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Final set of changes by Fred before 1.4beta3

This commit is contained in:
Guido van Rossum 1996-08-26 00:33:29 +00:00
parent d8a6d1c2e7
commit 8206fb9c4c
7 changed files with 589 additions and 155 deletions
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6
Demo/parser/Makefile
View File

@ -3,6 +3,10 @@ parser.dvi: parser.tex ../../Doc/libparser.tex
# Use a new name for this; the included file uses 'clean' already....
clean-parser:
rm -f *.log *.aux *.dvi *.pyc
rm -f *.log *.aux *.dvi *.pyc *.ps
dist:
(cd ../..; \
tar cf - `cat Demo/parser/FILES` | gzip >parsermodule-1.4.tar.gz)
include ../../Doc/Makefile

17
Demo/parser/README
View File

@ -4,12 +4,29 @@ to the Python Library Reference for more information.
Files:
------
FILES -- list of files associated with the parser module.
README -- this file.
example.py -- module that uses the `parser' module to extract
information from the parse tree of Python source
code.
docstring.py -- sample source file containing only a module docstring.
simple.py -- sample source containing a "short form" definition.
source.py -- sample source code used to demonstrate ability to
handle nested constructs easily using the functions
and classes in example.py.
pprint.py -- function to pretty-print Python values.
test_parser.py program to put the parser module through it's paces.
parser.tex -- LaTex driver file for formatting the parser module
documentation separately from the library reference.
Makefile -- `make' rule set to format the parser module manual.
Enjoy!

129
Demo/parser/example.py
View File

@ -1,6 +1,8 @@
"""Simple code to extract class & function docstrings from a module.
This code is used as an example in the library reference manual in the
section on using the parser module. Refer to the manual for a thorough
discussion of the operation of this code.
"""
import symbol
@ -23,12 +25,35 @@ def get_docs(fileName):
return ModuleInfo(tup, basename)
class DefnInfo:
class SuiteInfoBase:
_docstring = ''
_name = ''
def __init__(self, tree):
self._name = tree[2][1]
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)
def get_docstring(self):
return self._docstring
@ -36,38 +61,21 @@ class DefnInfo:
def get_name(self):
return self._name
class SuiteInfoBase(DefnInfo):
def __init__(self):
self._class_info = {}
self._function_info = {}
def get_class_names(self):
return self._class_info.keys()
def get_class_info(self, name):
return self._class_info[name]
def _extract_info(self, tree):
if len(tree) >= 4:
found, vars = match(DOCSTRING_STMT_PATTERN, tree[3])
if found:
self._docstring = eval(vars['docstring'])
for node in tree[1:]:
if (node[0] == symbol.stmt
and node[1][0] == symbol.compound_stmt):
if node[1][1][0] == symbol.funcdef:
name = node[1][1][2][1]
self._function_info[name] = \
FunctionInfo(node[1][1])
elif node[1][1][0] == symbol.classdef:
name = node[1][1][2][1]
self._class_info[name] = ClassInfo(node[1][1])
def __getitem__(self, name):
try:
return self._class_info[name]
except KeyError:
return self._function_info[name]
