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Convert docs to QuickBook.

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Stefan Seefeld
2015-07-29 17:43:42 -04:00
parent d4d7f018f9
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# Copyright David Abrahams 2006. Distributed under the Boost
# Software License, Version 1.0. (See accompanying
# Copyright (c) 2006 Joel de Guzman
# Copyright (c) 2015 Stefan Seefeld
#
# 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)
import docutils ;
import path ;
sources = building.rst ;
bases = $(sources:S=) ;
# This is a path relative to the html/ subdirectory where the
# generated output will eventually be moved.
stylesheet = "--stylesheet=../../../rst.css" ;
path-constant here : . ;
path-constant images : html/images ;
for local b in $(bases)
{
html $(b) : $(b).rst :
<docutils-html>"-gdt --source-url="./$(b).rst" --link-stylesheet --traceback --trim-footnote-reference-space --footnote-references=superscript "$(stylesheet)
;
}
alias htmls : $(bases) ;
stage . : $(bases) ;
project python/doc
: requirements <format>html:<xsl:param>boost.defaults=Boost
<format>html:<xsl:param>toc.max.depth=3
<format>html:<xsl:param>toc.section.depth=3
<format>html:<xsl:param>chunk.section.depth=3
;
import boostbook ;
import quickbook ;
boostbook python : python.qbk
: <format>html:<name>$(here)/html
<format>html:<xsl:param>generate.toc="library nop; chapter toc; section toc;"
<format>html:<xsl:param>html.stylesheet=boostbook.css
<format>html:<xsl:param>boost.image.src=images/boost.png
<format>html:<xsl:param>boost.graphics.root=images/
;
boostbook tutorial : tutorial.qbk
: <format>html:<name>$(here)/html/tutorial
<format>html:<xsl:param>html.stylesheet=../boostbook.css
<format>html:<xsl:param>boost.image.src=../images/boost.png
<format>html:<xsl:param>boost.graphics.root=../images/
;
boostbook reference : reference.qbk
: <format>html:<name>$(here)/html/reference
<format>html:<xsl:param>html.stylesheet=../boostbook.css
<format>html:<xsl:param>boost.image.src=../images/boost.png
<format>html:<xsl:param>boost.graphics.root=../images/
;

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[chapter Building and Testing
[quickbook 1.7]
[authors [Abrahams, David]]
[copyright 2002 - 2015 David Abrahams, Stefan Seefeld]
[id building]
]
[/ 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)
/]
[section Requirements]
Boost.Python requires [@http://www.python.org/2.2 Python 2.2]
[footnote Note that although we tested earlier versions of Boost.Python
with Python 2.2, and we don't *think* we've done anything to break
compatibility, this release of Boost.Python may not have been tested
with versions of Python earlier than 2.4, so we're not 100% sure that
python 2.2 and 2.3 are supported.] *or* [@http://www.python.org newer].
[endsect]
[section Background]
There are two basic models for combining C++ and Python:
* [@http://www.python.org/doc/current/ext/intro.html extending],
in which the end-user launches the Python interpreter
executable and imports Python “extension modules” written in C++.
Think of taking a library written in C++ and giving it a Python
interface so Python programmers can use it. From Python, these
modules look just like regular Python modules.
* [@http://www.python.org/doc/current/ext/embedding.html 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.
The key distinction between extending and embedding is the location
of the C++ `main()` function: in the Python interpreter executable,
or in some other program, respectively. Note that even when
embedding Python in another program, [@http://www.python.org/doc/current/ext/extending-with-embedding.html extension modules are often
the best way to make C/C++ functionality accessible to Python
code], so the use of extension modules is really at the heart of
both models.
Except in rare cases, extension modules are built as
dynamically-loaded libraries with a single entry point, which means
you can change them without rebuilding either the other extension
modules or the executable containing `main()`.
[endsect]
[section No-Install Quickstart]
There is no need to “install Boost” in order to get started using
Boost.Python. These instructions use [@http://boost.org/build] projects,
which will build those binaries as soon as they're needed. Your
first tests may take a little longer while you wait for
Boost.Python to build, but doing things this way will save you from
worrying about build intricacies like which library binaries to use
for a specific compiler configuration and figuring out the right
compiler options to use yourself.
[note Of course it's possible to use other build systems to
build Boost.Python and its extensions, but they are not
officially supported by Boost. Moreover *99% of all “I can't
build Boost.Python” problems come from trying to use another
build system* without first following these instructions.
If you want to use another system anyway, we suggest that you
follow these instructions, and then invoke ``bjam`` with the
`-a -o`\ *filename*
options to dump the build commands it executes to a file, so
you can see what your alternate build system needs to do.]
[section Basic Procedure]
1. Get Boost; see sections 1 and 2 of the _gsg_.
2. Get the `bjam` build driver. See section 5 of the _gsg_.
3. cd into the `example/quickstart/` directory of your
Boost.Python installation, which contains a small example project.
4. Invoke `bjam`. Replace the “\ `stage`\ “ argument from the
example invocation from section 5 of the _gsg_ with “\ `test`\ ,“ to
build all the test targets. Also add the argument “\ `--verbose-test`\ ”
to see the output generated by the tests when they are run.
On Windows, your `bjam` invocation might look something like:
``
C:\\boost_1_34_0\\...\\quickstart> bjam toolset=msvc --verbose-test test
``
and on Unix variants, perhaps,
``
~/boost_1_34_0/.../quickstart$ bjam toolset=gcc --verbose-test test
``
[note For the sake of concision, the rest of this guide will use
unix-style forward slashes in pathnames instead of the
backslashes with which Windows users may be more familiar. The forward
slashes should work everywhere except in
[@http://www.boost.org/more/getting_started/windows.html#command-prompt Command Prompt windows],
where you should use backslashes.]
If you followed this procedure successfully, you will have built an
extension module called `extending` and tested it by running a
Python script called `test_extending.py`. You will also have
built and run a simple application called `embedding` that embeds
python.
[endsect]
[section In Case of Trouble]
If you're seeing lots of compiler and/or linker error messages,
it's probably because Boost.Build is having trouble finding your
Python installation. You might want to pass the
`--debug-configuration` option to `bjam` the first few times
you invoke it, to make sure that Boost.Build is correctly locating
all the parts of your Python installation. If it isn't, consider
[link building.configuring_boost_build Configuring Boost.Build]
as detailed below.
If you're still having trouble, Someone on one of the following
mailing lists may be able to help:
* The _bb_list_ for issues related to Boost.Build
* The _bp_list_ for issues specifically related to Boost.Python
[endsect]
[section In Case Everything Seemed to Work]
Rejoice! If you're new to Boost.Python, at this point it might be
a good idea to ignore build issues for a while and concentrate on
learning the library by going through the _tutorial_ and perhaps
some of the _reference_, trying out what you've
learned about the API by modifying the quickstart project.
[endsect]
[section Modifying the Example Project]
If you're content to keep your extension module forever in one
source file called `extending.cpp`, inside your Boost.Python
distribution, and import it forever as `extending`, then you can
stop here. However, it's likely that you will want to make a few
changes. There are a few things you can do without having to learn
_bb_ in depth.
The project you just built is specified in two files in the current
directory: `boost-build.jam`, which tells `bjam` where it can
find the interpreted code of the Boost build system, and
`Jamroot`, which describes the targets you just built. These
files are heavily commented, so they should be easy to modify.
Take care, however, to preserve whitespace. Punctuation such as
`;` will not be recognized as intended by `bjam` if it is not
surrounded by whitespace.
[section Relocate the Project]
You'll probably want to copy this project elsewhere so you can
change it without modifying your Boost distribution. To do that,
simply
a. copy the entire `example/quickstart/` directory
into a new directory.
b. In the new copies of `boost-build.jam` and `Jamroot`, locate
the relative path near the top of the file that is clearly
marked by a comment, and edit that path so that it refers to the
same directory your Boost distribution as it referred to when
the file was in its original location in the
`example/quickstart/` directory.
For example, if you moved the project from
`/home/dave/boost_1_34_0/libs/python/example/quickstart` to
`/home/dave/my-project`, you could change the first path in
`boost-build.jam` from
``
../../../../tools/build/v2
``
to
``
/home/dave/boost_1_34_0/tools/build/v2
``
and change the first path in `Jamroot` from
``
../../../..
``
to
``
/home/dave/boost_1_34_0
``
[endsect]
[section Add New or Change Names of Existing Source Files]
The names of additional source files involved in building your
extension module or embedding application can be listed in
`Jamroot` right alongside `extending.cpp` or `embedding.cpp`
respectively. Just be sure to leave whitespace around each
filename:
``
… file1.cpp file2.cpp file3.cpp …
``
Naturally, if you want to change the name of a source file you can
tell Boost.Build about it by editing the name in `Jamroot`.
[endsect]
[section Change the Name of your Extension Module]
The name of the extension module is determined by two things:
1. the name in `Jamroot` immediately following `python-extension`, and
2. the name passed to ``BOOST_PYTHON_MODULE`` in `extending.cpp`.
To change the name of the extension module from `extending` to
`hello`, you'd edit `Jamroot`, changing
``
python-extension extending : extending.cpp ;
``
to
``
python-extension hello : extending.cpp ;
``
and you'd edit extending.cpp, changing
``
BOOST_PYTHON_MODULE(extending)
``
to
``
BOOST_PYTHON_MODULE(hello)
``
[endsect]
[endsect]
[section Installing Boost.Python on your System]
Since Boost.Python is a separately-compiled (as opposed to
`header-only`) library, its user relies on the services of a
Boost.Python library binary.
If you need a regular installation of the Boost.Python library
binaries on your system, the _gsg_ will
walk you through the steps of creating one. If building binaries
from source, you might want to supply the `--with-python`
argument to `bjam` (or the `--with-libraries=python` argument
to `configure`), so only the Boost.Python binary will be built,
rather than all the Boost binaries.
[endsect]
[section Configuring Boost.Build]
As described in the _reference_, a file called
`user-config.jam` in your home directory is used to
specify the tools and libraries available to the build system. You
may need to create or edit `user-config.jam` to tell Boost.Build
how to invoke Python, `#include` its headers, and link with its
libraries.
[note If you are using a unix-variant OS and you ran Boost's
`configure` script, it may have generated a
`user-config.jam` for you. [footnote `configure` overwrites the existing
`user-config.jam` in your home directory (if any) after making a backup of
the old version.] If your `configure`\ /\ `make` sequence was successful and
Boost.Python binaries were built, your `user-config.jam` file is probably already
correct.]
If you have one fairly “standard” python installation for your
platform, you might not need to do anything special to describe it. If
you haven't configured python in `user-config.jam` (and you don't
specify `--without-python` on the Boost.Build command line),
Boost.Build will automatically execute the equivalent of
``
import toolset : using ;
using python ;
``
which automatically looks for Python in the most likely places.
However, that only happens when using the Boost.Python project file
(e.g. when referred to by another project as in the quickstart
method). If instead you are linking against separately-compiled
Boost.Python binaries, you should set up a `user-config.jam` file
with at least the minimal incantation above.
[endsect]
[section Python Configuration Parameters]
If you have several versions of Python installed, or Python is
installed in an unusual way, you may want to supply any or all of
the following optional parameters to `using python`.
* *version*
the version of Python to use. Should be in Major.Minor
format, for example, `2.3`. Do not include the subminor
version (i.e. *not* `2.5.1`). If you have multiple Python
versions installed, the version will usually be the only
configuration argument required.
* *cmd-or-prefix*
preferably, a command that invokes a Python interpreter.
Alternatively, the installation prefix for Python libraries and
header files. Only use the alternative formulation if there is
no appropriate Python executable available.
* *includes*
the `#include` paths for Python headers. Normally the correct
path(s) will be automatically deduced from `version` and/or
`cmd-or-prefix`.
* *libraries*
the path to Python library binaries. On MacOS/Darwin,
you can also pass the path of the Python framework. Normally the
correct path(s) will be automatically deduced from `version`
and/or `cmd-or-prefix`.
* *condition*
if specified, should be a set of Boost.Build
properties that are matched against the build configuration when
Boost.Build selects a Python configuration to use. See examples
below for details.
* *extension-suffix*
A string to append to the name of extension
modules before the true filename extension. You almost certainly
don't need to use this. Usually this suffix is only used when
targeting a Windows debug build of Python, and will be set
automatically for you based on the value of the
|python-debugging|_ feature. However, at least one Linux
distribution (Ubuntu Feisty Fawn) has a specially configured
[@https://wiki.ubuntu.com/PyDbgBuilds <python-debugging>] package
that claims to use such a suffix.
[endsect]
[section Examples]
Note that in the examples below, case and *especially whitespace* are
significant.
* If you have both python 2.5 and python 2.4 installed,
`user-config.jam` might contain
``
using python : 2.5 ; # Make both versions of Python available
using python : 2.4 ; # To build with python 2.4, add python=2.4
# to your command line.
``
The first version configured (2.5) becomes the default. To build
against python 2.4, add `python=2.4` to the `bjam` command line.
* If you have python installed in an unusual location, you might
supply the path to the interpreter in the `cmd-or-prefix`
parameter:
``
using python : : /usr/local/python-2.6-beta/bin/python ;
``
* If you have a separate build of Python for use with a particular
toolset, you might supply that toolset in the `condition`
parameter:
``
using python ; # use for most toolsets
# Use with Intel C++ toolset
using python
: # version
: c:\\Devel\\Python-2.5-IntelBuild\\PCBuild\\python # cmd-or-prefix
: # includes
: # libraries
: <toolset>intel # condition
;
``
* If you have downloaded the Python sources and built both the
normal and the “\ `python debugging`_\ ” builds from source on
Windows, you might see:
``
using python : 2.5 : C:\\src\\Python-2.5\\PCBuild\\python ;
using python : 2.5 : C:\\src\\Python-2.5\\PCBuild\\python_d
: # includes
: # libs
: <python-debugging>on ;
``
* You can set up your user-config.jam so a bjam built under Windows
can build/test both Windows and Cygwin_ python extensions. Just pass
`<target-os>cygwin` in the `condition` parameter
for the cygwin python installation:
``
# windows installation
using python ;
# cygwin installation
using python : : c:\\cygwin\\bin\\python2.5 : : : <target-os>cygwin ;
``
when you put target-os=cygwin in your build request, it should build
with the cygwin version of python: [#flavor]_
``
bjam target-os=cygwin toolset=gcc
``
This is supposed to work the other way, too (targeting windows
python with a [@http://cygwin.com Cygwin] bjam) but it seems as though the support in
Boost.Build's toolsets for building that way is broken at the
time of this writing.
* Note that because of [@http://zigzag.cs.msu.su/boost.build/wiki/AlternativeSelection
the way Boost.Build currently selects target alternatives], you might have be very
explicit in your build requests. For example, given:
``
using python : 2.5 ; # a regular windows build
using python : 2.4 : : : : <target-os>cygwin ;
``
building with
``
bjam target-os=cygwin
``
will yield an error. Instead, you'll need to write
``
bjam target-os=cygwin/python=2.4
``
[endsect]
[endsect]
[section Choosing a Boost.Python Library Binary]
If—instead of letting Boost.Build construct and link with the right
libraries automatically—you choose to use a pre-built Boost.Python
library, you'll need to think about which one to link with. The
Boost.Python binary comes in both static and dynamic flavors. Take
care to choose the right flavor for your application. [footnote
Information about how to identify the static and dynamic builds of Boost.Python on
[@http://boost.org/getting_started/windows.html#library-naming Windows] /
[@http://boost.org/getting_started/unix-variants.html#library-naming Unix variants]]
[section The Dynamic Binary]
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. [footnote Because of the way most \*nix platforms
share symbols among dynamically-loaded objects, I'm not certain
that extension modules built with different compiler toolsets
will always use different copies of the Boost.Python library
when loaded into the same Python instance. Not using different
libraries could be a good thing if the compilers have compatible
ABIs, because extension modules built with the two libraries
would be interoperable. Otherwise, it could spell disaster,
since an extension module and the Boost.Python library would
have different ideas of such things as class layout. I would
appreciate someone doing the experiment to find out what
happens.]
* The library contains a type conversion registry. Because one
registry is shared among all extension modules, instances of a
class exposed to Python in one dynamically-loaded extension
module can be passed to functions exposed in another such module.
[endsect]
[section The Static Binary]
It might be 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 other than MacOS or AIX,
where the dynamically-loaded extension modules can “see” the
Boost.Python library symbols that are part of the executable.
* Or, 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 are able to
use the types exposed by your statically-linked extension
modules (and vice-versa).
[endsect]
[endsect]
[section `#include` Issues]
1. If you should ever have occasion to `#include "python.h"`
directly in a translation unit of a program using Boost.Python,
use `#include "boost/python/detail/wrap_python.hpp"` instead.
It handles several issues necessary for use with Boost.Python,
one of which is mentioned in the next section.
2. Be sure not to `#include` any system headers before
`wrap_python.hpp`. This restriction is actually imposed by
Python, or more properly, by Python's interaction with your
operating system. See
http://docs.python.org/ext/simpleExample.html for details.
[endsect]
[section Python Debugging Builds]
Python can be built in a special “python debugging” configuration
that adds extra checks and instrumentation that can be very useful
for developers of extension modules. The data structures used by
the debugging configuration contain additional members, so *a
Python executable built with python debugging enabled cannot be
used with an extension module or library compiled without it, and
vice-versa.*
Since pre-built “python debugging” versions of the Python
executable and libraries are not supplied with most distributions
of Python, [#get-debug-build]_ and we didn't want to force our users
to build them, Boost.Build does not automatically enable python
debugging in its `debug` build variant (which is the default).
Instead there is a special build property called
`python-debugging` that, when used as a build property, will
define the right preprocessor symbols and select the right
libraries to link with.
On unix-variant platforms, the debugging versions of Python's data
structures will only be used if the symbol `Py_DEBUG` is defined.
On many windows compilers, when extension modules are built with
the preprocessor symbol `_DEBUG`, Python defaults to force
linking with a special debugging version of the Python DLL. Since
that symbol is very commonly used even when Python is not present,
Boost.Python temporarily undefines _DEBUG when Python.h
is #included from `boost/python/detail/wrap_python.hpp` - unless
`BOOST_DEBUG_PYTHON` is defined. The upshot is that if you want
“python debugging”and you aren't using Boost.Build, you should make
sure `BOOST_DEBUG_PYTHON` is defined, or python debugging will be
suppressed.
[endsect]
[section Testing Boost.Python]
To run the full test suite for Boost.Python, invoke `bjam` in the
`test` subdirectory of your Boost.Python distribution.
[endsect]
[section Notes for MinGW (and Cygwin with -mno-cygwin) GCC Users]
If you are using a version of Python prior to 2.4.1 with a MinGW
prior to 3.0.0 (with binutils-2.13.90-20030111-1), you will need to
create a MinGW-compatible version of the Python library; the one
shipped with Python will only work with a Microsoft-compatible
linker. Follow the instructions in the “Non-Microsoft” section of
the “Building Extensions: Tips And Tricks” chapter in [@http://www.python.org/doc/current/inst/index.html Installing Python Modules] to create `libpythonXX.a`, where `XX`
corresponds to the major and minor version numbers of your Python
installation.
[endsect]

