Using the Tool

As a Library

TatSu can be used as a library, much like Python’s re, by embedding grammars as strings and generating grammar models instead of generating Python code.

  • tatsu.compile(grammar, name=None, **kwargs)

    Compiles the grammar and generates a model that can subsequently be used for parsing input with.

  • tatsu.parse(grammar, input, start=None, **kwargs)

    Compiles the grammar and parses the given input producing an AST as result. The result is equivalent to calling:

    model = compile(grammar)
    ast = model.parse(input)

    Compiled grammars are cached for efficiency.

  • tatsu.to_python_sourcecode(grammar, name=None, filename=None, **kwargs)

    Compiles the grammar to the Python sourcecode that implements the parser.

  • to_python_model(grammar, name=None, filename=None, **kwargs)

    Compiles the grammar and generates the Python sourcecode that implements the object model defined by rule annotations.

This is an example of how to use Tatsu as a library:


    start = expression $ ;

        | term '+' ~ expression
        | term '-' ~ expression
        | term

        | factor '*' ~ term
        | factor '/' ~ term
        | factor

        | '(' ~ @:expression ')'
        | number

    number = /\d+/ ;

def main():
    import pprint
    import json
    from tatsu import parse
    from tatsu.util import asjson

    ast = parse(GRAMMAR, '3 + 5 * ( 10 - 20 )')
    pprint.pprint(ast, indent=2, width=20)

    print(json.dumps(asjson(ast), indent=2))

if __name__ == '__main__':

And this is the output:

[ '3',
  [ '5',
    [ '10',


Compiling grammars to Python

Tatsu can be run from the command line:

$ python -m tatsu


$ scripts/tatsu

Or just:

$ tatsu

if Tatsu was installed using easy_install or pip.

The -h and –help parameters provide full usage information:

$ python -m tatsu -h
usage: tatsu [--generate-parser | --draw | --object-model | --pretty]
            [--color] [--trace] [--no-left-recursion] [--name NAME]
            [--no-nameguard] [--outfile FILE] [--object-model-outfile FILE]
            [--whitespace CHARACTERS] [--help] [--version]

TatSu takes a grammar in a variation of EBNF as input, and outputs a memoizing
PEG/Packrat parser in Python.

positional arguments:
GRAMMAR               the filename of the Tatsu grammar to parse

optional arguments:
--generate-parser     generate parser code from the grammar (default)
--draw, -d            generate a diagram of the grammar (requires --outfile)
--object-model, -g    generate object model from the class names given as
                        rule arguments
--pretty, -p          generate a prettified version of the input grammar

parse-time options:
--color, -c           use color in traces (requires the colorama library)
--trace, -t           produce verbose parsing output

generation options:
--no-left-recursion, -l
                        turns left-recusion support off
--name NAME, -m NAME  Name for the grammar (defaults to GRAMMAR base name)
--no-nameguard, -n    allow tokens that are prefixes of others
--outfile FILE, --output FILE, -o FILE
                        output file (default is stdout)
--object-model-outfile FILE, -G FILE
                        generate object model and save to FILE
                        characters to skip during parsing (use "" to disable)

common options:
--help, -h            show this help message and exit
--version, -v         provide version information and exit

The Generated Parsers

A Tatsu generated parser consists of the following classes:

  • A MyLanguageBuffer class derived from tatsu.buffering.Buffer that handles the grammar definitions for whitespace, comments, and case significance.
  • A MyLanguageParser class derived from tatsu.parsing.Parser which uses a MyLanguageBuffer for traversing input text, and implements the parser using one method for each grammar rule:
def _somerulename_(self):
  • A MyLanguageSemantics class with one semantic method per grammar rule. Each method receives as its single parameter the Abstract Syntax Tree (AST) built from the rule invocation:
def somerulename(self, ast):
    return ast
  • A if __name__ == '__main__': definition, so the generated parser can be executed as a Python script.

The methods in the delegate class return the same AST received as parameter, but custom semantic classes can override the methods to have them return anything (for example, a Semantic Graph). The semantics class can be used as a template for the final semantics implementation, which can omit methods for the rules that do not need semantic treatment.

If present, a _default() method will be called in the semantics class when no method matched the rule name:

def _default(self, ast):
    return ast

If present, a _postproc() method will be called in the semantics class after each rule (including the semantics) is processed. This method will receive the current parsing context as parameter:

def _postproc(self, context, ast):

Using the Generated Parser

To use the generated parser, just subclass the base or the abstract parser, create an instance of it, and invoke its parse() method passing the grammar to parse and the starting rule’s name as parameter:

from tatsu.util import asjson
from myparser import MyParser

parser = MyParser()
ast = parser.parse('text to parse', rule_name='start')
print(json.dumps(asjson(ast), indent=2))

The generated parsers’ constructors accept named arguments to specify whitespace characters, the regular expression for comments, case sensitivity, verbosity, and more (see below).

To add semantic actions, just pass a semantic delegate to the parse method:

model = parser.parse(text, rule_name='start', semantics=MySemantics())

If special lexical treatment is required (as in 80 column languages), then a descendant of tatsu.buffering.Buffer can be passed instead of the text:

class MySpecialBuffer(MyLanguageBuffer):

buf = MySpecialBuffer(text)
model = parser.parse(buf, rule_name='start', semantics=MySemantics())

The generated parser’s module can also be invoked as a script:

$ python inputfile startrule

As a script, the generated parser’s module accepts several options:

$ python -h
usage: [-h] [-c] [-l] [-n] [-t] [-w WHITESPACE] FILE [STARTRULE]

Simple parser for DBD.

positional arguments:
    FILE                  the input file to parse
    STARTRULE             the start rule for parsing

optional arguments:
    -h, --help            show this help message and exit
    -c, --color           use color in traces (requires the colorama library)
    -l, --list            list all rules and exit
    -n, --no-nameguard    disable the 'nameguard' feature
    -t, --trace           output trace information
    -w WHITESPACE, --whitespace WHITESPACE
                        whitespace specification