booze-tools 0.6.0.1

A panoply of tools for parsing, lexical analysis, and semantic processing

What is this?

At the moment, this is undirected exploration in the space of language recognition and translation. I'd like to try to discover the most ideal formulations of classic approaches.

What's New?

• The project moves back to alpha stage for the time being.

• Scanners stop being iterable. They find tokens and maybe dispatch actions. That is all. Edit: Iterable scanners remain an option for now.

• The interface to parsing must become push-mode.

What's Here?

For now there are four major components. Eventually there will be more. These are:

• MiniParse -- Provides Minimal-LR(1)* or LALR(1) or Canonical-LR(1) with operator-precedence grammar facilities (like Lemon, YACC, or Bison), error productions, and good-and-proper error recovery.

• MiniScan -- Provides a DFA-based backtracking scanner (like Flex or Lex) with a few extra goodies.

• MacroParse -- This is the crown jewel of the package right now. It:

  • provides for a separate document containing the definitions of both a scanner and parser.
  • supports error productions and error-recovery in the same manner as MiniParse.
  • uses markdown format to make just such a document into a literate program.
  • enables a single such definition to be used for different applications on different host languages.
  • supports a macro language for simplifying otherwise-redundant parser specifications.
  • provides a suitable runtime library so the examples run and pass the tests.
  • can prepare parse and scan tables ahead of time (serialized to JSON) or just-in-time according to your needs.
  • can generate DOT graphs from grammars.

• Support Library: generic bits and bobs that may also be useful in other contexts.

  • Strongly Connected Components
  • Transitive Closure
  • Visitor Pattern
  • Equivalence Classification
  • Hamming Distance
  • Breadth First Traversal
  • Various small array hacks

The "minimal-LR(1)" algorithm used here is -- I believe -- provably minimal, even while it respects precedence and associativity declarations in the usual way. It is strongly inspired by the IELR(1) algorithm, but it is NOT exactly that algorithm. As far as I can tell it is a new contribution. As such, I would appreciate feedback respecting your results with it.

Full documentation is moving from the wiki page over to ReadTheDocs. Worked examples may be found at /example/.

Priorities?

• These operate within a Python environment.
• They have some features not found in other such tools.
• Performance is accordingly NOT a top priority, but:
  • the profiler has been used to solve one or two problems,
  • if someone wants to play with the profiler they are welcome, and
  • contributions in that vein will be accepted as long as they are consistent with the higher priorities.

What Else?

There are several complete worked example scanners and parsers in the example folder. Start with the JSON ones: they have the best introductory commentary to walk you through getting started.

There are unit tests. They're not vast and imposing, but they exercise the interface both directly and via the example code.

There is a wiki linked above. It has background and more detail about what this is and how to use it.

Oh by the way..

I'm NOT a crack-pot. Really I'm not.