barcode generator in c# windows application codeproject Note In formal terms, an object that implements the __iter__ method is iterable, while the object in Font

Create PDF 417 in Font Note In formal terms, an object that implements the __iter__ method is iterable, while the object

Note In formal terms, an object that implements the __iter__ method is iterable, while the object
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implementing next is the iterator.
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First, make a Fibs object: >>> fibs = Fibs() You can then use it in a for loop for example, to find the smallest Fibonacci number that is greater than 1,000:
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CHAPTER 9 MAGIC METHODS, PROPERTIES, AND ITERATORS
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>>> for f in fibs: if f > 1000: print f break ... 1597 Here the loop stops because I issue a break inside it; if I didn t, the for loop would never end.
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Tip The built-in function iter can be used to get an iterator from an iterable object.
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Making Sequences from Iterators
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In addition to iterating over the iterators (which is what you normally do), you can convert them to sequences. In most contexts in which you can use a sequence (except in operations such as indexing or slicing), you can use an iterator instead. One useful example of this is explicitly converting an iterator to a list using the list constructor: >>> class TestIterator: value = 0 def next(self): self.value += 1 if self.value > 10: raise StopIteration return self.value def __iter__(self): return self ... >>> ti = TestIterator() >>> list(ti) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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Generators
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Generators (also called simple generators for historical reasons) are relatively new to Python, and are (along with iterators) perhaps one of the most powerful features to come along for years. Generators are a kind of iterators that are defined with normal function syntax. Exactly how they work is best shown through example. Let s first have a look at how you make them and use them, and then take a peek under the hood afterward.
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Making a Generator
Making a generator is simple; it s just like making a function. I m sure you are starting to tire of the good old Fibonacci sequence by now, so let me do something else. I ll make a function that flattens nested lists. The argument is a list that may look something like this: nested = [[1, 2], [3, 4], [5]]
CHAPTER 9 MAGIC METHODS, PROPERTIES, AND ITERATORS
In other words, a list of lists. My function should then give me the numbers in order. Here s a solution: def flatten(nested): for sublist in nested: for element in sublist: yield element Most of this function is pretty simple. First it iterates over all the sublists of the supplied nested list; then it iterates over the elements of each sublist in order. If the last line had been print element, for example, the function would have been easy to understand, right So what s new here is the yield statement. Any function that contains a yield statement is called a generator. And it s not just a matter of naming; it will behave quite differently from ordinary functions. The difference is that instead of returning one value, as you do with return, you can yield several values, one at a time. Each time a value is yielded (with yield), the function freezes: That is, it stops its execution at exactly that point and waits to be reawakened. When it is, it resumes its execution at the point where it stopped. I can make use of all the values by iterating over the generator: >>> nested = [[1, 2], [3, 4], [5]] >>> for num in flatten(nested): print num ... 1 2 3 4 5 or >>> list(flatten(nested)) [1, 2, 3, 4, 5]
A Recursive Generator
The generator I designed in the previous section could only deal with lists nested two levels deep, and to do that it used two for loops. What if you have a set of lists nested arbitrarily deeply Perhaps you use them to represent some tree structure, for example. (You can also do that with specific tree classes, but the strategy is the same.) You need a for loop for each level of nesting, but because you don t know how many levels there are, you have to change your solution to be more flexible. It s time to turn to the magic of recursion: def flatten(nested): try: for sublist in nested: for element in flatten(sublist): yield element except TypeError: yield nested
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