
``parser`` --- Access Python parse trees
****************************************

The ``parser`` module provides an interface to Python's internal
parser and byte-code compiler.  The primary purpose for this interface
is to allow Python code to edit the parse tree of a Python expression
and create executable code from this.  This is better than trying to
parse and modify an arbitrary Python code fragment as a string because
parsing is performed in a manner identical to the code forming the
application.  It is also faster.

Note: From Python 2.5 onward, it's much more convenient to cut in at the
  Abstract Syntax Tree (AST) generation and compilation stage, using
  the ``ast`` module.The ``parser`` module exports the names
  documented here also with "st" replaced by "ast"; this is a legacy
  from the time when there was no other AST and has nothing to do with
  the AST found in Python 2.5.  This is also the reason for the
  functions' keyword arguments being called *ast*, not *st*. The "ast"
  functions will be removed in Python 3.0.

There are a few things to note about this module which are important
to making use of the data structures created.  This is not a tutorial
on editing the parse trees for Python code, but some examples of using
the ``parser`` module are presented.

Most importantly, a good understanding of the Python grammar processed
by the internal parser is required.  For full information on the
language syntax, refer to *The Python Language Reference*.  The parser
itself is created from a grammar specification defined in the file
``Grammar/Grammar`` in the standard Python distribution.  The parse
trees stored in the ST objects created by this module are the actual
output from the internal parser when created by the ``expr()`` or
``suite()`` functions, described below.  The ST objects created by
``sequence2st()`` faithfully simulate those structures.  Be aware that
the values of the sequences which are considered "correct" will vary
from one version of Python to another as the formal grammar for the
language is revised.  However, transporting code from one Python
version to another as source text will always allow correct parse
trees to be created in the target version, with the only restriction
being that migrating to an older version of the interpreter will not
support more recent language constructs.  The parse trees are not
typically compatible from one version to another, whereas source code
has always been forward-compatible.

Each element of the sequences returned by ``st2list()`` or
``st2tuple()`` has a simple form.  Sequences representing non-terminal
elements in the grammar always have a length greater than one.  The
first element is an integer which identifies a production in the
grammar.  These integers are given symbolic names in the C header file
``Include/graminit.h`` and the Python module ``symbol``.  Each
additional element of the sequence represents a component of the
production as recognized in the input string: these are always
sequences which have the same form as the parent.  An important aspect
of this structure which should be noted is that keywords used to
identify the parent node type, such as the keyword ``if`` in an
``if_stmt``, are included in the node tree without any special
treatment.  For example, the ``if`` keyword is represented by the
tuple ``(1, 'if')``, where ``1`` is the numeric value associated with
all ``NAME`` tokens, including variable and function names defined by
the user.  In an alternate form returned when line number information
is requested, the same token might be represented as ``(1, 'if',
12)``, where the ``12`` represents the line number at which the
terminal symbol was found.

Terminal elements are represented in much the same way, but without
any child elements and the addition of the source text which was
identified.  The example of the ``if`` keyword above is
representative.  The various types of terminal symbols are defined in
the C header file ``Include/token.h`` and the Python module ``token``.

The ST objects are not required to support the functionality of this
module, but are provided for three purposes: to allow an application
to amortize the cost of processing complex parse trees, to provide a
parse tree representation which conserves memory space when compared
to the Python list or tuple representation, and to ease the creation
of additional modules in C which manipulate parse trees.  A simple
"wrapper" class may be created in Python to hide the use of ST
objects.

The ``parser`` module defines functions for a few distinct purposes.
The most important purposes are to create ST objects and to convert ST
objects to other representations such as parse trees and compiled code
objects, but there are also functions which serve to query the type of
parse tree represented by an ST object.

See also:

   Module ``symbol``
      Useful constants representing internal nodes of the parse tree.

   Module ``token``
      Useful constants representing leaf nodes of the parse tree and
      functions for testing node values.


