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ext/newtypes.tex \
ext/unix.tex \
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ext/embedding.tex \
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\chapter{Embedding Python in Another Application
\label{embedding}}
The previous chapters discussed how to extend Python, that is, how to
extend the functionality of Python by attaching a library of C
functions to it. It is also possible to do it the other way around:
enrich your C/\Cpp{} application by embedding Python in it. Embedding
provides your application with the ability to implement some of the
functionality of your application in Python rather than C or \Cpp.
This can be used for many purposes; one example would be to allow
users to tailor the application to their needs by writing some scripts
in Python. You can also use it yourself if some of the functionality
can be written in Python more easily.
Embedding Python is similar to extending it, but not quite. The
difference is that when you extend Python, the main program of the
application is still the Python interpreter, while if you embed
Python, the main program may have nothing to do with Python ---
instead, some parts of the application occasionally call the Python
interpreter to run some Python code.
So if you are embedding Python, you are providing your own main
program. One of the things this main program has to do is initialize
the Python interpreter. At the very least, you have to call the
function \cfunction{Py_Initialize()} (on Mac OS, call
\cfunction{PyMac_Initialize()} instead). There are optional calls to
pass command line arguments to Python. Then later you can call the
interpreter from any part of the application.
There are several different ways to call the interpreter: you can pass
a string containing Python statements to
\cfunction{PyRun_SimpleString()}, or you can pass a stdio file pointer
and a file name (for identification in error messages only) to
\cfunction{PyRun_SimpleFile()}. You can also call the lower-level
operations described in the previous chapters to construct and use
Python objects.
A simple demo of embedding Python can be found in the directory
\file{Demo/embed/} of the source distribution.
\begin{seealso}
\seetitle[../api/api.html]{Python/C API Reference Manual}{The
details of Python's C interface are given in this manual.
A great deal of necessary information can be found here.}
\end{seealso}
\section{Very High Level Embedding
\label{high-level-embedding}}
The simplest form of embedding Python is the use of the very
high level interface. This interface is intended to execute a
Python script without needing to interact with the application
directly. This can for example be used to perform some operation
on a file.
\begin{verbatim}
#include <Python.h>
int main()
{
Py_Initialize();
PyRun_SimpleString("from time import time,ctime\n"
"print 'Today is',ctime(time())\n");
Py_Finalize();
return 0;
}
\end{verbatim}
The above code first initializes the Python interpreter with
\cfunction{Py_Initialize()}, followed by the execution of a hard-coded
Python script that print the date and time. Afterwards, the
\cfunction{Py_Finalize()} call shuts the interpreter down, followed by
the end of the program. In a real program, you may want to get the
Python script from another source, perhaps a text-editor routine, a
file, or a database. Getting the Python code from a file can better
be done by using the \cfunction{PyRun_SimpleFile()} function, which
saves you the trouble of allocating memory space and loading the file
contents.
\section{Beyond Very High Level Embedding: An overview
\label{lower-level-embedding}}
The high level interface gives you the ability to execute
arbitrary pieces of Python code from your application, but
exchanging data values is quite cumbersome to say the least. If
you want that, you should use lower level calls. At the cost of
having to write more C code, you can achieve almost anything.
It should be noted that extending Python and embedding Python
is quite the same activity, despite the different intent. Most
topics discussed in the previous chapters are still valid. To
show this, consider what the extension code from Python to C
really does:
\begin{enumerate}
\item Convert data values from Python to C,
\item Perform a function call to a C routine using the
converted values, and
\item Convert the data values from the call from C to Python.
\end{enumerate}
When embedding Python, the interface code does:
\begin{enumerate}
\item Convert data values from C to Python,
\item Perform a function call to a Python interface routine
using the converted values, and
\item Convert the data values from the call from Python to C.
\end{enumerate}
As you can see, the data conversion steps are simply swapped to
accomodate the different direction of the cross-language transfer.
The only difference is the routine that you call between both
data conversions. When extending, you call a C routine, when
embedding, you call a Python routine.
This chapter will not discuss how to convert data from Python
to C and vice versa. Also, proper use of references and dealing
with errors is assumed to be understood. Since these aspects do not
differ from extending the interpreter, you can refer to earlier
chapters for the required information.
\section{Pure Embedding
\label{pure-embedding}}
The first program aims to execute a function in a Python
script. Like in the section about the very high level interface,
the Python interpreter does not directly interact with the
application (but that will change in th next section).
The code to run a function defined in a Python script is:
\verbatiminput{run-func.c}
This code loads a Python script using \code{argv[1]}, and calls the
function named in \code{argv[2]}. Its integer arguments are the other
values of the \code{argv} array. If you compile and link this
program (let's call the finished executable \program{call}), and use
it to execute a Python script, such as:
\begin{verbatim}
def multiply(a,b):
print "Thy shall add", a, "times", b
c = 0
for i in range(0, a):
c = c + b
return c
\end{verbatim}
then the result should be:
\begin{verbatim}
$ call multiply 3 2
Thy shall add 3 times 2
Result of call: 6
\end{verbatim} % $
Although the program is quite large for its functionality, most of the
code is for data conversion between Python and C, and for error
reporting. The interesting part with respect to embedding Python
starts with
\begin{verbatim}
Py_Initialize();
pName = PyString_FromString(argv[1]);
/* Error checking of pName left out */
pModule = PyImport_Import(pName);
\end{verbatim}
After initializing the interpreter, the script is loaded using
\cfunction{PyImport_Import()}. This routine needs a Python string
as its argument, which is constructed using the
\cfunction{PyString_FromString()} data conversion routine.
\begin{verbatim}
pDict = PyModule_GetDict(pModule);
/* pDict is a borrowed reference */
pFunc = PyDict_GetItemString(pDict, argv[2]);
/* pFun is a borrowed reference */
if (pFunc && PyCallable_Check(pFunc)) {
...
}
\end{verbatim}
Once the script is loaded, its dictionary is retrieved with
\cfunction{PyModule_GetDict()}. The dictionary is then searched using
the normal dictionary access routines for the function name. If the
name exists, and the object retunred is callable, you can safely
assume that it is a function. The program then proceeds by
constructing a tuple of arguments as normal. The call to the python
function is then made with:
\begin{verbatim}
pValue = PyObject_CallObject(pFunc, pArgs);
\end{verbatim}
Upon return of the function, \code{pValue} is either \NULL{} or it
contains a reference to the return value of the function. Be sure to
release the reference after examining the value.
\section{Extending Embedded Python
\label{extending-with-embedding}}
Until now, the embedded Python interpreter had no access to
functionality from the application itself. The Python API allows this
by extending the embedded interpreter. That is, the embedded
interpreter gets extended with routines provided by the application.
While it sounds complex, it is not so bad. Simply forget for a while
that the application starts the Python interpreter. Instead, consider
the application to be a set of subroutines, and write some glue code
that gives Python access to those routines, just like you would write
a normal Python extension. For example:
\begin{verbatim}
static int numargs=0;
/* Return the number of arguments of the application command line */
static PyObject*
emb_numargs(PyObject *self, PyObject *args)
{
if(!PyArg_ParseTuple(args, ":numargs"))
return NULL;
return Py_BuildValue("i", numargs);
}
static PyMethodDef EmbMethods[]={
{"numargs", emb_numargs, METH_VARARGS},
{NULL, NULL}
};
\end{verbatim}
Insert the above code just above the \cfunction{main()} function.
Also, insert the following two statements directly after
\cfunction{Py_Initialize()}:
\begin{verbatim}
numargs = argc;
Py_InitModule("emb", EmbMethods);
\end{verbatim}
These two lines initialize the \code{numargs} variable, and make the
\function{emb.numargs()} function accessible to the embedded Python
interpreter. With these extensions, the Python script can do things
like
\begin{verbatim}
import emb
print "Number of arguments", emb.numargs()
\end{verbatim}
In a real application, the methods will expose an API of the
application to Python.
%\section{For the future}
%
%You don't happen to have a nice library to get textual
%equivalents of numeric values do you :-) ?
%Callbacks here ? (I may be using information from that section
%?!)
%threads
%code examples do not really behave well if errors happen
% (what to watch out for)
\section{Embedding Python in \Cpp{}
\label{embeddingInCplusplus}}
It is also possible to embed Python in a \Cpp{} program; precisely how this
is done will depend on the details of the \Cpp{} system used; in general you
will need to write the main program in \Cpp{}, and use the \Cpp{} compiler
to compile and link your program. There is no need to recompile Python
itself using \Cpp{}.
\section{Linking Requirements
\label{link-reqs}}
While the \program{configure} script shipped with the Python sources
will correctly build Python to export the symbols needed by
dynamically linked extensions, this is not automatically inherited by
applications which embed the Python library statically, at least on
\UNIX. This is an issue when the application is linked to the static
runtime library (\file{libpython.a}) and needs to load dynamic
extensions (implemented as \file{.so} files).
The problem is that some entry points are defined by the Python
runtime solely for extension modules to use. If the embedding
application does not use any of these entry points, some linkers will
not include those entries in the symbol table of the finished
executable. Some additional options are needed to inform the linker
not to remove these symbols.
Determining the right options to use for any given platform can be
quite difficult, but fortunately the Python configuration already has
those values. To retrieve them from an installed Python interpreter,
start an interactive interpreter and have a short session like this:
\begin{verbatim}
>>> import distutils.sysconfig
>>> distutils.sysconfig.get_config_var('LINKFORSHARED')
'-Xlinker -export-dynamic'
\end{verbatim}
\refstmodindex{distutils.sysconfig}
The contents of the string presented will be the options that should
be used. If the string is empty, there's no need to add any
additional options. The \constant{LINKFORSHARED} definition
corresponds to the variable of the same name in Python's top-level
\file{Makefile}.
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\chapter{Extending Python with C or \Cpp{} \label{intro}}
It is quite easy to add new built-in modules to Python, if you know
how to program in C. Such \dfn{extension modules} can do two things
that can't be done directly in Python: they can implement new built-in
object types, and they can call C library functions and system calls.
To support extensions, the Python API (Application Programmers
Interface) defines a set of functions, macros and variables that
provide access to most aspects of the Python run-time system. The
Python API is incorporated in a C source file by including the header
\code{"Python.h"}.
The compilation of an extension module depends on its intended use as
well as on your system setup; details are given in later chapters.
\section{A Simple Example
\label{simpleExample}}
Let's create an extension module called \samp{spam} (the favorite food
of Monty Python fans...) and let's say we want to create a Python
interface to the C library function \cfunction{system()}.\footnote{An
interface for this function already exists in the standard module
\module{os} --- it was chosen as a simple and straightfoward example.}
This function takes a null-terminated character string as argument and
returns an integer. We want this function to be callable from Python
as follows:
\begin{verbatim}
>>> import spam
>>> status = spam.system("ls -l")
\end{verbatim}
Begin by creating a file \file{spammodule.c}. (Historically, if a
module is called \samp{spam}, the C file containing its implementation
is called \file{spammodule.c}; if the module name is very long, like
\samp{spammify}, the module name can be just \file{spammify.c}.)
The first line of our file can be:
\begin{verbatim}
#include <Python.h>
\end{verbatim}
which pulls in the Python API (you can add a comment describing the
purpose of the module and a copyright notice if you like).
All user-visible symbols defined by \code{"Python.h"} have a prefix of
\samp{Py} or \samp{PY}, except those defined in standard header files.
For convenience, and since they are used extensively by the Python
interpreter, \code{"Python.h"} includes a few standard header files:
\code{<stdio.h>}, \code{<string.h>}, \code{<errno.h>}, and
\code{<stdlib.h>}. If the latter header file does not exist on your
system, it declares the functions \cfunction{malloc()},
\cfunction{free()} and \cfunction{realloc()} directly.
The next thing we add to our module file is the C function that will
be called when the Python expression \samp{spam.system(\var{string})}
is evaluated (we'll see shortly how it ends up being called):
\begin{verbatim}
static PyObject *
spam_system(self, args)
PyObject *self;
PyObject *args;
{
char *command;
int sts;
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = system(command);
return Py_BuildValue("i", sts);
}
\end{verbatim}
There is a straightforward translation from the argument list in
Python (for example, the single expression \code{"ls -l"}) to the
arguments passed to the C function. The C function always has two
arguments, conventionally named \var{self} and \var{args}.
The \var{self} argument is only used when the C function implements a
built-in method, not a function. In the example, \var{self} will
always be a \NULL{} pointer, since we are defining a function, not a
method. (This is done so that the interpreter doesn't have to
understand two different types of C functions.)
The \var{args} argument will be a pointer to a Python tuple object
containing the arguments. Each item of the tuple corresponds to an
argument in the call's argument list. The arguments are Python
objects --- in order to do anything with them in our C function we have
to convert them to C values. The function \cfunction{PyArg_ParseTuple()}
in the Python API checks the argument types and converts them to C
values. It uses a template string to determine the required types of
the arguments as well as the types of the C variables into which to
store the converted values. More about this later.
