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backend
arpeggio-gm
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24c87952
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24c87952
authored
Oct 21, 2014
by
Igor Dejanovic
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WIP. Documentation addition.
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@@ -13,6 +13,25 @@ Arpeggio grammars are based on `PEG formalism <http://en.wikipedia.org/wiki/Pars
PEG grammars
------------
PEG is a type of formal grammar that is given as a set of rules for recognizing strings of the language.
In a way it is similar to context-free grammars with a very important distinction that PEG are always
unambiguous. This is achieved by making choice opearator ordered. In PEGs a first choice from
left to right that matches will be used.
In Arpeggio each PEG rule consists of atomic parsing expression which can be:
- **terminal match rules**:
- **String match** - a simple string that is matched literaly from the input string.
- **RegEx match** - regular expression match (based on python ``re`` module).
- **non-terminal match rules**:
- **Sequence**
TODO: Finis this section.
Grammar given in Python
-----------------------
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@@ -153,24 +172,152 @@ can parse the same language.
Parse tree
----------
Parse tree or concrete syntax tree is a tree structure built from the input string during parsing.
It represent the structure of the input string. Each node in the parse tree is either a ``terminal``
or ``non-terminal``. Terminals are the leafs of the tree while the inner nodes are non-terminals.
Here is an example parse tree for the ``calc`` grammar:
Terminals
~~~~~~~~~
Terminals in Arpeggio are created by the specializations of the ``Match`` class: ``StrMatch`` if
the literal string is matched from the input or ``RegExMatch`` if a regular expression is used to
match input.
NonTerminals
~~~~~~~~~~~~
Non-terminal nodes are non-leaf nodes of the parse tree. Children of non-terminals can be other non-terminals
or terminals.
For example, nodes .... from the above parse tree are non-terminal nodes.
Parse tree navigation
~~~~~~~~~~~~~~~~~~~~~
Usually we want to transform parse tree to some more usable form or to extract some data from it.
Parse tree can be navigated using following approaches:
Errors in grammar
Grammar debugging
-----------------
During grammar design you can make syntax and semantic errors. Arpeggio will report any syntax error
with all the necessary informations whether you are building parser from python expressions or from
a textual PEG notation.
For semantic error you have a debugging mode of operation which is entered by setting ``debug`` param
to ``True`` in the parser construction call. When Arpeggio runs in debug mode it will print a detailed
information of what it is doing. Furthermore a ``dot`` files will be generated that visually represents
your grammar (this is known in Arpeggio as ``the parser model``). In debug mode also a parse tree will
also be rendered to ``dot`` file when you parse your input with properly constructed parser.
You can visualize ``dot`` files using some of available dot viewer or you can convert dot file to image
using ``dot`` tool from ``graphviz`` package.
An example to convert ``calc_parser_model.dot`` to ``png`` file use:
.. code:: bash
$ dot -Tpng -O calc_parser_model.dot
Errors in the input
-------------------
If your grammar is correct but you get input string with syntax error parser will raise ``NoMatch`` exception
with the information where in the input stream error has occurred and what the parser expect to see at that
location.
The input location is given as the offset from the beginning of the input string. To convert it to row and column
use ``position_to_row_col`` method on the parser.
Example:
.. code:: python
Currently Arpeggio will report the first rule it tried at that location.
Arpeggio is backtracking parser, which means that it will go back and try another alternatives when the match
does not succeeds but it will nevertheless report the furthest place in the input where it failed.
Errors in input
---------------
Semantic analysis - Visitors
----------------------------
You will surely always want to extract some information from the parse tree or to transform it in some
more usable form.
The process of parse tree transformation to other forms is referred to as *semantic analysis*.
You could do that using plain tree navigation etc. but it is better to use some
standard mechanism.
In Arpeggio a visitor pattern is used for semantic analysis. You write a python class that has a methods named
``visit_<rule name>`` where rule name is a rule name from the grammar.
During a semantic analysis a parse tree is walked in the depth-first manner and for each node a proper visitor
method is called to transform it to some other form. The results are than fed to the parent node visitor method.
This is repeated until the final, top level parse tree node is processed (its visitor is called).
The result of the top level node is the final output of the semantic analysis.
To apply your visitor class on the parse tree use ``visit_parse_tree`` function.
Example:
.. code:: python
result = visit_parse_tree(parse_tree, CalcVisitor(debug=True))
The first parameter is a parse tree you get from the ``parser.parse`` call while the second parameter is an
instance of the your visitor class. Semantic analysis can be run in debug mode if you set ``debug`` parameter
to ``True`` during visitor construction.
During semantic analysis, each ``visitor_xxx`` method gets current parse tree node as the first parameter and
the evaluated children nodes as the second parameter.
For example, if you have ``expression`` rule in your grammar than the transformation of the non-terminal
matched by this rule can be done as:
.. code:: python
def visitor_expression(self, node, children):
...
return transformed node
``node`` is the current ``NonTerminal`` or ``Terminal`` from the parse tree while the ``children`` is
instance of ``SemanticResults`` class.
This class is a list like structure that holds the results of semantic evaluation from the children parse
tree nodes (analysis is done bottom-up).
In the ``calc.py`` example a semantic analysis will evaluate the expression. The parse tree is thus transformed
to a single numeric value that represent the result of the expression.
In the ``robot.py`` example a semantic analysis will evaluate robot program (transform its parse tree) to the
final robot location.
Semantic analysis can do a complex stuff. For example, see ``peg_peg.py`` example where the PEG parser for
the given language is built using semantic analysis.
SemanticResults
~~~~~~~~~~~~~~~
Class of object returned from the parse tree nodes evaluation. Used for filtering and navigation over evaluation
results on children nodes.
TODO: Describe class in more details.
Default actions
~~~~~~~~~~~~~~~
For each parse tree node that does not have an appropriate ``visitor_xxx`` call a default action is performed.
If the node is created by a plain string match action will return ``None`` and thus suppress this node.
This is handy for all those syntax noise (bracket, braces, keywords etc.).
For example, if your grammar is:
.. code::
number_in_brackets = "(" number ")"
number = r'\d+'
Than the default action for ``number`` will return number converted to string and the default action for
``(`` and ``)`` will return ``None`` and thus suppress this nodes so the visitor method for ``number_in_brackets``
rule will only see ``number`` child.
Indices and tables
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