Creating ST Objects
===================

ST objects may be created from source code or from a parse tree. When
creating an ST object from source, different functions are used to
create the ``'eval'`` and ``'exec'`` forms.

parser.expr(source)

   The ``expr()`` function parses the parameter *source* as if it were
   an input to ``compile(source, 'file.py', 'eval')``.  If the parse
   succeeds, an ST object is created to hold the internal parse tree
   representation, otherwise an appropriate exception is thrown.

parser.suite(source)

   The ``suite()`` function parses the parameter *source* as if it
   were an input to ``compile(source, 'file.py', 'exec')``.  If the
   parse succeeds, an ST object is created to hold the internal parse
   tree representation, otherwise an appropriate exception is thrown.

parser.sequence2st(sequence)

   This function accepts a parse tree represented as a sequence and
   builds an internal representation if possible.  If it can validate
   that the tree conforms to the Python grammar and all nodes are
   valid node types in the host version of Python, an ST object is
   created from the internal representation and returned to the
   called.  If there is a problem creating the internal
   representation, or if the tree cannot be validated, a
   ``ParserError`` exception is thrown.  An ST object created this way
   should not be assumed to compile correctly; normal exceptions
   thrown by compilation may still be initiated when the ST object is
   passed to ``compilest()``.  This may indicate problems not related
   to syntax (such as a ``MemoryError`` exception), but may also be
   due to constructs such as the result of parsing ``del f(0)``, which
   escapes the Python parser but is checked by the bytecode compiler.

   Sequences representing terminal tokens may be represented as either
   two-element lists of the form ``(1, 'name')`` or as three-element
   lists of the form ``(1, 'name', 56)``.  If the third element is
   present, it is assumed to be a valid line number.  The line number
   may be specified for any subset of the terminal symbols in the
   input tree.

parser.tuple2st(sequence)

   This is the same function as ``sequence2st()``.  This entry point
   is maintained for backward compatibility.


Converting ST Objects
=====================

ST objects, regardless of the input used to create them, may be
converted to parse trees represented as list- or tuple- trees, or may
be compiled into executable code objects.  Parse trees may be
extracted with or without line numbering information.

parser.st2list(ast[, line_info])

   This function accepts an ST object from the caller in *ast* and
   returns a Python list representing the equivalent parse tree.  The
   resulting list representation can be used for inspection or the
   creation of a new parse tree in list form.  This function does not
   fail so long as memory is available to build the list
   representation.  If the parse tree will only be used for
   inspection, ``st2tuple()`` should be used instead to reduce memory
   consumption and fragmentation.  When the list representation is
   required, this function is significantly faster than retrieving a
   tuple representation and converting that to nested lists.

   If *line_info* is true, line number information will be included
   for all terminal tokens as a third element of the list representing
   the token.  Note that the line number provided specifies the line
   on which the token *ends*. This information is omitted if the flag
   is false or omitted.

parser.st2tuple(ast[, line_info])

   This function accepts an ST object from the caller in *ast* and
   returns a Python tuple representing the equivalent parse tree.
   Other than returning a tuple instead of a list, this function is
   identical to ``st2list()``.

   If *line_info* is true, line number information will be included
   for all terminal tokens as a third element of the list representing
   the token.  This information is omitted if the flag is false or
   omitted.

parser.compilest(ast[, filename='<syntax-tree>'])

   The Python byte compiler can be invoked on an ST object to produce
   code objects which can be used as part of an ``exec`` statement or
   a call to the built-in ``eval()`` function. This function provides
   the interface to the compiler, passing the internal parse tree from
   *ast* to the parser, using the source file name specified by the
   *filename* parameter. The default value supplied for *filename*
   indicates that the source was an ST object.

   Compiling an ST object may result in exceptions related to
   compilation; an example would be a ``SyntaxError`` caused by the
   parse tree for ``del f(0)``: this statement is considered legal
   within the formal grammar for Python but is not a legal language
   construct.  The ``SyntaxError`` raised for this condition is
   actually generated by the Python byte-compiler normally, which is
   why it can be raised at this point by the ``parser`` module.  Most
   causes of compilation failure can be diagnosed programmatically by
   inspection of the parse tree.