\cfunction{PyArg_ParseTuple()} returns true (nonzero) if all arguments have
the right type and its components have been stored in the variables
whose addresses are passed. It returns false (zero) if an invalid
argument list was passed. In the latter case it also raises an
appropriate exception so the calling function can return
\NULL{} immediately (as we saw in the example).
\section{Intermezzo: Errors and Exceptions
\label{errors}}
An important convention throughout the Python interpreter is the
following: when a function fails, it should set an exception condition
and return an error value (usually a \NULL{} pointer). Exceptions
are stored in a static global variable inside the interpreter; if this
variable is \NULL{} no exception has occurred. A second global
variable stores the ``associated value'' of the exception (the second
argument to \keyword{raise}). A third variable contains the stack
traceback in case the error originated in Python code. These three
variables are the C equivalents of the Python variables
\code{sys.exc_type}, \code{sys.exc_value} and \code{sys.exc_traceback} (see
the section on module \module{sys} in the
\citetitle[../lib/lib.html]{Python Library Reference}). It is
important to know about them to understand how errors are passed
around.
The Python API defines a number of functions to set various types of
exceptions.
The most common one is \cfunction{PyErr_SetString()}. Its arguments
are an exception object and a C string. The exception object is
usually a predefined object like \cdata{PyExc_ZeroDivisionError}. The
C string indicates the cause of the error and is converted to a
Python string object and stored as the ``associated value'' of the
exception.
Another useful function is \cfunction{PyErr_SetFromErrno()}, which only
takes an exception argument and constructs the associated value by
inspection of the global variable \cdata{errno}. The most
general function is \cfunction{PyErr_SetObject()}, which takes two object
arguments, the exception and its associated value. You don't need to
\cfunction{Py_INCREF()} the objects passed to any of these functions.
You can test non-destructively whether an exception has been set with
\cfunction{PyErr_Occurred()}. This returns the current exception object,
or \NULL{} if no exception has occurred. You normally don't need
to call \cfunction{PyErr_Occurred()} to see whether an error occurred in a
function call, since you should be able to tell from the return value.
When a function \var{f} that calls another function \var{g} detects
that the latter fails, \var{f} should itself return an error value
(usually \NULL{} or \code{-1}). It should \emph{not} call one of the
\cfunction{PyErr_*()} functions --- one has already been called by \var{g}.
\var{f}'s caller is then supposed to also return an error indication
to \emph{its} caller, again \emph{without} calling \cfunction{PyErr_*()},
and so on --- the most detailed cause of the error was already
reported by the function that first detected it. Once the error
reaches the Python interpreter's main loop, this aborts the currently
executing Python code and tries to find an exception handler specified
by the Python programmer.
(There are situations where a module can actually give a more detailed
error message by calling another \cfunction{PyErr_*()} function, and in
such cases it is fine to do so. As a general rule, however, this is
not necessary, and can cause information about the cause of the error
to be lost: most operations can fail for a variety of reasons.)
To ignore an exception set by a function call that failed, the exception
condition must be cleared explicitly by calling \cfunction{PyErr_Clear()}.
The only time C code should call \cfunction{PyErr_Clear()} is if it doesn't
want to pass the error on to the interpreter but wants to handle it
completely by itself (possibly by trying something else, or pretending
nothing went wrong).
Every failing \cfunction{malloc()} call must be turned into an
exception --- the direct caller of \cfunction{malloc()} (or
\cfunction{realloc()}) must call \cfunction{PyErr_NoMemory()} and
return a failure indicator itself. All the object-creating functions
(for example, \cfunction{PyInt_FromLong()}) already do this, so this
note is only relevant to those who call \cfunction{malloc()} directly.
Also note that, with the important exception of
\cfunction{PyArg_ParseTuple()} and friends, functions that return an
integer status usually return a positive value or zero for success and
\code{-1} for failure, like \UNIX{} system calls.
Finally, be careful to clean up garbage (by making
\cfunction{Py_XDECREF()} or \cfunction{Py_DECREF()} calls for objects
you have already created) when you return an error indicator!
The choice of which exception to raise is entirely yours. There are
predeclared C objects corresponding to all built-in Python exceptions,
such as \cdata{PyExc_ZeroDivisionError}, which you can use directly.
Of course, you should choose exceptions wisely --- don't use
\cdata{PyExc_TypeError} to mean that a file couldn't be opened (that
should probably be \cdata{PyExc_IOError}). If something's wrong with
the argument list, the \cfunction{PyArg_ParseTuple()} function usually
raises \cdata{PyExc_TypeError}. If you have an argument whose value
must be in a particular range or must satisfy other conditions,
\cdata{PyExc_ValueError} is appropriate.
You can also define a new exception that is unique to your module.
For this, you usually declare a static object variable at the
beginning of your file:
\begin{verbatim}
static PyObject *SpamError;
\end{verbatim}
and initialize it in your module's initialization function
(\cfunction{initspam()}) with an exception object (leaving out
the error checking for now):
\begin{verbatim}
void
initspam()
{
PyObject *m, *d;
m = Py_InitModule("spam", SpamMethods);
d = PyModule_GetDict(m);
SpamError = PyErr_NewException("spam.error", NULL, NULL);
PyDict_SetItemString(d, "error", SpamError);
}
\end{verbatim}
Note that the Python name for the exception object is
\exception{spam.error}. The \cfunction{PyErr_NewException()} function
may create a class with the base class being \exception{Exception}
(unless another class is passed in instead of \NULL), described in the
\citetitle[../lib/lib.html]{Python Library Reference} under ``Built-in
Exceptions.''
Note also that the \cdata{SpamError} variable retains a reference to
the newly created exception class; this is intentional! Since the
exception could be removed from the module by external code, an owned
reference to the class is needed to ensure that it will not be
discarded, causing \cdata{SpamError} to become a dangling pointer.
Should it become a dangling pointer, C code which raises the exception
could cause a core dump or other unintended side effects.
\section{Back to the Example
\label{backToExample}}
Going back to our example function, you should now be able to
understand this statement:
\begin{verbatim}
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
\end{verbatim}
It returns \NULL{} (the error indicator for functions returning
object pointers) if an error is detected in the argument list, relying
on the exception set by \cfunction{PyArg_ParseTuple()}. Otherwise the
string value of the argument has been copied to the local variable
\cdata{command}. This is a pointer assignment and you are not supposed
to modify the string to which it points (so in Standard C, the variable
\cdata{command} should properly be declared as \samp{const char
*command}).
The next statement is a call to the \UNIX{} function
\cfunction{system()}, passing it the string we just got from
\cfunction{PyArg_ParseTuple()}:
\begin{verbatim}
sts = system(command);
\end{verbatim}
Our \function{spam.system()} function must return the value of
\cdata{sts} as a Python object. This is done using the function
\cfunction{Py_BuildValue()}, which is something like the inverse of
\cfunction{PyArg_ParseTuple()}: it takes a format string and an
arbitrary number of C values, and returns a new Python object.
More info on \cfunction{Py_BuildValue()} is given later.
\begin{verbatim}
return Py_BuildValue("i", sts);
\end{verbatim}
In this case, it will return an integer object. (Yes, even integers
are objects on the heap in Python!)
If you have a C function that returns no useful argument (a function
returning \ctype{void}), the corresponding Python function must return
\code{None}. You need this idiom to do so:
\begin{verbatim}
Py_INCREF(Py_None);
return Py_None;
\end{verbatim}
\cdata{Py_None} is the C name for the special Python object
\code{None}. It is a genuine Python object rather than a \NULL{}
pointer, which means ``error'' in most contexts, as we have seen.
\section{The Module's Method Table and Initialization Function
\label{methodTable}}
I promised to show how \cfunction{spam_system()} is called from Python
programs. First, we need to list its name and address in a ``method
table'':
\begin{verbatim}
static PyMethodDef SpamMethods[] = {
...
{"system", spam_system, METH_VARARGS},
...
{NULL, NULL} /* Sentinel */
};
\end{verbatim}
Note the third entry (\samp{METH_VARARGS}). This is a flag telling
the interpreter the calling convention to be used for the C
function. It should normally always be \samp{METH_VARARGS} or
\samp{METH_VARARGS | METH_KEYWORDS}; a value of \code{0} means that an
obsolete variant of \cfunction{PyArg_ParseTuple()} is used.
When using only \samp{METH_VARARGS}, the function should expect
the Python-level parameters to be passed in as a tuple acceptable for
parsing via \cfunction{PyArg_ParseTuple()}; more information on this
function is provided below.
The \constant{METH_KEYWORDS} bit may be set in the third field if
keyword arguments should be passed to the function. In this case, the
C function should accept a third \samp{PyObject *} parameter which
will be a dictionary of keywords. Use
\cfunction{PyArg_ParseTupleAndKeywords()} to parse the arguments to
such a function.
The method table must be passed to the interpreter in the module's
initialization function. The initialization function must be named
\cfunction{init\var{name}()}, where \var{name} is the name of the
module, and should be the only non-\keyword{static} item defined in
the module file:
\begin{verbatim}
void
initspam()
{
(void) Py_InitModule("spam", SpamMethods);
}
\end{verbatim}
Note that for \Cpp, this method must be declared \code{extern "C"}.
When the Python program imports module \module{spam} for the first
time, \cfunction{initspam()} is called. (See below for comments about
embedding Python.) It calls
\cfunction{Py_InitModule()}, which creates a ``module object'' (which
is inserted in the dictionary \code{sys.modules} under the key
\code{"spam"}), and inserts built-in function objects into the newly
created module based upon the table (an array of \ctype{PyMethodDef}
structures) that was passed as its second argument.
\cfunction{Py_InitModule()} returns a pointer to the module object
that it creates (which is unused here). It aborts with a fatal error
if the module could not be initialized satisfactorily, so the caller
doesn't need to check for errors.
When embedding Python, the \cfunction{initspam()} function is not
called automatically unless there's an entry in the
\cdata{_PyImport_Inittab} table. The easiest way to handle this is to
statically initialize your statically-linked modules by directly
calling \cfunction{initspam()} after the call to
\cfunction{Py_Initialize()} or \cfunction{PyMac_Initialize()}:
\begin{verbatim}
int main(int argc, char **argv)
{
/* Pass argv[0] to the Python interpreter */
Py_SetProgramName(argv[0]);
/* Initialize the Python interpreter. Required. */
Py_Initialize();
/* Add a static module */
initspam();
\end{verbatim}
An example may be found in the file \file{Demo/embed/demo.c} in the
Python source distribution.
\strong{Note:} Removing entries from \code{sys.modules} or importing
compiled modules into multiple interpreters within a process (or
following a \cfunction{fork()} without an intervening
\cfunction{exec()}) can create problems for some extension modules.
Extension module authors should exercise caution when initializing
internal data structures.
Note also that the \function{reload()} function can be used with
extension modules, and will call the module initialization function
(\cfunction{initspam()} in the example), but will not load the module
again if it was loaded from a dynamically loadable object file
(\file{.so} on \UNIX, \file{.dll} on Windows).
A more substantial example module is included in the Python source
distribution as \file{Modules/xxmodule.c}. This file may be used as a
template or simply read as an example. The \program{modulator.py}
script included in the source distribution or Windows install provides
a simple graphical user interface for declaring the functions and
objects which a module should implement, and can generate a template
which can be filled in. The script lives in the
\file{Tools/modulator/} directory; see the \file{README} file there
for more information.
\section{Compilation and Linkage
\label{compilation}}
There are two more things to do before you can use your new extension:
compiling and linking it with the Python system. If you use dynamic
loading, the details depend on the style of dynamic loading your
system uses; see the chapters about building extension modules on
\UNIX{} (chapter \ref{building-on-unix}) and Windows (chapter
\ref{building-on-windows}) for more information about this.
% XXX Add information about MacOS
If you can't use dynamic loading, or if you want to make your module a
permanent part of the Python interpreter, you will have to change the
configuration setup and rebuild the interpreter. Luckily, this is
very simple: just place your file (\file{spammodule.c} for example) in
the \file{Modules/} directory of an unpacked source distribution, add
a line to the file \file{Modules/Setup.local} describing your file:
\begin{verbatim}
spam spammodule.o
\end{verbatim}
and rebuild the interpreter by running \program{make} in the toplevel
directory. You can also run \program{make} in the \file{Modules/}
subdirectory, but then you must first rebuild \file{Makefile}
there by running `\program{make} Makefile'. (This is necessary each
time you change the \file{Setup} file.)
If your module requires additional libraries to link with, these can
be listed on the line in the configuration file as well, for instance:
\begin{verbatim}
spam spammodule.o -lX11
\end{verbatim}
\section{Calling Python Functions from C
\label{callingPython}}
So far we have concentrated on making C functions callable from
Python. The reverse is also useful: calling Python functions from C.
This is especially the case for libraries that support so-called
``callback'' functions. If a C interface makes use of callbacks, the
equivalent Python often needs to provide a callback mechanism to the
Python programmer; the implementation will require calling the Python
callback functions from a C callback. Other uses are also imaginable.
Fortunately, the Python interpreter is easily called recursively, and
there is a standard interface to call a Python function. (I won't
dwell on how to call the Python parser with a particular string as
input --- if you're interested, have a look at the implementation of
the \programopt{-c} command line option in \file{Python/pythonmain.c}
from the Python source code.)