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.. 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.html
.. section-numbering::
:depth: 2
.. contents:: Contents
:depth: 2
:class: sidebar small
.. |newer| replace:: *newer*
Requirements
============
Boost.Python requires `Python 2.2`_ [#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 “extension modules” written in C++.
Think of taking a library written in C++ and giving it a Python
interface so Python programmers can use it. 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 the 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 make C/C++ functionality accessible to Python
code`__, 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 rebuilding either the other extension
modules or the executable containing ``main()``.
.. _quickstart:
No-Install Quickstart
=====================
There is no need to “install Boost” in order to get started using
Boost.Python. These instructions use Boost.Build_ projects,
which will build those binaries as soon as they're needed. Your
first tests may take a little longer while you wait for
Boost.Python to build, but doing things this way will save you from
worrying about build intricacies like which library binaries to use
for a specific compiler configuration and figuring out the right
compiler options to use yourself.
.. .. raw:: html
<div style="width:50%">
.. Note:: Of course it's possible to use other build systems to
build Boost.Python and its extensions, but they are not
officially supported by Boost. Moreover **99% of all “I can't
build Boost.Python” problems come from trying to use another
build system** without first following these instructions.
If you want to use another system anyway, we suggest that you
follow these instructions, and then invoke ``bjam`` with the
.. parsed-literal::
``-a -o``\ *filename*
options to dump the build commands it executes to a file, so
you can see what your alternate build system needs to do.
.. .. raw:: html
</div>
.. _Boost.Build: ../../../tools/build/index.html
Basic Procedure
---------------
1. Get Boost; see sections 1 and 2 [`Unix/Linux`__, `Windows`__\ ] of the
Boost `Getting Started Guide`_.
__ ../../../more/getting_started/unix-variants.html#get-boost
__ ../../../more/getting_started/windows.html#get-boost
2. Get the ``bjam`` build driver. See section 5 [`Unix/Linux`__,
`Windows`__\ ] of the Boost `Getting Started Guide`_.
__ ../../../more/getting_started/unix-variants.html#prepare-to-use-a-boost-library-binary
__ ../../../more/getting_started/windows.html#prepare-to-use-a-boost-library-binary
3. cd into the ``libs/python/example/quickstart/`` directory of your
Boost installation, which contains a small example project.
4. Invoke ``bjam``. Replace the “\ ``stage``\ “ argument from the
example invocation from section 5 of the `Getting Started
Guide`_ with “\ ``test``\ ,“ to build all the test targets. Also add
the argument “\ ``--verbose-test``\ ” to see the output generated by
the tests when they are run.
On Windows, your ``bjam`` invocation might look something like:
.. parsed-literal::
C:\\boost_1_34_0\\…\\quickstart> **bjam toolset=msvc --verbose-test test**
and on Unix variants, perhaps,
.. parsed-literal::
~/boost_1_34_0/…/quickstart$ **bjam toolset=gcc --verbose-test test**
.. Admonition:: Note to Windows Users
For the sake of concision, the rest of this guide will use
unix-style forward slashes in pathnames instead of the
backslashes with which you may be more familiar. The forward
slashes should work everywhere except in `Command Prompt`_
windows, where you should use backslashes.
.. _Command Prompt: ../../../more/getting_started/windows.html#command-prompt
If you followed this procedure successfully, you will have built an
extension module called ``extending`` and tested it by running a
Python script called ``test_extending.py``. You will also have
built and run a simple application called ``embedding`` that embeds
python.
.. _Getting Started Guide: ../../../more/getting_started/index.html
In Case of Trouble
------------------
If you're seeing lots of compiler and/or linker error messages,
it's probably because Boost.Build is having trouble finding your
Python installation. You might want to pass the
``--debug-configuration`` option to ``bjam`` the first few times
you invoke it, to make sure that Boost.Build is correctly locating
all the parts of your Python installation. If it isn't, consider
`Configuring Boost.Build`_ as detailed below.
If you're still having trouble, Someone on one of the following
mailing lists may be able to help:
* The `Boost.Build mailing list`__ for issues related to Boost.Build
* The Python `C++ Sig`__ for issues specifically related to Boost.Python
__ http://www.boost.org/more/mailing_lists.htm#jamboost
__ http://www.boost.org/more/mailing_lists.htm#cplussig
In Case Everything Seemed to Work
---------------------------------
Rejoice! If you're new to Boost.Python, at this point it might be
a good idea to ignore build issues for a while and concentrate on
learning the library by going through the tutorial_ and perhaps
some of the `reference documentation`_, trying out what you've
learned about the API by modifying the quickstart project.
.. _reference documentation: v2/reference.html
.. _tutorial: tutorial/index.html
Modifying the Example Project
-----------------------------
If you're content to keep your extension module forever in one
source file called |extending.cpp|_, inside your Boost
distribution, and import it forever as ``extending``, then you can
stop here. However, it's likely that you will want to make a few
changes. There are a few things you can do without having to learn
Boost.Build_ in depth.
The project you just built is specified in two files in the current
directory: |boost-build.jam|_, which tells ``bjam`` where it can
find the interpreted code of the Boost build system, and
|Jamroot|_, which describes the targets you just built. These
files are heavily commented, so they should be easy to modify.
Take care, however, to preserve whitespace. Punctuation such as
``;`` will not be recognized as intended by ``bjam`` if it is not
surrounded by whitespace.
.. |boost-build.jam| replace:: ``boost-build.jam``
.. _boost-build.jam: ../example/quickstart/boost-build.jam
.. |Jamroot| replace:: ``Jamroot``
.. _Jamroot: ../example/quickstart/Jamroot
.. |extending.cpp| replace:: ``extending.cpp``
.. _extending.cpp: ../example/quickstart/extending.cpp
Relocate the Project
....................
You'll probably want to copy this project elsewhere so you can
change it without modifying your Boost distribution. To do that,
simply
a. copy the entire ``libs/python/example/quickstart/`` directory
into a new directory.
b. In the new copies of |boost-build.jam|_ and |Jamroot|_, locate
the relative path near the top of the file that is clearly
marked by a comment, and edit that path so that it refers to the
same directory your Boost distribution as it referred to when
the file was in its original location in the
``libs/python/example/quickstart/`` directory.
For example, if you moved the project from
``/home/dave/boost_1_34_0/libs/python/example/quickstart`` to
``/home/dave/my-project``, you could change the first path in
|boost-build.jam|_ from
.. parsed-literal::
**../../../..**\ /tools/build/v2
to
.. parsed-literal::
**/home/dave/boost_1_34_0**\ /tools/build/v2
and change the first path in |Jamroot|_ from
.. parsed-literal::
**../../../..**
to
.. parsed-literal::
**/home/dave/boost_1_34_0**
Add New or Change Names of Existing Source Files
................................................
The names of additional source files involved in building your
extension module or embedding application can be listed in
|Jamroot|_ right alongside ``extending.cpp`` or ``embedding.cpp``
respectively. Just be sure to leave whitespace around each
filename::
… file1.cpp file2.cpp file3.cpp …
Naturally, if you want to change the name of a source file you can
tell Boost.Build about it by editing the name in |Jamroot|_.
Change the Name of your Extension Module
........................................
The name of the extension module is determined by two things:
1. the name in |Jamroot|_ immediately following ``python-extension``, and
2. the name passed to ``BOOST_PYTHON_MODULE`` in |extending.cpp|_.
To change the name of the extension module from ``extending`` to
``hello``, you'd edit |Jamroot|_, changing
.. parsed-literal::
python-extension **extending** : extending.cpp ;
to
.. parsed-literal::
python-extension **hello** : extending.cpp ;
and you'd edit extending.cpp, changing
.. parsed-literal::
BOOST_PYTHON_MODULE(\ **extending**\ )
to
.. parsed-literal::
BOOST_PYTHON_MODULE(\ **hello**\ )
Installing Boost.Python on your System
======================================
Since Boost.Python is a separately-compiled (as opposed to
`header-only`_) library, its user relies on the services of a
Boost.Python library binary.
.. _header-only: ../../../more/getting_started/windows.html#header-only-libraries
If you need a regular installation of the Boost.Python library
binaries on your system, the Boost `Getting Started Guide`_ will
walk you through the steps of creating one. If building binaries
from source, you might want to supply the ``--with-python``
argument to ``bjam`` (or the ``--with-libraries=python`` argument
to ``configure``), so only the Boost.Python binary will be built,
rather than all the Boost binaries.
Configuring Boost.Build
=======================
As described in the `Boost.Build reference manual`__, a file called
``user-config.jam`` in your home directory [#home-dir]_ is used to
specify the tools and libraries available to the build system. You
may need to create or edit ``user-config.jam`` to tell Boost.Build
how to invoke Python, ``#include`` its headers, and link with its
libraries.
__ http://www.boost.orgdoc/html/bbv2/advanced.html#bbv2.advanced.configuration
.. Admonition:: Users of Unix-Variant OSes
If you are using a unix-variant OS and you ran Boost's
``configure`` script, it may have generated a
``user-config.jam`` for you. [#overwrite]_ If your ``configure``\
/\ ``make`` sequence was successful and Boost.Python binaries
were built, your ``user-config.jam`` file is probably already
correct.
If you have one fairly “standard” python installation for your
platform, you might not need to do anything special to describe it. If
you haven't configured python in ``user-config.jam`` (and you don't
specify ``--without-python`` on the Boost.Build command line),
Boost.Build will automatically execute the equivalent of ::
import toolset : using ;
using python ;
which automatically looks for Python in the most likely places.
However, that only happens when using the Boost.Python project file
(e.g. when referred to by another project as in the quickstart_
method). If instead you are linking against separately-compiled
Boost.Python binaries, you should set up a ``user-config.jam`` file
with at least the minimal incantation above.
Python Configuration Parameters
-------------------------------
If you have several versions of Python installed, or Python is
installed in an unusual way, you may want to supply any or all of
the following optional parameters to ``using python``.
version
the version of Python to use. Should be in Major.Minor
format, for example, ``2.3``. Do not include the subminor
version (i.e. *not* ``2.5.1``). If you have multiple Python
versions installed, the version will usually be the only
configuration argument required.
cmd-or-prefix
preferably, a command that invokes a Python interpreter.
Alternatively, the installation prefix for Python libraries and
header files. Only use the alternative formulation if there is
no appropriate Python executable available.
includes
the ``#include`` paths for Python headers. Normally the correct
path(s) will be automatically deduced from ``version`` and/or
``cmd-or-prefix``.
libraries
the path to Python library binaries. On MacOS/Darwin,
you can also pass the path of the Python framework. Normally the
correct path(s) will be automatically deduced from ``version``
and/or ``cmd-or-prefix``.
condition
if specified, should be a set of Boost.Build
properties that are matched against the build configuration when
Boost.Build selects a Python configuration to use. See examples
below for details.
extension-suffix
A string to append to the name of extension
modules before the true filename extension. You almost certainly
don't need to use this. Usually this suffix is only used when
targeting a Windows debug build of Python, and will be set
automatically for you based on the value of the
|python-debugging|_ feature. However, at least one Linux
distribution (Ubuntu Feisty Fawn) has a specially configured
`python-dbg`__ package that claims to use such a suffix.
.. |python-debugging| replace:: ``<python-debugging>``
__ https://wiki.ubuntu.com/PyDbgBuilds
Examples
--------
Note that in the examples below, case and *especially whitespace* are
significant.
- If you have both python 2.5 and python 2.4 installed,
``user-config.jam`` might contain::
using python : 2.5 ; # Make both versions of Python available
using python : 2.4 ; # To build with python 2.4, add python=2.4
# to your command line.
The first version configured (2.5) becomes the default. To build
against python 2.4, add ``python=2.4`` to the ``bjam`` command line.
- If you have python installed in an unusual location, you might
supply the path to the interpreter in the ``cmd-or-prefix``
parameter::
using python : : /usr/local/python-2.6-beta/bin/python ;
- If you have a separate build of Python for use with a particular
toolset, you might supply that toolset in the ``condition``
parameter::
using python ; # use for most toolsets
# Use with Intel C++ toolset
using python
: # version
: c:\\Devel\\Python-2.5-IntelBuild\\PCBuild\\python # cmd-or-prefix
: # includes
: # libraries
: <toolset>intel # condition
;
- If you have downloaded the Python sources and built both the
normal and the “\ `python debugging`_\ ” builds from source on
Windows, you might see::
using python : 2.5 : C:\\src\\Python-2.5\\PCBuild\\python ;
using python : 2.5 : C:\\src\\Python-2.5\\PCBuild\\python_d
: # includes
: # libs
: <python-debugging>on ;
- You can set up your user-config.jam so a bjam built under Windows
can build/test both Windows and Cygwin_ python extensions. Just pass
``<target-os>cygwin`` in the ``condition`` parameter
for the cygwin python installation::
# windows installation
using python ;
# cygwin installation
using python : : c:\\cygwin\\bin\\python2.5 : : : <target-os>cygwin ;
when you put target-os=cygwin in your build request, it should build
with the cygwin version of python: [#flavor]_
bjam target-os=cygwin toolset=gcc
This is supposed to work the other way, too (targeting windows
python with a Cygwin_ bjam) but it seems as though the support in
Boost.Build's toolsets for building that way is broken at the
time of this writing.
- Note that because of `the way Boost.Build currently selects target
alternatives`__, you might have be very explicit in your build
requests. For example, given::
using python : 2.5 ; # a regular windows build
using python : 2.4 : : : : <target-os>cygwin ;
building with ::
bjam target-os=cygwin
will yield an error. Instead, you'll need to write::
bjam target-os=cygwin/python=2.4
.. _Cygwin: http://cygwin.com
__ http://zigzag.cs.msu.su/boost.build/wiki/AlternativeSelection
Choosing a Boost.Python Library Binary
======================================
If—instead of letting Boost.Build construct and link with the right
libraries automatically—you choose to use a pre-built Boost.Python
library, you'll need to think about which one to link with. The
Boost.Python binary comes in both static and dynamic flavors. Take
care to choose the right flavor for your application. [#naming]_
The Dynamic Binary
------------------
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. [#toolset-specific]_
- The library contains a type conversion registry. Because one
registry is shared among all extension modules, instances of a
class exposed to Python in one dynamically-loaded extension
module can be passed to functions exposed in another such module.
The Static Binary
-----------------
It might be 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 other than MacOS or AIX,
where the dynamically-loaded extension modules can “see” the
Boost.Python library symbols that are part of the executable.
- Or, 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 are able to
use the types exposed by your statically-linked extension
modules (and vice-versa).
``#include`` Issues
===================
1. If you should ever have occasion to ``#include "python.h"``
directly in a translation unit of a program using Boost.Python,
use ``#include "boost/python/detail/wrap_python.hpp"`` instead.
It handles several issues necessary for use with Boost.Python,
one of which is mentioned in the next section.
2. Be sure not to ``#include`` any system headers before
``wrap_python.hpp``. This restriction is actually imposed by
Python, or more properly, by Python's interaction with your
operating system. See
http://docs.python.org/ext/simpleExample.html for details.
.. _python-debugging:
.. _python debugging:
Python Debugging Builds
=======================
Python can be built in a special “python debugging” configuration
that adds extra checks and instrumentation that can be very useful
for developers of extension modules. The data structures used by
the debugging configuration contain additional members, so **a
Python executable built with python debugging enabled cannot be
used with an extension module or library compiled without it, and
vice-versa.**
Since pre-built “python debugging” versions of the Python
executable and libraries are not supplied with most distributions
of Python, [#get-debug-build]_ and we didn't want to force our users
to build them, Boost.Build does not automatically enable python
debugging in its ``debug`` build variant (which is the default).
Instead there is a special build property called
``python-debugging`` that, when used as a build property, will
define the right preprocessor symbols and select the right
libraries to link with.
On unix-variant platforms, the debugging versions of Python's data
structures will only be used if the symbol ``Py_DEBUG`` is defined.
On many windows compilers, when extension modules are built with
the preprocessor symbol ``_DEBUG``, Python defaults to force
linking with a special debugging version of the Python DLL. Since
that symbol is very commonly used even when Python is not present,
Boost.Python temporarily undefines _DEBUG when Python.h
is #included from ``boost/python/detail/wrap_python.hpp`` - unless
``BOOST_DEBUG_PYTHON`` is defined. The upshot is that if you want
“python debugging”and you aren't using Boost.Build, you should make
sure ``BOOST_DEBUG_PYTHON`` is defined, or python debugging will be
suppressed.
Testing Boost.Python
====================
To run the full test suite for Boost.Python, invoke ``bjam`` in the
``libs/python/test`` subdirectory of your Boost distribution.
Notes for MinGW (and Cygwin with -mno-cygwin) GCC Users
=======================================================
If you are using a version of Python prior to 2.4.1 with a MinGW
prior to 3.0.0 (with binutils-2.13.90-20030111-1), you will need to
create a MinGW-compatible version of the Python library; the one
shipped with Python will only work with a Microsoft-compatible
linker. Follow the instructions in the “Non-Microsoft” section of
the “Building Extensions: Tips And Tricks” chapter in `Installing
Python Modules`__ to create ``libpythonXX.a``, where ``XX``
corresponds to the major and minor version numbers of your Python
installation.
__ http://www.python.org/doc/current/inst/index.html
-----------------------------
.. [#2.2] Note that although we tested earlier versions of
Boost.Python with Python 2.2, and we don't *think* we've done
anything to break compatibility, this release of Boost.Python
may not have been tested with versions of Python earlier than
2.4, so we're not 100% sure that python 2.2 and 2.3 are
supported.
.. [#naming] Information about how to identify the
static and dynamic builds of Boost.Python:
* `on Windows`__
* `on Unix variants`__
__ ../../../more/getting_started/windows.html#library-naming
__ ../../../more/getting_started/unix-variants.html#library-naming
.. [#toolset-specific] Because of the way most \*nix platforms
share symbols among dynamically-loaded objects, I'm not certain
that extension modules built with different compiler toolsets
will always use different copies of the Boost.Python library
when loaded into the same Python instance. Not using different
libraries could be a good thing if the compilers have compatible
ABIs, because extension modules built with the two libraries
would be interoperable. Otherwise, it could spell disaster,
since an extension module and the Boost.Python library would
have different ideas of such things as class layout. I would
appreciate someone doing the experiment to find out what
happens.
.. [#overwrite] ``configure`` overwrites the existing
``user-config.jam`` in your home directory
(if any) after making a backup of the old version.
.. [#flavor] Note that the ``<target-os>cygwin`` feature is
different from the ``<flavor>cygwin`` subfeature of the ``gcc``
toolset, and you might need handle both explicitly if you also
have a MinGW GCC installed.
.. [#home-dir] Windows users, your home directory can be
found by typing::
ECHO %HOMEDRIVE%%HOMEPATH%
into a `command prompt`_ window.
.. [#get-debug-build] On Unix and similar platforms, a debugging
python and associated libraries are built by adding
``--with-pydebug`` when configuring the Python build. On
Windows, the debugging version of Python is generated by
the "Win32 Debug" target of the Visual Studio project in the
PCBuild subdirectory of a full Python source code distribution.