Queries on ST Objects
=====================

Two functions are provided which allow an application to determine if
an ST was created as an expression or a suite.  Neither of these
functions can be used to determine if an ST was created from source
code via ``expr()`` or ``suite()`` or from a parse tree via
``sequence2st()``.

parser.isexpr(ast)

   When *ast* represents an ``'eval'`` form, this function returns
   true, otherwise it returns false.  This is useful, since code
   objects normally cannot be queried for this information using
   existing built-in functions.  Note that the code objects created by
   ``compilest()`` cannot be queried like this either, and are
   identical to those created by the built-in ``compile()`` function.

parser.issuite(ast)

   This function mirrors ``isexpr()`` in that it reports whether an ST
   object represents an ``'exec'`` form, commonly known as a "suite."
   It is not safe to assume that this function is equivalent to ``not
   isexpr(ast)``, as additional syntactic fragments may be supported
   in the future.


Exceptions and Error Handling
=============================

The parser module defines a single exception, but may also pass other
built-in exceptions from other portions of the Python runtime
environment.  See each function for information about the exceptions
it can raise.

exception parser.ParserError

   Exception raised when a failure occurs within the parser module.
   This is generally produced for validation failures rather than the
   built in ``SyntaxError`` thrown during normal parsing. The
   exception argument is either a string describing the reason of the
   failure or a tuple containing a sequence causing the failure from a
   parse tree passed to ``sequence2st()`` and an explanatory string.
   Calls to ``sequence2st()`` need to be able to handle either type of
   exception, while calls to other functions in the module will only
   need to be aware of the simple string values.

Note that the functions ``compilest()``, ``expr()``, and ``suite()``
may throw exceptions which are normally thrown by the parsing and
compilation process.  These include the built in exceptions
``MemoryError``, ``OverflowError``, ``SyntaxError``, and
``SystemError``.  In these cases, these exceptions carry all the
meaning normally associated with them. Refer to the descriptions of
each function for detailed information.


ST Objects
==========

Ordered and equality comparisons are supported between ST objects.
Pickling of ST objects (using the ``pickle`` module) is also
supported.

parser.STType

   The type of the objects returned by ``expr()``, ``suite()`` and
   ``sequence2st()``.

ST objects have the following methods:

ST.compile([filename])

   Same as ``compilest(st, filename)``.

ST.isexpr()

   Same as ``isexpr(st)``.

ST.issuite()

   Same as ``issuite(st)``.

ST.tolist([line_info])

   Same as ``st2list(st, line_info)``.

ST.totuple([line_info])

   Same as ``st2tuple(st, line_info)``.


Examples
========

The parser modules allows operations to be performed on the parse tree
of Python source code before the *bytecode* is generated, and provides
for inspection of the parse tree for information gathering purposes.
Two examples are presented.  The simple example demonstrates emulation
of the ``compile()`` built-in function and the complex example shows
the use of a parse tree for information discovery.


Emulation of ``compile()``
--------------------------

While many useful operations may take place between parsing and
bytecode generation, the simplest operation is to do nothing.  For
this purpose, using the ``parser`` module to produce an intermediate
data structure is equivalent to the code

   >>> code = compile('a + 5', 'file.py', 'eval')
   >>> a = 5
   >>> eval(code)
   10

The equivalent operation using the ``parser`` module is somewhat
longer, and allows the intermediate internal parse tree to be retained
as an ST object:

   >>> import parser
   >>> st = parser.expr('a + 5')
   >>> code = st.compile('file.py')
   >>> a = 5
   >>> eval(code)
   10

An application which needs both ST and code objects can package this
code into readily available functions:

   import parser

   def load_suite(source_string):
       st = parser.suite(source_string)
       return st, st.compile()

   def load_expression(source_string):
       st = parser.expr(source_string)
       return st, st.compile()


Information Discovery
---------------------

Some applications benefit from direct access to the parse tree.  The
remainder of this section demonstrates how the parse tree provides
access to module documentation defined in docstrings without requiring
that the code being examined be loaded into a running interpreter via
``import``.  This can be very useful for performing analyses of
untrusted code.