Calling a Python function is easy. First, the Python program must
somehow pass you the Python function object. You should provide a
function (or some other interface) to do this. When this function is
called, save a pointer to the Python function object (be careful to
\cfunction{Py_INCREF()} it!) in a global variable --- or wherever you
see fit. For example, the following function might be part of a module
definition:
\begin{verbatim}
static PyObject *my_callback = NULL;
static PyObject *
my_set_callback(dummy, args)
PyObject *dummy, *args;
{
PyObject *result = NULL;
PyObject *temp;
if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
if (!PyCallable_Check(temp)) {
PyErr_SetString(PyExc_TypeError, "parameter must be callable");
return NULL;
}
Py_XINCREF(temp); /* Add a reference to new callback */
Py_XDECREF(my_callback); /* Dispose of previous callback */
my_callback = temp; /* Remember new callback */
/* Boilerplate to return "None" */
Py_INCREF(Py_None);
result = Py_None;
}
return result;
}
\end{verbatim}
This function must be registered with the interpreter using the
\constant{METH_VARARGS} flag; this is described in section
\ref{methodTable}, ``The Module's Method Table and Initialization
Function.'' The \cfunction{PyArg_ParseTuple()} function and its
arguments are documented in section \ref{parseTuple}, ``Extracting
Parameters in Extension Functions.''
The macros \cfunction{Py_XINCREF()} and \cfunction{Py_XDECREF()}
increment/decrement the reference count of an object and are safe in
the presence of \NULL{} pointers (but note that \var{temp} will not be
\NULL{} in this context). More info on them in section
\ref{refcounts}, ``Reference Counts.''
Later, when it is time to call the function, you call the C function
\cfunction{PyEval_CallObject()}. This function has two arguments, both
pointers to arbitrary Python objects: the Python function, and the
argument list. The argument list must always be a tuple object, whose
length is the number of arguments. To call the Python function with
no arguments, pass an empty tuple; to call it with one argument, pass
a singleton tuple. \cfunction{Py_BuildValue()} returns a tuple when its
format string consists of zero or more format codes between
parentheses. For example:
\begin{verbatim}
int arg;
PyObject *arglist;
PyObject *result;
...
arg = 123;
...
/* Time to call the callback */
arglist = Py_BuildValue("(i)", arg);
result = PyEval_CallObject(my_callback, arglist);
Py_DECREF(arglist);
\end{verbatim}
\cfunction{PyEval_CallObject()} returns a Python object pointer: this is
the return value of the Python function. \cfunction{PyEval_CallObject()} is
``reference-count-neutral'' with respect to its arguments. In the
example a new tuple was created to serve as the argument list, which
is \cfunction{Py_DECREF()}-ed immediately after the call.
The return value of \cfunction{PyEval_CallObject()} is ``new'': either it
is a brand new object, or it is an existing object whose reference
count has been incremented. So, unless you want to save it in a
global variable, you should somehow \cfunction{Py_DECREF()} the result,
even (especially!) if you are not interested in its value.
Before you do this, however, it is important to check that the return
value isn't \NULL{}. If it is, the Python function terminated by
raising an exception. If the C code that called
\cfunction{PyEval_CallObject()} is called from Python, it should now
return an error indication to its Python caller, so the interpreter
can print a stack trace, or the calling Python code can handle the
exception. If this is not possible or desirable, the exception should
be cleared by calling \cfunction{PyErr_Clear()}. For example:
\begin{verbatim}
if (result == NULL)
return NULL; /* Pass error back */
...use result...
Py_DECREF(result);
\end{verbatim}
Depending on the desired interface to the Python callback function,
you may also have to provide an argument list to
\cfunction{PyEval_CallObject()}. In some cases the argument list is
also provided by the Python program, through the same interface that
specified the callback function. It can then be saved and used in the
same manner as the function object. In other cases, you may have to
construct a new tuple to pass as the argument list. The simplest way
to do this is to call \cfunction{Py_BuildValue()}. For example, if
you want to pass an integral event code, you might use the following
code:
\begin{verbatim}
PyObject *arglist;
...
arglist = Py_BuildValue("(l)", eventcode);
result = PyEval_CallObject(my_callback, arglist);
Py_DECREF(arglist);
if (result == NULL)
return NULL; /* Pass error back */
/* Here maybe use the result */
Py_DECREF(result);
\end{verbatim}
Note the placement of \samp{Py_DECREF(arglist)} immediately after the
call, before the error check! Also note that strictly spoken this
code is not complete: \cfunction{Py_BuildValue()} may run out of
memory, and this should be checked.
\section{Extracting Parameters in Extension Functions
\label{parseTuple}}
The \cfunction{PyArg_ParseTuple()} function is declared as follows:
\begin{verbatim}
int PyArg_ParseTuple(PyObject *arg, char *format, ...);
\end{verbatim}
The \var{arg} argument must be a tuple object containing an argument
list passed from Python to a C function. The \var{format} argument
must be a format string, whose syntax is explained below. The
remaining arguments must be addresses of variables whose type is
determined by the format string. For the conversion to succeed, the
\var{arg} object must match the format and the format must be
exhausted. On success, \cfunction{PyArg_ParseTuple()} returns true,
otherwise it returns false and raises an appropriate exception.
Note that while \cfunction{PyArg_ParseTuple()} checks that the Python
arguments have the required types, it cannot check the validity of the
addresses of C variables passed to the call: if you make mistakes
there, your code will probably crash or at least overwrite random bits
in memory. So be careful!
A format string consists of zero or more ``format units''. A format
unit describes one Python object; it is usually a single character or
a parenthesized sequence of format units. With a few exceptions, a
format unit that is not a parenthesized sequence normally corresponds
to a single address argument to \cfunction{PyArg_ParseTuple()}. In the
following description, the quoted form is the format unit; the entry
in (round) parentheses is the Python object type that matches the
format unit; and the entry in [square] brackets is the type of the C
variable(s) whose address should be passed. (Use the \samp{\&}
operator to pass a variable's address.)
Note that any Python object references which are provided to the
caller are \emph{borrowed} references; do not decrement their
reference count!
\begin{description}
\item[\samp{s} (string or Unicode object) {[char *]}]
Convert a Python string or Unicode object to a C pointer to a
character string. You must not provide storage for the string
itself; a pointer to an existing string is stored into the character
pointer variable whose address you pass. The C string is
null-terminated. The Python string must not contain embedded null
bytes; if it does, a \exception{TypeError} exception is raised.
Unicode objects are converted to C strings using the default
encoding. If this conversion fails, an \exception{UnicodeError} is
raised.
\item[\samp{s\#} (string, Unicode or any read buffer compatible object)
{[char *, int]}]
This variant on \samp{s} stores into two C variables, the first one a
pointer to a character string, the second one its length. In this
case the Python string may contain embedded null bytes. Unicode
objects pass back a pointer to the default encoded string version of the
object if such a conversion is possible. All other read buffer
compatible objects pass back a reference to the raw internal data
representation.
\item[\samp{z} (string or \code{None}) {[char *]}]
Like \samp{s}, but the Python object may also be \code{None}, in which
case the C pointer is set to \NULL{}.
\item[\samp{z\#} (string or \code{None} or any read buffer compatible object)
{[char *, int]}]
This is to \samp{s\#} as \samp{z} is to \samp{s}.
\item[\samp{u} (Unicode object) {[Py_UNICODE *]}]
Convert a Python Unicode object to a C pointer to a null-terminated
buffer of 16-bit Unicode (UTF-16) data. As with \samp{s}, there is no need
to provide storage for the Unicode data buffer; a pointer to the
existing Unicode data is stored into the Py_UNICODE pointer variable whose
address you pass.
\item[\samp{u\#} (Unicode object) {[Py_UNICODE *, int]}]
This variant on \samp{u} stores into two C variables, the first one
a pointer to a Unicode data buffer, the second one its length.
\item[\samp{es} (string, Unicode object or character buffer compatible
object) {[const char *encoding, char **buffer]}]
This variant on \samp{s} is used for encoding Unicode and objects
convertible to Unicode into a character buffer. It only works for
encoded data without embedded \NULL{} bytes.
The variant reads one C variable and stores into two C variables, the
first one a pointer to an encoding name string (\var{encoding}), and the
second a pointer to a pointer to a character buffer (\var{**buffer},
the buffer used for storing the encoded data).
The encoding name must map to a registered codec. If set to \NULL{},
the default encoding is used.
\cfunction{PyArg_ParseTuple()} will allocate a buffer of the needed
size using \cfunction{PyMem_NEW()}, copy the encoded data into this
buffer and adjust \var{*buffer} to reference the newly allocated
storage. The caller is responsible for calling
\cfunction{PyMem_Free()} to free the allocated buffer after usage.
\item[\samp{et} (string, Unicode object or character buffer compatible
object) {[const char *encoding, char **buffer]}]
Same as \samp{es} except that string objects are passed through without
recoding them. Instead, the implementation assumes that the string
object uses the encoding passed in as parameter.
\item[\samp{es\#} (string, Unicode object or character buffer compatible
object) {[const char *encoding, char **buffer, int *buffer_length]}]
This variant on \samp{s\#} is used for encoding Unicode and objects
convertible to Unicode into a character buffer. It reads one C
variable and stores into three C variables, the first one a pointer to
an encoding name string (\var{encoding}), the second a pointer to a
pointer to a character buffer (\var{**buffer}, the buffer used for
storing the encoded data) and the third one a pointer to an integer
(\var{*buffer_length}, the buffer length).
The encoding name must map to a registered codec. If set to \NULL{},
the default encoding is used.
There are two modes of operation:
If \var{*buffer} points a \NULL{} pointer,
\cfunction{PyArg_ParseTuple()} will allocate a buffer of the needed
size using \cfunction{PyMem_NEW()}, copy the encoded data into this
buffer and adjust \var{*buffer} to reference the newly allocated
storage. The caller is responsible for calling
\cfunction{PyMem_Free()} to free the allocated buffer after usage.
If \var{*buffer} points to a non-\NULL{} pointer (an already allocated
buffer), \cfunction{PyArg_ParseTuple()} will use this location as
buffer and interpret \var{*buffer_length} as buffer size. It will then
copy the encoded data into the buffer and 0-terminate it. Buffer
overflow is signalled with an exception.
In both cases, \var{*buffer_length} is set to the length of the
encoded data without the trailing 0-byte.
\item[\samp{et\#} (string, Unicode object or character buffer compatible
object) {[const char *encoding, char **buffer]}]
Same as \samp{es\#} except that string objects are passed through without
recoding them. Instead, the implementation assumes that the string
object uses the encoding passed in as parameter.
\item[\samp{b} (integer) {[char]}]
Convert a Python integer to a tiny int, stored in a C \ctype{char}.
\item[\samp{h} (integer) {[short int]}]
Convert a Python integer to a C \ctype{short int}.
\item[\samp{i} (integer) {[int]}]
Convert a Python integer to a plain C \ctype{int}.
\item[\samp{l} (integer) {[long int]}]
Convert a Python integer to a C \ctype{long int}.
\item[\samp{c} (string of length 1) {[char]}]
Convert a Python character, represented as a string of length 1, to a
C \ctype{char}.
\item[\samp{f} (float) {[float]}]
Convert a Python floating point number to a C \ctype{float}.
\item[\samp{d} (float) {[double]}]
Convert a Python floating point number to a C \ctype{double}.
\item[\samp{D} (complex) {[Py_complex]}]
Convert a Python complex number to a C \ctype{Py_complex} structure.
\item[\samp{O} (object) {[PyObject *]}]
Store a Python object (without any conversion) in a C object pointer.
The C program thus receives the actual object that was passed. The
object's reference count is not increased. The pointer stored is not
\NULL{}.
\item[\samp{O!} (object) {[\var{typeobject}, PyObject *]}]
Store a Python object in a C object pointer. This is similar to
\samp{O}, but takes two C arguments: the first is the address of a
Python type object, the second is the address of the C variable (of
type \ctype{PyObject *}) into which the object pointer is stored.
If the Python object does not have the required type,
\exception{TypeError} is raised.
\item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}]
Convert a Python object to a C variable through a \var{converter}
function. This takes two arguments: the first is a function, the
second is the address of a C variable (of arbitrary type), converted
to \ctype{void *}. The \var{converter} function in turn is called as
follows:
\var{status}\code{ = }\var{converter}\code{(}\var{object}, \var{address}\code{);}
where \var{object} is the Python object to be converted and
\var{address} is the \ctype{void *} argument that was passed to
\cfunction{PyArg_ConvertTuple()}. The returned \var{status} should be
\code{1} for a successful conversion and \code{0} if the conversion
has failed. When the conversion fails, the \var{converter} function
should raise an exception.
\item[\samp{S} (string) {[PyStringObject *]}]
Like \samp{O} but requires that the Python object is a string object.
Raises \exception{TypeError} if the object is not a string object.
The C variable may also be declared as \ctype{PyObject *}.
\item[\samp{U} (Unicode string) {[PyUnicodeObject *]}]
Like \samp{O} but requires that the Python object is a Unicode object.