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[chapter Configuration
[quickbook 1.7]
[authors [Abrahams, David]]
[copyright 2002 - 2015 David Abrahams, Stefan Seefeld]
[id configuration]
]
[section Configuration]
[section Introduction]
*Boost.Python* uses several configuration macros in `<boost/config.hpp>`, as well as configuration macros meant to be supplied by the application. These macros are documented here.
[endsect]
[section Application Defined Macros]
These are the macros that may be defined by an application using Boost.Python. Note that if you extend a strict interpretation of the C++ standard to cover dynamic libraries, using different values of these macros when compiling different libraries (including extension modules and the Boost.Python library itself) is a violation of the ODR. However, we know of no C++ implementations on which this particular violation is detectable or causes any problems.
[table
[[Macro][Default][Meaning]]
[[BOOST_PYTHON_MAX_ARITY]
[15]
[The maximum arity of any function, member function,
or constructor to be wrapped, invocation of a
Boost.Python function wich is specified as taking
arguments x1, x2,...Xn. This includes, in particular,
callback mechanisms such as object::operator()(...) or call_method<R>(... ).]]
[[BOOST_PYTHON_MAX_BASES][10]
[The maximum number of template arguments to the
`bases<...>` class template, which is used to specify
the bases of a wrapped C++ class..]]
[[BOOST_PYTHON_STATIC_MODULE]
[ /not defined/ ]
[If defined, prevents your module initialization
function from being treated as an exported symbol
on platforms which support that distinction in-code]]
[[BOOST_PYTHON_ENABLE_CDECL]
[ /not defined/ ]
[If defined, allows functions using the `__cdecl`
calling convention to be wrapped.]]
[[BOOST_PYTHON_ENABLE_STDCALL]
[ /not defined/ ]
[If defined, allows functions using the `__stdcall`
calling convention to be wrapped.]]
[[BOOST_PYTHON_ENABLE_FASTCALL]
[ /not defined/ ]
[If defined, allows functions using the `__fastcall`
calling convention to be wrapped.]]
]
[endsect]
[section Library Defined Defined Macros]
[table
[[Macro][Default][Meaning]]
[[BOOST_PYTHON_TYPE_ID_NAME][ /not defined/ ]
[If defined, this indicates that the type_info comparison across
shared library boundaries does not work on this platform.
In other words, if shared-lib-1 passes `typeid(T)` to a function
in shared-lib-2 which compares it to `typeid(T)`, that comparison
may return `false`. If this macro is #defined, Boost.Python uses
and compares `typeid(T).name()` instead of using and comparing
the `std::type_info` objects directly.]]
[[BOOST_PYTHON_NO_PY_SIGNATURES][ /not defined/ ]
[If defined for a module no pythonic signatures are generated for
the docstrings of the module functions, and no python type is
associated with any of the converters registered by the module.
This also reduces the binary size of the module by about 14%
(gcc compiled).
If defined for the boost_python runtime library, the default for
the `docstring_options.enable_py_signatures()` is set to `false`.]]
[[BOOST_PYTHON_SUPPORTS_PY_SIGNATURES]
[ /defined/ if `BOOST_PYTHON_NO_PY_SIGNATURES` is /undefined/ ]
[This macro is defined to enable a smooth transition from older
Boost.Python versions which do not support pythonic signatures.
For example usage see here.]]
[[BOOST_PYTHON_PY_SIGNATURES_PROPER_INIT_SELF_TYPE][ /not defined/ ]
[If defined the python type of `__init__` method "self" parameters
is properly generated, otherwise object is used. It is undefined by
default because it increases the binary size of the module by about
14% (gcc compiled).]]
]
[endsect]
[endsect]

