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Control Structures in Java Encoding code128b in Java Control Structures

2. Control Structures using barcode integrating for jdk control to generate, create code 128 image in jdk applications. console app Conversely, mutable objects, j2se code 128 code set c that is, parameters that are object references to mutable types like lists and dictionaries, can be changed inside functions. These parameter-passing behaviors are the same as in Java. All Python functions return a value.

This is done either explicitly by using a return or yield statement (covered next), or implicitly, in which case Python will return None for us. Unlike C++ or Java, we are not tied down to specifying one particular return type: We can return any type we want since what we return is an object reference that is bound to a variable of any type. Python functions always return a single value, but because that value can be a tuple or a list or any other collection, for all practical purposes, Python functions can return any number of values.

. Generator Functions If we replace the code at th j2ee barcode 128 e end of the frange() function as shown in the following code snippet, we will turn the function into a generator. Generators do not have return statements; instead, they have yield statements. If a generator runs out of values, that is, if control reaches the end of the function, instead of returning, Python automatically raises a StopIteration exception:.

# Build and return a list re sult = [] while start < (stop - (inc / 2.0)): result.append(start) start += inc return result # Return each value on demand while start < (stop - (inc / 2.

0)): yield start start += inc. Now, if we call frange(5), w jboss Code128 e will get back a generator object, not a list. We can force the generator to give us a list by doing this: list(frange(5)). But a more common use of generators is in loops:.

for x in frange(10): print x,. This will output 0.0 1.0 2.

code-128c for Java 0 3.0 4.0 5.

0 6.0 7.0 8.

0 9.0 whichever version we use. But for long lists the generator version will be much more ef cient, because rather than creating the whole list in memory like the list version, it creates only one item at a time.

The yield statement behaves like a return statement, but for one crucial difference: After yield has returned a value, when the generator is next called it will continue from the statement following the yield with all its previous state intact. So the rst time the frange() generator is called, assuming, say frange(5), it returns 0.0; the second time it returns 1.

0, and so on. After returning 9.0 the while expression evaluates to False and the function terminates.

. Mutable parameters in Pyth on are similar to Pascal s var parameters and to C++ s non-const references.. Functions Because the function is a ge nerator (and this is the case purely because we have used yield), when it nishes it does not return a value, but instead raises a StopIteration exception. In the context of a for loop, the for gracefully handles this particular exception, taking it not as an error, but as an indication that the iteration has completed, so the for loop ends and the ow of control moves to the for loop s else suite, or to the statement following the for loop s suite, if there is no else. Similarly, if we coerce a generator into a list, the list constructor will automatically handle the StopIteration exception.

A generator is an object that has a next() function, so we can explore the behavior of our frange() generator interactively if we wish:. >>> list(frange(1, tomcat barcode standards 128 3, 0.75)) [1, 1.75, 2.

5] >>> gen = frange(1, 3, 0.75) >>> gen.next() 1 >>> gen.

next() 1.75 >>> gen.next() 2.

5 >>> gen.next() Traceback (most recent call last): File <pyshell#126>, line 1, in -toplevelgen.next() StopIteration.

We generated the whole three -item list using list(), and then we used the generator returned by frange() to produce each successive value in the same way that a for loop does..
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