Raises \exception{TypeError} if the object is not a Unicode object.
The C variable may also be declared as \ctype{PyObject *}.
\item[\samp{t\#} (read-only character buffer) {[char *, int]}]
Like \samp{s\#}, but accepts any object which implements the read-only
buffer interface. The \ctype{char *} variable is set to point to the
first byte of the buffer, and the \ctype{int} is set to the length of
the buffer. Only single-segment buffer objects are accepted;
\exception{TypeError} is raised for all others.
\item[\samp{w} (read-write character buffer) {[char *]}]
Similar to \samp{s}, but accepts any object which implements the
read-write buffer interface. The caller must determine the length of
the buffer by other means, or use \samp{w\#} instead. Only
single-segment buffer objects are accepted; \exception{TypeError} is
raised for all others.
\item[\samp{w\#} (read-write character buffer) {[char *, int]}]
Like \samp{s\#}, but accepts any object which implements the
read-write buffer interface. The \ctype{char *} variable is set to
point to the first byte of the buffer, and the \ctype{int} is set to
the length of the buffer. Only single-segment buffer objects are
accepted; \exception{TypeError} is raised for all others.
\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}]
The object must be a Python sequence whose length is the number of
format units in \var{items}. The C arguments must correspond to the
individual format units in \var{items}. Format units for sequences
may be nested.
\strong{Note:} Prior to Python version 1.5.2, this format specifier
only accepted a tuple containing the individual parameters, not an
arbitrary sequence. Code which previously caused
\exception{TypeError} to be raised here may now proceed without an
exception. This is not expected to be a problem for existing code.
\end{description}
It is possible to pass Python long integers where integers are
requested; however no proper range checking is done --- the most
significant bits are silently truncated when the receiving field is
too small to receive the value (actually, the semantics are inherited
from downcasts in C --- your mileage may vary).
A few other characters have a meaning in a format string. These may
not occur inside nested parentheses. They are:
\begin{description}
\item[\samp{|}]
Indicates that the remaining arguments in the Python argument list are
optional. The C variables corresponding to optional arguments should
be initialized to their default value --- when an optional argument is
not specified, \cfunction{PyArg_ParseTuple()} does not touch the contents
of the corresponding C variable(s).
\item[\samp{:}]
The list of format units ends here; the string after the colon is used
as the function name in error messages (the ``associated value'' of
the exception that \cfunction{PyArg_ParseTuple()} raises).
\item[\samp{;}]
The list of format units ends here; the string after the semicolon is
used as the error message \emph{instead} of the default error message.
Clearly, \samp{:} and \samp{;} mutually exclude each other.
\end{description}
Some example calls:
\begin{verbatim}
int ok;
int i, j;
long k, l;
char *s;
int size;
ok = PyArg_ParseTuple(args, ""); /* No arguments */
/* Python call: f() */
\end{verbatim}
\begin{verbatim}
ok = PyArg_ParseTuple(args, "s", &s); /* A string */
/* Possible Python call: f('whoops!') */
\end{verbatim}
\begin{verbatim}
ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
/* Possible Python call: f(1, 2, 'three') */
\end{verbatim}
\begin{verbatim}
ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
/* A pair of ints and a string, whose size is also returned */
/* Possible Python call: f((1, 2), 'three') */
\end{verbatim}
\begin{verbatim}
{
char *file;
char *mode = "r";
int bufsize = 0;
ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
/* A string, and optionally another string and an integer */
/* Possible Python calls:
f('spam')
f('spam', 'w')
f('spam', 'wb', 100000) */
}
\end{verbatim}
\begin{verbatim}
{
int left, top, right, bottom, h, v;
ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
&left, &top, &right, &bottom, &h, &v);
/* A rectangle and a point */
/* Possible Python call:
f(((0, 0), (400, 300)), (10, 10)) */
}
\end{verbatim}
\begin{verbatim}
{
Py_complex c;
ok = PyArg_ParseTuple(args, "D:myfunction", &c);
/* a complex, also providing a function name for errors */
/* Possible Python call: myfunction(1+2j) */
}
\end{verbatim}
\section{Keyword Parameters for Extension Functions
\label{parseTupleAndKeywords}}
The \cfunction{PyArg_ParseTupleAndKeywords()} function is declared as
follows:
\begin{verbatim}
int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
char *format, char **kwlist, ...);
\end{verbatim}
The \var{arg} and \var{format} parameters are identical to those of the
\cfunction{PyArg_ParseTuple()} function. The \var{kwdict} parameter
is the dictionary of keywords received as the third parameter from the
Python runtime. The \var{kwlist} parameter is a \NULL{}-terminated
list of strings which identify the parameters; the names are matched
with the type information from \var{format} from left to right. On
success, \cfunction{PyArg_ParseTupleAndKeywords()} returns true,
otherwise it returns false and raises an appropriate exception.
\strong{Note:} Nested tuples cannot be parsed when using keyword
arguments! Keyword parameters passed in which are not present in the
\var{kwlist} will cause \exception{TypeError} to be raised.
Here is an example module which uses keywords, based on an example by
Geoff Philbrick (\email{philbrick@hks.com}):%
\index{Philbrick, Geoff}
\begin{verbatim}
#include <stdio.h>
#include "Python.h"
static PyObject *
keywdarg_parrot(self, args, keywds)
PyObject *self;
PyObject *args;
PyObject *keywds;
{
int voltage;
char *state = "a stiff";
char *action = "voom";
char *type = "Norwegian Blue";
static char *kwlist[] = {"voltage", "state", "action", "type", NULL};
if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
&voltage, &state, &action, &type))
return NULL;
printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
action, voltage);
printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);
Py_INCREF(Py_None);
return Py_None;
}
static PyMethodDef keywdarg_methods[] = {
/* The cast of the function is necessary since PyCFunction values
* only take two PyObject* parameters, and keywdarg_parrot() takes
* three.
*/
{"parrot", (PyCFunction)keywdarg_parrot, METH_VARARGS|METH_KEYWORDS},
{NULL, NULL} /* sentinel */
};
void
initkeywdarg()
{
/* Create the module and add the functions */
Py_InitModule("keywdarg", keywdarg_methods);
}
\end{verbatim}
\section{Building Arbitrary Values
\label{buildValue}}
This function is the counterpart to \cfunction{PyArg_ParseTuple()}. It is
declared as follows:
\begin{verbatim}
PyObject *Py_BuildValue(char *format, ...);
\end{verbatim}
It recognizes a set of format units similar to the ones recognized by
\cfunction{PyArg_ParseTuple()}, but the arguments (which are input to the
function, not output) must not be pointers, just values. It returns a
new Python object, suitable for returning from a C function called
from Python.
One difference with \cfunction{PyArg_ParseTuple()}: while the latter
requires its first argument to be a tuple (since Python argument lists
are always represented as tuples internally),
\cfunction{Py_BuildValue()} does not always build a tuple. It builds
a tuple only if its format string contains two or more format units.
If the format string is empty, it returns \code{None}; if it contains
exactly one format unit, it returns whatever object is described by
that format unit. To force it to return a tuple of size 0 or one,
parenthesize the format string.
When memory buffers are passed as parameters to supply data to build
objects, as for the \samp{s} and \samp{s\#} formats, the required data
is copied. Buffers provided by the caller are never referenced by the
objects created by \cfunction{Py_BuildValue()}. In other words, if
your code invokes \cfunction{malloc()} and passes the allocated memory
to \cfunction{Py_BuildValue()}, your code is responsible for
calling \cfunction{free()} for that memory once
\cfunction{Py_BuildValue()} returns.
In the following description, the quoted form is the format unit; the
entry in (round) parentheses is the Python object type that the format
unit will return; and the entry in [square] brackets is the type of
the C value(s) to be passed.
The characters space, tab, colon and comma are ignored in format
strings (but not within format units such as \samp{s\#}). This can be
used to make long format strings a tad more readable.
\begin{description}
\item[\samp{s} (string) {[char *]}]
Convert a null-terminated C string to a Python object. If the C
string pointer is \NULL{}, \code{None} is used.
\item[\samp{s\#} (string) {[char *, int]}]
Convert a C string and its length to a Python object. If the C string
pointer is \NULL{}, the length is ignored and \code{None} is
returned.
\item[\samp{z} (string or \code{None}) {[char *]}]
Same as \samp{s}.
\item[\samp{z\#} (string or \code{None}) {[char *, int]}]
Same as \samp{s\#}.
\item[\samp{u} (Unicode string) {[Py_UNICODE *]}]
Convert a null-terminated buffer of Unicode (UCS-2) data to a Python
Unicode object. If the Unicode buffer pointer is \NULL,
\code{None} is returned.
\item[\samp{u\#} (Unicode string) {[Py_UNICODE *, int]}]
Convert a Unicode (UCS-2) data buffer and its length to a Python
Unicode object. If the Unicode buffer pointer is \NULL, the length
is ignored and \code{None} is returned.
\item[\samp{i} (integer) {[int]}]
Convert a plain C \ctype{int} to a Python integer object.
\item[\samp{b} (integer) {[char]}]
Same as \samp{i}.
\item[\samp{h} (integer) {[short int]}]
Same as \samp{i}.
\item[\samp{l} (integer) {[long int]}]
Convert a C \ctype{long int} to a Python integer object.
\item[\samp{c} (string of length 1) {[char]}]
Convert a C \ctype{int} representing a character to a Python string of
length 1.
\item[\samp{d} (float) {[double]}]
Convert a C \ctype{double} to a Python floating point number.
\item[\samp{f} (float) {[float]}]
Same as \samp{d}.
\item[\samp{D} (complex) {[Py_complex *]}]
Convert a C \ctype{Py_complex} structure to a Python complex number.
\item[\samp{O} (object) {[PyObject *]}]
Pass a Python object untouched (except for its reference count, which
is incremented by one). If the object passed in is a \NULL{}
pointer, it is assumed that this was caused because the call producing
the argument found an error and set an exception. Therefore,
\cfunction{Py_BuildValue()} will return \NULL{} but won't raise an
exception. If no exception has been raised yet,
\cdata{PyExc_SystemError} is set.
\item[\samp{S} (object) {[PyObject *]}]
Same as \samp{O}.
\item[\samp{U} (object) {[PyObject *]}]
Same as \samp{O}.
\item[\samp{N} (object) {[PyObject *]}]
Same as \samp{O}, except it doesn't increment the reference count on
the object. Useful when the object is created by a call to an object
constructor in the argument list.
\item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}]
Convert \var{anything} to a Python object through a \var{converter}
function. The function is called with \var{anything} (which should be
compatible with \ctype{void *}) as its argument and should return a
``new'' Python object, or \NULL{} if an error occurred.
\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}]
Convert a sequence of C values to a Python tuple with the same number
of items.
\item[\samp{[\var{items}]} (list) {[\var{matching-items}]}]
Convert a sequence of C values to a Python list with the same number
of items.
\item[\samp{\{\var{items}\}} (dictionary) {[\var{matching-items}]}]
Convert a sequence of C values to a Python dictionary. Each pair of
consecutive C values adds one item to the dictionary, serving as key
and value, respectively.
\end{description}
If there is an error in the format string, the
\cdata{PyExc_SystemError} exception is raised and \NULL{} returned.
Examples (to the left the call, to the right the resulting Python value):
\begin{verbatim}
Py_BuildValue("") None
Py_BuildValue("i", 123) 123
Py_BuildValue("iii", 123, 456, 789) (123, 456, 789)
Py_BuildValue("s", "hello") 'hello'
Py_BuildValue("ss", "hello", "world") ('hello', 'world')
Py_BuildValue("s#", "hello", 4) 'hell'
Py_BuildValue("()") ()
Py_BuildValue("(i)", 123) (123,)
Py_BuildValue("(ii)", 123, 456) (123, 456)
Py_BuildValue("(i,i)", 123, 456) (123, 456)
Py_BuildValue("[i,i]", 123, 456) [123, 456]
Py_BuildValue("{s:i,s:i}",
"abc", 123, "def", 456) {'abc': 123, 'def': 456}
Py_BuildValue("((ii)(ii)) (ii)",
1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6))
\end{verbatim}
\section{Reference Counts
\label{refcounts}}
In languages like C or \Cpp{}, the programmer is responsible for
dynamic allocation and deallocation of memory on the heap. In C,
this is done using the functions \cfunction{malloc()} and
\cfunction{free()}. In \Cpp{}, the operators \keyword{new} and
\keyword{delete} are used with essentially the same meaning; they are
actually implemented using \cfunction{malloc()} and
\cfunction{free()}, so we'll restrict the following discussion to the
latter.
Every block of memory allocated with \cfunction{malloc()} should
eventually be returned to the pool of available memory by exactly one
call to \cfunction{free()}. It is important to call
\cfunction{free()} at the right time. If a block's address is
forgotten but \cfunction{free()} is not called for it, the memory it
occupies cannot be reused until the program terminates. This is
called a \dfn{memory leak}. On the other hand, if a program calls
\cfunction{free()} for a block and then continues to use the block, it
creates a conflict with re-use of the block through another
\cfunction{malloc()} call. This is called \dfn{using freed memory}.