Generally, the example will demonstrate how the parse tree may be
traversed to distill interesting information.  Two functions and a set
of classes are developed which provide programmatic access to high
level function and class definitions provided by a module.  The
classes extract information from the parse tree and provide access to
the information at a useful semantic level, one function provides a
simple low-level pattern matching capability, and the other function
defines a high-level interface to the classes by handling file
operations on behalf of the caller.  All source files mentioned here
which are not part of the Python installation are located in the
``Demo/parser/`` directory of the distribution.

The dynamic nature of Python allows the programmer a great deal of
flexibility, but most modules need only a limited measure of this when
defining classes, functions, and methods.  In this example, the only
definitions that will be considered are those which are defined in the
top level of their context, e.g., a function defined by a ``def``
statement at column zero of a module, but not a function defined
within a branch of an ``if`` ... ``else`` construct, though there are
some good reasons for doing so in some situations. Nesting of
definitions will be handled by the code developed in the example.

To construct the upper-level extraction methods, we need to know what
the parse tree structure looks like and how much of it we actually
need to be concerned about.  Python uses a moderately deep parse tree
so there are a large number of intermediate nodes.  It is important to
read and understand the formal grammar used by Python.  This is
specified in the file ``Grammar/Grammar`` in the distribution.
Consider the simplest case of interest when searching for docstrings:
a module consisting of a docstring and nothing else.  (See file
``docstring.py``.)

   """Some documentation.
   """

Using the interpreter to take a look at the parse tree, we find a
bewildering mass of numbers and parentheses, with the documentation
buried deep in nested tuples.

   >>> import parser
   >>> import pprint
   >>> st = parser.suite(open('docstring.py').read())
   >>> tup = st.totuple()
   >>> pprint.pprint(tup)
   (257,
    (264,
     (265,
      (266,
       (267,
        (307,
         (287,
          (288,
           (289,
            (290,
             (292,
              (293,
               (294,
                (295,
                 (296,
                  (297,
                   (298,
                    (299,
                     (300, (3, '"""Some documentation.\n"""'))))))))))))))))),
      (4, ''))),
    (4, ''),
    (0, ''))

The numbers at the first element of each node in the tree are the node
types; they map directly to terminal and non-terminal symbols in the
grammar. Unfortunately, they are represented as integers in the
internal representation, and the Python structures generated do not
change that.  However, the ``symbol`` and ``token`` modules provide
symbolic names for the node types and dictionaries which map from the
integers to the symbolic names for the node types.

In the output presented above, the outermost tuple contains four
elements: the integer ``257`` and three additional tuples.  Node type
``257`` has the symbolic name ``file_input``.  Each of these inner
tuples contains an integer as the first element; these integers,
``264``, ``4``, and ``0``, represent the node types ``stmt``,
``NEWLINE``, and ``ENDMARKER``, respectively. Note that these values
may change depending on the version of Python you are using; consult
``symbol.py`` and ``token.py`` for details of the mapping.  It should
be fairly clear that the outermost node is related primarily to the
input source rather than the contents of the file, and may be
disregarded for the moment.  The ``stmt`` node is much more
interesting.  In particular, all docstrings are found in subtrees
which are formed exactly as this node is formed, with the only
difference being the string itself.  The association between the
docstring in a similar tree and the defined entity (class, function,
or module) which it describes is given by the position of the
docstring subtree within the tree defining the described structure.

