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diff --git a/Doc/ext.tex b/Doc/ext.tex deleted file mode 100644 index 01d2428..0000000 --- a/Doc/ext.tex +++ /dev/null @@ -1,1502 +0,0 @@ -\documentclass{manual} - -% XXX PM Modulator - -\title{Extending and Embedding the Python Interpreter} - -\input{boilerplate} - -% Tell \index to actually write the .idx file -\makeindex - -\begin{document} - -\maketitle - -\input{copyright} - -\begin{abstract} - -\noindent -Python is an interpreted, object-oriented programming language. This -document describes how to write modules in \C{} or \Cpp{} to extend the -Python interpreter with new modules. Those modules can define new -functions but also new object types and their methods. The document -also describes how to embed the Python interpreter in another -application, for use as an extension language. Finally, it shows how -to compile and link extension modules so that they can be loaded -dynamically (at run time) into the interpreter, if the underlying -operating system supports this feature. - -This document assumes basic knowledge about Python. For an informal -introduction to the language, see the Python Tutorial. The \emph{Python -Reference Manual} gives a more formal definition of the language. The -\emph{Python Library Reference} documents the existing object types, -functions and modules (both built-in and written in Python) that give -the language its wide application range. - -For a detailed description of the whole Python/\C{} API, see the separate -\emph{Python/\C{} API Reference Manual}. \strong{Note:} While that -manual is still in a state of flux, it is safe to say that it is much -more up to date than the manual you're reading currently (which has -been in need for an upgrade for some time now). - - -\end{abstract} - -\tableofcontents - - -\chapter{Extending Python with \C{} or \Cpp{} code} - - -%\section{Introduction} -\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 a later section. - - -\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}. (In general, 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 (e.g.\ 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. This will be discussed later. 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 by 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 \emph{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 (\UNIX{}) 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 -(e.g. \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 (e.g. by trying something else or pretending -nothing happened). - -Note that a 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 -(\cfunction{PyInt_FromLong()} etc.) already do this, so only if you -call \cfunction{malloc()} directly this note is of importance. - -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, -e.g. \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 -which 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, e.g. - -\begin{verbatim} -static PyObject *SpamError; -\end{verbatim} - -and initialize it in your module's initialization function -(\cfunction{initspam()}) with an exception object, e.g. (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 either a string or class, depending on whether the -\samp{-X} flag was passed to the interpreter. If \samp{-X} was used, -\cdata{SpamError} will be a string object, otherwise it will be a -class object with the base class being \exception{Exception}, -described in the \emph{Python Library Reference} under ``Built-in -Exceptions.'' - - -\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 arguemts to such a function. - -The method table must be passed to the interpreter in the module's -initialization function (which should be the only non-\code{static} -item defined in the module file): - -\begin{verbatim} -void -initspam() -{ - (void) Py_InitModule("spam", SpamMethods); -} -\end{verbatim} - -When the Python program imports module \module{spam} for the first -time, \cfunction{initspam()} is called. 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. - - -\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 chapter ``Dynamic Loading'' for more information -about this. - -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, 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 \samp{-c} command line option in \file{Python/pythonmain.c}.) - -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 whereever 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, arg) - PyObject *dummy, *arg; -{ - Py_XDECREF(my_callback); /* Dispose of previous callback */ - Py_XINCREF(arg); /* Add a reference to new callback */ - my_callback = arg; /* Remember new callback */ - /* Boilerplate to return "None" */ - Py_INCREF(Py_None); - return Py_None; -} -\end{verbatim} - -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. More info on them in the section on -Reference Counts below. - -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{Format Strings for \cfunction{PyArg_ParseTuple()}} -\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. - -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.) - -\begin{description} - -\item[\samp{s} (string) {[char *]}] -Convert a Python string 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. - -\item[\samp{s\#} (string) {[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. - -\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}) {[char *, int]}] -This is to \samp{s\#} as \samp{z} is to \samp{s}. - -\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, a -\exception{TypeError} exception 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: - -\code{\var{status} = \var{converter}(\var{object}, \var{address});} - -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 a \exception{TypeError} exception if the object is not a string -object. The \C{} variable may also be declared as \ctype{PyObject *}. - -\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}] -The object must be a Python tuple 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 tuples may -be nested. - -\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 milage 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 exceptions that \cfunction{PyArg_ParseTuple()} raises). - -\item[\samp{;}] -The list of format units ends here; the string after the colon 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() */ - - ok = PyArg_ParseTuple(args, "s", &s); /* A string */ - /* Possible Python call: f('whoops!') */ - - ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */ - /* Possible Python call: f(1, 2, 'three') */ - - 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') */ - - { - 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) */ - } - - { - 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)) */ - } - - { - 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 Parsing with \cfunction{PyArg_ParseTupleAndKeywords()}} -\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. - -\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[] = { - {"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{The \cfunction{Py_BuildValue()} Function} -\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. - -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 returned. - -\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{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{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{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} - -%\subsection{Introduction} - -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 (i.e., 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, e.g.\ \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 CPU 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 slower. - -It is better to test for \NULL{} only at the ``source'', i.e.\ -when a pointer that may be \NULL{} is received, e.g.\ 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. - - -\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). - -\chapter{Embedding Python in another application} -\label{embedding} - -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()}. 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}. - - -\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{}. - - -\chapter{Dynamic Loading} -\label{dynload} - -On most modern systems it is possible to configure Python to support -dynamic loading of extension modules implemented in \C{}. When shared -libraries are used dynamic loading is configured automatically; -otherwise you have to select it as a build option (see below). Once -configured, dynamic loading is trivial to use: when a Python program -executes \code{import spam}, the search for modules tries to find a -file \file{spammodule.o} (\file{spammodule.so} when using shared -libraries) in the module search path,% -\indexiii{module}{search}{path} -and if one is found, it is loaded into the executing binary and -executed. Once loaded, the module acts just like a built-in extension -module. - -The advantages of dynamic loading are twofold: the ``core'' Python -binary gets smaller, and users can extend Python with their own -modules implemented in \C{} without having to build and maintain their -own copy of the Python interpreter. There are also disadvantages: -dynamic loading isn't available on all systems (this just means that -on some systems you have to use static loading), and dynamically -loading a module that was compiled for a different version of Python -(e.g. with a different representation of objects) may dump core. - - -\section{Configuring and Building the Interpreter for Dynamic Loading} -\label{dynloadConfig} - -There are three styles of dynamic loading: one using shared libraries, -one using SGI IRIX 4 dynamic loading, and one using GNU dynamic -loading. - -\subsection{Shared Libraries} -\label{sharedlibs} - -The following systems support dynamic loading using shared libraries: -SunOS 4; Solaris 2; SGI IRIX 5 (but not SGI IRIX 4!), Linux, FreeBSD, -NetBSD; and probably all systems derived from SVR4, or at least those -SVR4 derivatives that support shared libraries (are there any that -don't?). - -You don't need to do anything to configure dynamic loading on these -systems --- the \file{configure} detects the presence of the -\code{<dlfcn.h>} header file and automatically configures dynamic -loading. - -\subsection{SGI IRIX 4 Dynamic Loading} -\label{irixDynload} - -Only SGI IRIX 4 supports dynamic loading of modules using SGI dynamic -loading. (SGI IRIX 5 might also support it but it is inferior to -using shared libraries so there is no reason to; a small test didn't -work right away so I gave up trying to support it.) - -Before you build Python, you first need to fetch and build the -\code{dl} package written by Jack Jansen. This is available by -anonymous ftp from \url{ftp://ftp.cwi.nl/pub/dynload/}, file -\file{dl-1.6.tar.Z}. (The version number may change.) Follow the -instructions in the package's \file{README} file to build it. - -Once you have built \code{dl}, you can configure Python to use it. To -this end, you run the \program{configure} script with the option -\code{--with-dl=\var{directory}} where \var{directory} is the absolute -pathname of the \code{dl} directory. - -Now build and install Python as you normally would (see the -\file{README} file in the toplevel Python directory.) - -\subsection{GNU Dynamic Loading} -\label{gnuDynload} - -GNU dynamic loading supports (according to its \file{README} file) the -following hardware and software combinations: VAX (Ultrix), Sun 3 -(SunOS 3.4 and 4.0), Sparc (SunOS 4.0), Sequent Symmetry (Dynix), and -Atari ST. There is no reason to use it on a Sparc; I haven't seen a -Sun 3 for years so I don't know if these have shared libraries or not. - -You need to fetch and build two packages. -One is GNU DLD. All development of this code has been done with DLD -version 3.2.3, which is available by anonymous ftp from -\url{ftp://ftp.cwi.nl/pub/dynload}, file -\file{dld-3.2.3.tar.Z}. (A more recent version of DLD is available -via \url{http://www-swiss.ai.mit.edu/~jaffer/DLD.html} but this has -not been tested.) -The other package needed is an -emulation of Jack Jansen's \code{dl} package that I wrote on top of -GNU DLD 3.2.3. This is available from the same host and directory, -file \file{dl-dld-1.1.tar.Z}. (The version number may change --- but I doubt -it will.) Follow the instructions in each package's \file{README} -file to configure and build them. - -Now configure Python. Run the \file{configure} script with the option -\code{--with-dl-dld=\var{dl-directory},\var{dld-directory}} where -\var{dl-directory} is the absolute pathname of the directory where you -have built the \file{dl-dld} package, and \var{dld-directory} is that -of the GNU DLD package. The Python interpreter you build hereafter -will support GNU dynamic loading. - - -\section{Building a Dynamically Loadable Module} -\label{makedynload} - -Since there are three styles of dynamic loading, there are also three -groups of instructions for building a dynamically loadable module. -Instructions common for all three styles are given first. Assuming -your module is called \module{spam}, the source filename must be -\file{spammodule.c}, so the object name is \file{spammodule.o}. The -module must be written as a normal Python extension module (as -described earlier). - -Note that in all cases you will have to create your own Makefile that -compiles your module file(s). This Makefile will have to pass two -\samp{-I} arguments to the \C{} compiler which will make it find the -Python header files. If the Make variable \makevar{PYTHONTOP} points to -the toplevel Python directory, your \makevar{CFLAGS} Make variable should -contain the options \samp{-I\$(PYTHONTOP) -I\$(PYTHONTOP)/Include}. -(Most header files are in the \file{Include/} subdirectory, but the -\file{config.h} header lives in the toplevel directory.) - - -\subsection{Shared Libraries} -\label{linking} - -You must link the \file{.o} file to produce a shared library. This is -done using a special invocation of the \UNIX{} loader/linker, -\manpage{ld}{1}. Unfortunately the invocation differs slightly per -system. - -On SunOS 4, use -\begin{verbatim} -ld spammodule.o -o spammodule.so -\end{verbatim} - -On Solaris 2, use -\begin{verbatim} -ld -G spammodule.o -o spammodule.so -\end{verbatim} - -On SGI IRIX 5, use -\begin{verbatim} -ld -shared spammodule.o -o spammodule.so -\end{verbatim} - -On other systems, consult the manual page for \manpage{ld}{1} to find -what flags, if any, must be used. - -If your extension module uses system libraries that haven't already -been linked with Python (e.g. a windowing system), these must be -passed to the \program{ld} command as \samp{-l} options after the -\samp{.o} file. - -The resulting file \file{spammodule.so} must be copied into a directory -along the Python module search path. - - -\subsection{SGI IRIX 4 Dynamic Loading} -\label{irixLinking} - -\strong{IMPORTANT:} You must compile your extension module with the -additional \C{} flag \samp{-G0} (or \samp{-G 0}). This instructs the -assembler to generate position-independent code. - -You don't need to link the resulting \file{spammodule.o} file; just -copy it into a directory along the Python module search path.% -\indexiii{module}{search}{path} - -The first time your extension is loaded, it takes some extra time and -a few messages may be printed. This creates a file -\file{spammodule.ld} which is an image that can be loaded quickly into -the Python interpreter process. When a new Python interpreter is -installed, the \code{dl} package detects this and rebuilds -\file{spammodule.ld}. The file \file{spammodule.ld} is placed in the -directory where \file{spammodule.o} was found, unless this directory is -unwritable; in that case it is placed in a temporary -directory.\footnote{Check the manual page of the \code{dl} package for -details.} - -If your extension modules uses additional system libraries, you must -create a file \file{spammodule.libs} in the same directory as the -\file{spammodule.o}. This file should contain one or more lines with -whitespace-separated options that will be passed to the linker --- -normally only \samp{-l} options or absolute pathnames of libraries -(\samp{.a} files) should be used. - - -\subsection{GNU Dynamic Loading} -\label{gnuLinking} - -Just copy \file{spammodule.o} into a directory along the Python module -search path.% -\indexiii{module}{search}{path} - -If your extension modules uses additional system libraries, you must -create a file \file{spammodule.libs} in the same directory as the -\file{spammodule.o}. This file should contain one or more lines with -whitespace-separated absolute pathnames of libraries (\samp{.a} -files). No \samp{-l} options can be used. - - -\end{document} |