It has the same bad consequences as referencing uninitialized data ---
core dumps, wrong results, mysterious crashes.
Common causes of memory leaks are unusual paths through the code. For
instance, a function may allocate a block of memory, do some
calculation, and then free the block again. Now a change in the
requirements for the function may add a test to the calculation that
detects an error condition and can return prematurely from the
function. It's easy to forget to free the allocated memory block when
taking this premature exit, especially when it is added later to the
code. Such leaks, once introduced, often go undetected for a long
time: the error exit is taken only in a small fraction of all calls,
and most modern machines have plenty of virtual memory, so the leak
only becomes apparent in a long-running process that uses the leaking
function frequently. Therefore, it's important to prevent leaks from
happening by having a coding convention or strategy that minimizes
this kind of errors.
Since Python makes heavy use of \cfunction{malloc()} and
\cfunction{free()}, it needs a strategy to avoid memory leaks as well
as the use of freed memory. The chosen method is called
\dfn{reference counting}. The principle is simple: every object
contains a counter, which is incremented when a reference to the
object is stored somewhere, and which is decremented when a reference
to it is deleted. When the counter reaches zero, the last reference
to the object has been deleted and the object is freed.
An alternative strategy is called \dfn{automatic garbage collection}.
(Sometimes, reference counting is also referred to as a garbage
collection strategy, hence my use of ``automatic'' to distinguish the
two.) The big advantage of automatic garbage collection is that the
user doesn't need to call \cfunction{free()} explicitly. (Another claimed
advantage is an improvement in speed or memory usage --- this is no
hard fact however.) The disadvantage is that for C, there is no
truly portable automatic garbage collector, while reference counting
can be implemented portably (as long as the functions \cfunction{malloc()}
and \cfunction{free()} are available --- which the C Standard guarantees).
Maybe some day a sufficiently portable automatic garbage collector
will be available for C. Until then, we'll have to live with
reference counts.
\subsection{Reference Counting in Python
\label{refcountsInPython}}
There are two macros, \code{Py_INCREF(x)} and \code{Py_DECREF(x)},
which handle the incrementing and decrementing of the reference count.
\cfunction{Py_DECREF()} also frees the object when the count reaches zero.
For flexibility, it doesn't call \cfunction{free()} directly --- rather, it
makes a call through a function pointer in the object's \dfn{type
object}. For this purpose (and others), every object also contains a
pointer to its type object.
The big question now remains: when to use \code{Py_INCREF(x)} and
\code{Py_DECREF(x)}? Let's first introduce some terms. Nobody
``owns'' an object; however, you can \dfn{own a reference} to an
object. An object's reference count is now defined as the number of
owned references to it. The owner of a reference is responsible for
calling \cfunction{Py_DECREF()} when the reference is no longer
needed. Ownership of a reference can be transferred. There are three
ways to dispose of an owned reference: pass it on, store it, or call
\cfunction{Py_DECREF()}. Forgetting to dispose of an owned reference
creates a memory leak.
It is also possible to \dfn{borrow}\footnote{The metaphor of
``borrowing'' a reference is not completely correct: the owner still
has a copy of the reference.} a reference to an object. The borrower
of a reference should not call \cfunction{Py_DECREF()}. The borrower must
not hold on to the object longer than the owner from which it was
borrowed. Using a borrowed reference after the owner has disposed of
it risks using freed memory and should be avoided
completely.\footnote{Checking that the reference count is at least 1
\strong{does not work} --- the reference count itself could be in
freed memory and may thus be reused for another object!}
The advantage of borrowing over owning a reference is that you don't
need to take care of disposing of the reference on all possible paths
through the code --- in other words, with a borrowed reference you
don't run the risk of leaking when a premature exit is taken. The
disadvantage of borrowing over leaking is that there are some subtle
situations where in seemingly correct code a borrowed reference can be
used after the owner from which it was borrowed has in fact disposed
of it.
A borrowed reference can be changed into an owned reference by calling
\cfunction{Py_INCREF()}. This does not affect the status of the owner from
which the reference was borrowed --- it creates a new owned reference,
and gives full owner responsibilities (the new owner must
dispose of the reference properly, as well as the previous owner).
\subsection{Ownership Rules
\label{ownershipRules}}
Whenever an object reference is passed into or out of a function, it
is part of the function's interface specification whether ownership is
transferred with the reference or not.
Most functions that return a reference to an object pass on ownership
with the reference. In particular, all functions whose function it is
to create a new object, such as \cfunction{PyInt_FromLong()} and
\cfunction{Py_BuildValue()}, pass ownership to the receiver. Even if in
fact, in some cases, you don't receive a reference to a brand new
object, you still receive ownership of the reference. For instance,
\cfunction{PyInt_FromLong()} maintains a cache of popular values and can
return a reference to a cached item.
Many functions that extract objects from other objects also transfer
ownership with the reference, for instance
\cfunction{PyObject_GetAttrString()}. The picture is less clear, here,
however, since a few common routines are exceptions:
\cfunction{PyTuple_GetItem()}, \cfunction{PyList_GetItem()},
\cfunction{PyDict_GetItem()}, and \cfunction{PyDict_GetItemString()}
all return references that you borrow from the tuple, list or
dictionary.
The function \cfunction{PyImport_AddModule()} also returns a borrowed
reference, even though it may actually create the object it returns:
this is possible because an owned reference to the object is stored in
\code{sys.modules}.
When you pass an object reference into another function, in general,
the function borrows the reference from you --- if it needs to store
it, it will use \cfunction{Py_INCREF()} to become an independent
owner. There are exactly two important exceptions to this rule:
\cfunction{PyTuple_SetItem()} and \cfunction{PyList_SetItem()}. These
functions take over ownership of the item passed to them --- even if
they fail! (Note that \cfunction{PyDict_SetItem()} and friends don't
take over ownership --- they are ``normal.'')
When a C function is called from Python, it borrows references to its
arguments from the caller. The caller owns a reference to the object,
so the borrowed reference's lifetime is guaranteed until the function
returns. Only when such a borrowed reference must be stored or passed
on, it must be turned into an owned reference by calling
\cfunction{Py_INCREF()}.
The object reference returned from a C function that is called from
Python must be an owned reference --- ownership is tranferred from the
function to its caller.
\subsection{Thin Ice
\label{thinIce}}
There are a few situations where seemingly harmless use of a borrowed
reference can lead to problems. These all have to do with implicit
invocations of the interpreter, which can cause the owner of a
reference to dispose of it.
The first and most important case to know about is using
\cfunction{Py_DECREF()} on an unrelated object while borrowing a
reference to a list item. For instance:
\begin{verbatim}
bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0); /* BUG! */
}
\end{verbatim}
This function first borrows a reference to \code{list[0]}, then
replaces \code{list[1]} with the value \code{0}, and finally prints
the borrowed reference. Looks harmless, right? But it's not!
Let's follow the control flow into \cfunction{PyList_SetItem()}. The list
owns references to all its items, so when item 1 is replaced, it has
to dispose of the original item 1. Now let's suppose the original
item 1 was an instance of a user-defined class, and let's further
suppose that the class defined a \method{__del__()} method. If this
class instance has a reference count of 1, disposing of it will call
its \method{__del__()} method.
Since it is written in Python, the \method{__del__()} method can execute
arbitrary Python code. Could it perhaps do something to invalidate
the reference to \code{item} in \cfunction{bug()}? You bet! Assuming
that the list passed into \cfunction{bug()} is accessible to the
\method{__del__()} method, it could execute a statement to the effect of
\samp{del list[0]}, and assuming this was the last reference to that
object, it would free the memory associated with it, thereby
invalidating \code{item}.
The solution, once you know the source of the problem, is easy:
temporarily increment the reference count. The correct version of the
function reads:
\begin{verbatim}
no_bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
Py_INCREF(item);
PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0);
Py_DECREF(item);
}
\end{verbatim}
This is a true story. An older version of Python contained variants
of this bug and someone spent a considerable amount of time in a C
debugger to figure out why his \method{__del__()} methods would fail...
The second case of problems with a borrowed reference is a variant
involving threads. Normally, multiple threads in the Python
interpreter can't get in each other's way, because there is a global
lock protecting Python's entire object space. However, it is possible
to temporarily release this lock using the macro
\code{Py_BEGIN_ALLOW_THREADS}, and to re-acquire it using
\code{Py_END_ALLOW_THREADS}. This is common around blocking I/O
calls, to let other threads use the processor while waiting for the I/O to
complete. Obviously, the following function has the same problem as
the previous one:
\begin{verbatim}
bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
Py_BEGIN_ALLOW_THREADS
...some blocking I/O call...
Py_END_ALLOW_THREADS
PyObject_Print(item, stdout, 0); /* BUG! */
}
\end{verbatim}
\subsection{NULL Pointers
\label{nullPointers}}
In general, functions that take object references as arguments do not
expect you to pass them \NULL{} pointers, and will dump core (or
cause later core dumps) if you do so. Functions that return object
references generally return \NULL{} only to indicate that an
exception occurred. The reason for not testing for \NULL{}
arguments is that functions often pass the objects they receive on to
other function --- if each function were to test for \NULL{},
there would be a lot of redundant tests and the code would run more
slowly.
It is better to test for \NULL{} only at the ``source:'' when a
pointer that may be \NULL{} is received, for example, from
\cfunction{malloc()} or from a function that may raise an exception.
The macros \cfunction{Py_INCREF()} and \cfunction{Py_DECREF()}
do not check for \NULL{} pointers --- however, their variants
\cfunction{Py_XINCREF()} and \cfunction{Py_XDECREF()} do.
The macros for checking for a particular object type
(\code{Py\var{type}_Check()}) don't check for \NULL{} pointers ---
again, there is much code that calls several of these in a row to test
an object against various different expected types, and this would
generate redundant tests. There are no variants with \NULL{}
checking.
The C function calling mechanism guarantees that the argument list
passed to C functions (\code{args} in the examples) is never
\NULL{} --- in fact it guarantees that it is always a tuple.\footnote{
These guarantees don't hold when you use the ``old'' style
calling convention --- this is still found in much existing code.}
It is a severe error to ever let a \NULL{} pointer ``escape'' to
the Python user.
% Frank Stajano:
% A pedagogically buggy example, along the lines of the previous listing,
% would be helpful here -- showing in more concrete terms what sort of
% actions could cause the problem. I can't very well imagine it from the
% description.
\section{Writing Extensions in \Cpp{}
\label{cplusplus}}
It is possible to write extension modules in \Cpp{}. Some restrictions
apply. If the main program (the Python interpreter) is compiled and
linked by the C compiler, global or static objects with constructors
cannot be used. This is not a problem if the main program is linked
by the \Cpp{} compiler. Functions that will be called by the
Python interpreter (in particular, module initalization functions)
have to be declared using \code{extern "C"}.
It is unnecessary to enclose the Python header files in
\code{extern "C" \{...\}} --- they use this form already if the symbol
\samp{__cplusplus} is defined (all recent \Cpp{} compilers define this
symbol).
\section{Providing a C API for an Extension Module
\label{using-cobjects}}
\sectionauthor{Konrad Hinsen}{hinsen@cnrs-orleans.fr}
Many extension modules just provide new functions and types to be
used from Python, but sometimes the code in an extension module can
be useful for other extension modules. For example, an extension
module could implement a type ``collection'' which works like lists
without order. Just like the standard Python list type has a C API
which permits extension modules to create and manipulate lists, this
new collection type should have a set of C functions for direct
manipulation from other extension modules.
At first sight this seems easy: just write the functions (without
declaring them \keyword{static}, of course), provide an appropriate
header file, and document the C API. And in fact this would work if
all extension modules were always linked statically with the Python
interpreter. When modules are used as shared libraries, however, the
symbols defined in one module may not be visible to another module.
The details of visibility depend on the operating system; some systems
use one global namespace for the Python interpreter and all extension
modules (Windows, for example), whereas others require an explicit
list of imported symbols at module link time (AIX is one example), or
offer a choice of different strategies (most Unices). And even if
symbols are globally visible, the module whose functions one wishes to
call might not have been loaded yet!
Portability therefore requires not to make any assumptions about
symbol visibility. This means that all symbols in extension modules
should be declared \keyword{static}, except for the module's
initialization function, in order to avoid name clashes with other
extension modules (as discussed in section~\ref{methodTable}). And it
means that symbols that \emph{should} be accessible from other
extension modules must be exported in a different way.
Python provides a special mechanism to pass C-level information
(pointers) from one extension module to another one: CObjects.
A CObject is a Python data type which stores a pointer (\ctype{void
*}). CObjects can only be created and accessed via their C API, but
they can be passed around like any other Python object. In particular,
they can be assigned to a name in an extension module's namespace.
Other extension modules can then import this module, retrieve the
value of this name, and then retrieve the pointer from the CObject.
There are many ways in which CObjects can be used to export the C API
of an extension module. Each name could get its own CObject, or all C
API pointers could be stored in an array whose address is published in
a CObject. And the various tasks of storing and retrieving the pointers
can be distributed in different ways between the module providing the
code and the client modules.