By replacing the actual docstring with something to signify a variable
component of the tree, we allow a simple pattern matching approach to
check any given subtree for equivalence to the general pattern for
docstrings.  Since the example demonstrates information extraction, we
can safely require that the tree be in tuple form rather than list
form, allowing a simple variable representation to be
``['variable_name']``.  A simple recursive function can implement the
pattern matching, returning a Boolean and a dictionary of variable
name to value mappings.  (See file ``example.py``.)

   from types import ListType, TupleType

   def match(pattern, data, vars=None):
       if vars is None:
           vars = {}
       if type(pattern) is ListType:
           vars[pattern[0]] = data
           return 1, vars
       if type(pattern) is not TupleType:
           return (pattern == data), vars
       if len(data) != len(pattern):
           return 0, vars
       for pattern, data in map(None, pattern, data):
           same, vars = match(pattern, data, vars)
           if not same:
               break
       return same, vars

Using this simple representation for syntactic variables and the
symbolic node types, the pattern for the candidate docstring subtrees
becomes fairly readable. (See file ``example.py``.)

   import symbol
   import token

   DOCSTRING_STMT_PATTERN = (
       symbol.stmt,
       (symbol.simple_stmt,
        (symbol.small_stmt,
         (symbol.expr_stmt,
          (symbol.testlist,
           (symbol.test,
            (symbol.and_test,
             (symbol.not_test,
              (symbol.comparison,
               (symbol.expr,
                (symbol.xor_expr,
                 (symbol.and_expr,
                  (symbol.shift_expr,
                   (symbol.arith_expr,
                    (symbol.term,
                     (symbol.factor,
                      (symbol.power,
                       (symbol.atom,
                        (token.STRING, ['docstring'])
                        )))))))))))))))),
        (token.NEWLINE, '')
        ))

Using the ``match()`` function with this pattern, extracting the
module docstring from the parse tree created previously is easy:

   >>> found, vars = match(DOCSTRING_STMT_PATTERN, tup[1])
   >>> found
   1
   >>> vars
   {'docstring': '"""Some documentation.\n"""'}

Once specific data can be extracted from a location where it is
expected, the question of where information can be expected needs to
be answered.  When dealing with docstrings, the answer is fairly
simple: the docstring is the first ``stmt`` node in a code block
(``file_input`` or ``suite`` node types).  A module consists of a
single ``file_input`` node, and class and function definitions each
contain exactly one ``suite`` node.  Classes and functions are readily
identified as subtrees of code block nodes which start with ``(stmt,
(compound_stmt, (classdef, ...`` or ``(stmt, (compound_stmt, (funcdef,
...``.  Note that these subtrees cannot be matched by ``match()``
since it does not support multiple sibling nodes to match without
regard to number.  A more elaborate matching function could be used to
overcome this limitation, but this is sufficient for the example.

Given the ability to determine whether a statement might be a
docstring and extract the actual string from the statement, some work
needs to be performed to walk the parse tree for an entire module and
extract information about the names defined in each context of the
module and associate any docstrings with the names.  The code to
perform this work is not complicated, but bears some explanation.

The public interface to the classes is straightforward and should
probably be somewhat more flexible.  Each "major" block of the module
is described by an object providing several methods for inquiry and a
constructor which accepts at least the subtree of the complete parse
tree which it represents.  The ``ModuleInfo`` constructor accepts an
optional *name* parameter since it cannot otherwise determine the name
of the module.

The public classes include ``ClassInfo``, ``FunctionInfo``, and
``ModuleInfo``.  All objects provide the methods ``get_name()``,
``get_docstring()``, ``get_class_names()``, and ``get_class_info()``.
The ``ClassInfo`` objects support ``get_method_names()`` and
``get_method_info()`` while the other classes provide
``get_function_names()`` and ``get_function_info()``.

Within each of the forms of code block that the public classes
represent, most of the required information is in the same form and is
accessed in the same way, with classes having the distinction that
functions defined at the top level are referred to as "methods." Since
the difference in nomenclature reflects a real semantic distinction
from functions defined outside of a class, the implementation needs to
maintain the distinction. Hence, most of the functionality of the
public classes can be implemented in a common base class,
``SuiteInfoBase``, with the accessors for function and method
information provided elsewhere. Note that there is only one class
which represents function and method information; this parallels the
use of the ``def`` statement to define both types of elements.