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[chapter FAQ
[quickbook 1.7]
[id faq]
]
[section How can I wrap a function which takes a function pointer as an argument?]
If what you're trying to do is something like this:
``
typedef boost::function<void (string s) > funcptr;
void foo(funcptr fp)
{
fp("hello,world!");
}
BOOST_PYTHON_MODULE(test)
{
def("foo",foo);
}
``
And then:
``
>>> def hello(s):
... print s
...
>>> foo(hello)
hello, world!
``
The short answer is: "you can't". This is not a
Boost.Python limitation so much as a limitation of C++. The
problem is that a Python function is actually data, and the only
way of associating data with a C++ function pointer is to store it
in a static variable of the function. The problem with that is
that you can only associate one piece of data with every C++
function, and we have no way of compiling a new C++ function
on-the-fly for every Python function you decide to pass
to `foo`. In other words, this could work if the C++
function is always going to invoke the /same/ Python
function, but you probably don't want that.
If you have the luxury of changing the C++ code you're
wrapping, pass it an `object` instead and call that;
the overloaded function call operator will invoke the Python
function you pass it behind the `object`.
[endsect]
[section I'm getting the "attempt to return dangling reference" error.
What am I doing wrong?]
That exception is protecting you from causing a nasty crash. It usually
happens in response to some code like this:
``
period const &get_floating_frequency() const
{
return boost::python::call_method<period const &>(
m_self,"get_floating_frequency");
}
``
And you get:
``
ReferenceError: Attempt to return dangling reference to object of type:
class period
``
In this case, the Python method invoked by `call_method`
constructs a new Python object. You're trying to return a reference to a
C++ object (an instance of `class period`) contained within
and owned by that Python object. Because the called method handed back a
brand new object, the only reference to it is held for the duration of
`get_floating_frequency()` above. When the function returns,
the Python object will be destroyed, destroying the instance of
`class period`, and leaving the returned reference dangling.
That's already undefined behavior, and if you try to do anything with
that reference you're likely to cause a crash. Boost.Python detects this
situation at runtime and helpfully throws an exception instead of letting
you do that.
[endsect]
[section Is return_internal_reference efficient?]
[*Q:] /I have an object composed of 12 doubles. A const& to
this object is returned by a member function of another class. From the
viewpoint of using the returned object in Python I do not care if I get
a copy or a reference to the returned object. In Boost.Python Version 2
I have the choice of using copy_const_reference or
return_internal_reference. Are there considerations that would lead me
to prefer one over the other, such as size of generated code or memory
overhead?/
[*A:] `copy_const_reference` will make an instance with storage
for one of your objects, size = base_size + 12 * sizeof(double).
return_internal_reference will make an instance with storage for a
pointer to one of your objects, size = base_size + sizeof(void*).
However, it will also create a weak reference object which goes in the
source object's weakreflist and a special callback object to manage the
lifetime of the internally-referenced object. My guess?
copy_const_reference is your friend here, resulting in less overall
memory use and less fragmentation, also probably fewer total
cycles.
[endsect]
[section How can I wrap functions which take C++ containers as arguments?]
Ralf W. Grosse-Kunstleve provides these notes:
# Using the regular `class_<>` wrapper:
``
class_<std::vector<double> >("std_vector_double")
.def(...)
...
;
``
This can be moved to a template so that several types (double, int,
long, etc.) can be wrapped with the same code. This technique is used
in the file `scitbx/include/scitbx/array_family/boost_python/flex_wrapper.h`
in the "scitbx" package. The file could easily be modified for
wrapping `std::vector<>` instantiations.
This type of C++/Python binding is most suitable for containers
that may contain a large number of elements (>10000).
# Using custom rvalue converters. Boost.Python "rvalue converters"
match function signatures such as:
``
void foo(std::vector<double> const &array); // pass by const-reference
void foo(std::vector<double> array); // pass by value
``
Some custom rvalue converters are implemented in the file
`scitbx/include/scitbx/boost_python/container_conversions.h`
This code can be used to convert from C++ container types such as
`std::vector<>` or `std::list<>` to Python tuples and vice
versa. A few simple examples can be found in the file
`scitbx/array_family/boost_python/regression_test_module.cpp`
Automatic C++ container <-> Python tuple conversions are most
suitable for containers of moderate size. These converters generate
significantly less object code compared to alternative 1 above.
A disadvantage of using alternative 2 is that operators such as
arithmetic +,-,*,/,% are not available. It would be useful to have custom
rvalue converters that convert to a "math_array" type instead of tuples.
This is currently not implemented but is possible within the framework of
Boost.Python V2 as it will be released in the next couple of weeks. [ed.:
this was posted on 2002/03/10]
It would also be useful to also have "custom lvalue converters" such
as `std::vector<>` <-> Python list. These converters would
support the modification of the Python list from C++. For example:
C++:
``
void foo(std::vector<double> &array)
{
for(std::size_t i=0;i&lt;array.size();i++) {
array[i] *= 2;
}
}
``
Python:
``
>>> l = [1, 2, 3]
>>> foo(l)
>>> print l
[2, 4, 6]
``
Custom lvalue converters require changes to the Boost.Python core library
and are currently not available.
P.S.:
The "scitbx" files referenced above are available via anonymous
CVS:
``
cvs -d:pserver:anonymous@cvs.cctbx.sourceforge.net:/cvsroot/cctbx login
cvs -d:pserver:anonymous@cvs.cctbx.sourceforge.net:/cvsroot/cctbx co scitbx
``
[endsect]
[section fatal error C1204:Compiler limit:internal structure overflow]
[*Q:] /I get this error message when compiling a large source file. What can I do?/
[*A:] You have two choices:
# Upgrade your compiler (preferred)
# Break your source file up into multiple translation units.
`my_module.cpp`:
``
...
void more_of_my_module();
BOOST_PYTHON_MODULE(my_module)
{
def("foo", foo);
def("bar", bar);
...
more_of_my_module();
}
``
`more_of_my_module.cpp`:
``
void more_of_my_module()
{
def("baz", baz);
...
}
``
If you find that a [link reference.class-spec class_]<...> declaration
can't fit in a single source file without triggering the error, you
can always pass a reference to the `class_` object to a
function in another source file, and call some of its member
functions (e.g. `.def(...)`) in the auxilliary source
file:
`more_of_my_class.cpp`:
``
void more_of_my_class(class&lt;my_class&gt;&amp; x)
{
x
.def("baz", baz)
.add_property("xx", &my_class::get_xx, &my_class::set_xx)
;
...
}
``
[endsect]
[section How do I debug my Python extensions?]
Greg Burley gives the following answer for Unix GCC users:
[:Once you have created a boost python extension for your c++ library or
class, you may need to debug the code. Afterall this is one of the
reasons for wrapping the library in python. An expected side-effect or
benefit of using BPL is that debugging should be isolated to the c++
library that is under test, given that python code is minimal and
boost::python either works or it doesn't. (ie. While errors can occur
when the wrapping method is invalid, most errors are caught by the
compiler ;-).
The basic steps required to initiate a gdb session to debug a c++
library via python are shown here. Note, however that you should start
the gdb session in the directory that contains your BPL my_ext.so
module.
``
(gdb) target exec python
(gdb) run
>>> from my_ext import *
>>> [C-c]
(gdb) break MyClass::MyBuggyFunction
(gdb) cont
>>> pyobj = MyClass()
>>> pyobj.MyBuggyFunction()
Breakpoint 1, MyClass::MyBuggyFunction ...
Current language: auto; currently c++
(gdb) do debugging stuff
``
]
Greg's approach works even better using Emacs' "gdb"
command, since it will show you each line of source as you step through it.
On *Windows*, my favorite debugging solution is the debugger that
comes with Microsoft Visual C++ 7. This debugger seems to work with code
generated by all versions of Microsoft and Metrowerks toolsets; it's rock
solid and "just works" without requiring any special tricks from the
user.
Raoul Gough has provided the following for gdb on Windows:
[:gdb support for Windows DLLs has improved lately, so it is
now possible to debug Python extensions using a few
tricks. Firstly, you will need an up-to-date gdb with support
for minimal symbol extraction from a DLL. Any gdb from version 6
onwards, or Cygwin gdb-20030214-1 and onwards should do. A
suitable release will have a section in the gdb.info file under
Configuration - Native - Cygwin Native -
Non-debug DLL symbols. Refer to that info section for more
details of the procedures outlined here.
Secondly, it seems necessary to set a breakpoint in the
Python interpreter, rather than using ^C to break execution. A
good place to set this breakpoint is PyOS_Readline, which will
stop execution immediately before reading each interactive
Python command. You have to let Python start once under the
debugger, so that it loads its own DLL, before you can set the
breakpoint:
``
$ gdb python
GNU gdb 2003-09-02-cvs (cygwin-special)
[...]
(gdb) run
Starting program: /cygdrive/c/Python22/python.exe
Python 2.2.2 (#37, Oct 14 2002, 17:02:34) [MSC 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> ^Z
Program exited normally.
(gdb) break *&PyOS_Readline
Breakpoint 1 at 0x1e04eff0
(gdb) run
Starting program: /cygdrive/c/Python22/python.exe
Python 2.2.2 (#37, Oct 14 2002, 17:02:34) [MSC 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Breakpoint 1, 0x1e04eff0 in python22!PyOS_Readline ()
from /cygdrive/c/WINNT/system32/python22.dll
(gdb) cont
Continuing.
>>> from my_ext import *
Breakpoint 1, 0x1e04eff0 in python22!PyOS_Readline ()
from /cygdrive/c/WINNT/system32/python22.dll
(gdb) # my_ext now loaded (with any debugging symbols it contains)
``
]
[h2 Debugging extensions through Boost.Build]
If you are launching your extension module tests with _bb_ using the
`boost-python-runtest` rule, you can ask it to launch your
debugger for you by adding "--debugger=/debugger/" to your bjam
command-line:
``
bjam -sTOOLS=vc7.1 "--debugger=devenv /debugexe" test
bjam -sTOOLS=gcc -sPYTHON_LAUNCH=gdb test
``
It can also be extremely useful to add the `-d+2` option when
you run your test, because Boost.Build will then show you the exact
commands it uses to invoke it. This will invariably involve setting up
PYTHONPATH and other important environment variables such as
LD_LIBRARY_PATH which may be needed by your debugger in order to get
things to work right.
[endsect]
[section Why doesn't my `*=` operator work?]
[*Q:] ['I have exported my class to python, with many overloaded
operators. it works fine for me except the `*=`
operator. It always tells me "can't multiply sequence with non int
type". If I use `p1.__imul__(p2)` instead of
`p1 *= p2`, it successfully executes my code. What's
wrong with me?]
[*A:] There's nothing wrong with you. This is a bug in Python
2.2. You can see the same effect in Pure Python (you can learn a lot
about what's happening in Boost.Python by playing with new-style
classes in Pure Python).
``
>>> class X(object):
... def __imul__(self, x):
... print 'imul'
...
>>> x = X()
>>> x *= 1
``
To cure this problem, all you need to do is upgrade your Python to
version 2.2.1 or later.
[endsect]
[section Does Boost.Python work with Mac OS X?]
It is known to work under 10.2.8 and 10.3 using
Apple's gcc 3.3 compiler:
``gcc (GCC) 3.3 20030304 (Apple Computer, Inc. build 1493)``
Under 10.2.8 get the August 2003 gcc update (free at [@http://connect.apple.com]).
Under 10.3 get the Xcode Tools v1.0 (also free).
Python 2.3 is required. The Python that ships with 10.3 is
fine. Under 10.2.8 use these commands to install Python
as a framework:
``./configure --enable-framework
make
make frameworkinstall``
The last command requires root privileges because the target
directory is `/Library/Frameworks/Python.framework/Versions/2.3`.
However, the installation does not interfere with the Python
version that ships with 10.2.8.
It is also crucial to increase the `stacksize` before
starting compilations, e.g.:
``limit stacksize 8192k``
If the `stacksize` is too small the build might crash with
internal compiler errors.
Sometimes Apple's compiler exhibits a bug by printing an error
like the following while compiling a
`boost::python::class_<your_type>`
template instantiation:
``
.../inheritance.hpp:44: error: cannot
dynamic_cast `p' (of type `struct cctbx::boost_python::<unnamed>::add_pair*
') to type `void*' (source type is not polymorphic)
``
We do not know a general workaround, but if the definition of
`your_type` can be modified the following was found
to work in all cases encountered so far:
``
struct your_type
{
// before defining any member data
#if defined(__MACH__) &amp;&amp; defined(__APPLE_CC__) &amp;&amp; __APPLE_CC__ == 1493
bool dummy_;
#endif
// now your member data, e.g.
double x;
int j;
// etc.
};
``
[endsect]
[section How can I find the existing PyObject that holds a C++ object?]
[: "I am wrapping a function that always returns a pointer to an
already-held C++ object."]
One way to do that is to hijack the mechanisms used for wrapping a class
with virtual functions. If you make a wrapper class with an initial
PyObject* constructor argument and store that PyObject* as "self", you
can get back to it by casting down to that wrapper type in a thin wrapper
function. For example:
``
class X { X(int); virtual ~X(); ... };
X* f(); // known to return Xs that are managed by Python objects
// wrapping code
struct X_wrap : X
{
X_wrap(PyObject* self, int v) : self(self), X(v) {}
PyObject* self;
};
handle<> f_wrap()
{
X_wrap* xw = dynamic_cast<X_wrap*>(f());
assert(xw != 0);
return handle<>(borrowed(xw->self));
}
...
def("f", f_wrap());
class_<X,X_wrap,boost::noncopyable>("X", init<int>())
...
;
``
Of course, if X has no virtual functions you'll have to use
`static_cast` instead of `dynamic_cast` with no
runtime check that it's valid. This approach also only works if the
`X` object was constructed from Python, because
`X`\ s constructed from C++ are of course never
`X_wrap` objects.
Another approach to this requires you to change your C++ code a bit;
if that's an option for you it might be a better way to go. work we've
been meaning to get to anyway. When a `shared_ptr<X>` is
converted from Python, the shared_ptr actually manages a reference to the
containing Python object. When a shared_ptr<X> is converted back to
Python, the library checks to see if it's one of those "Python object
managers" and if so just returns the original Python object. So you could
just write `object(p)` to get the Python object back. To
exploit this you'd have to be able to change the C++ code you're wrapping
so that it deals with shared_ptr instead of raw pointers.
There are other approaches too. The functions that receive the Python
object that you eventually want to return could be wrapped with a thin
wrapper that records the correspondence between the object address and
its containing Python object, and you could have your f_wrap function
look in that mapping to get the Python object out.
[endsect]
[section How can I wrap a function which needs to take ownership of a raw pointer?]
[*Q:] Part of an API that I'm wrapping goes something like this:
``
struct A {}; struct B { void add( A* ); }
where B::add() takes ownership of the pointer passed to it.
``
However:
``
a = mod.A()
b = mod.B()
b.add( a )
del a
del b
# python interpreter crashes
# later due to memory corruption.
``
Even binding the lifetime of a to b via `with_custodian_and_ward` doesn't prevent
the python object a from ultimately trying to delete the object it's pointing to.
Is there a way to accomplish a 'transfer-of-ownership' of a wrapped C++ object?
--Bruce Lowery
Yes: Make sure the C++ object is held by auto_ptr:
``
class_<A, std::auto_ptr<A> >("A")
...
;
``
Then make a thin wrapper function which takes an auto_ptr parameter:
``
void b_insert(B &b, std::auto_ptr<A> a)
{
b.insert(a.get());
a.release();
}
``
Wrap that as B.add. Note that pointers returned via [link reference.manage_new_object manage_new_object]
will also be held by `auto_ptr`, so this transfer-of-ownership
will also work correctly.
[endsect]
[section Compilation takes too much time and eats too much memory!
What can I do to make it faster?]
Please refer to the [link tutorial.python.reducing_compiling_time Reducing Compiling Time] section in the tutorial.
[endsect]
[section How do I create sub-packages using Boost.Python?]
Please refer to the [link tutorial.python.creating_packages Creating Packages] section in the tutorial.
[endsect]
[section error C2064: term does not evaluate to a function taking 2 arguments]
/Niall Douglas provides these notes:/
If you see Microsoft Visual C++ 7.1 (MS Visual Studio .NET 2003) issue
an error message like the following it is most likely due to a bug
in the compiler:
``
boost\boost\python\detail\invoke.hpp(76):
error C2064: term does not evaluate to a function taking 2 arguments"
``
This message is triggered by code like the following:
``
#include <boost/python.hpp>
using namespace boost::python;
class FXThread
{
public:
bool setAutoDelete(bool doso) throw();
};
void Export_FXThread()
{
class_< FXThread >("FXThread")
.def("setAutoDelete", &amp;FXThread::setAutoDelete)
;
}
``
The bug is related to the `throw()` modifier.
As a workaround cast off the modifier. E.g.:
``
.def("setAutoDelete", (bool (FXThread::*)(bool)) &FXThread::setAutoDelete)
``
(The bug has been reported to Microsoft.)
[endsect]
[section How can I automatically convert my custom string type to and from a Python string?]
/Ralf W. Grosse-Kunstleve provides these notes:/
Below is a small, self-contained demo extension module that shows
how to do this. Here is the corresponding trivial test:
``
import custom_string
assert custom_string.hello() == "Hello world."
assert custom_string.size("california") == 10
``
If you look at the code you will find:
* A custom `to_python` converter (easy):
`custom_string_to_python_str`
*A custom lvalue converter (needs more code):
`custom_string_from_python_str`
The custom converters are registered in the global Boost.Python
registry near the top of the module initialization function. Once
flow control has passed through the registration code the automatic
conversions from and to Python strings will work in any module
imported in the same process.
``
#include <boost/python/module.hpp>
#include <boost/python/def.hpp>
#include <boost/python/to_python_converter.hpp>
namespace sandbox { namespace {
class custom_string
{
public:
custom_string() {}
custom_string(std::string const &value) : value_(value) {}
std::string const &value() const { return value_; }
private:
std::string value_;
};
struct custom_string_to_python_str
{
static PyObject* convert(custom_string const &s)
{
return boost::python::incref(boost::python::object(s.value()).ptr());
}
};
struct custom_string_from_python_str
{
custom_string_from_python_str()
{
boost::python::converter::registry::push_back(
&convertible,
&construct,
boost::python::type_id<custom_string>());
}
static void* convertible(PyObject* obj_ptr)
{
if (!PyString_Check(obj_ptr)) return 0;
return obj_ptr;
}
static void construct(
PyObject* obj_ptr,
boost::python::converter::rvalue_from_python_stage1_data* data)
{
const char* value = PyString_AsString(obj_ptr);
if (value == 0) boost::python::throw_error_already_set();
void* storage = (
(boost::python::converter::rvalue_from_python_storage<custom_string>*)
data)->storage.bytes;
new (storage) custom_string(value);
data->convertible = storage;
}
};
custom_string hello() { return custom_string("Hello world."); }
std::size_t size(custom_string const &s) { return s.value().size(); }
void init_module()
{
using namespace boost::python;
boost::python::to_python_converter<
custom_string,
custom_string_to_python_str>();
custom_string_from_python_str();
def("hello", hello);
def("size", size);
}
}} // namespace sandbox::<anonymous>
BOOST_PYTHON_MODULE(custom_string)
{
sandbox::init_module();
}
``
[endsect]
[section Why is my automatic to-python conversion not being found?]
/Niall Douglas provides these notes:/
If you define custom converters similar to the ones
shown above the `def_readonly()` and `def_readwrite()`
member functions provided by `boost::python::class_` for
direct access to your member data will not work as expected.
This is because `def_readonly("bar",&foo::bar)` is
equivalent to:
``
.add_property("bar", make_getter(&foo::bar, return_internal_reference()))
``
Similarly, `def_readwrite("bar",&foo::bar)` is
equivalent to:
``
.add_property("bar", make_getter(&foo::bar, return_internal_reference()),
make_setter(&foo::bar, return_internal_reference())
``
In order to define return value policies compatible with the
custom conversions replace `def_readonly()` and
`def_readwrite()` by `add_property()`. E.g.:
``
.add_property("bar", make_getter(&foo::bar, return_value_policy<return_by_value>()),
make_setter(&foo::bar, return_value_policy<return_by_value>()))
``
[endsect]
[section Is Boost.Python thread-aware/compatible with multiple interpreters?]
/Niall Douglas provides these notes:/
The quick answer to this is: no.
The longer answer is that it can be patched to be so, but it's
complex. You will need to add custom lock/unlock wrapping of every
time your code enters Boost.Python (particularly every virtual
function override) plus heavily modify
`boost/python/detail/invoke.hpp` with custom unlock/lock
wrapping of every time Boost.Python enters your code. You must
furthermore take care to /not/ unlock/lock when Boost.Python
is invoking iterator changes via `invoke.hpp`.
There is a patched `invoke.hpp` posted on the C++-SIG
mailing list archives and you can find a real implementation of all
the machinery necessary to fully implement this in the TnFOX
project at [@http://sourceforge.net/projects/tnfox/ this]
SourceForge project location.
[endsect]

