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if sysplatformstartswith("win"): def get_files(names): for name in names: if ospathisfile(name): yield name else: for file in globiglob(name): if not ospathisfile(file): continue yield file else: def get_files(names): return (file for file in names if ospathisfile(file))

The CLDC and MIDP standards intend to bring the benefits of Java technology to resource-constrained wireless devices with limited Internet connectivity This chapter reviews the goals, requirements, and scope of these standards [ Team LiB ]

Custom Using The second approach to using the is to use the interface, which is generally used in advanced scenarios where intercomplete control over serialization is needed The , , and With face supports three methods: method was deprecated and replaced with the NET 20, the attribute The other two methods are and These methods correspond to methods used to deserialize and serialize from and to XML Listing 633 demonstrates this

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In either case the function is expected to be called with a list of lenames, for example, sysargv[1:], as its argument On Windows the function iterates over all the names listed For each lename, the function yields the name, but for non les (usually directories), the glob module s globiglob() function is used to return an iterator to the names of the les that the name represents after wildcard expansion For an ordinary name like autoexecbat an iterator that produces one item (the name) is returned, and for a name that uses wildcards like *txt an iterator that produces all the matching les (in this case those with extension txt) is returned (There is also a globglob() function that returns a list rather than an iterator) On Unix the shell does wildcard expansion for us, so we just need to return a generator for all the les whose names we have been given Generator functions can also be used as coroutines, if we structure them correctly Coroutines are functions that can be suspended in mid-execution (at the yield expression), waiting for the yield to provide a result to work on, and once received they continue processing As we will see in the coroutines subsection later in this chapter ( 399), coroutines can be used to distribute work and to create processing pipelines

There are some occasions when it is easier to write a piece of code that generates the code we need than to write the needed code directly And in some contexts it is useful to let users enter code (eg, functions in a spreadsheet), and to let Python execute the entered code for us rather than to write a parser and handle it ourselves although executing arbitrary code like this is a potential security risk, of course Another use case for dynamic code execution is to provide plug-ins to extend a program s functionality Using plug-ins has the disadvantage that all the necessary functionality is not built into the program (which can make the program more dif cult to deploy and runs the risk of plug-ins getting lost), but has the advantages that plug-ins can be upgraded individually and can be provided separately, perhaps to provide enhancements that were not originally envisaged

The easiest way to execute an expression is to use the built-in eval() function we rst saw in 6 For example:

[ Team LiB ]

The result of using the is that we can work with XSD schemas as the starting point for our contracts The drawback to this approach is that there could potentially be much more code to write

The globglob() functions are not as powerful as, say, the Unix bash shell, since although they support the *, , and [] syntaxes, they don t support the {} syntax

This is ne for user-entered expressions, but what if we need to create a function dynamically For that we can use the built-in exec() function For example, the user might give us a formula such as 4 r2 and the name area of sphere , which they want turned into a function Assuming that we replace with mathpi, the function they want can be created like this:

import math code = ''' def area_of_sphere(r): return 4 * mathpi * r ** 2 ''' context = {} context["math"] = math exec(code, context)

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