class SuiteInfo(SuiteInfoBase):
def __init__(self, tree):
SuiteInfoBase.__init__(self)
self._extract_info(tree)
class SuiteFuncInfo:
# Mixin class providing access to function names and info.
def get_function_names(self):
return self._function_info.keys()
@ -76,23 +84,16 @@ class SuiteInfo(SuiteInfoBase):
return self._function_info[name]
class FunctionInfo(SuiteInfo):
def __init__(self, tree):
DefnInfo.__init__(self, tree)
suite = tree[-1]
if len(suite) >= 4:
found, vars = match(DOCSTRING_STMT_PATTERN, suite[3])
if found:
self._docstring = eval(vars['docstring'])
SuiteInfoBase.__init__(self)
self._extract_info(suite)
class FunctionInfo(SuiteInfoBase, SuiteFuncInfo):
def __init__(self, tree = None):
self._name = tree[2][1]
SuiteInfoBase.__init__(self, tree and tree[-1] or None)
class ClassInfo(SuiteInfoBase):
def __init__(self, tree):
SuiteInfoBase.__init__(self)
DefnInfo.__init__(self, tree)
self._extract_info(tree[-1])
def __init__(self, tree = None):
self._name = tree[2][1]
SuiteInfoBase.__init__(self, tree and tree[-1] or None)
def get_method_names(self):
return self._function_info.keys()
@ -101,19 +102,40 @@ class ClassInfo(SuiteInfoBase):
return self._function_info[name]
class ModuleInfo(SuiteInfo):
def __init__(self, tree, name="<string>"):
class ModuleInfo(SuiteInfoBase, SuiteFuncInfo):
def __init__(self, tree = None, name = "<string>"):
self._name = name
SuiteInfo.__init__(self, tree)
found, vars = match(DOCSTRING_STMT_PATTERN, tree[1])
if found:
self._docstring = vars["docstring"]
SuiteInfoBase.__init__(self, tree)
if tree:
found, vars = match(DOCSTRING_STMT_PATTERN, tree[1])
if found:
self._docstring = vars["docstring"]
from types import ListType, TupleType
def match(pattern, data, vars=None):
"""
"""Match `data' to `pattern', with variable extraction.
pattern
Pattern to match against, possibly containing variables.
data
Data to be checked and against which variables are extracted.
vars
Dictionary of variables which have already been found. If not
provided, an empty dictionary is created.
The `pattern' value may contain variables of the form ['varname'] which
are allowed to match anything. The value that is matched is returned as
part of a dictionary which maps 'varname' to the matched value. 'varname'
is not required to be a string object, but using strings makes patterns
and the code which uses them more readable.
This function returns two values: a boolean indicating whether a match
was found and a dictionary mapping variable names to their associated
values.
"""
if vars is None:
vars = {}
@ -131,6 +153,15 @@ def match(pattern, data, vars=None):
return same, vars
# This pattern identifies compound statements, allowing them to be readily
# differentiated from simple statements.
#
COMPOUND_STMT_PATTERN = (
symbol.stmt,
(symbol.compound_stmt, ['compound'])
)
# This pattern will match a 'stmt' node which *might* represent a docstring;
# docstrings require that the statement which provides the docstring be the
# first statement in the class or function, which this pattern does not check.

