Metadata-Version: 1.1
Name: parsimonious
Version: 0.3
Summary: (Soon to be) the fastest pure-Python PEG parser I could muster
Home-page: https://github.com/erikrose/parsimonious
Author: Erik Rose
Author-email: erikrose@grinchcentral.com
License: MIT
Description: ============
        Parsimonious
        ============
        
        Parsimonious aims to be the fastest arbitrary-lookahead parser written in pure
        Python. It's based on parsing expression grammars (PEGs), which means you
        feed it a simplified sort of EBNF notation. Parsimonious was designed to
        undergird a MediaWiki parser that wouldn't take 5 seconds or a GB of RAM to do
        one page.
        
        Beyond speed, secondary goals include...
        
        * Frugal RAM use
        * Minimalistic, understandable, idiomatic Python code
        * Readable grammars
        * Extensible grammars
        * Complete test coverage
        * Separation of concerns. Some Python parsing kits mix recognition with
          instructions about how to turn the resulting tree into some kind of other
          representation. This is limiting when you want to do several different things
          with a tree: for example, render wiki markup to HTML *or* to text.
        * Good error reporting. I want the parser to work *with* me as I develop a
          grammar.
        
        
        Example
        =======
        
        Here's how to build a simple grammar::
        
            >>> from parsimonious.grammar import Grammar
            >>> grammar = Grammar(
            ...     """
            ...     bold_text  = bold_open text bold_close
            ...     text       = ~"[A-Z 0-9]*"i
            ...     bold_open  = "(("
            ...     bold_close = "))"
            ...     """)
        
        You can have forward references and even right recursion; it's all taken care
        of by the grammar compiler. The first rule is taken to be the default start
        symbol, but you can override that.
        
        Next, let's parse something and get an abstract syntax tree::
        
            >>> grammar.parse('((bold stuff))')
            <Node called "bold_text" matching "((bold stuff))">
                <Node called "bold_open" matching "((">
                <RegexNode called "text" matching "bold stuff">
                <Node called "bold_close" matching "))">
        
        You'd typically then use a ``nodes.NodeVisitor`` subclass (see below) to walk
        the tree and do something useful with it.
        
        
        Status
        ======
        
        0.3 is a pretty usable release for inputs that aren't huge. I haven't really
        started optimizing yet. And note that there may be API changes until we get to
        1.0.
        
        * Everything that exists works. Test coverage is good.
        * I don't plan on making any backward-incompatible changes to the rule syntax
          in the future, so you can write grammars without fear.
        * It may be slow and use a lot of RAM; I haven't measured either yet. However,
          I have several macro- and micro-optimizations in mind.
        * Error reporting is fairly uninformative, and debugging is nonexistent.
          However, ``repr`` methods of expressions, grammars, and nodes are very clear
          and helpful. Ones of ``Grammar`` objects are even round-trippable! Huge
          things are planned for grammar debugging in the future.
        * The grammar extensibility story is underdeveloped at the moment. You should
          be able to extend a grammar by simply concatening more rules onto the
          existing ones; later rules of the same name should override previous ones.
          However, this is untested and may not be the final story.
        * Sphinx docs are coming, but the docstrings are quite useful now.
        
        Coming up soon...
        
        * Optimizations to make Parsimonious worthy of its name.
        * Tighter RAM use
        * Better-thought-out grammar extensibility story
        
        
        A Little About PEG Parsers
        ==========================
        
        PEG parsers don't draw a distinction between lexing and parsing; everything's
        done at once. As a result, there is no lookahead limit, as there is with, for
        instance, Yacc. And, due to both of these properties, PEG grammars are easier
        to write: they're basically just a more practical dialect of EBNF. With
        caching, they take O(grammar size * text length) memory (though I plan to do
        better), but they run in O(text length) time.
        
        More Technically
        ----------------
        
        PEGs can describe a superset of LL(k) languages, any deterministic LR(k)
        language, and many others, including some that aren't context-free
        (http://www.brynosaurus.com/pub/lang/peg.pdf). They can also deal with what
        would be ambiguous languages if described in canonical EBNF. They do this by
        trading the ``|`` alternation operator for the ``/`` operator, which works the
        same except that it makes priority explicit: ``a / b / c`` first tries matching
        ``a``. If that fails, it tries ``b``, and, failing that, moves on to ``c``.
        Thus, ambiguity is resolved by always yielding the first successful recognition.
        
