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.. include:: links.rst
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:
.. code:: python
GRAMMAR = '''
@@grammar::Calc
start = expression $ ;
expression
=
| term '+' ~ expression
| term '-' ~ expression
| term
;
term
=
| factor '*' ~ term
| factor '/' ~ term
| factor
;
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 )')
print('PPRINT')
pprint.pprint(ast, indent=2, width=20)
print()
print('JSON')
print(json.dumps(asjson(ast), indent=2))
print()
if __name__ == '__main__':
main()
And this is the output:
.. code:: bash
PPRINT
[ '3',
'+',
[ '5',
'*',
[ '10',
'-',
'20']]]
JSON
[
"3",
"+",
[
"5",
"*",
[
"10",
"-",
"20"
]
]
]
Compiling grammars to Python
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Tatsu** can be run from the command line:
.. code:: bash
$ python -m tatsu
Or:
.. code:: bash
$ scripts/tatsu
Or just:
.. code:: bash
$ tatsu
if **Tatsu** was installed using *easy\_install* or *pip*.
The *-h* and *--help* parameters provide full usage information:
.. code:: bash
$ 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]
GRAMMAR
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-recursion 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
--whitespace CHARACTERS, -w CHARACTERS
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:
.. code:: python
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:
.. code:: python
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:
.. code:: python
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:
.. code:: python
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:
.. code:: python
from tatsu.util import asjson
from myparser import MyParser
parser = MyParser()
ast = parser.parse('text to parse', start='start')
print(ast)
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:
.. code:: python
model = parser.parse(text, start='start', semantics=MySemantics())
If special lexical treatment is required (as in *80 column* languages),
then a descendant of ``tatsu.tokenizing.Tokenizer`` can be passed instead of
the text:
.. code:: python
class MySpecialTokenizer(Tokenizer):
...
tokenizer = MySpecialTokenizer(text)
model = parser.parse(tokenizer, start='start', semantics=MySemantics())
The generated parser's module can also be invoked as a script:
.. code:: bash
$ python myparser.py inputfile startrule
As a script, the generated parser's module accepts several options:
.. code:: bash
$ python myparser.py -h
usage: myparser.py [-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
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