87
Demo/parser/pprint.py
View File

@ -1,7 +1,7 @@
# pprint.py
#
# Author: Fred L. Drake, Jr.
# fdrake@vt.edu
# fdrake@cnri.reston.va.us, fdrake@intr.net
#
# This is a simple little module I wrote to make life easier. I didn't
# see anything quite like it in the library, though I may have overlooked
@ -9,35 +9,29 @@
# tuples with fairly non-descriptive content. This is modelled very much
# after Lisp/Scheme - style pretty-printing of lists. If you find it
# useful, thank small children who sleep at night.
#
"""Support to pretty-print lists, tuples, & dictionaries recursively.
Very simple, but at least somewhat useful, especially in debugging
data structures.
Very simple, but useful, especially in debugging data structures.
INDENT_PER_LEVEL -- Amount of indentation to use for each new
recursive level. The default is 1. This
must be a non-negative integer, and may be
set by the caller before calling pprint().
Constants
---------
MAX_WIDTH -- Maximum width of the display. This is only
used if the representation *can* be kept
less than MAX_WIDTH characters wide. May
be set by the user before calling pprint().
INDENT_PER_LEVEL
Amount of indentation to use for each new recursive level. The
default is 1. This must be a non-negative integer, and may be set
by the caller before calling pprint().
TAB_WIDTH -- The width represented by a single tab. This
value is typically 8, but 4 is the default
under MacOS. Can be changed by the user if
desired, but is probably not a good idea.
MAX_WIDTH
Maximum width of the display. This is only used if the
representation *can* be kept less than MAX_WIDTH characters wide.
May be set by the user before calling pprint().
pprint(seq [, stream]) -- The pretty-printer. This takes a Python
object (presumably a sequence, but that
doesn't matter) and an optional output
stream. See the function documentation
for details.
TAB_WIDTH
The width represented by a single tab. This value is typically 8,
but 4 is the default under MacOS. Can be changed by the user if
desired, but is probably not a good idea.
"""
INDENT_PER_LEVEL = 1
MAX_WIDTH = 80
@ -46,46 +40,45 @@ import os
TAB_WIDTH = (os.name == 'mac' and 4) or 8
del os
from types import DictType, ListType, TupleType
def _indentation(cols):
"Create tabbed indentation string COLS columns wide."
# This is used to reduce the byte-count for the output, allowing
# files created using this module to use as little external storage
# as possible. This is primarily intended to minimize impact on
# a user's quota when storing resource files, or for creating output
# intended for transmission.
"""Create tabbed indentation string.
cols
Width of the indentation, in columns.
"""
return ((cols / TAB_WIDTH) * '\t') + ((cols % TAB_WIDTH) * ' ')
def pprint(seq, stream = None, indent = 0, allowance = 0):
"""Pretty-print a list, tuple, or dictionary.
pprint(seq [, stream]) ==> None
seq
List, tuple, or dictionary object to be pretty-printed. Other
object types are permitted by are not specially interpreted.
If STREAM is provided, output is written to that stream, otherwise
sys.stdout is used. Indentation is done according to
INDENT_PER_LEVEL, which may be set to any non-negative integer
before calling this function. The output written on the stream is
a perfectly valid representation of the Python object passed in,
with indentation to suite human-readable interpretation. The
output can be used as input without error, given readable
representations of all sequence elements are available via repr().
Output is restricted to MAX_WIDTH columns where possible. The
STREAM parameter must support the write() method with a single
parameter, which will always be a string. The output stream may be
a StringIO.StringIO object if the result is needed as a string.
stream
Output stream. If not provided, `sys.stdout' is used. This
parameter must support the `write()' method with a single
parameter, which will always be a string. It may be a
`StringIO.StringIO' object if the result is needed as a
string.
Indentation is done according to `INDENT_PER_LEVEL', which may be
set to any non-negative integer before calling this function. The
output written on the stream is a perfectly valid representation
of the Python object passed in, with indentation to assist
human-readable interpretation. The output can be used as input
without error, given readable representations of all elements are
available via `repr()'. Output is restricted to `MAX_WIDTH'
columns where possible.
"""
if stream is None:
import sys
stream = sys.stdout
from types import DictType, ListType, TupleType
rep = `seq`
typ = type(seq)
sepLines = len(rep) > (MAX_WIDTH - 1 - indent - allowance)
@ -140,4 +133,4 @@ def pprint(seq, stream = None, indent = 0, allowance = 0):
#
# end of pprint.py
# end of file