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[chapter Glossary
[id glossary]
]
[variablelist
[[arity]
[The number of argumnts accepted by a function or member function.
Unless otherwise specified, the hidden `this` argument to member
functions is not counted when specifying arity.]]
[[ntbs]
[Null-Terminated Byte String, or 'C'-string. C++ string literals are *ntbs*\ es.
An *ntbs* must never be null.]]
[[raise]
[Exceptions in Python are "raised", not "thrown", as they are in C++.
When this documentation says that some Python exception is "raised" in
the context of C++ code, it means that the corresponding Python exception
is set via the [@http://www.python.org/doc/current/api/exceptionHandling.html Python/'C' API],
and [link reference.errors.throw_error_already_set throw_error_already_set()] is called.]]
[[POD]
[A technical term from the C++ standard. Short for "Plain Ol'Data":
A POD-struct is an aggregate class that has no non-static data members
of type pointer to member, non-POD-struct, non-POD-union (or array of such
types) or reference, and has no user-defined copy assign- ment operator and
no user-defined destructor. Similarly, a POD-union is an aggregate union that
has no non-static data members of type pointer to member, non-POD-struct,
non-POD-union (or array of such types) or reference, and has no
user-defined copy assignment operator and no user-defined destructor. A
POD class is a class that is either a POD-struct or a POD-union. An
aggregate is an array or a class (clause 9) with no user-declared
constructors (12.1), no private or protected non-static data members
(clause 11), no base classes (clause 10), and no virtual functions
(10.3).]]
[[ODR]
[The "One Definition Rule", which says that any entity in a C++ program must have
the same definition in all translation units (object files) which make up a program.]]
]

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font-size: 80%;
}
table.simplelist
{
width: auto !important;
margin: 0em !important;
padding: 0em !important;
border: none !important;
}
table.simplelist td
{
margin: 0em !important;
padding: 0em !important;
text-align: left !important;
font-size: 9pt !important;
border: none !important;
}
/*=============================================================================
Blurbs
=============================================================================*/
div.note,
div.tip,
div.important,
div.caution,
div.warning,
p.blurb
{
font-size: 9pt; /* A little bit smaller than the main text */
line-height: 1.2;
display: block;
margin: 1pc 4% 0pc 4%;
padding: 0.5pc 0.5pc 0.5pc 0.5pc;
}
p.blurb img
{
padding: 1pt;
}
/*=============================================================================
Variable Lists
=============================================================================*/
div.variablelist
{
margin: 1em 0;
}
/* Make the terms in definition lists bold */
div.variablelist dl dt,
span.term
{
font-weight: bold;
font-size: 10pt;
}
div.variablelist table tbody tr td
{
text-align: left;
vertical-align: top;
padding: 0em 2em 0em 0em;
font-size: 10pt;
margin: 0em 0em 0.5em 0em;
line-height: 1;
}
div.variablelist dl dt
{
margin-bottom: 0.2em;
}
div.variablelist dl dd
{
margin: 0em 0em 0.5em 2em;
font-size: 10pt;
}
div.variablelist table tbody tr td p,
div.variablelist dl dd p
{
margin: 0em 0em 0.5em 0em;
line-height: 1;
}
/*=============================================================================
Misc
=============================================================================*/
/* Title of books and articles in bibliographies */
span.title
{
font-style: italic;
}
span.underline
{
text-decoration: underline;
}
span.strikethrough
{
text-decoration: line-through;
}
/* Copyright, Legal Notice */
div div.legalnotice p
{
text-align: left
}
/*=============================================================================
Colors
=============================================================================*/
@media screen
{
body {
background-color: #FFFFFF;
color: #000000;
}
/* Syntax Highlighting */
.keyword { color: #0000AA; }
.identifier { color: #000000; }
.special { color: #707070; }
.preprocessor { color: #402080; }
.char { color: teal; }
.comment { color: #800000; }
.string { color: teal; }
.number { color: teal; }
.white_bkd { background-color: #FFFFFF; }
.dk_grey_bkd { background-color: #999999; }
/* Links */
a, a .keyword, a .identifier, a .special, a .preprocessor
a .char, a .comment, a .string, a .number
{
color: #005a9c;
}
a:visited, a:visited .keyword, a:visited .identifier,
a:visited .special, a:visited .preprocessor a:visited .char,
a:visited .comment, a:visited .string, a:visited .number
{
color: #9c5a9c;
}
h1 a, h2 a, h3 a, h4 a, h5 a, h6 a,
h1 a:hover, h2 a:hover, h3 a:hover, h4 a:hover, h5 a:hover, h6 a:hover,
h1 a:visited, h2 a:visited, h3 a:visited, h4 a:visited, h5 a:visited, h6 a:visited
{
text-decoration: none; /* no underline */
color: #000000;
}
/* Copyright, Legal Notice */
.copyright
{
color: #666666;
font-size: small;
}
div div.legalnotice p
{
color: #666666;
}
/* Program listing */
pre.synopsis
{
border: 1px solid #DCDCDC;
}
.programlisting,
.screen
{
border: 1px solid #DCDCDC;
}
td .programlisting,
td .screen
{
border: 0px solid #DCDCDC;
}
/* Blurbs */
div.note,
div.tip,
div.important,
div.caution,
div.warning,
p.blurb
{
border: 1px solid #DCDCDC;
}
/* Table of contents */
div.toc
{
border: 1px solid #DCDCDC;
}
/* Tables */
div.informaltable table tr td,
div.table table tr td
{
border: 1px solid #DCDCDC;
}
div.informaltable table tr th,
div.table table tr th
{
background-color: #F0F0F0;
border: 1px solid #DCDCDC;
}
.copyright-footer
{
color: #8F8F8F;
}
/* Misc */
span.highlight
{
color: #00A000;
}
}
@media print
{
/* Links */
a
{
color: black;
}
a:visited
{
color: black;
}
.spirit-nav
{
display: none;
}
/* Program listing */
pre.synopsis
{
border: 1px solid gray;
}
.programlisting,
.screen
{
border: 1px solid gray;
}
td .programlisting,
td .screen
{
border: 0px solid #DCDCDC;
}
/* Table of contents */
div.toc
{
border: 1px solid gray;
}
.informaltable table,
.table table
{
border: 1px solid gray;
border-collapse: collapse;
}
/* Tables */
div.informaltable table tr td,
div.table table tr td
{
border: 1px solid gray;
}
div.informaltable table tr th,
div.table table tr th
{
border: 1px solid gray;
}
table.simplelist tr td
{
border: none !important;
}
/* Misc */
span.highlight
{
font-weight: bold;
}
}
/*=============================================================================
Images
=============================================================================*/
span.inlinemediaobject img
{
vertical-align: middle;
}
/*==============================================================================
Super and Subscript: style so that line spacing isn't effected, see
http://www.adobe.com/cfusion/communityengine/index.cfm?event=showdetails&productId=1&postId=5341
==============================================================================*/
sup,
sub {
height: 0;
line-height: 1;
vertical-align: baseline;
position: relative;
}
/* For internet explorer: */
* html sup,
* html sub {
vertical-align: bottom;
}
sup {
bottom: 1ex;
}
sub {
top: .5ex;
}
/*==============================================================================
Indexes: pretty much the same as the TOC.
==============================================================================*/
.index
{
font-size: 80%;
padding-top: 0px;
padding-bottom: 0px;
margin-top: 0px;
margin-bottom: 0px;
margin-left: 0px;
}
.index ul
{
padding-left: 3em;
}
.index p
{
padding: 2px;
margin: 2px;
}
.index-entry-level-0
{
font-weight: bold;
}
.index em
{
font-weight: bold;
}
/*==============================================================================
Alignment and coloring use 'role' feature, available from Quickbook 1.6 up.
Added from Niall Douglas for role color and alignment.
http://article.gmane.org/gmane.comp.lib.boost.devel/243318
*/
/* Add text alignment (see http://www.w3schools.com/cssref/pr_text_text-align.asp) */
span.aligncenter
{
display: inline-block; width: 100%; text-align: center;
}
span.alignright
{
display: inline-block; width: 100%; text-align: right;
}
/* alignleft is the default. */
span.alignleft
{
display: inline-block; width: 100%; text-align: left;
}
/* alignjustify stretches the word spacing so that each line has equal width
within a chosen fraction of page width (here arbitrarily 20%).
*Not* useful inside table items as the column width remains the total string width.
Nor very useful, except to temporarily restrict the width.
*/
span.alignjustify
{
display: inline-block; width: 20%; text-align: justify;
}
/* Text colors.
Names at http://www.w3.org/TR/2002/WD-css3-color-20020219/ 4.3. X11 color keywords.
Quickbook Usage: [role red Some red text]
*/
span.red { inline-block; color: red; }
span.green { color: green; }
span.lime { color: #00FF00; }
span.blue { color: blue; }
span.navy { color: navy; }
span.yellow { color: yellow; }
span.magenta { color: magenta; }
span.indigo { color: #4B0082; }
span.cyan { color: cyan; }
span.purple { color: purple; }
span.gold { color: gold; }
span.silver { color: silver; } /* lighter gray */
span.gray { color: #808080; } /* light gray */

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[library Boost.Python
[quickbook 1.3]
[authors [Abrahams, David], [Seefeld, Stefan]]
[copyright 2002 2003 2004 2005 2015 David Abrahams, Stefan Seefeld]
[category inter-language support]
[id python]
[purpose
Reflects C++ classes and functions into Python
]
[license
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])
]
]
[/ QuickBook Document version 1.3 ]
[include preface.qbk]

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[preface Introduction
[quickbook 1.6]
]
[section Synopsis]
Welcome to version 2 of Boost.Python, a C++ library which enables seamless interoperability between C++ and the Python programming language. The new version has been rewritten from the ground up, with a more convenient and flexible interface, and many new capabilities, including support for:
* References and Pointers
* Globally Registered Type Coercions
* Automatic Cross-Module Type Conversions
* Efficient Function Overloading
* C++ to Python Exception Translation
* Default Arguments
* Keyword Arguments
* Manipulating Python objects in C++
* Exporting C++ Iterators as Python Iterators
* Documentation Strings
The development of these features was funded in part by grants to Boost Consulting from the Lawrence Livermore National Laboratories and by the Computational Crystallography Initiative at Lawrence Berkeley National Laboratories.
[endsect]
[section Articles]
"Building Hybrid Systems With Boost Python", by Dave Abrahams and Ralf W. Grosse-Kunstleve (PDF)
[endsect]

