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A good book on regular expressions is Mastering Regular Expressions by Jeffrey E F Friedl, ISBN 0596528124 It does not explicitly cover Python, but Python s re module offers very similar functionality to the Perl regular expression engine that the book covers in depth

[ Team LiB ]

regexes are widely and successfully used to create parsers, they do have a limitation in that area: They are only able to deal with recursively structured text if the maximum level of recursion is known Also, large and complex regexes can be dif cult to read and maintain So apart from simple cases, for parsing the best approach is to use a tool designed for the purpose for example, use a dedicated XML parser for XML If such a parser isn t available, then an alternative to using regexes is to use a generic parsing tool, an approach that is covered in 14 At its simplest a regular expression is an expression (eg, a literal character), optionally followed by a quanti er More complex regexes consist of any number of quanti ed expressions and may include assertions and may be in uenced by ags This chapter s rst section introduces and explains all the key regular expression concepts and shows pure regular expression syntax it makes minimal reference to Python itself Then the second section shows how to use regular expressions in the context of Python programming, drawing on all the material covered in the earlier sections Readers familiar with regular expressions who just want to learn how they work in Python could skip to the second section ( 499) The chapter covers the complete regex language offered by the re module, including all the assertions and ags We indicate regular expressions in the text using bold, show where they match using underlining, and show captures using shading

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We have cut out a few lines and reduced the indentation that is present in the le The le is about 840K in size, so we have compressed it using gzip to a more manageable 72K Unfortunately, the element tree parser requires either a lename or a le object to read, but we cannot give it the compressed le since that will just appear to be random binary data We can solve this problem with two initial steps:

Because WCF received an unhandled exception at the service host, it assumed the exception was indicative of a fatal issue and therefore faulted , the established security the server channel In our case with session is no longer valid, so communications must be reestablished by recreating the client proxy

binary = gzipopen(filename)read() fh = ioStringIO(binarydecode("utf8"))

The Form class has a layout policy that is organized around rows A Form grows vertically and scrolls vertically as necessary The height of a Form varies depending upon the number of rows and the height of each row The height of each row is determined by the items that are positioned on that row

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The gzip module s gzipopen() function is similar to the built-in open() except that it reads gzip-compressed les (those with extension gz) as raw binary data We need the data available as a le that the element tree parser can work with, so we use the bytesdecode() method to convert the binary data to a string using UTF-8 encoding (which is what the XML le uses), and we create a le-like ioStringIO object with the string containing the entire XML le as its data

Operations with one-way designations by design do not receive a message from the called service, regardless of whether that call was successful Because no message is returned, there is no indication to the client that a fault has occurred In addition, if that fault was caused by an unhandled exception, the server channel will be considered faulted, but the client will not be aware of that fact In a session-dependent interaction, continued calls ) until the proxy will fail (with is re-created Be certain to account for this behavior in your client and server logic when utilizing one-way operations

tree = xmletreeElementTreeElementTree() root = treeparse(fh) stations = [] for element in treegetiterator("station_name"): stationsappend(elementtext)

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