1
Demo/parser/simple.py Normal file
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@ -0,0 +1 @@
def f(): "maybe a docstring"

252
Doc/lib/libparser.tex
View File

@ -236,19 +236,25 @@ to the descriptions of each function for detailed information.
\subsection{AST Objects}
AST objects (returned by \code{expr()}, \code{suite()}, and
\code{tuple2ast()}, described above) have no methods of their own.
\code{sequence2ast()}, described above) have no methods of their own.
Some of the functions defined which accept an AST object as their
first argument may change to object methods in the future.
Ordered and equality comparisons are supported between AST objects.
\subsection{Example}
\subsection{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 as
well. While many useful operations may take place between parsing and
well. Two examples are presented. The simple example demonstrates
emulation of the \code{compile()} built-in function and the complex
example shows the use of a parse tree for information discovery.
\subsubsection{Emulation of {\tt 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 \code{parser} module to produce an
intermediate data structure is equivelent to the code
@ -273,6 +279,25 @@ as an AST object:
10
\end{verbatim}
An application which needs both AST and code objects can package this
code into readily available functions:
\begin{verbatim}
import parser
def load_suite(source_string):
ast = parser.suite(source_string)
code = parser.compileast(ast)
return ast, code
def load_expression(source_string):
ast = parser.expr(source_string)
code = parser.compileast(ast)
return ast, code
\end{verbatim}
\subsubsection{Information Discovery}
Some applications can benfit from access to the parse tree itself, and
can take advantage of the intermediate data structure provided by the
\code{parser} module. The remainder of this section of examples will
@ -293,6 +318,16 @@ operations on behalf of the caller. All source files mentioned here
which are not part of the Python installation are located in the
\file{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 \code{def}
statement at column zero of a module, but not a function defined
within a branch of an \code{if} ... \code{else} construct, thought
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,
@ -300,7 +335,8 @@ 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 \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:
a module consisting of a docstring and nothing else. (See file
\file{docstring.py}.)
\begin{verbatim}
"""Some documentation.
@ -376,7 +412,7 @@ extraction, we can safely require that the tree be in tuple form
rather than list form, allowing a simple variable representation to be
\code{['variable\_name']}. A simple recursive function can implement
the pattern matching, returning a boolean and a dictionary of variable
name to value mappings.
name to value mappings. (See file \file{example.py}.)
\begin{verbatim}
from types import ListType, TupleType
@ -399,32 +435,36 @@ def match(pattern, data, vars=None):
\end{verbatim}
Using this simple recursive pattern matching function and the symbolic
node types, the pattern for the candidate docstring subtrees becomes:
node types, the pattern for the candidate docstring subtrees becomes
fairly readable. (See file \file{example.py}.)
\begin{verbatim}
>>> 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, '')
... ))
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, '')
))
\end{verbatim}
Using the \code{match()} function with this pattern, extracting the
@ -453,6 +493,160 @@ 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 \code{ModuleInfo} constructor
accepts an optional \code{\var{name}} parameter since it cannot
otherwise determine the name of the module.
The public classes include \code{ClassInfo}, \code{FunctionInfo},
and \code{ModuleInfo}. All objects provide the
methods \code{get_name()}, \code{get_docstring()},
\code{get_class_names()}, and \code{get_class_info()}. The
\code{ClassInfo} objects support \code{get_method_names()} and
\code{get_method_info()} while the other classes provide
\code{get_function_names()} and \code{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
access 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, our
implementation needs to maintain the same measure of distinction.
Hence, most of the functionality of the public classes can be
implemented in a common base class, \code{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 mirrors the use of the \code{def} statement to
define both types of functions.
Most of the accessor functions are declared in \code{SuiteInfoBase}
and do not need to be overriden by subclasses. More importantly, the
extraction of most information from a parse tree is handled through a
method called by the \code{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 \code{SuiteInfoBase} definition from \file{example.py}:
\begin{verbatim}
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)
\end{verbatim}
After initializing some internal state, the constructor calls the
\code{_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 \code{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
\begin{verbatim}
def square(x): "Square an argument."; return x ** 2
\end{verbatim}
while the long form uses an indented block and allows nested
definitions:
\begin{verbatim}
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
\end{verbatim}
When the short form is used, the code block may contain a docstring as
the first, and possibly only, \code{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
given in the code, the docstring will only be found if there is only
one \code{small_stmt} node in the \code{simple_stmt} node. Since most
functions and methods which use the short form do not provide
docstring, this may be considered sufficient. The extraction of the
docstring proceeds using the \code{match()} function as described
above, and the value of the docstring is stored as an attribute of the
\code{SuiteInfoBase} object.
After docstring extraction, the operates a simple definition discovery
algorithm on the \code{stmt} nodes of the \code{suite} node. The
special case of the short form is not tested; since there are no
\code{stmt} nodes in the short form, the algorithm will silently skip
the single \code{simple_stmt} node and correctly not discover any
nested definitions.
Each statement in the code block bing examined is categorized as being
a class definition, function definition (including methods), or
something else. For the definition statements, the name of the
element being defined is extracted and representation object
appropriate to the definition is created with the defining subtree
passed as an argument to the constructor. The repesentation 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 \code{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:
\begin{verbatim}
def get_docs(fileName):
source = open(fileName).read()
import os
basename = os.path.basename(os.path.splitext(fileName)[0])
import parser
ast = parser.suite(source)
tup = parser.ast2tuple(ast)
return ModuleInfo(tup, basename)
\end{verbatim}
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.
%%

