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Enumeration enumVals = attrgetStringValues(); // Enumerate on values for this attribute boolean hasVals = false; while ( (enumVals != null) && enumValshasMoreElements() ) { String val = (String)enumValsnextElement(); Systemoutprintln( attrs[i] + ": " + val ); hasVals = true; } if ( !hasVals ) { Systemoutprintln( attrs[i] + " has no values" ); }

One implementation consideration for Java virtual machines is that every eld and array element is considered distinct; updates to one eld or element must not interact with reads or updates of any other eld or element In particular, two threads that update adjacent elements of a byte array separately must not interfere or interact and do not need synchronization to ensure sequential consistency Some processors do not provide the ability to write to a single byte It would be illegal to implement byte array updates on such a processor by simply reading an entire word, updating the appropriate byte, and then writing the entire word back to memory This problem is sometimes known as word tearing, and on processors that cannot easily update a single byte in isolation some other approach will be required

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In general, if Python already has a suitable parser in the standard library, or as a third-party add-on, it is usually best to use it rather than to write our own When it comes to parsing data formats or DSLs for which no parser is available, rather than handcrafting a parser, we can use one of Python s third-party general-purpose parsing modules In this chapter we will introduce two of the most popular third-party parsers One of these is Paul McGuire s PyParsing module, which takes a unique and very Pythonic approach The other is David Beazley s PLY (Python Lex Yacc), which is closely modeled on the classic Unix lex and yacc tools, and that makes extensive use of regexes Many other parsers are available, with many listed at wwwdabeazcom/ply (at the bottom of the page), and of course, in the Python Package Index, pypipythonorg/pypi This chapter s rst section provides a brief introduction to the standard BNF (Backus Naur Form) syntax used to describe the grammars of data formats and DSLs In that section we will also explain the basic terminology The remaining sections all cover parsing itself, with the second section covering handcrafted parsers, using regexes, and using recursive descent, as a natural follow-on from the regular expressions chapter The third section introduces the PyParsing module The initial examples are the same as those for which handcrafted parsers are created in the second section this is to help learn the PyParsing approach, and also to provide the opportunity to compare and contrast The section s last example has a more ambitious grammar and is new in this section The last section introduces the PLY module, and shows the same examples we used in the PyParsing section, again for ease of learning and to provide a basis for comparison Note that with one exception, the handcrafted parsers section is where each data format and DSL is described, its BNF given, and an example of the data or DSL shown, with the other sections providing backreferences to these where appropriate The exception is the rst-order logic parser whose details are given in the PyParsing section, with corresponding backreferences in the PLY section.

Finally, we mark the end of the output for this record:

Here is a test case to detect word tearing:

Parsing is a means of transforming data that is in some structured format whether the data represents actual data, or statements in a programming language, or some mixture of both into a representation that re ects the data s structure and that can be used to infer the meaning that the data represents The parsing process is most often done in two phases: lexing (also called lexical analysis, tokenizing, or scanning), and parsing proper (also called syntactic analysis)

} Systemoutprintln(" "); } }

public class WordTearing extends Thread { static final int LENGTH = 8; static final int ITERS = 1000000; static byte[] counts = new byte[LENGTH];

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