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[book Boost.Python
[quickbook 1.7]
[authors [Abrahams, David], [Seefeld, Stefan]]
[copyright 2002 - 2015 David Abrahams, Stefan Seefeld]
[category inter-language support]
[id python]
[purpose
Reflects C++ classes and functions into Python
]
[license
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])
]
]
[def _boost_ [@http://www.boost.org Boost]]
[def _bb_ [@http://www.boost.org/build Boost.Build]]
[def _bb_list_ [@http://www.boost.org/more/mailing_lists.htm#jamboost Boost.Build mailing list]]
[def _bp_list_ [@http://www.boost.org/more/mailing_lists.htm#cplussig Boost.Python mailing list]]
[def _tutorial_ [@tutorial/index.html Tutorial]]
[def _reference_ [@reference/index.html Reference Manual]]
[def _gsg_ Boost [@http://www.boost.org/more/getting_started/ Getting Started Guide]]
[h2 Synopsis]
Welcome to Boost.Python, a C++ library which enables seamless interoperability between C++ and the Python programming language. The library includes support for:
* References and Pointers
* Globally Registered Type Coercions
* Automatic Cross-Module Type Conversions
* Efficient Function Overloading
* C++ to Python Exception Translation
* Default Arguments
* Keyword Arguments
* Manipulating Python objects in C++
* Exporting C++ Iterators as Python Iterators
* Documentation Strings
The development of these features was funded in part by grants to [@http://www.boost-consulting.com Boost Consulting] from the [@http://www.llnl.gov Lawrence Livermore National Laboratories] and by the [@http://cci.lbl.gov Computational Crystallography Initiative] at Lawrence Berkeley National Laboratories.
[section Contents]
* _tutorial_
* [link building Building and Testing]
* _reference_
* [link configuration Configuration Information]
* [link glossary Glossary]
* Support Resources
* [link faq Frequently Asked Questions (FAQs)]
[endsect]
[h2 Articles]
"Building Hybrid Systems With Boost Python", by Dave Abrahams and Ralf W. Grosse-Kunstleve (PDF)
[include building.qbk]
[include configuration.qbk]
[include faq.qbk]
[include glossary.qbk]

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[book Boost.Python Reference Manual
[quickbook 1.7]
[authors [Abrahams, David], [Seefeld, Stefan]]
[copyright 2002 2003 2004 2005 2015 David Abrahams, Stefan Seefeld]
[category inter-language support]
[id reference]
[dirname reference]
[purpose
Reflects C++ classes and functions into Python
]
[license
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]
]
]
[include reference/concepts.qbk]
[include reference/components.qbk]
[include reference/objects.qbk]
[include reference/functions.qbk]
[include reference/conversion.qbk]
[include reference/embedding.qbk]
[include reference/utility.qbk]
[include reference/topics.qbk]

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[section args.hpp]
[section Introduction]
Supplies a family of overloaded functions for specifying argument keywords for wrapped C++ functions.
[section keyword-expressions]
A keyword-expression results in an object which holds a sequence of ntbses, and whose type encodes the number of keywords specified. The keyword-expression may contain default values for some or all of the keywords it holds
[endsect]
[endsect]
[section Class `arg`]
The objects of class arg are keyword-expressions holding one keyword ( size one )
``
namespace boost { namespace python
{
struct arg
{
template <class T>
arg &operator = (T const &value);
explicit arg (char const *name){elements[0].name = name;}
};
}}
``
[endsect]
[section Class `arg` constructor]
``arg(char const* name);``
[variablelist
[[Requires][The argument must be a ntbs.]]
[[Effects][Constructs an arg object holding a keyword with name name.]]
]
[endsect]
[section Class `arg` operator=]
``template <class T> arg &operator = (T const &value);``
[variablelist
[[Requires][The argument must convertible to python.]]
[[Effects][Assigns default value for the keyword.]]
[[Returns][Reference to `this`.]]
]
[endsect]
[section Keyword-expression operator,]
``
keyword-expression operator , (keyword-expression, const arg &kw) const
keyword-expression operator , (keyword-expression, const char *name) const;
``
[variablelist
[[Requires][The argument name must be a ntbs.]]
[[Effects][Extends the keyword-expression argument with one more keyword.]]
[[Returns][The extended keyword-expression.]]
]
[endsect]
[section Example]
``
#include <boost/python/def.hpp>
using namespace boost::python;
int f(double x, double y, double z=0.0, double w=1.0);
BOOST_PYTHON_MODULE(xxx)
{
def("f", f
, ( arg("x"), "y", arg("z")=0.0, arg("w")=1.0 )
);
}
``
[endsect]
[endsect]

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[section call.hpp]
[section Introduction]
<boost/python/call.hpp> defines the call family of overloaded function templates, used to invoke Python callable objects from C++.
[endsect]
[section Function `call`]
``
template <class R, class A1, class A2, ... class An>
R call(PyObject* callable, A1 const&, A2 const&, ... An const&)
``
[variablelist
[[Requires][R is a pointer type, reference type, or a complete type with an accessible copy constructor]]
[[Effects][Invokes callable(a1, a2, ...an) in Python, where a1...an are the arguments to call(), converted to Python objects. ]]
[[Returns][The result of the Python call, converted to the C++ type R.]]
[[Rationale][For a complete semantic description and rationale, see this page. ]]
]
[endsect]
[section Example]
The following C++ function applies a Python callable object to its two arguments and returns the result. If a Python exception is raised or the result can't be converted to a double, an exception is thrown.
``
double apply2(PyObject* func, double x, double y)
{
return boost::python::call<double>(func, x, y);
}
``
[endsect]
[endsect]

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[section call_method.hpp]
[section Introduction]
<boost/python/call_method.hpp> defines the call_method family of overloaded function templates, used to invoke callable attributes of Python objects from C++.
[endsect]
[section Function `call_method`]
``
template <class R, class A1, class A2, ... class An>
R call_method(PyObject* self, char const* method, A1 const&, A2 const&, ... An const&)
``
[variablelist
[[Requires][R is a pointer type, reference type, or a complete type with an accessible copy constructor]]
[[Effects][Invokes self.method(a1, a2, ...an) in Python, where a1...an are the arguments to call_method(), converted to Python objects. For a complete semantic description, see this page.]]
[[Returns][The result of the Python call, converted to the C++ type R.]]
[[Rationale][call_method is critical to implementing C++ virtual functions which are overridable in Python, as shown by the example below.]]
]
[endsect]
[section Example]
The following C++ illustrates the use of call_method in wrapping a class with a virtual function that can be overridden in Python:
C++ Module Definition
``
#include <boost/python/module.hpp>
#include <boost/python/class.hpp>
#include <boost/utility.hpp>
#include <cstring>
// class to be wrapped
class Base
{
public:
virtual char const* class_name() const { return "Base"; }
virtual ~Base();
};
bool is_base(Base* b)
{
return !std::strcmp(b->class_name(), "Base");
}
// Wrapper code begins here
using namespace boost::python;
// Callback class
class Base_callback : public Base
{
public:
Base_callback(PyObject* self) : m_self(self) {}
char const* class_name() const { return call_method<char const*>(m_self, "class_name"); }
char const* Base_name() const { return Base::class_name(); }
private:
PyObject* const m_self;
};
using namespace boost::python;
BOOST_PYTHON_MODULE(my_module)
{
def("is_base", is_base);
class_<Base,Base_callback, noncopyable>("Base")
.def("class_name", &Base_callback::Base_name)
;
}
``
Python code:
``
>>> from my_module import *
>>> class Derived(Base):
... def __init__(self):
... Base.__init__(self)
... def class_name(self):
... return self.__class__.__name__
...
>>> is_base(Base()) # calls the class_name() method from C++
1
>>> is_base(Derived())
0
``
[endsect]
[endsect]

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[section Calling Python Functions and Methods]
[section Introduction]
The simplest way to call a Python function from C++, given an object instance f holding the function, is simply to invoke its function call operator.
``f("tea", 4, 2) // In Python: f('tea', 4, 2)``
And of course, a method of an object instance x can be invoked by using the function-call operator of the corresponding attribute:
``x.attr("tea")(4, 2); // In Python: x.tea(4, 2)``
If you don't have an object instance, Boost.Python provides two families of function templates, call and call_method, for invoking Python functions and methods respectively on PyObject*s. The interface for calling a Python function object (or any Python callable object) looks like:
``call<ResultType>(callable_object, a1, a2... aN);``
Calling a method of a Python object is similarly easy:
``call_method<ResultType>(self_object, "method-name", a1, a2... aN);``
This comparitively low-level interface is the one you'll use when implementing C++ virtual functions that can be overridden in Python.
[endsect]
[section Argument Handling]
Arguments are converted to Python according to their type. By default, the arguments a1...aN are copied into new Python objects, but this behavior can be overridden by the use of ptr() and ref():
``
class X : boost::noncopyable
{
...
};
void apply(PyObject* callable, X& x)
{
// Invoke callable, passing a Python object which holds a reference to x
boost::python::call<void>(callable, boost::ref(x));
}
``
In the table below, x denotes the actual argument object and cv denotes an optional cv-qualification: "const", "volatile", or "const volatile".
[table
[[Argument Type][Behavior]]
[[`T cv &`
`T cv`][The Python argument is created by the same means used for the return value of a wrapped C++ function returning T. When T is a class type, that normally means *x is copy-constructed into the new Python object.]]
[[T*][If x == 0, the Python argument will be None. Otherwise, the Python argument is created by the same means used for the return value of a wrapped C++ function returning T. When T is a class type, that normally means *x is copy-constructed into the new Python object.]]
[[boost::reference_wrapper<T>][The Python argument contains a pointer to, rather than a copy of, x.get(). Note: failure to ensure that no Python code holds a reference to the resulting object beyond the lifetime of *x.get() may result in a crash!]]
[[pointer_wrapper<T>][If x.get() == 0, the Python argument will be None. Otherwise, the Python argument contains a pointer to, rather than a copy of, *x.get(). Note: failure to ensure that no Python code holds a reference to the resulting object beyond the lifetime of *x.get() may result in a crash!]]
]
[endsect]
[section Result Handling]
In general, call<ResultType>() and call_method<ResultType>() return ResultType by exploiting all lvalue and rvalue from_python converters registered for ResultType and returning a copy of the result. However, when ResultType is a pointer or reference type, Boost.Python searches only for lvalue converters. To prevent dangling pointers and references, an exception will be thrown if the Python result object has only a single reference count.
[endsect]
[section Rationale]
In general, to get Python arguments corresponding to a1...aN, a new Python object must be created for each one; should the C++ object be copied into that Python object, or should the Python object simply hold a reference/pointer to the C++ object? In general, the latter approach is unsafe, since the called function may store a reference to the Python object somewhere. If the Python object is used after the C++ object is destroyed, we'll crash Python.
In keeping with the philosophy that users on the Python side shouldn't have to worry about crashing the interpreter, the default behavior is to copy the C++ object, and to allow a non-copying behavior only if the user writes boost::ref(a1) instead of a1 directly. At least this way, the user doesn't get dangerous behavior "by accident". It's also worth noting that the non-copying ("by-reference") behavior is in general only available for class types, and will fail at runtime with a Python exception if used otherwise[1].
However, pointer types present a problem: one approach is to refuse to compile if any aN has pointer type: after all, a user can always pass *aN to pass "by-value" or ref(*aN) to indicate a pass-by-reference behavior. However, this creates a problem for the expected null pointer to None conversion: it's illegal to dereference a null pointer value.
The compromise I've settled on is this:
# The default behavior is pass-by-value. If you pass a non-null pointer, the pointee is copied into a new Python object; otherwise the corresponding Python argument will be None.
# if you want by-reference behavior, use ptr(aN) if aN is a pointer and ref(aN) otherwise. If a null pointer is passed to ptr(aN), the corresponding Python argument will be None.
As for results, we have a similar problem: if ResultType is allowed to be a pointer or reference type, the lifetime of the object it refers to is probably being managed by a Python object. When that Python object is destroyed, our pointer dangles. The problem is particularly bad when the ResultType is char const* - the corresponding Python String object is typically uniquely-referenced, meaning that the pointer dangles as soon as call<char const*>(...) returns.
The old Boost.Python v1 deals with this issue by refusing to compile any uses of call<char const*>(), but this goes both too far and not far enough. It goes too far because there are cases where the owning Python string object survives beyond the call (just for instance, when it's the name of a Python class), and it goes not far enough because we might just as well have the same problem with a returned pointer or reference of any other type.
In Boost.Python v2 this is dealt with by:
# lifting the compile-time restriction on const char* callback returns
# detecting the case when the reference count on the result Python object is 1 and throwing an
exception inside of call<U>(...) when U is a pointer or reference type.
This should be acceptably safe because users have to explicitly specify a pointer/reference for U in call<U>, and they will be protected against dangles at runtime, at least long enough to get out of the call<U>(...) invocation.
[endsect]
[endsect]