252
Doc/libparser.tex
View File

@ -236,19 +236,25 @@ to the descriptions of each function for detailed information.
\subsection{AST Objects}
AST objects (returned by \code{expr()}, \code{suite()}, and
\code{tuple2ast()}, described above) have no methods of their own.
\code{sequence2ast()}, described above) have no methods of their own.
Some of the functions defined which accept an AST object as their
first argument may change to object methods in the future.
Ordered and equality comparisons are supported between AST objects.
\subsection{Example}
\subsection{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 as
well. While many useful operations may take place between parsing and
well. Two examples are presented. The simple example demonstrates
emulation of the \code{compile()} built-in function and the complex
example shows the use of a parse tree for information discovery.
\subsubsection{Emulation of {\tt 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 \code{parser} module to produce an
intermediate data structure is equivelent to the code
@ -273,6 +279,25 @@ as an AST object:
10
\end{verbatim}
An application which needs both AST and code objects can package this
code into readily available functions:
\begin{verbatim}
import parser
def load_suite(source_string):
ast = parser.suite(source_string)
code = parser.compileast(ast)
return ast, code
def load_expression(source_string):
ast = parser.expr(source_string)
code = parser.compileast(ast)
return ast, code
\end{verbatim}
\subsubsection{Information Discovery}
Some applications can benfit from access to the parse tree itself, and
can take advantage of the intermediate data structure provided by the
\code{parser} module. The remainder of this section of examples will
@ -293,6 +318,16 @@ operations on behalf of the caller. All source files mentioned here
which are not part of the Python installation are located in the
\file{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 \code{def}
statement at column zero of a module, but not a function defined
within a branch of an \code{if} ... \code{else} construct, thought
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,
@ -300,7 +335,8 @@ 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 \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:
a module consisting of a docstring and nothing else. (See file
\file{docstring.py}.)
\begin{verbatim}
"""Some documentation.
@ -376,7 +412,7 @@ extraction, we can safely require that the tree be in tuple form
rather than list form, allowing a simple variable representation to be
\code{['variable\_name']}. A simple recursive function can implement
the pattern matching, returning a boolean and a dictionary of variable
name to value mappings.
name to value mappings. (See file \file{example.py}.)
\begin{verbatim}
from types import ListType, TupleType
@ -399,32 +435,36 @@ def match(pattern, data, vars=None):
\end{verbatim}
Using this simple recursive pattern matching function and the symbolic
node types, the pattern for the candidate docstring subtrees becomes:
node types, the pattern for the candidate docstring subtrees becomes
fairly readable. (See file \file{example.py}.)
\begin{verbatim}
>>> 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, '')
... ))
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, '')
))
\end{verbatim}
Using the \code{match()} function with this pattern, extracting the
@ -453,6 +493,160 @@ 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 \code{ModuleInfo} constructor
accepts an optional \code{\var{name}} parameter since it cannot
otherwise determine the name of the module.
The public classes include \code{ClassInfo}, \code{FunctionInfo},
and \code{ModuleInfo}. All objects provide the
methods \code{get_name()}, \code{get_docstring()},
\code{get_class_names()}, and \code{get_class_info()}. The
\code{ClassInfo} objects support \code{get_method_names()} and
\code{get_method_info()} while the other classes provide
\code{get_function_names()} and \code{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
access 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, our
implementation needs to maintain the same measure of distinction.
Hence, most of the functionality of the public classes can be
implemented in a common base class, \code{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 mirrors the use of the \code{def} statement to
define both types of functions.
Most of the accessor functions are declared in \code{SuiteInfoBase}
and do not need to be overriden by subclasses. More importantly, the
extraction of most information from a parse tree is handled through a
method called by the \code{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 \code{SuiteInfoBase} definition from \file{example.py}:
\begin{verbatim}
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)
\end{verbatim}
After initializing some internal state, the constructor calls the
\code{_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 \code{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
\begin{verbatim}
def square(x): "Square an argument."; return x ** 2
\end{verbatim}
while the long form uses an indented block and allows nested
definitions:
\begin{verbatim}
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
\end{verbatim}
When the short form is used, the code block may contain a docstring as
the first, and possibly only, \code{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
given in the code, the docstring will only be found if there is only
one \code{small_stmt} node in the \code{simple_stmt} node. Since most
functions and methods which use the short form do not provide
docstring, this may be considered sufficient. The extraction of the
docstring proceeds using the \code{match()} function as described
above, and the value of the docstring is stored as an attribute of the
\code{SuiteInfoBase} object.
After docstring extraction, the operates a simple definition discovery
algorithm on the \code{stmt} nodes of the \code{suite} node. The
special case of the short form is not tested; since there are no
\code{stmt} nodes in the short form, the algorithm will silently skip
the single \code{simple_stmt} node and correctly not discover any
nested definitions.
Each statement in the code block bing examined is categorized as being
a class definition, function definition (including methods), or
something else. For the definition statements, the name of the
element being defined is extracted and representation object
appropriate to the definition is created with the defining subtree
passed as an argument to the constructor. The repesentation 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 \code{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:
\begin{verbatim}
def get_docs(fileName):
source = open(fileName).read()
import os
basename = os.path.basename(os.path.splitext(fileName)[0])
import parser
ast = parser.suite(source)
tup = parser.ast2tuple(ast)
return ModuleInfo(tup, basename)
\end{verbatim}
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.
%%