You should probably use the https://github.com/peter17/pijnu official repository...

Pijnu is a PEG parser generator and processor, written in Python, intended to be clear, easy, practical.

Pijnu was created in 2009 by Denis "Spir" Derman and then transferred to Peter Potrowl (peter17 on GitHub) in June 2011.

Presentation

See the wiki pages for details.

Syntax and grammar

Pijnu syntax is a custom, extended version of Parsing Expression Grammars (PEG); which itself is a kind of mix of BNF and regular expressions.

The major difference is that PEG is a grammar to express string recognition patterns, while BNF or regexp express string generation. As a consequence, PEG is better suited for parsing tasks. A PEG grammar clearly encodes the algorithm to parse a source string, that simply needs to be rewritten into a parser coded in a programming language.

Pijnu generated parsers use a library to parse source texts. This library mainly holds pattern classes, a Node type for the resulting parse tree, and several kinds of tool functions.

The parser is produced from the grammar by a generator which, indeed, itself "meta-uses" the library. For the story, all but the first generator were themselves "meta-produced" by previous versions of the generator: Pijnu is bootstrapped.

Example:

### simple arithmetics with '+' and '*' only
formula
<definition>
# tokens
ADD        : '+'
MULT       : '*'
LPAREN     : '('
RPAREN     : ')'
digit      : [0..9.]
number     : digit+
# operations
mult       : (grup/number) MULT (grup/mult/number)
add        : (grup/mult/number) ADD (grup/add/mult/number)
grup       : LPAREN (add / mult) RPAREN
formula    : add / mult / number

Post-process & transformations

A parsing phase produces a parse tree in which every node was yielded by a pattern. Simple leaf nodes hold the matched string snippet while branch nodes contain a sequence of child nodes. A major issue in text processing applications is that a raw parse tree is far from having a form well suited for further processing.

Using Pijnu, one can do far more that getting a parse tree. The grammar allows assigning transformation functions to patterns, that will then be applied to every generated node. Numerous in-built transformations are provided in order to easily restructure the resulting parse tree and/or modify specific nodes.

Moreover, a user can write custom functions right inside the grammar that will then be applied to directly perform further processing. This is a both very simple and highly powerful method. In most cases, one can get final results "like magic".

For instance, to compute the actual result from the above formula grammar, one needs only 2 single-line functions: one for each operation indeed. Then, the result of the parsing/processing process is the result of the expressed formula.

Another example that will generate XHTML from wiki-text styled lines (possibly nested), using a single 3-lines function:

### parse wiki-text styled lines and rewrite them into XHTML
wikInline
<toolset>
def styledSpan(node):
    klass = node.typ
    text = node.value
    node.value = '<span class="%s">%s</span>' %(klass,text)

<definition>
# codes
    ESCAPE         : '~'
    DISTINCT       : "//"                                : drop
    IMPORTANT      : "**"                                : drop
    styleCode      : (DISTINCT / IMPORTANT)
# character expression
    escChar        : ESCAPE ('*' / '!' / '/' / ESCAPE)   : join
    validChar      : [\\x20..\\xff  !!/!*~]
    rawText        : (escChar / (!styleCode validChar))+ : join
# text kinds
    distinctText   : DISTINCT inlineText DISTINCT        : liftValue
    importantText  : IMPORTANT inlineText IMPORTANT      : liftValue
    styledText     : distinctText / importantText        : styledSpan
    inlineText     : (styledText / rawText)+             : @ join

The column on right side assigns transformations to patterns. drop, join, and liftValue are builtin. styledSpan is a custom transformation. '@' denotes a recursive pattern.

Practical use

See the guide & tutorial in the wiki for details.

As a tool, Pijnu is hopefully clear and efficient for the user.

It provides highly informative feedback about patterns, results and exceptions.

Custom extensions from PEG help defining legible grammars -- there may be more in the future. There are also pre-processing functions and configuration parameters that may be worthful in practical cases, but still need be fully integrated.

Typically, a user will define the grammar, import the generator and let it write a corresponding parser. This parser comes in the form of a python module from which a parser object can be imported. The said parser object and each of its patterns can be used to match a source text partially or completely, find first or all occurrences of matches, or replace found matches. In most cases, transformation will restructure and further process the resulting parse tree:

from pijnu import makeParser
myGrammar = file("grammar.pijnu").read()
make_parser = makeParser(myGrammar)
parser = make_parser()
parser.match(source)

It is also possible to directly produce a parser from the command line using the gen.py module (later may be renamed to pijnu.py):

python gen.py myGrammar.pijnu myParser.py