Update of /cvsroot/boost/boost/libs/python/doc
In directory sc8-pr-cvs3.sourceforge.net:/tmp/cvs-serv16275
Added Files:
building.rst
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New build instructions in progress
--- NEW FILE: building.rst ---
.. Copyright David Abrahams 2006. Distributed under the Boost
.. Software License, Version 1.0. (See accompanying
.. file LICENSE_1_0.txt or copy at
.. http://www.boost.org/LICENSE_1_0.txt)
==============================================
|(logo)|__ Boost.Python Build and Test HOWTO
==============================================
.. |(logo)| image:: ../boost.png
:alt: Boost C++ Libraries:
:class: boost-logo
__ ../index.htm
.. section-numbering::
:depth: 2
.. contents:: Contents
:depth: 2
:class: sidebar small
.. |newer| replace:: *newer*
Requirements
============
Boost.Python requires `Python 2.2`_ *or* |newer|__.
.. _Python 2.2: http://www.python.org/2.2
__ http://www.python.org
Background
==========
There are two basic models for combining C++ and Python:
- extending_, in which the end-user launches the Python
interpreter executable and imports Python âextension modulesâ
written in C++. Think of providing libraries written in C++ for
Python programmers to use. From Python, these modules look just
like regular Python modules.
- embedding_, in which the end-user launches a program written
in C++ that in turn invokes the Python interpreter as a library
subroutine. Think of adding scriptability to an existing
application.
.. _extending: http://www.python.org/doc/current/ext/intro.html
.. _embedding: http://www.python.org/doc/current/ext/embedding.html
The key distinction between extending and embedding is the location
of C++' ``main()`` function: in the Python interpreter executable,
or in some other program, respectively. Note that even when
embedding Python in another program, `extension modules are often the best
way to give Python code access to C/C++ functionality`__, so the use
of extension modules is really at the heart of both models.
__ http://www.python.org/doc/current/ext/extending-with-embedding.html
Except in rare cases, extension modules are built as
dynamically-loaded libraries with a single entry point, which means
you can change them without recompiling either the other extension
modules or the executable containing ``main()``.
Choosing a Boost.Python Library Binary
======================================
Boost.Python extension modules draw on the services of the
Boost.Python library binary. Since Boost.Python is a
separately-compiled (as opposed to `header-only`_) library, it
comes in both static and dynamic flavors. Take care to choose the
right flavor for your application. The dynamic library is the
safest and most-versatile choice:
- A single copy of the library code is used by all extension
modules built with a given toolset.
- The library contains a type conversion registry. Because one
registry is shared among all extension modules, a instances of a
class exposed to Python in one dynamically-loaded extension
module can be passed to functions exposed in another such module.
It is appropriate to use the static Boost.Python library in any of
the following cases:
- You are extending_ python and the types exposed in your
dynamically-loaded extension module don't need to be used by any
other Boost.Python extension modules, and you don't care if the
core library code is duplicated among them.
- You are embedding_ python in your application and either:
- You are targeting a Unix variant OS, where the
dynamically-loaded extension modules can âseeâ the Boost.Python
library symbols that are part of the executable.
- You have statically linked some Boost.Python extension modules
into your application and you don't care if any
dynamically-loaded Boost.Python extension modules can use the
types exposed by your statically-linked extension modules (and
vice-versa).
.. _header-only: ../../../more/getting_started.html#header-only-libraries
Although they are supported under Boost.Python, we're not going to
cover the esoteric case of statically-linked extension modules here
in detail.
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