not every feature of the language has been covered here and in the previous chapters, but those that have not are obscure and rarely used None of the subsequent chapters introduces new language features, although all of them make use of modules from the standard library that have not been covered before, and some of them take techniques shown in this and earlier chapters further than we have seen so far Furthermore, the programs shown in the following chapters have none of the constraints that have applied previously (ie, to only use aspects of the language that had been covered up to the point they were introduced), so they are the book s most idiomatic examples

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None of the rst three exercises described here requires writing a lot of code although the fourth one does and none of them are easy! 1 Copy the magic-numberspy program and delete its get_function() functions, and all but one of its load_modules() functions Add a GetFunction functor class that has two caches, one to hold functions that have been found and one to hold functions that could not be found (to avoid repeatedly looking for a function in a module that does not have the function) The only modi cations to main() are to add get_function = GetFunction() before the loop, and to use a with statement to avoid the need for a finally block Also, check that the module functions are callable using collectionsCallable rather than using hasattr() The class can be written in about twenty lines A solution is in magic-numbers_anspy 2 Create a new module le and in it de ne three functions: is_ascii() that returns True if all the characters in the given string have code points less than 127; is_ascii_punctuation() that returns True if all the characters are in the stringpunctuation string; and is_ascii_printable() that returns True if all the characters are in the stringprintable string The last two are structurally the same Each function should be created using lambda and can be done in one or two lines using functional-style code Be sure to add a docstring for each one with doctests and to make the module run the doctests The functions require only three to ve lines for all three of them, with the whole module fewer than 25 lines including doctests A solution is given in Asciipy 3 Create a new module le and in it de ne the Atomic context manager class This class should work like the AtomicList class shown in this chapter, except that instead of working only with lists it should work with any mutable collection type The __init__() method should check the suitability.

At this point in the process you should have a complete logical model set up and documented in a data modeling tool Depending on the tool, you may have the ability to translate what you have in the logical model into a physical model If your tool doesn t have this option, you need to find another way to document your physical model You can model it directly in SQL Server by using a database diagram, which would actually create the tables and other objects, or you can set up another logical model but this time follow your rules for physical modeling In our case, the tool supports both a physical and a logical model When we created our logical model, we used the tool to separate the entities into logical groupings called submodels Submodels simplify working with large data models, because they allow you to view a subset of entities instead of 300 entities at once The submodels we created are as follows

This package was the basis for the new facilities to support concurrency introduced via JSR166 in Java 2 15 Its author, Doug Lea, was a lead in the JSR effort The FJTask framework is not part of Java 2 15, but remains available in [Lea00b]

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