The following example demonstrates an approach that puts most of the
burden on the writer of the exporting module, which is appropriate
for commonly used library modules. It stores all C API pointers
(just one in the example!) in an array of \ctype{void} pointers which
becomes the value of a CObject. The header file corresponding to
the module provides a macro that takes care of importing the module
and retrieving its C API pointers; client modules only have to call
this macro before accessing the C API.
The exporting module is a modification of the \module{spam} module from
section~\ref{simpleExample}. The function \function{spam.system()}
does not call the C library function \cfunction{system()} directly,
but a function \cfunction{PySpam_System()}, which would of course do
something more complicated in reality (such as adding ``spam'' to
every command). This function \cfunction{PySpam_System()} is also
exported to other extension modules.
The function \cfunction{PySpam_System()} is a plain C function,
declared \keyword{static} like everything else:
\begin{verbatim}
static int
PySpam_System(command)
char *command;
{
return system(command);
}
\end{verbatim}
The function \cfunction{spam_system()} is modified in a trivial way:
\begin{verbatim}
static PyObject *
spam_system(self, args)
PyObject *self;
PyObject *args;
{
char *command;
int sts;
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = PySpam_System(command);
return Py_BuildValue("i", sts);
}
\end{verbatim}
In the beginning of the module, right after the line
\begin{verbatim}
#include "Python.h"
\end{verbatim}
two more lines must be added:
\begin{verbatim}
#define SPAM_MODULE
#include "spammodule.h"
\end{verbatim}
The \code{\#define} is used to tell the header file that it is being
included in the exporting module, not a client module. Finally,
the module's initialization function must take care of initializing
the C API pointer array:
\begin{verbatim}
void
initspam()
{
PyObject *m;
static void *PySpam_API[PySpam_API_pointers];
PyObject *c_api_object;
m = Py_InitModule("spam", SpamMethods);
/* Initialize the C API pointer array */
PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;
/* Create a CObject containing the API pointer array's address */
c_api_object = PyCObject_FromVoidPtr((void *)PySpam_API, NULL);
if (c_api_object != NULL) {
/* Create a name for this object in the module's namespace */
PyObject *d = PyModule_GetDict(m);
PyDict_SetItemString(d, "_C_API", c_api_object);
Py_DECREF(c_api_object);
}
}
\end{verbatim}
Note that \code{PySpam_API} is declared \code{static}; otherwise
the pointer array would disappear when \code{initspam} terminates!
The bulk of the work is in the header file \file{spammodule.h},
which looks like this:
\begin{verbatim}
#ifndef Py_SPAMMODULE_H
#define Py_SPAMMODULE_H
#ifdef __cplusplus
extern "C" {
#endif
/* Header file for spammodule */
/* C API functions */
#define PySpam_System_NUM 0
#define PySpam_System_RETURN int
#define PySpam_System_PROTO (char *command)
/* Total number of C API pointers */
#define PySpam_API_pointers 1
#ifdef SPAM_MODULE
/* This section is used when compiling spammodule.c */
static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;
#else
/* This section is used in modules that use spammodule's API */
static void **PySpam_API;
#define PySpam_System \
(*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])
#define import_spam() \
{ \
PyObject *module = PyImport_ImportModule("spam"); \
if (module != NULL) { \
PyObject *module_dict = PyModule_GetDict(module); \
PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); \
if (PyCObject_Check(c_api_object)) { \
PySpam_API = (void **)PyCObject_AsVoidPtr(c_api_object); \
} \
} \
}
#endif
#ifdef __cplusplus
}
#endif
#endif /* !defined(Py_SPAMMODULE_H */
\end{verbatim}
All that a client module must do in order to have access to the
function \cfunction{PySpam_System()} is to call the function (or
rather macro) \cfunction{import_spam()} in its initialization
function:
\begin{verbatim}
void
initclient()
{
PyObject *m;
Py_InitModule("client", ClientMethods);
import_spam();
}
\end{verbatim}
The main disadvantage of this approach is that the file
\file{spammodule.h} is rather complicated. However, the
basic structure is the same for each function that is
exported, so it has to be learned only once.
Finally it should be mentioned that CObjects offer additional
functionality, which is especially useful for memory allocation and
deallocation of the pointer stored in a CObject. The details
are described in the \citetitle[../api/api.html]{Python/C API
Reference Manual} in the section ``CObjects'' and in the
implementation of CObjects (files \file{Include/cobject.h} and
\file{Objects/cobject.c} in the Python source code distribution).
Doc/ext/newtypes.tex 0 → 100644
Dosyayı görüntüle @ cc8f44b8
\chapter{Defining New Types
\label{defining-new-types}}
\sectionauthor{Michael Hudson}{mwh21@cam.ac.uk}
\sectionauthor{Dave Kuhlman}{dkuhlman@rexx.com}
As mentioned in the last chapter, Python allows the writer of an
extension module to define new types that can be manipulated from
Python code, much like strings and lists in core Python.
This is not hard; the code for all extension types follows a pattern,
but there are some details that you need to understand before you can
get started.
\section{The Basics
\label{dnt-basics}}
The Python runtime sees all Python objects as variables of type
\ctype{PyObject*}. A \ctype{PyObject} is not a very magnificent
object - it just contains the refcount and a pointer to the object's
``type object''. This is where the action is; the type object
determines which (C) functions get called when, for instance, an
attribute gets looked up on an object or it is multiplied by another
object. I call these C functions ``type methods'' to distinguish them
from things like \code{[].append} (which I will call ``object
methods'' when I get around to them).
So, if you want to define a new object type, you need to create a new
type object.
This sort of thing can only be explained by example, so here's a
minimal, but complete, module that defines a new type:
\begin{verbatim}
#include <Python.h>
staticforward PyTypeObject noddy_NoddyType;
typedef struct {
PyObject_HEAD
} noddy_NoddyObject;
static PyObject*
noddy_new_noddy(PyObject* self, PyObject* args)
{
noddy_NoddyObject* noddy;
if (!PyArg_ParseTuple(args,":new_noddy"))
return NULL;
noddy = PyObject_New(noddy_NoddyObject, &noddy_NoddyType);
return (PyObject*)noddy;
}
static void
noddy_noddy_dealloc(PyObject* self)
{
PyObject_Del(self);
}
static PyTypeObject noddy_NoddyType = {
PyObject_HEAD_INIT(NULL)
0,
"Noddy",
sizeof(noddy_NoddyObject),
0,
noddy_noddy_dealloc, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
0, /*tp_compare*/
0, /*tp_repr*/
0, /*tp_as_number*/
0, /*tp_as_sequence*/
0, /*tp_as_mapping*/
0, /*tp_hash */
};
static PyMethodDef noddy_methods[] = {
{ "new_noddy", noddy_new_noddy, METH_VARARGS },
{NULL, NULL}
};
DL_EXPORT(void)
initnoddy(void)
{
noddy_NoddyType.ob_type = &PyType_Type;
Py_InitModule("noddy", noddy_methods);
}
\end{verbatim}
Now that's quite a bit to take in at once, but hopefully bits will
seem familiar from the last chapter.
The first bit that will be new is:
\begin{verbatim}
staticforward PyTypeObject noddy_NoddyType;
\end{verbatim}
This names the type object that will be defining further down in the
file. It can't be defined here because its definition has to refer to
functions that have no yet been defined, but we need to be able to
refer to it, hence the declaration.
The \code{staticforward} is required to placate various brain dead
compilers.
\begin{verbatim}
typedef struct {
PyObject_HEAD
} noddy_NoddyObject;
\end{verbatim}
This is what a Noddy object will contain. In this case nothing more
than every Python object contains - a refcount and a pointer to a type
object. These are the fields the \code{PyObject_HEAD} macro brings
in. The reason for the macro is to standardize the layout and to
enable special debugging fields to be brought in debug builds.
For contrast
\begin{verbatim}
typedef struct {
PyObject_HEAD
long ob_ival;
} PyIntObject;
\end{verbatim}
is the corresponding definition for standard Python integers.
Next up is:
\begin{verbatim}
static PyObject*
noddy_new_noddy(PyObject* self, PyObject* args)
{
noddy_NoddyObject* noddy;
if (!PyArg_ParseTuple(args,":new_noddy"))
return NULL;
noddy = PyObject_New(noddy_NoddyObject, &noddy_NoddyType);
return (PyObject*)noddy;
}
\end{verbatim}
This is in fact just a regular module function, as described in the
last chapter. The reason it gets special mention is that this is
where we create our Noddy object. Defining PyTypeObject structures is
all very well, but if there's no way to actually \emph{create} one
of the wretched things it is not going to do anyone much good.
Almost always, you create objects with a call of the form:
\begin{verbatim}
PyObject_New(<type>, &<type object>);
\end{verbatim}
This allocates the memory and then initializes the object (sets
the reference count to one, makes the \cdata{ob_type} pointer point at
the right place and maybe some other stuff, depending on build options).
You \emph{can} do these steps separately if you have some reason to
--- but at this level we don't bother.
We cast the return value to a \ctype{PyObject*} because that's what
the Python runtime expects. This is safe because of guarantees about
the layout of structures in the C standard, and is a fairly common C
programming trick. One could declare \cfunction{noddy_new_noddy} to
return a \ctype{noddy_NoddyObject*} and then put a cast in the
definition of \cdata{noddy_methods} further down the file --- it
doesn't make much difference.
Now a Noddy object doesn't do very much and so doesn't need to
implement many type methods. One you can't avoid is handling
deallocation, so we find
\begin{verbatim}
static void
noddy_noddy_dealloc(PyObject* self)
{
PyObject_Del(self);
}
\end{verbatim}
This is so short as to be self explanatory. This function will be
called when the reference count on a Noddy object reaches \code{0} (or
it is found as part of an unreachable cycle by the cyclic garbage
collector). \cfunction{PyObject_Del()} is what you call when you want
an object to go away. If a Noddy object held references to other
Python objects, one would decref them here.
Moving on, we come to the crunch --- the type object.
\begin{verbatim}
static PyTypeObject noddy_NoddyType = {
PyObject_HEAD_INIT(NULL)
0,
"Noddy",
sizeof(noddy_NoddyObject),
0,
noddy_noddy_dealloc, /*tp_dealloc*/
0, /*tp_print*/
0, /*tp_getattr*/
0, /*tp_setattr*/
0, /*tp_compare*/
0, /*tp_repr*/
0, /*tp_as_number*/
0, /*tp_as_sequence*/
0, /*tp_as_mapping*/
0, /*tp_hash */
};
\end{verbatim}
Now if you go and look up the definition of \ctype{PyTypeObject} in
\file{object.h} you'll see that it has many, many more fields that the
definition above. The remaining fields will be filled with zeros by
the C compiler, and it's common practice to not specify them
explicitly unless you need them.
This is so important that I'm going to pick the top of it apart still
further:
\begin{verbatim}
PyObject_HEAD_INIT(NULL)
\end{verbatim}
This line is a bit of a wart; what we'd like to write is:
\begin{verbatim}
PyObject_HEAD_INIT(&PyType_Type)
\end{verbatim}
as the type of a type object is ``type'', but this isn't strictly
conforming C and some compilers complain. So instead we fill in the
\cdata{ob_type} field of \cdata{noddy_NoddyType} at the earliest
oppourtunity --- in \cfunction{initnoddy()}.
\begin{verbatim}
0,
\end{verbatim}
XXX why does the type info struct start PyObject_*VAR*_HEAD??
\begin{verbatim}
"Noddy",
\end{verbatim}
The name of our type. This will appear in the default textual
representation of our objects and in some error messages, for example:
\begin{verbatim}
>>> "" + noddy.new_noddy()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
TypeError: cannot add type "Noddy" to string
\end{verbatim}
\begin{verbatim}
sizeof(noddy_NoddyObject),
\end{verbatim}
This is so that Python knows how much memory to allocate when you call
\cfunction{PyObject_New}.
\begin{verbatim}
0,
\end{verbatim}
This has to do with variable length objects like lists and strings.
Ignore for now...
Now we get into the type methods, the things that make your objects
different from the others. Of course, the Noddy object doesn't
implement many of these, but as mentioned above you have to implement
the deallocation function.
\begin{verbatim}
noddy_noddy_dealloc, /*tp_dealloc*/
\end{verbatim}
From here, all the type methods are nil so I won't go over them yet -
that's for the next section!
Everything else in the file should be familiar, except for this line
in \cfunction{initnoddy}:
\begin{verbatim}
noddy_NoddyType.ob_type = &PyType_Type;
\end{verbatim}
This was alluded to above --- the \cdata{noddy_NoddyType} object should
have type ``type'', but \code{\&PyType_Type} is not constant and so
can't be used in its initializer. To work around this, we patch it up
in the module initialization.
That's it! All that remains is to build it; put the above code in a
file called \file{noddymodule.c} and
\begin{verbatim}
from distutils.core import setup, Extension
setup(name = "noddy", version = "1.0",
ext_modules = [Extension("noddy", ["noddymodule.c"])])
\end{verbatim}
in a file called \file{setup.py}; then typing
\begin{verbatim}
$ python setup.py build%$
\end{verbatim}
at a shell should produce a file \file{noddy.so} in a subdirectory;
move to that directory and fire up Python --- you should be able to
\code{import noddy} and play around with Noddy objects.
