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If we had created unordered dicts the same way and asked for their keys, the order of the returned keys would be arbitrary But for ordered dictionaries, we can rely on the keys to be returned in the same order they were inserted So for these examples, if we wrote list(dkeys()), we are guaranteed to get the list ['z', 'e', 'k'], and if we wrote list(taskskeys()), we are guaranteed to get the list [8031, 4027, 5733] One other nice feature of ordered dictionaries is that if we change an item s value that is, if we insert an item with the same key as an existing key the order is not changed So if we did tasks[8031] = "Daily backup", and then asked for the list of keys, we would get exactly the same list in exactly the same order as before If we want to move an item to the end, we must delete it and then reinsert it We can also call popitem() to remove and return the last key value item in the ordered dictionary; or we can call popitem(last=False), in which case the rst item will be removed and returned Another, slightly more specialized use for ordered dictionaries is to produce sorted dictionaries Given a dictionary, d, we can convert it into a sorted dictionary like this: d = collectionsOrderedDict(sorted(ditems())) Note that if we were to insert any additional keys they would be inserted at the end, so after any insertion, to preserve the sorted order, we would have to re-create the dictionary by executing the same code we used to create it in the rst place Doing insertions and re-creating isn t quite as inef cient as it sounds, since Python s sorting algorithm is highly optimized, especially for partially sorted data, but it is still potentially expensive In general, using an ordered dictionary to produce a sorted dictionary makes sense only if we expect to iterate over the dictionary multiple times, and if we do not expect to do any insertions (or very few), once the sorted dictionary has.

A MIDP 20 device must recognize key usage (see RFC 2459 [reference 7] section 4213) and basic constraints (see RFC 2459 [reference 7] section 42110) It must be able to process certificates with unknown distinguished name attributes and unknown non-critical certificate extensions It must also recognize the serialNumber attribute defined by WAPCert [reference 15] in distinguished names for Issuer and Subject

In the previous subsection the Point class included a distance_from_origin() method, and the Circle class had the area(), circumference(), and edge_distance_from_origin() methods All these methods return a single float value, so from the point of view of a user of these classes they could just as well be data attributes, but read-only, of course In the ShapeAltpy le alternative implementations of Point and Circle are provided, and all the methods mentioned here are provided as properties This allows us to write code like this:

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circle = ShapeCircle(5, 28, 45) circleradius circleedge_distance_from_origin # assumes: import ShapeAlt as Shape # returns: 5 # returns: 480

The maximum time to wait for the connection to be closed Speci es the method for hostname comparison when parsing URIs Maximum size of any buffer pools used by the transport Maximum number of bytes used to buffer incoming messages in memory The maximum number of outbound or inbound connections Outbound and inbound connections are counted separately The maximum size of an incoming message The name of the binding The maximum time to wait for an open connection operation to complete Specify the complexity of messages that can be processed (for example, size) The maximum time to wait for a receive operation to complete

Here are the implementations of the getter methods for the ShapeAltCircle class s area and edge_ distance_from_origin properties:

[ Team LiB ]

@property def area(self): return mathpi * (selfradius ** 2) @property def edge_distance_from_origin(self): return abs(selfdistance_from_origin - selfradius)

If we provide only getters as we have done here, the properties are read-only The code for the area property is the same as for the previous area() method The edge_distance_from_origin s code is slightly different from before because it now accesses the base class s distance_from_origin property instead of calling a distance_from_origin() method The most notable difference to both is the property decorator A decorator is a function that takes a function or method as its argument and returns a decorated version, that is, a version of the function or method that is modi ed in some way A decorator is indicated by preceding its name with an at symbol (@) For now, just treat decorators as syntax in 8 we will see how to create custom decorators The property() decorator function is built-in and takes up to four arguments: a getter function, a setter function, a deleter function, and a docstring The effect of using @property is the same as calling the property() function with just one argument, the getter function We could have created the area property like this:

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def area(self): return mathpi * (selfradius ** 2) area = property(area)

[ Team LiB ]

In the previous subsection we noted that no validation is performed on the Circle s radius attribute We can provide validation by making radius into a property This does not require any changes to the Circle__init__() method, and any code that accesses the Circleradius attribute will continue to work unchanged only now the radius will be validated whenever it is set Python programmers normally use properties rather than the explicit getters and setters (eg, getRadius() and setRadius()) that are so commonly used in other object-oriented languages This is because it is so easy to change a data attribute into a property without affecting the use of the class To turn an attribute into a readable/writable property we must create a private attribute where the data is actually held and supply getter and setter methods Here is the radius s getter, setter, and docstring in full:

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