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gives rise to an unchecked warning, because it is not possible to ascertain, either at compile-time (within the limits of the compile-time type checking rules) or at run-time, whether the variable l does indeed refer to a List<String> If the code above is executed, heap pollution arises, as the variable ls, declared to be a List<String>, refers to a value that is not in fact a List<String> The problem cannot be identi ed at run-time because type variables are not rei ed, and thus instances do not carry any information at run-time regarding the actual type parameters used to create them In a simple example as given above, it may appear that it should be straightforward to identify the situation at compile-time and give a compilation error However, in the general (and typical) case, the value of the variable l may be the result of an invocation of a separately compiled method, or its value may depend upon arbitrary control ow The code above is therefore very atypical, and indeed very bad style Assignment from a value of a raw type to a variable of a parameterized type should only be used when combining legacy code which does not make use of parameterized types with more modern code that does If no operation that requires an unchecked warning to be issued takes place, heap pollution cannot occur Note that this does not imply that heap pollution only occurs if an unchecked warning actually occurred It is possible to run a program where some of the binaries were compiled by a compiler for an older version of the Java programming language, or by a compiler that allows the unchecked warnings to suppressed This practice is unhealthy at best Conversely, it is possible that despite executing code that could (and perhaps did) give rise to an unchecked warning, no heap pollution takes place Indeed, good programming practice requires that the programmer satisfy herself that despite any unchecked warning, the code is correct and heap pollution will not occur.

This function is used to parse one block s data up to the given end point in the text and to add a corresponding Block object to the stack of blocks We start by trying to nd a color, and if we nd one, we advance over it Next we try to nd the block s text (its name), although this can legitimately be empty If we have a block with no name or color we create an empty Block; otherwise we create a Block with the given name and color Once the Block has been created we add it as the last child of the stack of block s top block (Initially the top block is the root block, but if we have nested blocks it could be some other block that has been pushed on top) At the end we return the block so that it can be pushed onto the stack of blocks something we do only if the block has other blocks nested inside it

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The variable will always refer to an object that is an instance of a class that implements the parameterized type

Then there are accessors for the common data types:

def parse_new_row(data): datastack[-1]childrenappend(Blockget_new_row()) dataadvance_by(1)

4123 Kinds of Variables There are seven kinds of variables:

This is the easiest of the parsing functions It simply adds a new row as the last child of the stack of block s top block, and advances over the new row character This completes the review of the blocks recursive descent parser The parser does not require a huge amount of code, fewer than 100 lines, but that s still more than 50 percent more lines than the PyParsing version needs, and about 33 percent more lines than the PLY version needs And as we will see, using PyParsing or PLY is much easier than handcrafting a recursive descent parser and they also lead to parsers that are much easier to maintain The conversion into an SVG le using the BlockOutputsave_blocks_as_svg() function is the same for all the blocks parsers, since they all produce the same root block and children structures We won t review the function s code since it isn t relevant to parsing as such it is in the BlockOutputpy module le that comes with the book s examples We have now nished reviewing the handcrafted parsers In the following two sections we will show PyParsing and PLY versions of these parsers In addition, we will show a parser for a DSL that would need a quite sophisticated recursive descent parser if we did it by hand, and that really shows that as our needs grow, using a generic parser scales much better than a handcrafted solution

/** * Retrieve a single String-valued preference value * * @param key key for the particular preference * @return value of the preference * @exception MissingResourceException if not found */ public String getString( String key ) { byte[] val = getBytes( key ); String s = null; if ( val != null ) { try { s = new String( val, "UTF8" ); } catch ( UnsupportedEncodingException e ) { } } return s; } /** * Retrieve a single preference value of integer type * * @param key key for the particular preference * @return value of the preference * @exception MissingResourceException if not found */ public int getInteger( String key ) { String s = getString( key ); if ( s != null ) { return IntegervalueOf(s)intValue(); } // Will never get this far, because there will be a // MissingResourceException or NumberFormatException // on error return -1; } /** * Retrieve a single preference value of boolean type * * @param key key for the particular preference

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