That wasn't so hard, was it?
\section{Type Methods
\label{dnt-type-methods}}
This section aims to give a quick fly-by on the various type methods
you can implement and what they do.
Here is the definition of \ctype{PyTypeObject}, with some fields only
used in debug builds omitted:
\begin{verbatim}
typedef struct _typeobject {
PyObject_VAR_HEAD
char *tp_name; /* For printing */
int tp_basicsize, tp_itemsize; /* For allocation */
/* Methods to implement standard operations */
destructor tp_dealloc;
printfunc tp_print;
getattrfunc tp_getattr;
setattrfunc tp_setattr;
cmpfunc tp_compare;
reprfunc tp_repr;
/* Method suites for standard classes */
PyNumberMethods *tp_as_number;
PySequenceMethods *tp_as_sequence;
PyMappingMethods *tp_as_mapping;
/* More standard operations (here for binary compatibility) */
hashfunc tp_hash;
ternaryfunc tp_call;
reprfunc tp_str;
getattrofunc tp_getattro;
setattrofunc tp_setattro;
/* Functions to access object as input/output buffer */
PyBufferProcs *tp_as_buffer;
/* Flags to define presence of optional/expanded features */
long tp_flags;
char *tp_doc; /* Documentation string */
/* Assigned meaning in release 2.0 */
/* call function for all accessible objects */
traverseproc tp_traverse;
/* delete references to contained objects */
inquiry tp_clear;
/* Assigned meaning in release 2.1 */
/* rich comparisons */
richcmpfunc tp_richcompare;
/* weak reference enabler */
long tp_weaklistoffset;
/* Added in release 2.2 */
/* Iterators */
getiterfunc tp_iter;
iternextfunc tp_iternext;
/* Attribute descriptor and subclassing stuff */
struct PyMethodDef *tp_methods;
struct memberlist *tp_members;
struct getsetlist *tp_getset;
struct _typeobject *tp_base;
PyObject *tp_dict;
descrgetfunc tp_descr_get;
descrsetfunc tp_descr_set;
long tp_dictoffset;
initproc tp_init;
allocfunc tp_alloc;
newfunc tp_new;
destructor tp_free; /* Low-level free-memory routine */
PyObject *tp_bases;
PyObject *tp_mro; /* method resolution order */
PyObject *tp_defined;
} PyTypeObject;
\end{verbatim}
Now that's a \emph{lot} of methods. Don't worry too much though - if
you have a type you want to define, the chances are very good that you
will only implement a handful of these.
As you probably expect by now, we're going to go over this and give
more information about the various handlers. We won't go in the order
they are defined in the structure, because there is a lot of
historical baggage that impacts the ordering of the fields; be sure
your type initializaion keeps the fields in the right order! It's
often easiest to find an example that includes all the fields you need
(even if they're initialized to \code{0}) and then change the values
to suit your new type.
\begin{verbatim}
char *tp_name; /* For printing */
\end{verbatim}
The name of the type - as mentioned in the last section, this will
appear in various places, almost entirely for diagnostic purposes.
Try to choose something that will be helpful in such a situation!
\begin{verbatim}
int tp_basicsize, tp_itemsize; /* For allocation */
\end{verbatim}
These fields tell the runtime how much memory to allocate when new
objects of this typed are created. Python has some builtin support
for variable length structures (think: strings, lists) which is where
the \cdata{tp_itemsize} field comes in. This will be dealt with
later.
\begin{verbatim}
char *tp_doc;
\end{verbatim}
Here you can put a string (or its address) that you want returned when
the Python script references \code{obj.__doc__} to retrieve the
docstring.
Now we come to the basic type methods---the ones most extension types
will implement.
\subsection{Finalization and De-allocation}
\begin{verbatim}
destructor tp_dealloc;
\end{verbatim}
This function is called when the reference count of the instance of
your type is reduced to zero and the Python interpreter wants to
reclaim it. If your type has memory to free or other clean-up to
perform, put it here. The object itself needs to be freed here as
well. Here is an example of this function:
\begin{verbatim}
static void
newdatatype_dealloc(newdatatypeobject * obj)
{
free(obj->obj_UnderlyingDatatypePtr);
PyObject_DEL(obj);
}
\end{verbatim}
\subsection{Object Representation}
In Python, there are three ways to generate a textual representation
of an object: the \function{repr()}\bifuncindex{repr} function (or
equivalent backtick syntax), the \function{str()}\bifuncindex{str}
function, and the \keyword{print} statement. For most objects, the
\keyword{print} statement is equivalent to the \function{str()}
function, but it is possible to special-case printing to a
\ctype{FILE*} if necessary; this should only be done if efficiency is
identified as a problem and profiling suggests that creating a
temporary string object to be written to a file is too expensive.
These handlers are all optional, and most types at most need to
implement the \member{tp_str} and \member{tp_repr} handlers.
\begin{verbatim}
reprfunc tp_repr;
reprfunc tp_str;
printfunc tp_print;
\end{verbatim}
The \member{tp_repr} handler should return a string object containing
a representation of the instance for which it is called. Here is a
simple example:
\begin{verbatim}
static PyObject *
newdatatype_repr(newdatatypeobject * obj)
{
char buf[4096];
sprintf(buf, "Repr-ified_newdatatype{{size:%d}}",
obj->obj_UnderlyingDatatypePtr->size);
return PyString_FromString(buf);
}
\end{verbatim}
If no \member{tp_repr} handler is specified, the interpreter will
supply a representation that uses the type's \member{tp_name} and a
uniquely-identifying value for the object.
The \member{tp_str} handler is to \function{str()} what the
\member{tp_repr} handler described above is to \function{repr()}; that
is, it is called when Python code calls \function{str()} on an
instance of your object. It's implementation is very similar to the
\member{tp_repr} function, but the resulting string is intended to be
human consumption. It \member{tp_str} is not specified, the
\member{tp_repr} handler is used instead.
Here is a simple example:
\begin{verbatim}
static PyObject *
newdatatype_str(newdatatypeobject * obj)
{
PyObject *pyString;
char buf[4096];
sprintf(buf, "Stringified_newdatatype{{size:%d}}",
obj->obj_UnderlyingDatatypePtr->size
);
pyString = PyString_FromString(buf);
return pyString;
}
\end{verbatim}
The print function will be called whenever Python needs to "print" an
instance of the type. For example, if 'node' is an instance of type
TreeNode, then the print function is called when Python code calls:
\begin{verbatim}
print node
\end{verbatim}
There is a flags argument and one flag, \constant{Py_PRINT_RAW}, and
it suggests that you print without string quotes and possibly without
interpreting escape sequences.
The print function receives a file object as an argument. You will
likely want to write to that file object.
Here is a sampe print function:
\begin{verbatim}
static int
newdatatype_print(newdatatypeobject *obj, FILE *fp, int flags)
{
if (flags & Py_PRINT_RAW) {
fprintf(fp, "<{newdatatype object--size: %d}>",
obj->obj_UnderlyingDatatypePtr->size);
}
else {
fprintf(fp, "\"<{newdatatype object--size: %d}>\"",
obj->obj_UnderlyingDatatypePtr->size);
}
return 0;
}
\end{verbatim}
\subsection{Attribute Management Functions}
\begin{verbatim}
getattrfunc tp_getattr;
setattrfunc tp_setattr;
\end{verbatim}
The \member{tp_getattr} handle is called when the object requires an
attribute look-up. It is called in the same situations where the
\method{__getattr__()} method of a class would be called.
A likely way to handle this is (1) to implement a set of functions
(such as \cfunction{newdatatype_getSize()} and
\cfunction{newdatatype_setSize()} in the example below), (2) provide a
method table listing these functions, and (3) provide a getattr
function that returns the result of a lookup in that table.
Here is an example:
\begin{verbatim}
static PyMethodDef newdatatype_methods[] = {
{"getSize", (PyCFunction)newdatatype_getSize, METH_VARARGS},
{"setSize", (PyCFunction)newdatatype_setSize, METH_VARARGS},
{NULL, NULL} /* sentinel */
};
static PyObject *
newdatatype_getattr(newdatatypeobject *obj, char *name)
{
return Py_FindMethod(newdatatype_methods, (PyObject *)obj, name);
}
\end{verbatim}
The \member{tp_setattr} handler is called when the
\method{__setattr__()} or \method{__delattr__()} method of a class
instance would be called. When an attribute should be deleted, the
third parameter will be \NULL. Here is an example that simply raises
an exception; if this were really all you wanted, the
\member{tp_setattr} handler should be set to \NULL.
\begin{verbatim}
static int
newdatatype_setattr(newdatatypeobject *obj, char *name, PyObject *v)
{
char buf[1024];
sprintf(buf, "Set attribute not supported for attribute %s", name);
PyErr_SetString(PyExc_RuntimeError, buf);
return -1;
}
\end{verbatim}
\subsection{Object Comparison}
\begin{verbatim}
cmpfunc tp_compare;
\end{verbatim}
The \member{tp_compare} handler is called when comparisons are needed
are the object does not implement the specific rich comparison method
which matches the requested comparison. (It is always used if defined
and the \cfunction{PyObject_Compare()} or \cfunction{PyObject_Cmp()}
functions are used, or if \function{cmp()} is used from Python.)
It is analogous to the \method{__cmp__()} method. This function
should return a negative integer if \var{obj1} is less than
\var{obj2}, \code{0} if they are equal, and a positive integer if
\var{obj1} is greater than
\var{obj2}.
Here is a sample implementation:
\begin{verbatim}
static int
newdatatype_compare(newdatatypeobject * obj1, newdatatypeobject * obj2)
{
long result;
if (obj1->obj_UnderlyingDatatypePtr->size <
obj2->obj_UnderlyingDatatypePtr->size) {
result = -1;
}
else if (obj1->obj_UnderlyingDatatypePtr->size >
obj2->obj_UnderlyingDatatypePtr->size) {
result = 1;
}
else {
result = 0;
}
return result;
}
\end{verbatim}
\subsection{Abstract Protocol Support}
\begin{verbatim}
tp_as_number;
tp_as_sequence;
tp_as_mapping;
\end{verbatim}
If you wish your object to be able to act like a number, a sequence,
or a mapping object, then you place the address of a structure that
implements the C type \ctype{PyNumberMethods},
\ctype{PySequenceMethods}, or \ctype{PyMappingMethods}, respectively.
It is up to you to fill in this structure with appropriate values. You
can find examples of the use of each of these in the \file{Objects}
directory of the Python source distribution.
\begin{verbatim}
hashfunc tp_hash;
\end{verbatim}
This function, if you choose to provide it, should return a hash
number for an instance of your datatype. Here is a moderately
pointless example:
\begin{verbatim}
static long
newdatatype_hash(newdatatypeobject *obj)
{
long result;
result = obj->obj_UnderlyingDatatypePtr->size;
result = result * 3;
return result;
}
\end{verbatim}
\begin{verbatim}
ternaryfunc tp_call;
\end{verbatim}
This function is called when an instance of your datatype is "called",
for example, if \code{obj1} is an instance of your datatype and the Python
script contains \code{obj1('hello')}, the \member{tp_call} handler is
invoked.
This function takes three arguments:
\begin{enumerate}
\item
\var{arg1} is the instance of the datatype which is the subject of
the call. If the call is \code{obj1('hello')}, then \var{arg1} is
\code{obj1}.
\item
\var{arg2} is a tuple containing the arguments to the call. You
can use \cfunction{PyArg_ParseTuple()} to extract the arguments.
\item
\var{arg3} is a dictionary of keyword arguments that were passed.
If this is non-\NULL{} and you support keyword arguments, use
\cfunction{PyArg_ParseTupleAndKeywords()} to extract the
arguments. If you do not want to support keyword arguments and
this is non-\NULL, raise a \exception{TypeError} with a message
saying that keyword arguments are not supported.
\end{enumerate}
Here is a desultory example of the implementation of call function.
\begin{verbatim}
/* Implement the call function.
* obj1 is the instance receiving the call.
* obj2 is a tuple containing the arguments to the call, in this
* case 3 strings.
*/
static PyObject *
newdatatype_call(newdatatypeobject *obj, PyObject *args, PyObject *other)
{
PyObject *result;
char *arg1;
char *arg2;
char *arg3;
char buf[4096];
if (!PyArg_ParseTuple(args, "sss:call", &arg1, &arg2, &arg3)) {
return NULL;
}
sprintf(buf,
"Returning -- value: [%d] arg1: [%s] arg2: [%s] arg3: [%s]\n",
obj->obj_UnderlyingDatatypePtr->size,
arg1, arg2, arg3);
printf(buf);
return PyString_FromString(buf);
}
\end{verbatim}
\subsection{More Suggestions}
Remember that you can omit most of these functions, in which case you
provide \code{0} as a value.