        
        Writing Grammars
        ================
        
        Grammars are defined by a series of rules, one per line. The syntax should be
        familiar to anyone who uses regexes or reads programming language manuals. An
        example will serve best::
        
            styled_text = bold_text / italic_text
            bold_text   = "((" text "))"
            italic_text = "''" text "''"
            text        = ~"[A-Z 0-9]*"i
        
        Here's a syntax reference:
        
        ``"some literal"``
          Used to quote literals. Backslash escaping and Python conventions for "raw"
          and Unicode strings help support fiddly characters.
        space
          Sequences are made out of space- or tab-delimited things. ``a b c`` matches
          spots where those 3 terms appear in that order.
        ``a / b``
          Alternatives. The first to succeed of ``a / b / c`` wins.
        ``thing?``
          An optional expression. This is greedy, always consuming ``thing`` if it
          exists.
        ``&thing``
          A lookahead assertion. Ensures ``thing`` matches at the current position but
          does not consume it.
        ``!thing``
          A negative lookahead assertion. Matches if ``thing`` isn't found here.
          Doesn't consume any text.
        ``things*``
          Zero or more things. This is greedy, always consuming as many repetitions as
          it can.
        ``things+``
          One or more things. This is greedy, always consuming as many repetitions as
          it can.
        ``~r"regex"ilmsux``
          Regexes have ``~`` in front and are quoted like literals. Any flags follow
          the end quotes as single chars. Regexes are good for representing character
          classes (``[a-z0-9]``) and optimizing for speed. The downside is that they
          won't be able to take advantage of our fancy debugging, once we get that
          working. Ultimately, I'd like to deprecate explicit regexes and instead have
          Parsimonious build them dynamically out of simpler primitives.
        
        
        Optimizing Grammars
        ===================
        
        Don't repeat expressions. If you need a ``~"[a-z0-9]"i`` at two points in your
        grammar, don't type it twice; make it a rule of its own, and reference it from
        wherever you need it. You'll get the most out of the caching this way, since
        cache lookups are by expression object identity (for speed). Even if you have
        an expression that's very simple, not repeating it will save RAM, as there can,
        at worst, be a cached int for every char in the text you're parsing. In the
        future, we may identify repeated subexpressions automatically and factor them
        up while building the grammar.
        
        How much should you shove into one regex, versus how much should you break them
        up to not repeat yourself? That's a fine balance and worthy of benchmarking.
        More stuff jammed into a regex will execute faster, because it doesn't have to
        run any Python between pieces, but a broken-up one will give better cache
        performance if the individual pieces are re-used elsewhere. If the pieces of a
        regex aren't used anywhere else, by all means keep the whole thing together.
        
        Quantifiers: bring your ``?`` and ``*`` quantifiers up to the highest level you
        can. Otherwise, lower-level patterns could succeed but be empty and put a bunch
        of useless nodes in your tree that didn't really match anything.
        
        
        Processing Parse Trees
        ======================
        
        A parse tree has a node for each expression matched, even if it matched a
        zero-length string, like ``"thing"?`` might do.
        
        The ``NodeVisitor`` class provides an inversion-of-control framework for
        walking a tree and returning a new construct (tree, string, or whatever) based
        on it. For now, have a look at its docstrings for more detail. There's also a
        good example in ``grammar.RuleVisitor``. Notice how we take advantage of nodes'
        iterability by using tuple unpacks in the formal parameter lists::
        
            def visit_or_term(self, or_term, (_, slash, term)):
                ...
        
        When something goes wrong in your visitor, you get a nice error like this::
        
            [normal traceback here...]
            VisitationException: 'Node' object has no attribute 'foo'
        
            Parse tree:
            <Node called "rules" matching "number = ~"[0-9]+"">  <-- *** We were here. ***
                <Node matching "number = ~"[0-9]+"">
                    <Node called "rule" matching "number = ~"[0-9]+"">
                        <Node matching "">
                        <Node called "label" matching "number">
                        <Node matching " ">
                            <Node called "_" matching " ">
                        <Node matching "=">
                        <Node matching " ">
                            <Node called "_" matching " ">
                        <Node called "rhs" matching "~"[0-9]+"">
                            <Node called "term" matching "~"[0-9]+"">
                                <Node called "atom" matching "~"[0-9]+"">
                                    <Node called "regex" matching "~"[0-9]+"">
                                        <Node matching "~">
                                        <Node called "literal" matching ""[0-9]+"">
                                        <Node matching "">
                        <Node matching "">
                        <Node called "eol" matching "
                        ">
                <Node matching "">
        
        The parse tree is tacked onto the exception, and the node whose visitor method
        raised the error is pointed out.
        