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[section class.hpp]
[section Introduction]
`<boost/python/class.hpp>` defines the interface through which users expose their C++ classes to Python. It declares the `class_` class template, which is parameterized on the class type being exposed. It also exposes the `init`, `optional` and `bases` utility class templates, which are used in conjunction with `class_`.
`<boost/python/class_fwd.hpp>` contains a forward declaration of the `class_` class template.
[endsect]
[section Classes]
[section Class template `class_<T, Bases, HeldType, NonCopyable>`]
Creates a Python class associated with the C++ type passed as its first parameter. Although it has four template parameters, only the first one is required. The three optional arguments can actually be supplied *in any order*\ ; Boost.Python determines the role of the argument from its type.
[table
[[Template Parameter][Requirements][Semantics][Default]]
[[`T`][A class type.][The class being wrapped][]]
[[Bases]
[A specialization of `bases<...>` which specifies previously-exposed C++ base classes of `T`.]
[Registers `from_python` conversions from wrapped `T` instances to each of its exposed direct and indirect bases. For each polymorphic base `B`, registers conversions from indirectly-held wrapped `B` instances to `T`.][`bases<>`]]
[[HeldType][Must be `T`, a class derived from `T`, or a `Dereferenceable` type for which `pointee<HeldType>::type` is `T` or a class derived from `T`.][Specifies the type that is actually embedded in a Python object wrapping a `T` instance when `T`\ 's constructor is called or when a `T` or `T*` is converted to Python without the use of `ptr`, `ref`, or Call Policies such as `return_internal_reference`. More details below.][`T`]]
[[NonCopyable][If supplied, must be `boost::noncopyable`.][Suppresses automatic registration of `to_python` conversions which copy `T` instances. Required when `T` has no publicly-accessible copy constructor.][An unspecified type other than boost::noncopyable.]]
]
[section HeldType Semantics]
# If HeldType is derived from `T`, its exposed constructor(s) must accept an initial `PyObject*` argument which refers back to the Python object that contains the HeldType instance, as shown in this example. This argument is not included in the init-expression passed to `def(init_expr)`, below, nor is it passed explicitly by users when Python instances of `T` are created. This idiom allows C++ virtual functions which will be overridden in Python to access the Python object so the Python method can be invoked. Boost.Python automatically registers additional converters which allow wrapped instances of `T` to be passed to wrapped C++ functions expecting HeldType arguments.
# Because Boost.Python will always allow wrapped instances of `T` to be passed in place of HeldType arguments, specifying a smart pointer for HeldType allows users to pass Python `T` instances where a smart pointer-to-T is expected. Smart pointers such as `std::auto_ptr<>` or `boost::shared_ptr<>` which contain a nested type `element_type` designating the referent type are automatically supported; additional smart pointer types can be supported by specializing `pointee<HeldType>`.
# As in case 1 above, when HeldType is a smart pointer to a class derived from `T`, the initial `PyObject*` argument must be supplied by all of HeldType's exposed constructors.
# Except in cases 1 and 3, users may optionally specify that T itself gets initialized with a similar initial `PyObject*` argument by specializing `has_back_reference<T>`.
[endsect]
[section Class template `class_` synopsis]
``
namespace boost { namespace python
{
template <class T
, class Bases = bases<>
, class HeldType = T
, class NonCopyable = unspecified
>
class class_ : public object
{
// Constructors with default __init__
class_(char const* name);
class_(char const* name, char const* docstring);
// Constructors, specifying non-default __init__
template <class Init>
class_(char const* name, Init);
template <class Init>
class_(char const* name, char const* docstring, Init);
// Exposing additional __init__ functions
template <class Init>
class_& def(Init);
// defining methods
template <class F>
class_& def(char const* name, F f);
template <class Fn, class A1>
class_& def(char const* name, Fn fn, A1 const&);
template <class Fn, class A1, class A2>
class_& def(char const* name, Fn fn, A1 const&, A2 const&);
template <class Fn, class A1, class A2, class A3>
class_& def(char const* name, Fn fn, A1 const&, A2 const&, A3 const&);
// declaring method as static
class_& staticmethod(char const* name);
// exposing operators
template <unspecified>
class_& def(detail::operator_<unspecified>);
// Raw attribute modification
template <class U>
class_& setattr(char const* name, U const&);
// exposing data members
template <class D>
class_& def_readonly(char const* name, D T::*pm);
template <class D>
class_& def_readwrite(char const* name, D T::*pm);
// exposing static data members
template <class D>
class_& def_readonly(char const* name, D const& d);
template <class D>
class_& def_readwrite(char const* name, D& d);
// property creation
template <class Get>
void add_property(char const* name, Get const& fget, char const* doc=0);
template <class Get, class Set>
void add_property(
char const* name, Get const& fget, Set const& fset, char const* doc=0);
template <class Get>
void add_static_property(char const* name, Get const& fget);
template <class Get, class Set>
void add_static_property(char const* name, Get const& fget, Set const& fset);
// pickle support
template <typename PickleSuite>
self& def_pickle(PickleSuite const&);
self& enable_pickling();
};
}}
``
[endsect]
[section Class template `class_` constructors]
``
class_(char const* name);
class_(char const* name, char const* docstring);
template <class Init>
class_(char const* name, Init init_spec);
template <class Init>
class_(char const* name, char const* docstring, Init init_spec);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules. If docstring is supplied, it must be an ntbs. If `init_spec` is supplied, it must be either the special enumeration constant `no_init` or an init-expression compatible with `T`.]]
[[Effects][Constructs a `class_` object holding a Boost.Python extension class named name. The named attribute of the current scope is bound to the new extension class.
* If supplied, the value of docstring is bound to the `__doc__` attribute of the extension class.
* If `init_spec` is `no_init`, a special `__init__` function is generated which always raises a Python exception. Otherwise, `this->def(init_spec)` is called.
* If `init_spec` is not supplied, `this->def(init<>())` is called.]]
[[Rationale][Allowing the user to specify constructor arguments in the `class_<>` constructor helps her to avoid the common run-time errors which result from invoking wrapped member functions without having exposed an `__init__` function which creates the requisite `T` instance. Types which are not default-constructible will cause a compile-time error unless `Init` is supplied. The user must always supply name as there is currently no portable method to derive the text of the class name from its type.]]
]
[endsect]
[section Class template `class_` modifier functions]
``
template <class Init>
class_& def(Init init_expr);
``
[variablelist
[[Requires][`init_expr` is the result of an init-expression compatible with `T`.]]
[[Effects][For each valid prefix `P` of `Init`, adds an `__init__(...)` function overload to the extension class accepting P as arguments. Each overload generated constructs an object of HeldType according to the semantics described above, using a copy of init_expr's call policies. If the longest valid prefix of Init contains N types and init_expr holds M keywords, an initial sequence of the keywords are used for all but the first N - M arguments of each overload.]]
[[Returns][`*this`]]
[[Rationale][Allows users to easily expose a class' constructor to Python.]]
]
``
template <class F>
class_& def(char const* name, Fn fn);
template <class Fn, class A1>
class_& def(char const* name, Fn fn, A1 const& a1);
template <class Fn, class A1, class A2>
class_& def(char const* name, Fn fn, A1 const& a1, A2 const& a2);
template <class Fn, class A1, class A2, class A3>
class_& def(char const* name, Fn fn, A1 const& a1, A2 const& a2, A3 const& a3);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules.
* If a1 is the result of an overload-dispatch-expression, only the second form is allowed and fn must be a pointer to function or pointer to member function whose arity is the same as A1's maximum arity.
[*Effects:] For each prefix P of Fn's sequence of argument types, beginning with the one whose length is A1's minimum arity, adds a name(...) method overload to the extension class. Each overload generated invokes a1's call-expression with P, using a copy of a1's call policies. If the longest valid prefix of A1 contains N types and a1 holds M keywords, an initial sequence of the keywords are used for all but the first N - M arguments of each overload.
* Otherwise, a single method overload is built around fn, which must not be null:
* If fn is a function pointer, its first argument must be of the form U, U cv&, U cv*, or U cv* const&, where T* is convertible to U*, and a1-a3, if supplied, may be selected in any order from the table below.
* Otherwise, if fn is a member function pointer, its target must be T or one of its public base classes, and a1-a3, if supplied, may be selected in any order from the table below.
* Otherwise, Fn must be [derived from] object, and a1-a2, if supplied, may be selcted in any order from the first two rows of the table below. To be useful, fn should be callable.
[table
[[Mnemonic Name][Requirements/Type properties][Effects]]
[[docstring][Any ntbs][Value will be bound to the __doc__ attribute of the resulting method overload. If an earlier overload supplied a docstring, two newline characters and the new docstring are appended to it.]]
[[policies][A model of CallPolicies][A copy will be used as the call policies of the resulting method overload.]]
[[keywords][The result of a keyword-expression specifying no more arguments than the arity of fn.][A copy will be used as the call policies of the resulting method overload.]]
]
]]
[[Returns][`*this`]]
]
``class_& staticmethod(char const* name);``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules, and corresponds to a method whose overloads have all been defined.]]
[[Effects][Replaces the existing named attribute x with the result of invoking staticmethod(x) in Python. Specifies that the corresponding method is static and therefore no object instance will be passed to it. This is equivalent to the Python statement:]]
]
``setattr(self, name, staticmethod(getattr(self, name)))``
[variablelist
[[Note][Attempting to invoke def(name,...) after invoking staticmethod(name) will raise a RuntimeError.]]
[[Returns][`*this`]]
]
``
template <class U>
class_& setattr(char const* name, U const& u);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules.]]
[[Effects][Converts u to Python and adds it to the attribute dictionary of the extension class:
``PyObject_SetAttrString(this->ptr(), name, object(u).ptr());``]]
[[Returns][`*this`]]
]
``
template <class Get>
void add_property(char const* name, Get const& fget, char const* doc=0);
template <class Get, class Set>
void add_property(
char const* name, Get const& fget, Set const& fset, char const* doc=0);
``
[variablelist
[[Requires][name is an ntbs which conform to Python's identifier naming rules.]]
[[Effects][Creates a new Python property class instance, passing object(fget) (and object(fset) in the second form) with an (optional) docstring doc to its constructor, then adds that property to the Python class object under construction with the given attribute name.]]
[[Returns][`*this`]]
[[Rationale][Allows users to easily expose functions that can be invoked from Python with attribute access syntax.]]
]
``
template <class Get>
void add_static_property(char const* name, Get const& fget);
template <class Get, class Set>
void add_static_property(char const* name, Get const& fget, Set const& fset);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules.]]
[[Effects][Creates a Boost.Python.StaticProperty object, passing object(fget) (and object(fset) in the second form) to its constructor, then adds that property to the Python class under construction with the given attribute name. StaticProperty is a special subclass of Python's property class which can be called without an initial self argument.]]
[[Returns][`*this`]]
[[Rationale][Allows users to easily expose functions that can be invoked from Python with static attribute access syntax.]]
]
``
template <class D>
class_& def_readonly(char const* name, D T::*pm, char const* doc=0);
template <class D>
class_& def_readonly(char const* name, D const& d);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules. doc is also an ntbs.]]
[[Effects][``this->add_property(name, make_getter(pm), doc);`` and ``this->add_static_property(name, make_getter(d));`` respectively.]]
[[Returns][`*this`]]
[[Rationale][Allows users to easily expose a class' data member or free variable such that it can be inspected from Python with a natural syntax.]]
]
``
template <class D>
class_& def_readwrite(char const* name, D T::*pm, char const* doc=0);
template <class D>
class_& def_readwrite(char const* name, D& d);
``
[variablelist
[[Effects][``this->add_property(name, make_getter(pm), make_setter(pm), doc);`` and ``this->add_static_property(name, make_getter(d), make_setter(d));`` respectively.]]
[[Returns][`*this`]]
[[Rationale][Allows users to easily expose a class' data or free variable member such that it can be inspected and set from Python with a natural syntax.]]
]
``
template <typename PickleSuite>
class_& def_pickle(PickleSuite const&);
``
[variablelist
[[Requires][PickleSuite must be publically derived from pickle_suite.]]
[[Effects][Defines a legal combination of the special attributes and methods: __getinitargs__, __getstate__, __setstate__, __getstate_manages_dict__, __safe_for_unpickling__, __reduce__]]
[[Returns][`*this`]]
[[Rationale][Provides an easy to use high-level interface for establishing complete pickle support for the wrapped class. The user is protected by compile-time consistency checks.]]
]
``class_& enable_pickling();``
[variablelist
[[Effects][Defines the __reduce__ method and the __safe_for_unpickling__ attribute.]]
[[Returns][`*this`]]
[[Rationale][Light-weight alternative to def_pickle(). Enables implementation of pickle support from Python.]]
]
[endsect]
[endsect]
[section Class template bases<T1, T2, ...TN>]
An MPL sequence which can be used in class_<...> instantiations indicate a list of base classes.
[section Class template bases synopsis]
``
namespace boost { namespace python
{
template <T1 = unspecified,...Tn = unspecified>
struct bases
{};
}}
``
[endsect]
[endsect]
[endsect]
[section Examples]
Given a C++ class declaration:
``
class Foo : public Bar, public Baz
{
public:
Foo(int x, char const* y);
Foo(double);
std::string const& name() { return m_name; }
void name(char const*);
double value; // public data
private:
...
};
``
A corresponding Boost.Python extension class can be created with:
``
using namespace boost::python;
class_<Foo,bases<Bar,Baz> >("Foo",
"This is Foo's docstring."
"It describes our Foo extension class",
init<int,char const*>(args("x","y"), "__init__ docstring")
)
.def(init<double>())
.def("get_name", &Foo::get_name, return_internal_reference<>())
.def("set_name", &Foo::set_name)
.def_readwrite("value", &Foo::value);
``
[endsect]
[endsect]

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[chapter High Level Components
[quickbook 1.7]
]
[include class.qbk]
[include def.qbk]
[include def_visitor.qbk]
[include docstring_options.qbk]
[include enum.qbk]
[include errors.qbk]
[include exception_translator.qbk]
[include init.qbk]
[include iterator.qbk]
[include module.qbk]
[include operators.qbk]
[include scope.qbk]
[include stl_iterator.qbk]
[include wrapper.qbk]