In the \file{Objects} directory of the Python source distribution,
there is a file \file{xxobject.c}, which is intended to be used as a
template for the implementation of new types. One useful strategy
for implementing a new type is to copy and rename this file, then
read the instructions at the top of it.
There are type definitions for each of the functions you must
provide. They are in \file{object.h} in the Python include
directory that comes with the source distribution of Python.
In order to learn how to implement any specific method for your new
datatype, do the following: Download and unpack the Python source
distribution. Go the the \file{Objects} directory, then search the
C source files for \code{tp_} plus the function you want (for
example, \code{tp_print} or \code{tp_compare}). You will find
examples of the function you want to implement.
When you need to verify that the type of an object is indeed the
object you are implementing and if you use xxobject.c as an starting
template for your implementation, then there is a macro defined for
this purpose. The macro definition will look something like this:
\begin{verbatim}
#define is_newdatatypeobject(v) ((v)->ob_type == &Newdatatypetype)
\end{verbatim}
And, a sample of its use might be something like the following:
\begin{verbatim}
if (!is_newdatatypeobject(objp1) {
PyErr_SetString(PyExc_TypeError, "arg #1 not a newdatatype");
return NULL;
}
\end{verbatim}
%For a reasonably extensive example, from which most of the snippits
%above were taken, see \file{newdatatype.c} and \file{newdatatype.h}.
Doc/ext/unix.tex 0 → 100644
Dosyayı görüntüle @ cc8f44b8
\chapter{Building C and \Cpp{} Extensions on \UNIX{}
\label{building-on-unix}}
\sectionauthor{Jim Fulton}{jim@zope.com}
%The make file make file, building C extensions on Unix
Starting in Python 1.4, Python provides a special make file for
building make files for building dynamically-linked extensions and
custom interpreters. The make file make file builds a make file
that reflects various system variables determined by configure when
the Python interpreter was built, so people building module's don't
have to resupply these settings. This vastly simplifies the process
of building extensions and custom interpreters on Unix systems.
The make file make file is distributed as the file
\file{Misc/Makefile.pre.in} in the Python source distribution. The
first step in building extensions or custom interpreters is to copy
this make file to a development directory containing extension module
source.
The make file make file, \file{Makefile.pre.in} uses metadata
provided in a file named \file{Setup}. The format of the \file{Setup}
file is the same as the \file{Setup} (or \file{Setup.dist}) file
provided in the \file{Modules/} directory of the Python source
distribution. The \file{Setup} file contains variable definitions:
\begin{verbatim}
EC=/projects/ExtensionClass
\end{verbatim}
and module description lines. It can also contain blank lines and
comment lines that start with \character{\#}.
A module description line includes a module name, source files,
options, variable references, and other input files, such
as libraries or object files. Consider a simple example:
\begin{verbatim}
ExtensionClass ExtensionClass.c
\end{verbatim}
This is the simplest form of a module definition line. It defines a
module, \module{ExtensionClass}, which has a single source file,
\file{ExtensionClass.c}.
This slightly more complex example uses an \strong{-I} option to
specify an include directory:
\begin{verbatim}
EC=/projects/ExtensionClass
cPersistence cPersistence.c -I$(EC)
\end{verbatim} % $ <-- bow to font lock
This example also illustrates the format for variable references.
For systems that support dynamic linking, the \file{Setup} file should
begin:
\begin{verbatim}
*shared*
\end{verbatim}
to indicate that the modules defined in \file{Setup} are to be built
as dynamically linked modules. A line containing only \samp{*static*}
can be used to indicate the subsequently listed modules should be
statically linked.
Here is a complete \file{Setup} file for building a
\module{cPersistent} module:
\begin{verbatim}
# Set-up file to build the cPersistence module.
# Note that the text should begin in the first column.
*shared*
# We need the path to the directory containing the ExtensionClass
# include file.
EC=/projects/ExtensionClass
cPersistence cPersistence.c -I$(EC)
\end{verbatim} % $ <-- bow to font lock
After the \file{Setup} file has been created, \file{Makefile.pre.in}
is run with the \samp{boot} target to create a make file:
\begin{verbatim}
make -f Makefile.pre.in boot
\end{verbatim}
This creates the file, Makefile. To build the extensions, simply
run the created make file:
\begin{verbatim}
make
\end{verbatim}
It's not necessary to re-run \file{Makefile.pre.in} if the
\file{Setup} file is changed. The make file automatically rebuilds
itself if the \file{Setup} file changes.
\section{Building Custom Interpreters \label{custom-interps}}
The make file built by \file{Makefile.pre.in} can be run with the
\samp{static} target to build an interpreter:
\begin{verbatim}
make static
\end{verbatim}
Any modules defined in the \file{Setup} file before the
\samp{*shared*} line will be statically linked into the interpreter.
Typically, a \samp{*shared*} line is omitted from the
\file{Setup} file when a custom interpreter is desired.
\section{Module Definition Options \label{module-defn-options}}
Several compiler options are supported:
\begin{tableii}{l|l}{programopt}{Option}{Meaning}
\lineii{-C}{Tell the C pre-processor not to discard comments}
\lineii{-D\var{name}=\var{value}}{Define a macro}
\lineii{-I\var{dir}}{Specify an include directory, \var{dir}}
\lineii{-L\var{dir}}{Specify a link-time library directory, \var{dir}}
\lineii{-R\var{dir}}{Specify a run-time library directory, \var{dir}}
\lineii{-l\var{lib}}{Link a library, \var{lib}}
\lineii{-U\var{name}}{Undefine a macro}
\end{tableii}
Other compiler options can be included (snuck in) by putting them
in variables.
Source files can include files with \file{.c}, \file{.C}, \file{.cc},
\file{.cpp}, \file{.cxx}, and \file{.c++} extensions.
Other input files include files with \file{.a}, \file{.o}, \file{.sl},
and \file{.so} extensions.
\section{Example \label{module-defn-example}}
Here is a more complicated example from \file{Modules/Setup.dist}:
\begin{verbatim}
GMP=/ufs/guido/src/gmp
mpz mpzmodule.c -I$(GMP) $(GMP)/libgmp.a
\end{verbatim}
which could also be written as:
\begin{verbatim}
mpz mpzmodule.c -I$(GMP) -L$(GMP) -lgmp
\end{verbatim}
\section{Distributing your extension modules
\label{distributing}}
There are two ways to distribute extension modules for others to use.
The way that allows the easiest cross-platform support is to use the
\module{distutils}\refstmodindex{distutils} package. The manual
\citetitle[../dist/dist.html]{Distributing Python Modules} contains
information on this approach. It is recommended that all new
extensions be distributed using this approach to allow easy building
and installation across platforms. Older extensions should migrate to
this approach as well.
What follows describes the older approach; there are still many
extensions which use this.
When distributing your extension modules in source form, make sure to
include a \file{Setup} file. The \file{Setup} file should be named
\file{Setup.in} in the distribution. The make file make file,
\file{Makefile.pre.in}, will copy \file{Setup.in} to \file{Setup} if
the person installing the extension doesn't do so manually.
Distributing a \file{Setup.in} file makes it easy for people to
customize the \file{Setup} file while keeping the original in
\file{Setup.in}.
It is a good idea to include a copy of \file{Makefile.pre.in} for
people who do not have a source distribution of Python.
Do not distribute a make file. People building your modules
should use \file{Makefile.pre.in} to build their own make file. A
\file{README} file included in the package should provide simple
instructions to perform the build.
Doc/ext/windows.tex 0 → 100644
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\chapter{Building C and \Cpp{} Extensions on Windows
\label{building-on-windows}}
This chapter briefly explains how to create a Windows extension module
for Python using Microsoft Visual \Cpp{}, and follows with more
detailed background information on how it works. The explanatory
material is useful for both the Windows programmer learning to build
Python extensions and the \UNIX{} programmer interested in producing
software which can be successfully built on both \UNIX{} and Windows.
\section{A Cookbook Approach \label{win-cookbook}}
\sectionauthor{Neil Schemenauer}{neil_schemenauer@transcanada.com}
This section provides a recipe for building a Python extension on
Windows.
Grab the binary installer from \url{http://www.python.org/} and
install Python. The binary installer has all of the required header
files except for \file{pyconfig.h}.
Get the source distribution and extract it into a convenient location.
Copy the \file{pyconfig.h} from the \file{PC/} directory into the
\file{include/} directory created by the installer.
Create a \file{Setup} file for your extension module, as described in
chapter \ref{building-on-unix}.
Get David Ascher's \file{compile.py} script from
\url{http://starship.python.net/crew/da/compile/}. Run the script to
create Microsoft Visual \Cpp{} project files.
Open the DSW file in Visual \Cpp{} and select \strong{Build}.
If your module creates a new type, you may have trouble with this line:
\begin{verbatim}
PyObject_HEAD_INIT(&PyType_Type)
\end{verbatim}
Change it to:
\begin{verbatim}
PyObject_HEAD_INIT(NULL)
\end{verbatim}
and add the following to the module initialization function:
\begin{verbatim}
MyObject_Type.ob_type = &PyType_Type;
\end{verbatim}
Refer to section 3 of the
\citetitle[http://www.python.org/doc/FAQ.html]{Python FAQ} for details
on why you must do this.
\section{Differences Between \UNIX{} and Windows
\label{dynamic-linking}}
\sectionauthor{Chris Phoenix}{cphoenix@best.com}
\UNIX{} and Windows use completely different paradigms for run-time
loading of code. Before you try to build a module that can be
dynamically loaded, be aware of how your system works.
In \UNIX{}, a shared object (\file{.so}) file contains code to be used by the
program, and also the names of functions and data that it expects to
find in the program. When the file is joined to the program, all
references to those functions and data in the file's code are changed
to point to the actual locations in the program where the functions
and data are placed in memory. This is basically a link operation.
In Windows, a dynamic-link library (\file{.dll}) file has no dangling
references. Instead, an access to functions or data goes through a
lookup table. So the DLL code does not have to be fixed up at runtime
to refer to the program's memory; instead, the code already uses the
DLL's lookup table, and the lookup table is modified at runtime to
point to the functions and data.
In \UNIX{}, there is only one type of library file (\file{.a}) which
contains code from several object files (\file{.o}). During the link
step to create a shared object file (\file{.so}), the linker may find
that it doesn't know where an identifier is defined. The linker will
look for it in the object files in the libraries; if it finds it, it
will include all the code from that object file.
In Windows, there are two types of library, a static library and an
import library (both called \file{.lib}). A static library is like a
\UNIX{} \file{.a} file; it contains code to be included as necessary.
An import library is basically used only to reassure the linker that a
certain identifier is legal, and will be present in the program when
the DLL is loaded. So the linker uses the information from the
import library to build the lookup table for using identifiers that
are not included in the DLL. When an application or a DLL is linked,
an import library may be generated, which will need to be used for all
future DLLs that depend on the symbols in the application or DLL.
Suppose you are building two dynamic-load modules, B and C, which should
share another block of code A. On \UNIX{}, you would \emph{not} pass
\file{A.a} to the linker for \file{B.so} and \file{C.so}; that would
cause it to be included twice, so that B and C would each have their
own copy. In Windows, building \file{A.dll} will also build
\file{A.lib}. You \emph{do} pass \file{A.lib} to the linker for B and
C. \file{A.lib} does not contain code; it just contains information
which will be used at runtime to access A's code.
In Windows, using an import library is sort of like using \samp{import
spam}; it gives you access to spam's names, but does not create a
separate copy. On \UNIX{}, linking with a library is more like
\samp{from spam import *}; it does create a separate copy.
\section{Using DLLs in Practice \label{win-dlls}}
\sectionauthor{Chris Phoenix}{cphoenix@best.com}
Windows Python is built in Microsoft Visual \Cpp{}; using other
compilers may or may not work (though Borland seems to). The rest of
this section is MSV\Cpp{} specific.
When creating DLLs in Windows, you must pass \file{python15.lib} to
the linker. To build two DLLs, spam and ni (which uses C functions
found in spam), you could use these commands:
\begin{verbatim}
cl /LD /I/python/include spam.c ../libs/python15.lib
cl /LD /I/python/include ni.c spam.lib ../libs/python15.lib
\end{verbatim}
The first command created three files: \file{spam.obj},
\file{spam.dll} and \file{spam.lib}. \file{Spam.dll} does not contain
any Python functions (such as \cfunction{PyArg_ParseTuple()}), but it
does know how to find the Python code thanks to \file{python15.lib}.
The second command created \file{ni.dll} (and \file{.obj} and
\file{.lib}), which knows how to find the necessary functions from
spam, and also from the Python executable.
Not every identifier is exported to the lookup table. If you want any
other modules (including Python) to be able to see your identifiers,
you have to say \samp{_declspec(dllexport)}, as in \samp{void
_declspec(dllexport) initspam(void)} or \samp{PyObject
_declspec(dllexport) *NiGetSpamData(void)}.
Developer Studio will throw in a lot of import libraries that you do
not really need, adding about 100K to your executable. To get rid of
them, use the Project Settings dialog, Link tab, to specify
\emph{ignore default libraries}. Add the correct
\file{msvcrt\var{xx}.lib} to the list of libraries.
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