        Why No Streaming Tree Processing?
        ---------------------------------
        
        Some have asked why we don't process the tree as we go, SAX-style. There are
        two main reasons:
        
        1. It wouldn't work. With a PEG parser, no parsing decision is final until the
           whole text is parsed. If we had to change a decision, we'd have to backtrack
           and redo the SAX-style interpretation as well, which would involve
           reconstituting part of the AST and quite possibly scuttling whatever you
           were doing with the streaming output. (Note that some bursty SAX-style
           processing may be possible in the future if we use cuts.)
        
        2. It interferes with the ability to derive multiple representations from the
           AST: for example, first HTML and then text from wiki markup.
        
        
        Future Directions
        =================
        
        Rule Syntax Changes
        -------------------
        
        * Maybe support left-recursive rules like PyMeta, if anybody cares.
        * Ultimately, I'd like to get rid of explicit regexes and break them into more
          atomic things like character classes. Then we can dynamically compile bits
          of the grammar into regexes as necessary to boost speed.
        
        Optimizations
        -------------
        
        * Make RAM use almost constant by automatically inserting "cuts", as described
          in
          http://ialab.cs.tsukuba.ac.jp/~mizusima/publications/paste513-mizushima.pdf.
          This would also improve error reporting, as we wouldn't backtrack out of
          everything informative before finally failing.
        * Find all the distinct subexpressions, and unify duplicates for a better cache
          hit ratio.
        * Think about having the user (optionally) provide some representative input
          along with a grammar. We can then profile against it, see which expressions
          are worth caching, and annotate the grammar. Perhaps there will even be
          positions at which a given expression is more worth caching. Or we could keep
          a count of how many times each cache entry has been used and evict the most
          useless ones as RAM use grows.
        * We could possibly compile the grammar into VM instructions, like in "A
          parsing machine for PEGs" by Medeiros.
        * If the recursion gets too deep in practice, use trampolining to dodge it.
        * It looks like we could make an architecture-independent .o file and use LLVM
          to JIT it to whatever arch we're on: https://github.com/dabeaz/bitey/. Of
          course, then everybody has to have LLVM, which is even harder to set up than
          a vanilla C toolchain.
        
        Niceties
        --------
        
        * Pijnu has a raft of tree manipulators. I don't think I want all of them, but
          a judicious subset might be nice. Don't get into mixing formatting with tree
          manipulation.
          https://github.com/erikrose/pijnu/blob/master/library/node.py#L333. PyPy's
          parsing lib exposes a sane subset:
          http://doc.pypy.org/en/latest/rlib.html#tree-transformations.
        
        
        Version History
        ===============
        
        0.3
          * Support comments, the ``!`` ("not") operator, and parentheses in grammar
            definition syntax.
          * Change the ``&`` operator to a prefix operator to conform to the original
            PEG syntax. The version in Parsing Techniques was infix, and that's what I
            used as a reference. However, the unary version is more convenient, as it
            lets you spell ``AB & A`` as simply ``A &B``.
          * Take the ``print`` statements out of the benchmark tests.
          * Give Node an evaluate-able ``__repr__``.
        
        0.2
          * Support matching of prefixes and other not-to-the-end slices of strings by
            making ``match()`` public and able to initialize a new cache. Add
            ``match()`` callthrough method to ``Grammar``.
          * Report a ``BadGrammar`` exception (rather than crashing) when there are
            mistakes in a grammar definition.
          * Simplify grammar compilation internals: get rid of superfluous visitor
            methods and factor up repetitive ones. Simplify rule grammar as well.
          * Add ``NodeVisitor.lift_child`` convenience method.
          * Rename ``VisitationException`` to ``VisitationError`` for consistency with
            the standard Python exception hierarchy.
          * Rework ``repr`` and ``str`` values for grammars and expressions. Now they
            both look like rule syntax. Grammars are even round-trippable! This fixes a
            unicode encoding error when printing nodes that had parsed unicode text.
          * Add tox for testing. Stop advertising Python 2.5 support, which never
            worked (and won't unless somebody cares a lot, since it makes Python 3
            support harder).
          * Settle (hopefully) on the term "rule" to mean "the string representation of
            a production". Get rid of the vague, mysterious "DSL".
        
        0.1
          * A rough but useable preview release
        
Keywords: parse,parser,parsing,peg,packrat,grammar,language
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Text Processing :: General