Most of the accessor functions are declared in ``SuiteInfoBase`` and
do not need to be overridden by subclasses.  More importantly, the
extraction of most information from a parse tree is handled through a
method called by the ``SuiteInfoBase`` constructor.  The example code
for most of the classes is clear when read alongside the formal
grammar, but the method which recursively creates new information
objects requires further examination.  Here is the relevant part of
the ``SuiteInfoBase`` definition from ``example.py``:

   class SuiteInfoBase:
       _docstring = ''
       _name = ''

       def __init__(self, tree = None):
           self._class_info = {}
           self._function_info = {}
           if tree:
               self._extract_info(tree)

       def _extract_info(self, tree):
           # extract docstring
           if len(tree) == 2:
               found, vars = match(DOCSTRING_STMT_PATTERN[1], tree[1])
           else:
               found, vars = match(DOCSTRING_STMT_PATTERN, tree[3])
           if found:
               self._docstring = eval(vars['docstring'])
           # discover inner definitions
           for node in tree[1:]:
               found, vars = match(COMPOUND_STMT_PATTERN, node)
               if found:
                   cstmt = vars['compound']
                   if cstmt[0] == symbol.funcdef:
                       name = cstmt[2][1]
                       self._function_info[name] = FunctionInfo(cstmt)
                   elif cstmt[0] == symbol.classdef:
                       name = cstmt[2][1]
                       self._class_info[name] = ClassInfo(cstmt)

After initializing some internal state, the constructor calls the
``_extract_info()`` method.  This method performs the bulk of the
information extraction which takes place in the entire example.  The
extraction has two distinct phases: the location of the docstring for
the parse tree passed in, and the discovery of additional definitions
within the code block represented by the parse tree.

The initial ``if`` test determines whether the nested suite is of the
"short form" or the "long form."  The short form is used when the code
block is on the same line as the definition of the code block, as in

   def square(x): "Square an argument."; return x ** 2

while the long form uses an indented block and allows nested
definitions:

   def make_power(exp):
       "Make a function that raises an argument to the exponent `exp'."
       def raiser(x, y=exp):
           return x ** y
       return raiser

When the short form is used, the code block may contain a docstring as
the first, and possibly only, ``small_stmt`` element.  The extraction
of such a docstring is slightly different and requires only a portion
of the complete pattern used in the more common case.  As implemented,
the docstring will only be found if there is only one ``small_stmt``
node in the ``simple_stmt`` node. Since most functions and methods
which use the short form do not provide a docstring, this may be
considered sufficient.  The extraction of the docstring proceeds using
the ``match()`` function as described above, and the value of the
docstring is stored as an attribute of the ``SuiteInfoBase`` object.

After docstring extraction, a simple definition discovery algorithm
operates on the ``stmt`` nodes of the ``suite`` node.  The special
case of the short form is not tested; since there are no ``stmt``
nodes in the short form, the algorithm will silently skip the single
``simple_stmt`` node and correctly not discover any nested
definitions.

Each statement in the code block is categorized as a class definition,
function or method definition, or something else.  For the definition
statements, the name of the element defined is extracted and a
representation object appropriate to the definition is created with
the defining subtree passed as an argument to the constructor.  The
representation objects are stored in instance variables and may be
retrieved by name using the appropriate accessor methods.

The public classes provide any accessors required which are more
specific than those provided by the ``SuiteInfoBase`` class, but the
real extraction algorithm remains common to all forms of code blocks.
A high-level function can be used to extract the complete set of
information from a source file.  (See file ``example.py``.)

   def get_docs(fileName):
       import os
       import parser

       source = open(fileName).read()
       basename = os.path.basename(os.path.splitext(fileName)[0])
       st = parser.suite(source)
       return ModuleInfo(st.totuple(), basename)

This provides an easy-to-use interface to the documentation of a
module.  If information is required which is not extracted by the code
of this example, the code may be extended at clearly defined points to
provide additional capabilities.