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[chapter Concepts
[quickbook 1.7]
]
[section CallPolicies]
[section Introduction]
Models of the CallPolicies concept are used to specialize the behavior of Python callable objects
generated by Boost.Python to wrapped C++ objects like function and member function pointers,
providing three behaviors:
# `precall` - Python argument tuple management before the wrapped object is invoked
# `result_converter` - C++ return value handling
# `postcall` - Python argument tuple and result management after the wrapped object is invoked
# `extract_return_type` - metafunction for extracting the return type from a given signature type sequence
[endsect]
[section CallPolicies Composition]
In order to allow the use of multiple models of CallPolicies in the same callable object,
Boost.Python's CallPolicies class templates provide a chaining interface which allows them to be
recursively composed. This interface takes the form of an optional template parameter, Base which
defaults to default_call_policies. By convention, the precall function of the Base is invoked after
the precall function supplied by the outer template, and the postcall function of the Base is invoked
before the postcall function of the outer template. If a result_converter is supplied by the outer
template, it replaces any result_converter supplied by the Base. For an example, see
return_internal_reference.
[endsect]
[section Concept Requirements]
[table
[[Expression][Type][Result/Semantics]]
[[`x.precall(a)`][convertible to bool]
[returns false and PyErr_Occurred() != 0 upon failure, true otherwise.]]
[[`P::result_converter`][A model of ResultConverterGenerator.]
[An MPL unary Metafunction Class used produce the "preliminary" result object.]]
[[`x.postcall(a, r)`][convertible to PyObject*]
[0 0 and PyErr_Occurred() != 0 upon failure. Must "conserve references" even in the event of an exception. In other words, if r is not returned, its reference count must be decremented; if another existing object is returned, its reference count must be incremented.]]
[[`P::extract_return_type`][A model of Metafunction.]
[An MPL unary Metafunction used extract the return type from a given signature. By default it is derived from mpl::front.]]
]
[endsect]
[endsect]
[section Dereferenceable]
[section Introduction]
Instances of a Dereferenceable type can be used like a pointer to access an lvalue.
[endsect]
[section Concept Requirements]
In the table below, T is a model of Dereferenceable, and x denotes an object of type T. In addition, all pointers are Dereferenceable.
[table
[[Expression][Result][Operational Semantics]]
[[`get_pointer(x)`][convertible to pointee<T>::type*]
[&*x, or a null pointer ]]
]
[endsect]
[endsect]
[section Extractor]
[section Introduction]
An Extractor is a class which Boost.Python can use to extract C++ objects from Python objects, and is typically used by facilities that define from_python conversions for "traditional" Python extension types.
[endsect]
[section Concept Requirements]
In the table below, X denotes a model of Extractor and a denotes an instance of a Python object type.
[table
[[Expression][Type][Semantics]]
[[`X::execute(a)`][non-void ]
[Returns the C++ object being extracted. The execute function must not be overloaded.]]
[[`&a.ob_type`][PyTypeObject**]
[Points to the ob_type field of an object which is layout-compatible with PyObject]]
]
[endsect]
[section Notes]
Informally, an Extractor's execute member must be a non-overloaded static function whose single argument is a Python object type. Acceptable Python object types include those publicly (and unambiguously) derived from PyObject, and POD types which are layout-compatible with PyObject.
[endsect]
[endsect]
[section HolderGenerator]
[section Introduction]
A HolderGenerator is a unary metafunction class which returns types suitable for holding instances of its argument in a wrapped C++ class instance.
[endsect]
[section Concept Requirements]
In the table below, G denotes an type which models HolderGenerator, and X denotes a class type.
[table
[[Expression][Requirements]]
[[`G::apply<X>::type`][A concrete subclass of instance_holder which can hold objects of type X. ]]
]
[endsect]
[endsect]
[section ResultConverter]
[section Introduction]
A ResultConverter for a type T is a type whose instances can be used to convert C++ return values of type T to_python. A ResultConverterGenerator is an MPL unary metafunction class which, given the return type of a C++ function, returns a ResultConverter for that type. ResultConverters in Boost.Python generally inspect library's registry of converters to find a suitable converter, but converters which don't use the registry are also possible.
[endsect]
[section ResultConverter Concept Requirements]
In the table below, C denotes a ResultConverter type for a type R , c denotes an object of type C , and r denotes an object of type R.
[table
[[Expression][Type][Semantics]]
[[`C c`][]
[Constructs a c object.]]
[[`c.convertible()`][convertible to bool]
[false iff no conversion from any R value to a Python object is possible.]]
[[`c(r)`][convertible to PyObject*]
[A pointer to a Python object corresponding to r, or 0 iff r could not be converted to_python, in which case PyErr_Occurred should return non-zero.]]
[[`c.get_pytype()`][PyTypeObject const *]
[A pointer to a Python Type object corresponding to result of the conversion, or 0. Used for documentation generation. If 0 is returned the generated type in the documentation will be object .]]
]
[endsect]
[section ResultConverterGenerator Concept Requirements]
In the table below, G denotes a ResultConverterGenerator type and R denotes a possible C++ function return type.
[table
[[Expression][Requirements]]
[[`G::apply<R>::type`][A ResultConverter type for R.]]
]
[endsect]
[endsect]
[section ObjectWrapper]
[section Introduction]
This page defines two concepts used to describe classes which manage a Python objects, and which are intended to support usage with a Python-like syntax.
[endsect]
[section ObjectWrapper Concept Requirements]
Models of the ObjectWrapper concept have object as a publicly-accessible base class, and are used to supply special construction behavior and/or additional convenient functionality through (often templated) member functions. Except when the return type R is itself an TypeWrapper, a member function invocation of the form ``x.some_function(a1, a2,...an)`` always has semantics equivalent to:
``extract<R>(x.attr("some_function")(object(a1), object(a2),...object(an)))()`` (see [link caveat] below).
[endsect]
[section TypeWrapper Concept Requirements]
TypeWrapper is a refinement of ObjectWrapper which is associated with a particular Python type X. For a given TypeWrapper T, a valid constructor expression ``T(a1, a2,...an)`` builds a new T object managing the result of invoking X with arguments corresponding to ``object(a1), object(a2),...object(an)``.
When used as arguments to wrapped C++ functions, or as the template parameter to extract<>, only instances of the associated Python type will be considered a match.
[endsect]
[section Caveat]
The upshot of the special member function invocation rules when the return type is a TypeWrapper is that it is possible for the returned object to manage a Python object of an inappropriate type. This is not usually a serious problem; the worst-case result is that errors will be detected at runtime a little later than they might otherwise be. For an example of how this can occur, note that the dict member function items returns an object of type list. Now suppose the user defines this dict subclass in Python:
``
>>> class mydict(dict):
... def items(self):
... return tuple(dict.items(self)) # return a tuple
``
Since an instance of mydict is also an instance of dict, when used as an argument to a wrapped C++ function, boost::python::dict can accept objects of Python type mydict. Invoking items() on this object can result in an instance of boost::python::list which actually holds a Python tuple. Subsequent attempts to use list methods (e.g. append, or any other mutating operation) on this object will raise the same exception that would occur if you tried to do it from Python.
[endsect]
[endsect]

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[chapter To/From Python Type Conversion
[quickbook 1.7]
]
[include extract.qbk]
[include implicit.qbk]
[include lvalue_from_pytype.qbk]
[include opaque_pointer_converter.qbk]
[include to_python_converter.qbk]
[include register_ptr_to_python.qbk]

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[section copy_const_reference.hpp]
[section Class `copy_const_reference`]
copy_const_reference is a model of ResultConverterGenerator which can be used to wrap C++ functions returning a reference-to-const type such that the referenced value is copied into a new Python object.
``
namespace boost { namespace python
{
struct copy_const_reference
{
template <class T> struct apply;
};
}}
``
[endsect]
[section Class `copy_const_reference` metafunctions]
``template <class T> struct apply``
[variablelist
[[Requires][T is U const& for some U.]]
[[Returns][typedef to_python_value<T> type;]]
]
[endsect]
[section Example]
C++ module definition:
``
#include <boost/python/module.hpp>
#include <boost/python/class.hpp>
#include <boost/python/copy_const_reference.hpp>
#include <boost/python/return_value_policy.hpp>
// classes to wrap
struct Bar { int x; }
struct Foo {
Foo(int x) : { b.x = x; }
Bar const& get_bar() const { return b; }
private:
Bar b;
};
// Wrapper code
using namespace boost::python;
BOOST_PYTHON_MODULE(my_module)
{
class_<Bar>("Bar");
class_<Foo>("Foo", init<int>())
.def("get_bar", &Foo::get_bar
, return_value_policy<copy_const_reference>())
;
}
``
Python code:
``
>>> from my_module import *
>>> f = Foo(3) # create a Foo object
>>> b = f.get_bar() # make a copy of the internal Bar object
``
[endsect]
[endsect]

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[section copy_non_const_reference.hpp]
[section Class `copy_non_const_reference`]
copy_non_const_reference is a model of ResultConverterGenerator which can be used to wrap C++ functions returning a reference-to-non-const type such that the referenced value is copied into a new Python object.
``
namespace boost { namespace python
{
struct copy_non_const_reference
{
template <class T> struct apply;
};
}}
``
[endsect]
[section Class `copy_non_const_reference` metafunctions]
``template <class T> struct apply``
[variablelist
[[Requires][`T` is `U &` for some non-const `U`.]]
[[Returns][`typedef to_python_value<T> type`;]]
]
[endsect]
[section Example]
C++ module definition:
``
#include <boost/python/module.hpp>
#include <boost/python/class.hpp>
#include <boost/python/copy_non_const_reference.hpp>
#include <boost/python/return_value_policy.hpp>
// classes to wrap
struct Bar { int x; }
struct Foo {
Foo(int x) : { b.x = x; }
Bar& get_bar() { return b; }
private:
Bar b;
};
// Wrapper code
using namespace boost::python;
BOOST_PYTHON_MODULE(my_module)
{
class_<Bar>("Bar");
class_<Foo>("Foo", init<int>())
.def("get_bar", &Foo::get_bar
, return_value_policy<copy_non_const_reference>())
;
}
``
Python code:
``
>>> from my_module import *
>>> f = Foo(3) # create a Foo object
>>> b = f.get_bar() # make a copy of the internal Bar object
``
[endsect]
[endsect]

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[section data_members.hpp]
[section Introduction]
make_getter() and make_setter() are the functions used internally by class_<>::def_readonly and class_<>::def_readwrite to produce Python callable objects which wrap C++ data members.
[endsect]
[section Functions]
``
template <class C, class D>
object make_getter(D C::*pm);
template <class C, class D, class Policies>
object make_getter(D C::*pm, Policies const& policies);
``
[variablelist
[[Requires][Policies is a model of CallPolicies.]]
[[Effects][Creates a Python callable object which accepts a single argument that can be converted from_python to C*, and returns the corresponding member D member of the C object, converted to_python. If policies is supplied, it will be applied to the function as described here. Otherwise, the library attempts to determine whether D is a user-defined class type, and if so uses return_internal_reference<>
for Policies. Note that this test may inappropriately choose return_internal_reference<> in some cases when D is a smart pointer type. This is a known defect.]]
[[Returns][An instance of object which holds the new Python callable object.]]
]
``
template <class D>
object make_getter(D const& d);
template <class D, class Policies>
object make_getter(D const& d, Policies const& policies);
template <class D>
object make_getter(D const* p);
template <class D, class Policies>
object make_getter(D const* p, Policies const& policies);
``
[variablelist
[[Requires][Policies is a model of CallPolicies.]]
[[Effects][Creates a Python callable object which accepts no arguments and returns d or *p, converted to_python on demand. If policies is supplied, it will be applied to the function as described here. Otherwise, the library attempts to determine whether D is a user-defined class type, and if so uses reference_existing_object for Policies.]]
[[Returns][An instance of object which holds the new Python callable object.]]
]
``
template <class C, class D>
object make_setter(D C::*pm);
template <class C, class D, class Policies>
object make_setter(D C::*pm, Policies const& policies);
``
[variablelist
[[Requires][Policies is a model of CallPolicies.]]
[[Effects][Creates a Python callable object which, when called from Python, expects two arguments which can be converted from_python to C* and D const&, respectively, and sets the corresponding D member of the C object. If policies is supplied, it will be applied to the function as described here.]]
[[Returns][An instance of object which holds the new Python callable object.]]
]
``
template <class D>
object make_setter(D& d);
template <class D, class Policies>
object make_setter(D& d, Policies const& policies);
template <class D>
object make_setter(D* p);
template <class D, class Policies>
object make_setter(D* p, Policies const& policies);
``
[variablelist
[[Requires][Policies is a model of CallPolicies.]]
[[Effects][Creates a Python callable object which accepts one argument, which is converted from Python to D const& and written into d or *p, respectively. If policies is supplied, it will be applied to the function as described here.]]
[[Returns][An instance of object which holds the new Python callable object.]]
]
[endsect]
[section Example]
The code below uses make_getter and make_setter to expose a data member as functions:
``
#include <boost/python/data_members.hpp>
#include <boost/python/module.hpp>
#include <boost/python/class.hpp>
struct X
{
X(int x) : y(x) {}
int y;
};
using namespace boost::python;
BOOST_PYTHON_MODULE_INIT(data_members_example)
{
class_<X>("X", init<int>())
.def("get", make_getter(&X::y))
.def("set", make_setter(&X::y))
;
}
``
It can be used this way in Python:
``
>>> from data_members_example import *
>>> x = X(1)
>>> x.get()
1
>>> x.set(2)
>>> x.get()
2
``
[endsect]
[endsect]

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[section def.hpp]
[section Introduction]
`def()` is the function which can be used to expose C++ functions and callable objects as Python functions in the current scope.
[endsect]
[section Functions]
``
template <class F>
void def(char const* name, F f);
template <class Fn, class A1>
void def(char const* name, Fn fn, A1 const&);
template <class Fn, class A1, class A2>
void def(char const* name, Fn fn, A1 const&, A2 const&);
template <class Fn, class A1, class A2, class A3>
void def(char const* name, Fn fn, A1 const&, A2 const&, A3 const&);
``
[variablelist
[[Requires][name is an ntbs which conforms to Python's identifier naming rules.
* If `Fn` is [derived from] object, it will be added to the current scope as a single overload. To be useful, fn should be callable.
* If a1 is the result of an overload-dispatch-expression, only the second form is allowed and fn must be a pointer to function or pointer to member function whose arity is the same as A1's maximum arity.
[*Effects:] For each prefix P of Fn's sequence of argument types, beginning with the one whose length is A1's minimum arity, adds a name(...) function overload to the current scope. Each overload generated invokes a1's call-expression with P, using a copy of a1's call policies. If the longest valid prefix of A1 contains N types and a1 holds M keywords, an initial sequence of the keywords are used for all but the first N - M arguments of each overload.
* Otherwise, fn must be a non-null function or member function pointer, and a single function overload built around fn is added to the current scope. If any of a1-a3 are supplied, they may be selected in any order from the table below.
[table
[[Mnemonic Name][Requirements/Type properties][Effects]]
[[docstring][Any ntbs][Value will be bound to the __doc__ attribute of the resulting method overload.]]
[[policies][A model of CallPolicies][A copy will be used as the call policies of the resulting method overload.]]
[[keywords][The result of a keyword-expression specifying no more arguments than the arity of fn.][A copy will be used as the call policies of the resulting method overload.]]
]
]]
]
[endsect]
[section Example]
``
#include <boost/python/def.hpp>
#include <boost/python/module.hpp>
#include <boost/python/args.hpp>
using namespace boost::python;
char const* foo(int x, int y) { return "foo"; }
BOOST_PYTHON_MODULE(def_test)
{
def("foo", foo, args("x", "y"), "foo's docstring");
}
``
[endsect]
[endsect]

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[section def_visitor.hpp]
[section Introduction]
<boost/python/def_visitor.hpp> provides a generic visitation interface through which the class_ def member functionality can be extended non-intrusively to avoid cluttering the class_ interface. It declares the def_visitor<T> class template, which is parameterized on the derived type DerivedVisitor, which provides the actual def functionality through its visit member functions.
[endsect]
[section Class `def_visitor`]
The class def_visitor is a base class paramaterized by its derived class. The def_visitor class is a protocol class. Its derived class, DerivedVisitor, is expected to have a member function visit. The def_visitor class is never instantiated directly. Instead, an instance of its subclass, DerivedVisitor, is passed on as an argument to the class_ def member function.
``
namespace boost { namespace python {
template <class DerivedVisitor>
class def_visitor {};
}
``
[variablelist
[[Requires][The client supplied class DerivedVisitor template parameter is expected to:
* be privately derived from def_visitor
* grant friend access to class def_visitor_access
* define either or both visit member functions listed in the table below:
[table
[[Expression][Return Type][Requirements][Effects]]
[[`visitor.visit(cls)`][`void`]
[cls is an instance of a class_ being wrapped to Python. visitor is a def_visitor derived class.]
[A call to cls.def(visitor) forwards to this member function.]]
[[`visitor.visit(cls, name, options)`][`void`]
[cls is a class_ instance, name is a C string. visitor is a def_visitor derived class. options is a context specific optional argument.]
[A call to cls.def(name, visitor) or cls.def(name, visitor, options) forwards to this member function. ]]]
]]
]
[endsect]
[section Example]
``
class X {/*...*/};
class my_def_visitor : boost::python::def_visitor<my_def_visitor>
{
friend class def_visitor_access;
template <class classT>
void visit(classT& c) const
{
c
.def("foo", &my_def_visitor::foo)
.def("bar", &my_def_visitor::bar)
;
}
static void foo(X& self);
static void bar(X& self);
};
BOOST_PYTHON_MODULE(my_ext)
{
class_<X>("X")
.def(my_def_visitor())
;
}
``
[endsect]
[endsect]

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