Python advanced 1.handle error, generator, decorator and decriptor


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Python advanced 1.handle error, generator, decorator and decriptor

  1. 1. HANDLE ERROR, GENERATOR AND DECORATOR John Saturday, December 21, 2013
  3. 3. Brief introduction • Python provide 2 ways to handle unexpected error: exception and assert. • Exception handling: is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions. • The exceptions are defined in the built-in class exceptions • For example: If divided by 0, we want to raise an exception
  4. 4. Built-in exceptions
  5. 5. Warnings • It is defined on the warnings module
  6. 6. Raising Exceptions • The raise statement allows the programmer to force a specified exception to occur raise NameError('HiThere') • Raise statement is to raise an exceptions, tryexception-finally clause is to catch an exceptions and decide how to do.
  7. 7. Try …except…finally structure • • • • First, the try clause(print 100/0) is executed If no exception occurs, the except clause is skipped Otherwise, the rest of the try clause is skipped. Go to the line its type matches the exception name(ZeroDivisionError). Clean-up info in the finally sentence. It executed under all conditions
  8. 8. Write User-defined Exceptions >>> class MyError(Exception): ... def __init__(self, value): ... self.value = value ... def __str__(self): ... return repr(self.value) • Define user-defined exception MyError. • Raise an exception when x == 0. Also write the try-except-finally clause • When call f(0,100), the exception is raised and catched.
  9. 9. Brief introduction of assert • The assert clause is used on situation or condition that should never happen. For example: assert 1>0 • “assert” statement is removed when the compilation is optimized (-O and -OO option, it is because __debug__ change to False when -O or -OO option are added). • So It is a convenient way to insert debugging assertion into a program
  10. 10. Quick example • We can see assert is ignored when add -O option
  11. 11. GENERATOR
  12. 12. Brief introduction • Generator s are a simple and powerful tool for create iterators. • Use yield statement instead of return to return data • the __iter__() and next() methods are created automatically. The local and execution state are saved automatically. • When generator terminate, it raise StopIteration
  13. 13. Quick example • When you call the generator function, the co de does not run. It just return the generator object.
  14. 14. The difference between generator and sequence type >>> mylist = [x*x for x in range(3)] >>> mygenerator = (x*x for x in range(3)) •Both mylist and mygenerator are iterable •But you can only read generator once. •Generator do not store all the values in memory, they generate the values on the fly.
  15. 15. DECORATOR
  16. 16. Brief introduction • Functions are objects in python. • We can define other function inside function definition. • We can pass a function as argument of other function.
  17. 17. Quick example • benchmark function accept func as input argument. • We can see @benchmark equal to apply benchmark function on f f = benchmark(f) • This is the typical usage of decorator: Use func as input argument. define wrapper function inside function definition
  18. 18. Official document • PEP - 318 Decorators for Functions and Methods
  19. 19. DESCRIPTOR
  20. 20. Brief introduction • A descriptor is an object attribute with “binding behavior”. • If any of __get__(), __set__() and __delete__() are defined for an object, it is said to be a descriptor.
  21. 21. Descriptor protocol • If an object defines both __get__() and __set__(), it is considered a data descriptor. • Descriptors that only define __get__() are called non-data descriptors • descriptors are invoked by the __getattribute__() method • overriding __getattribute__() prevents automatic descriptor calls
  22. 22. Descriptor example • Define __set__ and __get__ method.
  23. 23. Implement the property() method • Calling property() is a succinct way of building a data descriptor
  24. 24. Let us write the similar property() descriptor
  25. 25. Function are non-data descriptor • All functions include __get__() method for binding methods.
  26. 26. Non-data descriptor staticmethod • The pure python verson of static method should be like: Use static method
  27. 27. Non-data descriptor classmethod • Pure python version of classmethod looks like: